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Auricle Global Society of Education and Research
Analysis of Resistance Reduction via Support Vector Regression and Experiment on a Submerged Floating Vehicle Fitted with an Air Curtain Traversing Deep Soft Terrains
To improve the driving efficiency of submerged floating vehicles (SFVs) in deep soft terrains, the effect of an active air curtain method on the reduction and optimization of sliding resistance of a floating device was explored. The sliding resistance reduction principle of the air curtain was introduced, then, the resistance was analyzed. The soil bin experiment for air curtain resistance reduction test was built. The orthogonal tests were employed to ascertain the influence of the air curtain, ground contact pressure, sliding velocity, and soil moisture content upon the sliding resistance. The results show that the resistance with air curtain is less than that without air curtain, and the resistance reduction rate up to 11.17%. The resistance augments in tandem with the elevation of the ground contact pressure and the sliding velocity. When the soil moisture content approximates the liquid limit, the rate of resistance reduction reaches its peak. Furthermore, a support vector regression (SVR) method is put forward to formulate a regression prediction model for the sliding resistance. The predictions yielded by this method were proximate to the experimental outcomes. It implies that the SVR model exhibits a superior prediction capacity even with a restricted number of samples. The research discloses the impact of the air curtain, ground contact pressure, the floating device’s sliding velocity, and soil moisture level on the sliding resistance. It lays a solid research foundation for the drag reduction design of submerged floating vehicles in deep soft terrains.
Improving Overseas Aftermarket Aircraft Spare Parts Supply Logistics Using Simulation Modeling
Computer simulation technology not only enhances the understanding of complex systems but also provides crucial support for decision-making and design, offering an effective method for studying the supply of aftermarket aircraft spare parts (ASPs). The demand for ASPs is different from that for aftermarket general-equipment spare parts in terms of supplier quantity, supply process, and supply conditions. These differences in demand characteristics lead to changes in supply logistics. This study investigates the factors affecting the aftermarket ASP supply and proposes an overseas supply logistics model for aftermarket ASPs. A simulation model was used to reproduce the aftermarket ASP supply and compare different scenarios. The ordinary mode and full-lift-cycle coverage mode are modeled to compare their efficiency. The full-lift-cycle coverage mode was also tested to evaluate the feasibility of its supply process. The results confirm that the full-lift-cycle coverage mode improves supply logistics performance, offering benefits for the overseas supply of aftermarket ASPs for Chinese-made civil aircraft.
Application of Learning Pass Information Technology Platform in Blended Teaching Module on Integrated Moral-art Education
The in-depth integration of modern information technology with contemporary education and teaching is a major development strategy for China's education and a trend in the development of world education. This study takes Learning Pass information technology platform as the teaching platform and introduces blended teaching into the classroom teaching of Chinese moral education course. Then the construction of resources and the design of blended teaching process are carried out to explore the effect of blended teaching based on Learning Pass information technology platform. Through specific teaching practice, the problems in the design and application of blended teaching can be improved to enhance the effect of moral education teaching. This study adopts a quantitative research approach to collect and analyse receipts. Questionnaires were distributed to learners and interviews were conducted to obtain more accurate data. It was found that the blended teaching module on integrated moral-art education implemented on the basis of Learning Pass information technology platform was significantly better than the traditional classroom teaching. The blended teaching approach with the help of information technology not only enhances students' independent learning ability, but also stimulates students' interest in learning and enables them to master the knowledge of the course more comprehensively. Teachers can better grasp the learning status of students through the Learning Pass information technology platform and provide personalized services to students.
Driving Impact of Sci-Tech Finance on Companies’ Green Productivity: An Empirical Analysis Using High-Dimensional Fixed Effects and Difference-in-Differences Models
Achieving high-quality economic performance and cultivating new quality productive forces are important goals of the Chinese government currently. Green development serves as a foundation for high-quality economic performance, where new quality productive forces are essentially green productivity. Sci-tech finance refers to a financing framework aimed at fostering technological innovation and advancing the commercialization of R&D achievements. To investigate how the development of Sci-tech finance influences companies’ green productivity, this research utilizes panel data from listed companies in Zhejiang Province from 2007 to 2021, using high-dimensional fixed effects model and difference-in-differences model. The findings reveal that Sci-tech finance plays a crucial role in boosting companies’ green total factor productivity, encouraging green patent applications, and driving the cultivation and growth of green productivity. Heterogeneity analysis shows that the impact of sci-tech finance is particularly significant in high-income regions, private companies, small and medium-sized enterprises, and firms not audited by the Big Four accounting firms. Furthermore, by constructing a quasi-natural experiment, it is verified that the “Pilot policy for synergizing technology and finance” considerably boosts the green productivity of companies in pilot cities. Finally, the research makes policy proposals for the growth of green productivity in companies, such as strengthening Sci-tech finance policy support, promoting regional coordinated development, and providing more financing channels for small and medium-sized companies.
A Study on Generative Artificial Intelligence to Enable Urban Red Literature Inheritance
In the current era of rapid development of informationization, the problem of urban cultural inheritance is becoming more and more prominent, especially the protection and development of red cultural lineage has attracted much attention. This study focuses on the inheritance needs of urban red cultural heritage, and explores the application mode and strategy of generative artificial intelligence technology in it. In this paper, we first systematically sort out the urban red cultural heritage, describe the key technology of generative artificial intelligence, and establish a research framework that integrates the two, aiming to realize an innovative way for the protection of urban cultural heritage. Based on rich experimental materials and combined with practical cases, this paper constructs a corresponding generative model to design an effective red cultural heritage inheritance program. Through the analysis of experimental data, this study demonstrates the potential of generative AI in the digital display, educational dissemination, and cultural experience of the red vein, highlighting its significant contribution to improving the efficiency of cultural inheritance. Summarizing the experimental results, the paper discusses the application prospects of the technology, points out the limitations of the study, and provides references and insights for future phase-related work.
Research on the Site Selection and Transfer Path Optimization of Garbage Transfer Station in East Lake Scenic Area Based on Analytic Hierarchy Process
With the development of social economy, people's living standards have been significantly improved, the output of garbage in daily life has also increased greatly, and the effect of garbage disposal has a great impact on the environment and society. The scenic area is a densely populated area, and its garbage production is also very high. In order to reduce the workload of garbage transportation, reduce labor costs, shorten the total transportation distance and reduce transportation costs, the research object of this paper is the East Lake Ecological Scenic Area in Wuhan, and the garbage transfer station in the scenic area is mainly selected and the garbage transfer route is planned. In this paper, the analytic hierarchy process is used to select the location of the garbage transfer station, Get optimal route planning. The research ideas and methods in this paper have certain reference significance for the site selection and transfer path planning of other waste transfer stations.
Application of Computer Technology in Martial Arts Short Weapons Training
In the era of rapid advancements in artificial intelligence technology, we should deeply integrate advanced technologies from the computer field, such as motion capture, virtual reality simulation, and intelligent assistive teaching, with martial arts short weapons training. By empowering police training with technology, we aim to comprehensively enhance their professional competence and combat capabilities. In the face of increasingly complex criminal tactics, such as high-tech cyber fraud and street violence, the police, as a solid line of defense for maintaining social stability and ensuring public safety, face a crucial and urgent need to strengthen their professional competence. Martial arts short weapons, as a martial arts combat form that combines both offensive and defensive techniques, highlight their unique value in this context. Through a comprehensive application of research methods including literature analysis, empirical research, case studies, and expert interviews, this paper delves into the characteristics, advantages, and potential value of integrating computer technology with martial arts short weapons for practical combat training in enhancing police professional competence. The study reveals that the highly intensive combative environment trains participants' mental resilience and enhances their ability to cope with various psychological pressures; it also strengthens their offensive and defensive awareness, proficiency in handling police equipment, and tactical awareness. Therefore, increasing resource investment is the foundation for improving the effectiveness of martial arts short weapons training, utilizing computer technology to optimize training methods is the key to enhancing training quality, enhancing the integration with practical combat is an important goal of martial arts short weapons training, and strengthening the application and integration of technology is a significant trend in martial arts short weapons training.
Comparative Analysis of Talent Cultivation Systems for Civil Engineering Majors in Higher and Vocational Colleges Based on Mathematical Models
This paper aims to explore the logical relationship between talent cultivation objectives and curriculum system differences under the integrated cultivation system of higher vocational education and vocational schools, based on mathematical models. By comparing the talent cultivation standards and curriculum standards of civil engineering majors in vocational and higher vocational colleges in Chongqing, it is found that while both emphasize the cultivation of technically skilled talents, higher vocational colleges place more emphasis on developing high-quality technical skilled talents with strong sustainable development ability and industry adaptability. In terms of career orientation, both vocational and higher vocational colleges set corresponding vocational qualification certificates as development goals, but adjustments must be made in practice according to the characteristics of the profession and market demand. Vocational colleges focus more on the proficiency of basic skills, while higher vocational colleges add courses that align with industry development trends and the need for higher-level engineering personnel, such as modular construction, 3D scanning, and digital construction technologies, reflecting the different levels of educational objectives and focuses. Moreover, the teaching content of core courses also shows hierarchical differences, with higher vocational colleges expanding both depth and breadth to meet the needs of more advanced career development.
An Empirical Study on the Impact of Economic aid on the Sustainable Development Ability of Impoverished College Students from the Perspective Conservation of Resource Theory Based on Computer Data Analysis
For some poor students in colleges and universities, they are facing huge economic pressure, which may have a negative impact on their studies and their future sustainable development ability. Therefore, the importance of economic aid to the poor students is self-evident. From the perspective of resource conservation theory, a survey of 682 impoverished students from five universities was conducted based on computer data analysis. The results showed that: (1) economic aid has a positive impact on the sustainable development ability of poor students in colleges and universities; (2) affects the sustainable development ability through the effect of internal and external attribution and self-efficacy; (3) having the necessary resources is the basic condition for the sustainable development of poor students in colleges and universities. The empirical study on the impact of economic aid based on computer data analysis on the sustainable development ability of impoverished students in universities has enlightening significance for adjusting and optimizing the aid policy system and promoting the sustainable development of impoverished students.
Research on the Risk of Material Misstatement in M&A Audit under Fuzzy Comprehensive Evaluation
With the development of economy, the demand for merger and acquisition of enterprises is increasingly strong, but there are many risks in merger and acquisition that make the process of merger and acquisition not smooth. This paper focuses on the quantification of audit risk, the main difficulties lie in the assessment of audit risk factors, the selection of audit risk factors, the weight distribution of factors and so on. This paper introduces the fuzzy comprehensive evaluation method and takes W Group's merger and acquisition of K enterprise as an example to illustrate the application of this method. The case involved complex accounting measures, unique corporate cultures, and the legal implications of unprecedented policy challenges, factors that made audit risk particularly prominent in the M&A process compared to other companies. The research results show that the risk of material misstatement in M&A audit for W Group's merger and acquisition of K company is medium and high risk, and indicates that the risk assessment model of material misstatement in M&A audit based on fuzzy comprehensive evaluation method can identify and quantify various risk factors more accurately, and provide a powerful decision support tool for auditors. At the same time, the model also has strong flexibility and adaptability, and can be adjusted and optimized according to the characteristics of different M&A cases.
Exploration of Micro-specialty Construction Based on Big Data and AI from the Perspective of Medical and Industrial Integration: A Case Study of Intelligent Technology Aging Application Micro-specialty in Anhui Sanlian University
With the aggravation of population aging, the demand of the elderly for old-age services is becoming increasingly diversified and personalized, which poses a severe challenge to the traditional old-age service model. In this context, it is particularly important to build a micro-specialty of "intelligent technology suitable for aging application", which combines big data analysis with artificial intelligence technology. Based on the practical exploration Faculty of Modern Health Care Industry College of Anhui Sanlian University in the field of health care industry, this paper deeply analyzes the social background and necessity of micro-specialty construction and expounds in detail how to use big data and artificial intelligence technology to design the content, implement path planning and build the evaluation system of micro-specialty construction. This micro-major trains compound talents who have mastered medicine, engineering and other fields, and have big data analysis and artificial intelligence applications, so as to provide safer, more convenient, and personalized aged care services and provide strong support for the innovation and development of the health care industry.
Co/Ni-MOF@MXene Electrode Material for High-performance Supercapacitor
Rapid advances in the electronics industry have significantly increased the demand for high performance energy storage materials. In response, there has been a growing need in recent years to research and develop innovative materials that offer both stability and high capacity for application in sustainable energy conversion and storage systems. Large specific surface area, controllable pore pattern, tunable framework architecture, excellent electrochemical energy storage performance of MOFs. Nevertheless, in the areas of energy storage, practical applications are restricted because of their stability and structure. Herein, Co/Ni-MOF nanowires were synthesised on Ti3C2Tx MXene by a two-step hydrothermal method. Benefitting from excellent wettability of Ti3C2Tx MXene with the electrolyte and its high conductivity. The electrochemical exploration of Co/Ni-MOF@Ti3C2Tx MXene composite via CV, GCD, and EIS measurements showed excellent performance. It had a specific capacitance of 1019.5 F/g at 1 A/g due to synergistic effects. The MXene's structure provided a large surface area, facilitated electron transport and ion diffusion, and enhanced activity. The composite exhibited long-term stability, retaining 75.0% capacitance after 3500 cycles at 8 A/g. The MXene structure stabilized the MOF, enhancing energy storage. Findings suggest a strategy for using MOFs as electrode materials, overcoming challenges and opening new design avenues for energy storage systems.
Exploration and Implementation of the "Problem-Oriented + Progressive Case" Teaching Model Reform: A Case Study of Probability Theory and Mathematical Statistics
Mathematics is the most brilliant pearl of human wisdom. As a critical branch of modern mathematics, probability theory and mathematical statistics serve as foundational courses for engineering, economics, and management disciplines. Presently, the inherently mathematical nature of these courses often leads students to focus excessively on calculations while neglecting conceptual understanding, resulting in weak knowledge transfer abilities. Bridging "concrete phenomena" with "the principles of probability and statistics" in modeling requires high levels of various student competencies, and the disconnect between traditional teaching content and the current environment has led to outdated teaching cases. Additionally, a singular assessment method discourages student enthusiasm and results in low learning efficiency. To address these issues, our team adopted a "problem-oriented + hierarchical planning" teaching model. We restructured the teaching content, reshaped the teaching process, rebuilt online teaching resources, re-established multiple assessment methods, and integrated ideological and political elements. This reform achieved initial success and received high recognition from students. Furthermore, our team mentors guide students to participate in competitions such as the National College Students Mathematical Modeling Competition, the "Internet +" College Students Innovation and Entrepreneurship Competition, and the National College Students Mathematics Competition, thereby fostering the development of application-oriented talents.
Research on Onboard PNT Information Integrity Monitoring
Abstract With the Global Navigation Satellite System (GNSS) development, the GNSS applications have been widely used in the maritime field, especially for onboard equipments. As GNSS signals are susceptible to interference, the International Maritime Organization (IMO) has been investigating resilient PNT as the primary resolution for Positioning, Navigation and Timing (PNT). Resilient PNT is based on all available PNT sources and integrated positioning algorithms, not dependent on GNSS completely. It improves the PNT information integrity by the error compensation capability of the different navigation systems. This paper summarizes technology standards related to navigation systems and PNT integrity in the maritime field. On the basis of these analyses and investigations, a framework for onboard PNT integrity monitoring is proposed. It can provide a timely alarm to users when the navigation system cannot be trusted or used for regular operation. The proposed integrity monitoring framework is in line with the IMO e-Navigation strategy. It can provide resilient PNT information for the Maritime Autonomous Surface Ship (MASS) and the future intelligent development of the maritime field.
A Game Strategy for Getting Electricity Investment Interface Extension in Adaptation to the Construction of Hainan's High-Level Free Trade Port
This study focuses on the extension of the ' Getting Electricity ' investment interface as its main theme, taking the construction of key parks in the Hainan Free Trade Port ((Hainan Province, China)) as the real-world background. this study constructs an evolutionary model for the cooperative operation mechanism among the power grid company, customers, and government in the extension of the electricity investment interface. It analyzes the strategic interactions among the three parties during the implementation of the interface extension. Furthermore, the study conducts a stability analysis of the equilibrium among the government, power grid enterprises, and park customers under scenarios where value-added services are provided by the power grid enterprises. Different pathways for coordinating the interests of the three parties under various standards of investment interface extension are presented. This provides critical support for formulating targeted policies on ‘Getting Electricity' investment extensions and maximizing the comprehensive efficiency of electric power resources at a broader level.
Construction and Application of PE-PCK Characteristic Training Case Teaching Model for Excellent PE Teachers based on Artificial Intelligence Technology
In order to improve the quality of physical education teacher training, with the help of artificial intelligence technology, this study constructs and applies a case teaching model based on the PE-PCK characteristics of excellent physical education teachers. The feasibility of the case teaching model in the construction of this characteristic is demonstrated, and the PCK of normal students and excellent physical education teachers is compared and analyzed. Based on the content, characteristics, and construction path of PE-PCK for excellent physical education teachers, a case teaching model for PE-PCK excellent characteristics training of normal students is established. Selecting physical education teacher students from Qujing Normal University for implementation, analyzing the results of the formation of PCK in the curriculum before and after implementation, verifying the effectiveness of constructing excellent physical education teacher PCK characteristics for teacher students, and improving the model through interviews. The results indicate that this model helps teachers accumulate teaching cases, enhance their ability to promote student development, educational theory literacy, and subject teaching theory knowledge.
Utilizing Waste Glass Aggregates in Concrete: Enhancing Performance and Reducing Carbon Emissions through Life Cycle Assessment
To effectively address the issue of waste glass disposal, this paper proposed a novel approach of using waste glass aggregates as mineral admixtures in concrete. The study investigated the characteristics of waste glass aggregates and their effects on the compressive strength, flexural strength, and fluidity of concrete. Additionally, based on the life cycle assessment (LCA) method, the carbon emission reduction benefits of using waste glass aggregates were analyzed and calculated. The results indicate that with the increase in the content of glass fine aggregates, the fluidity of concrete increases, with larger particle sizes contributing to higher fluidity. Compared to ordinary concrete, the compressive and flexural strengths of waste glass aggregate concrete do not show a significant reduction. Moreover, the recycling and utilization of waste glass can reduce energy consumption and production costs, save resources, and reduce environmental pollution, which is significant for achieving the "dual carbon" goals in the construction industry.
Sustainable Building Materials: The Role of Waste Glass Powder in Improving UHPC Performance and Lowering CO2 Emissions
With the widespread use of glass products, a large amount of glass solid waste is generated worldwide annually. In the preparation of ultra-high performance concrete (UHPC), waste glass powder (WGP) can be used to replace part of river sand, thereby reducing the consumption of river sand (RS), promoting the reuse of waste glass, and lowering CO2 emissions. By replacing RS with WGP in different proportions, followed the standard test method of the spread diameter of freshly mixed UHPC in both vertical and horizontal directions, the standard test method of compressive and flexural strengths, the fluidity, compressive strength, and flexural strength of UHPC were tested. The experimental results show that the incorporation of WGP does not affect various performance indicators of concrete materials. It not only reduces the usage of RS but also lowers carbon emissions. The findings of this study are significant for promoting the research and development of ultra-low energy consumption building materials and the large-scale development of low-carbon buildings.
Exploration of the Impact of Digital Labor on the Capital Production Process based on Big Data Algorithms
In the era of digital economy, the computing and storage capabilities of computers drive the rapid development of digital technologies such as big data algorithms. Digital capital relies on digital technology to establish digital enterprises, making digital labor a new form of labor. However, there is controversy in academia over whether digital labor creates value and surplus value. This article analyzes the impact of digital labor on capital production from the perspective of big data algorithms, and finds that although digital labor changes the premise of capital production, it still belongs to "productive labor" and can create value and surplus value. On the surface, digital enterprises provide free and flexible work, but in reality, they are conducive to capital production. By monitoring workers through big data algorithms, they ensure production efficiency and quality. This "zero cost" form of employment breaks the capital limit of increasing the number of employees and enables the rapid proliferation of digital capital. Therefore, digital labor has not changed the essence of capital production, but has made digital labor production appear "technologically invisible" through the use of digital technology.
Design of Urban Waterlogging Emergency Rescue Boat from the Perspective of Human-Computer Interaction
With the acceleration of the urbanization process, urban waterlogging occurs frequently and the demand for emergency rescue is increasing. However, the existing urban waterlogging emergency rescue boats have deficiencies in human-computer interaction design, such as poor operation convenience and low information visualization, which affect the rescue efficiency. This study aims to improve the rescue efficiency and ease of use. The Delphi method, Analytic Hierarchy Process (AHP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) are used to construct the human-computer interaction design factor system and quantify the factor weights and relationships. The design factors are determined through discussions among experts in multiple fields. The relative weights are calculated by AHP, and the causal relationships are clarified by DEMATEL, and key factors such as safety protection and space layout are obtained. A mother-child rescue boat and auxiliary equipment are designed, including clear functional positioning, optimization of the shape structure and color matching, etc., which improves the comfort and rescue efficiency. This study provides a basis for the human-computer interaction design of rescue boats, improves the deficiencies in related research, provides ideas for similar designs, and can integrate advanced technologies and continue to be optimized in the future to deal with complex rescue scenarios.
Research on Reinforcement of Connection Nodes of Transmission Line Towers
The existing research on the reinforcement of transmission lines focuses on the main materials and foundations. To address the key issue of reinforcing the connection nodes between the upper structure and foundation of transmission line towers, a finite element model of this node was established using Abaqus, considering different stress conditions under natural disasters. Comparisons with the calculation results of both original and current specifications revealed that after reinforcing the upper structure, the weakest point is the shear failure of the connecting bolts between the shoe plate and the main material of the node, necessitating an increase in overall load-bearing capacity. Therefore, a node reinforcement plan compatible with the reinforcement type of the upper structure main material was proposed, which allows for construction without power outage and was verified through true type tests. This research on node reinforcement contributes to improving the service value of transmission lines and provides new insights for the reinforcement of similar prefabricated building nodes.
Guanxi: A Systematic Review of Interpersonal Relationships within Modern Workplace
Guanxi, a concept central to Chinese interpersonal relationships, extends into organisational settings, influencing various aspects of workplace interactions. While prior research has concentrated on supervisor-subordinate relationships, the complexity of modern workplaces necessitates a broader examination of all interpersonal connections. Understanding workplace Guanxi is vital for organisations operating in or with Chinese-speaking regions. This systematic review synthesizes research on Guanxi between 2019 and October 2024 across different workplace Guanxi. It examines how emerging technologies, such as big data analytics, offer novel avenues for investigating Guanxi's complexities and provide innovative insights into its role in modern organisational contexts. This research aims to achieve two objectives. The first is to conduct a comprehensive review of existing studies related to workplace Guanxi. By examining the factors that contribute to the formation and maintenance of Guanxi, as well as the outcomes that are influenced by these relationships, we aim to enrich the theoretical landscape and inform practical strategies for managing interpersonal relationships in diverse organisational contexts. The second goal focuses on identifying shortcomings in the current research and recommending directions for further exploration. By presenting a thorough framework for analysing Guanxi, this study enhances both academic understanding and practical approaches within cross-cultural management.
Study on the Optimization of Logistics Procurement Mode in the Background of Supply Chain Internet of Things
In the traditional logistics procurement model, there are problems such as incomplete information acquisition and information lag. The information between suppliers and purchasers cannot be exchanged in a timely manner, which leads to certain risks and increased costs when both parties formulate strategies. The application of Internet of Things (IoT) technology in supply chain management, through real-time data collection, intelligent processing, and optimized decision-making, significantly enhances the transparency, efficiency, and response speed of the supply chain. It is conducive to promoting internal management innovation and supplier collaborative innovation, reducing the cost of information acquisition and communication with suppliers during the procurement process, and thereby promoting the improvement of total factor productivity. This paper analyzes the problems existing in the logistics procurement model in the traditional environment, compares and analyzes the role and innovation of the procurement model under the background of supply chain IoT, and finally compares and demonstrates the logistics service levels of two companies before and after the optimization of the procurement model under the background of supply chain IoT. The optimization of the procurement model under the background of supply chain IoT can effectively improve the service quality of enterprises, enhance the overall management level, assist enterprises in sharing risks, effectively reduce the inventory costs of enterprises, and effectively strengthen the integration effect of logistics information. Therefore, the research on the optimization of the procurement model under the background of supply chain IoT is very necessary.
Chatgpt and Ai-Enabled Technologies for Personalized Learning: A Scientometric Analysis using Citespace
With the rapid development of ChatGPT and artificial intelligence (AI) technologies, education has gradually begun to apply ChatGPT and AI techniques to enhance teaching effectiveness and personalized learning experiences in recent years. Personalized learning has attracted considerable interest because of its ability to improve educational results by customizing teaching to meet the specific needs of each learner. There has been a surge in research exploring the application of AI technologies in personalized learning. This paper presents a scientometric analysis of research on AI-enabled technologies for personalized learning Using CiteSpace for visualization and analysis. The study provides an overview of the research landscape, identifying key themes, influential authors, and highly cited publications. The analysis reveals the interdisciplinary nature of the research, with contributions from fields such as educational technology, cognitive science, and machine learning. Additionally, it highlights the emergence of novel AI techniques, including adaptive learning systems, intelligent tutoring systems, and recommender systems. The findings contribute to understanding the current state of the art, identifying research trends, and guiding future studies in developing and implementing AI-enabled technologies for personalized learning.
Analysis of Chinese Time-Honored Restaurant Selection Embedded in Customers’ Nostalgia: based on Contextual Analysis Model
Revitalizing China's old restaurant brands is of great significance to the cultural self-confidence of the Chinese nation. In this study, nine old brands in Beijing, Shanghai and Guangzhou were selected, and a total of 543 valid questionnaires were obtained to analyze consumers' willingness to choose old brands. Specifically, based on consumers' perceived attractiveness and visit resistance to old firms, the authors used the contextual analysis model in the hierarchical linear model to explore the perturbation mechanism of the two nostalgic emotions in consumers' choices. The results of the analysis are as follows: first, perceived attractiveness has a positive effect on customer choice, while visit resistance has a negative effect on customer choice. Second, individual nostalgia has a positive effect on customer choice and plays a moderating role in the relationship between attractiveness, visit resistance and choice. Third, urban nostalgia also plays an important role in the relationship between attractiveness, visit resistance and choice. Overall, based on the contextual analysis model, this paper discovered the intrinsic choice mechanism of consumers for Chinese old restaurants, which can play a guiding role for their development.
The Influence of Social Media Users' Pre-Understanding on Polarization Behavior in the Process of International Information "Backflow" Assisted by Artificial Intelligence: A Study Based on Structural Equation Model
With the acceleration of globalization, social media has become a core channel for transnational information dissemination, playing an increasingly crucial role in the process of international information "backflow." This study analyzes the impact of social media users' pre-understanding on information polarization behavior through artificial intelligence (AI) recommendation systems and verifies the influence pathways of various factors on polarization behavior using structural equation modeling (SEM). The findings reveal that users' ideological orientations, cultural affiliations, and individual psychological attributes significantly affect their willingness to engage in polarized information behavior. Moreover, narrative comparison serves as a mediating variable between ideological orientation, cultural affiliation, individual psychological attributes, and the willingness to engage in polarization. AI recommendation systems continuously adjust information flows based on users' historical preferences, further exacerbating information polarization. This study provides new perspectives on the cognitive and emotional mechanisms involved in modern information dissemination and offers a theoretical foundation for improving cross-cultural communication and optimizing information technologies.
Improving Students’ Engagement with Feedback in Mathematics Classrooms: Based on Big Data Analytics
With the shift in educational paradigms from “teaching-centered” to “learning-centered”, mathematics classrooms are undergoing significant transformations. Feedback is a crucial factor influencing students’ learning and academic achievement, and students’ active engagement is considered a key prerequisite for effective feedback. However, current research has not fully explored how students engage with feedback in mathematics classrooms or the factors that influence the engagement. Additionally, providing timely and meaningful feedback remains a significant challenge for teachers. This article examines the nature of feedback, analyzes the critical role of student engagement with it, and identifies key factors influencing this engagement in mathematics classrooms. Based on the application of big data analysis, the study proposes strategies to enhance students’ engagement with feedback, aiming to promote mathematics learning and teaching. These findings provide a theoretical basis for improving students’ engagement with feedback and for guiding the strategic design of interventions aimed at improving this engagement.
Research on the Influence of Social Media on the Us Presidential Election in the Era of Big Data
Background: The impact of social media on the U.S. presidential election is a multifaceted and profound issue. Social media has revolutionized the way information is disseminated and consumed, playing a pivotal role in shaping voters' perceptions and behaviors. This paper explores the various ways in which social media influences voters' perceptions of the fairness of election rules, electoral opportunities, and their electoral intentions. Objectives: To analyze the role of social media in affecting voters' understanding of election rules, the formation of public opinions, and the shaping of electoral intentions. The study aims to highlight the challenges posed by misinformation, the polarization of public opinion, and the potential for manipulation of voter behavior through social media platforms. Methods:The research employs a comprehensive review of existing literature, case studies of recent U.S. presidential elections, and an analysis of social media trends and user interactions. It examines the efficiency and breadth of information transmission, the reliability of information, the risk of division and information filtering, and the promotion of voter mobilization and participation. Results: The study finds that social media significantly impacts voters' perceptions of the fairness of election rules, with both positive and negative effects. On one hand, it facilitates the rapid and wide dissemination of election-related information, enhancing voter awareness and participation. On the other hand, it poses risks due to the spread of false information, the reinforcement of existing biases through algorithmic recommendations, and the potential for targeted political advertising to influence voter behavior. Conclusions: Social media is a double-edged sword in the context of U.S. presidential elections. While it has the potential to increase civic engagement and political participation, it also introduces new challenges that threaten the integrity of the electoral process. The paper emphasizes the need for stronger regulations, improved platform responsibility, and enhanced media literacy among voters to ensure the fairness and transparency of elections in the digital age.
Multi-Objective Optimization and Intelligent Algorithm Study for 1.5mw Doubly Fed Wind Turbine Blades
Background: The demand for renewable energy has catapulted wind power to the forefront of sustainable energy options. Designing wind turbine blades is a multi-objective optimization problem with conflicting objectives. Optimization methods such as the weighted sum or the goal programming method fail in this context of conflicting objectives: minimum weight, cost, and strength. The study discusses the integration of artificial intelligence algorithms with conventional design approaches for optimal blades of a 1.5MW DFIG wind turbine. Advanced computational methods are used to integrate aerodynamic efficiency, structural durability, and economic feasibility. Methods: This paper uses a broad meta-analytical approach with the integration of finite element analysis (FEA)and artificial intelligence optimization algorithms. This paper will consider three critical optimization techniques: genetic algorithms (GA), particle swarm optimization (PSO), and gray wolf optimization (GWO). The blade performance will be considered for various operating conditions, including aerodynamic efficiency, structural integrity, and cost. This analysis will be performed based on IEC 61400 standards and new frontiers of innovative design optimization techniques. Results: Blade design optimization showed considerable improvement in using AI-driven approaches. For the GWO algorithm, better convergence is around 20% faster than the one obtained with traditional methods. This optimized design reduces weight by 8% and improves structural durability by an increase of 25% in fatigue life. For combined blade design, the maximum value of the power coefficient is 0.27, thus showing considerable improvement compared to the conventional designs. Besides, innovative material selection and design optimization could reduce the cost index by 17.6%. Conclusion: These results highlight that incorporating AI algorithms with traditional methods has significantly enhanced wind turbine blade optimization. This methodology was developed in such a way that it achieved a balanced solution for multi-objective designs, including computational efficiency. This study stresses industrial feasibility by using advanced optimization techniques for real applications, thus demonstrating their impact on the future of wind energy technological development.
Effect of Using Geogrid on Some Properties of Concrete
Geogrid use is important for civil engineering projects. The use of geogrids as an alternative to steel bars for reinforcing concrete, particularly in retaining walls, has been accepted by a few researchers. As a result, geogrids are being used in many other constructions, such as pavements and dams. The purpose of this study is to determine how the properties of Portland cement concrete are affected by geogrid, which is made of steel and plastic. The results showed that utilizing a single layer of plastic geogrid for every test produced the greatest results. However, when compared to concrete reinforced with a single layer of steel geogrid, concrete reinforced with two layers of steel geogrid showed substantial results in every test
Effect of Bovine Serum on Quorum Sensing Genes Expression in MDR Resistant P. Aeruginosa
Background: Pseudomonas aeruginosa, similar to several bacteria, use chemical signals for intercellular communication via use of quorum sensing (QS) mechanisms. QS enable the bacteria groups to detect population density and, in reaction to variations in cell density, to synchronize their behaviors. The current study intended to investigatethe effectof bovine serum on quorum sensing genes expression in Pseudomonas aeruginosaduring early stages of growth. Methodology: Pseudomonas aeruginosa isolates were obtained from burns, wounds, urine, and ears. Bacteria isolates were identified in accordance to their biochemical reactions, then antibiotic susceptibility and The isolates' ability to build biofilms was examined, and each bacterial isolate's genomic DNA was extracted in order to find quorum sensing genes (rhlR, rhlI, lasR, and lasI). Result: From different clinicalspecimens, twenty-five Pseudomonas aeruginosa isolates were identified,antibiotic resistance was present in these isolates., and it was found that only 10(40%) of these isolates were biofilm producers. Biofilm virulence genes lasR, lasI, rhlR, and rhlI were detected in all Pseudomonas aeruginosa isolates examined. Conclusion: lasR, lasI, rhlR, and rhlI genes were common inPseudomonas aeruginosa isolates that have high rate of resistance to all antibiotics, and strong ability in biofilm formation.
A Novel Computational Model of Insulin-Glucose Chaotic System to representβ-Cells Effects, Type 1 Diabetes Mellitus (T1DM) and Hyperglycemia
The math model is a very good way to look at how glucose and insulin work together. This work brings in a new math model of control of insulin and glucose in the human body, based on the well-known Prey-Predator model. Chaos is one of the known things in bio systems and hard systems from the info that is out there. The results of this study match up similarly with the known results, showing that the insulin-glucose control system is a very hard one in different backgrounds. This study, then, might make an extra part of seeing how reg system work in diabetes; mainly Type 1 diabetes and hyperinsulinemia.
Mathematical Modeling in Agricultural Sciences: Strategies for Reducing Water Consumption in Irrigation
Water scarcity is one of the most significant issues worldwide because agricultural water usage constitutes the largest share of freshwater resources. Only water-efficient irrigation practices can ensure food security without wasting water. The present work aims at improving mathematical models to optimize irrigation water use without reducing crop yields. An analysis of a 500-entry dataset using regression modeling and optimization was conducted based on input variables such as soil moisture, rainfall, crop type, and water consumption. These models predicted water usage and developed sustainable irrigation schedules in accordance with environmental factors. The approach can reduce water consumption to up to 30% with no yield penalties. The sensitivity analysis revealed that the most important parameters for the selection of irrigation frequency were soil moisture and rainfall, whereas Monte Carlo simulations validated the model's robustness under varying conditions. This study provides actionable knowledge for farmers and policymakers with scalable and adaptable solutions for sustainable water management. The study's strength is that predictive regression models can be combined with the optimization frameworks developed, and each crop-specific strategy will be able to adapt, using a very flexible and scalable approach to irrigate and challenge water scarcity issues.
Effect of magnesium fertilization and boron spraying on the growth and yield of two variety of potatoes (Solanum tuberosom L.) cultivars in calcareous soil in northern Iraq
A field experiment was conducted to study the effect of magnesium fertilization and boron spraying on some growth and yield characteristics of two potato cultivars in one of the fields of the College of Agriculture and Forestry/University of Mosul in northern Iraq. The field was divided into 3 panels (each panel represents a replicate) and each field was divided into 24 experimental units. Then, the potato cultivars CARRERA (v1) and PREMABELLE (v2) were planted. Magnesium was added at four levels (0, 40, 80, 120) kg h-1 and the symbols were given (Mg0, Mg1, Mg2, Mg3) respectively, while boron was added at three levels (0, 25, 50) mg L1 and the symbols were given (B0, B1, B2) respectively. The results showed the superiority of the (Mg3) treatment in all the studied traits and recorded the highest value for the trait of dry weight of the vegetative part (43.69) g plant-1, the total yield of tubers (56.48) tons ha-1, the percentage of dry matter in tubers (17.60)%, the percentage of starch in tubers (11.68)%, the percentage of protein in tubers (9.35)%, the magnesium content in the plant (23.26) mg kg-1, and the boron content in the plant (1.13) mg B kg-1 plant. As for the effect of spraying with boron, treatment (B2) was superior and recorded the highest value for the trait of dry weight of the vegetative part (41.95) g plant-1, total yield of tubers (52.48) tons h-1, percentage of dry matter in tubers (18.05)%, percentage of starch in tubers (12.08)%, percentage of protein in tubers (10.66)%, magnesium content in the plant (21.97) mg kg-1, and boron content in the plant (1.33) mg B kg-1 plant.
Effect of preparation temperature of titanium dioxide deposited on FTO glass on the UV photoresponse of photodetectors.
In this study, titanium dioxide (TiO2) nanocomposite membranes were prepared Installation on FTO bases by hydrothermal method and at high temperatures (150OC , 160OC,170OC and 180OC ) by holding the titanium dioxide concentration constant at 0.5 ml and time Constant deposition for 4h. These films were prepared by adding 0.5 ml. Of titanium butoxide (TiO2), 20 ml of hydrochloric acid, and 20 ml of deionized water. Heat treatment was carried out at 450 OC for two hours , UV tests were conducted on FTO samples (T1, T2, T3 and T4) at different temperatures, and the absorbance was studied and the energy gaps of the samples were calculated. XRD examinations also showed the peaks of each model, FESEM images also showed the shapes of nanorods for all models. Using a photoluminescence device, it was found that the photoluminescence value was concentrated at (415 – 405 nm) in the samples prepared with TiO2 , and its intensity was observed to decrease with increasing temperature. FTO/TiO2 photodetectors prepared at different temperatures, where the sensitivity (140 %, 175%,315.4% and 589% ) and photoresponse (0.007,0.014, 0.041and 0.171 A/W ) to temperatures (150OC , 160OC ,170OC and 180OC ) respectively. These parameters were calculated using ultraviolet light with a wavelength of 385 nm and an intensity of 0.05 mw/cm2 at room temperature , applying a bias voltage of )1volt (, an on time of 20 seconds, and an off time of 20 seconds. These rays were shone perpendicularly to the photodetectors at a distance of 5 cm. In this study, we will focus on the best results resulting from the measurements
A Hybrid Early Earthquake Prediction Model Using IoT Networks and Deep Learning Algorithms
Earthquakes are one of the most serious natural disasters that threaten human life and property, which makes their early prediction extremely important to reduce their devastating effects. This research aims to develop an intelligent earthquake prediction system based on the integration of Internet of Things (IoT) technologies with deep learning algorithms, specifically recurrent neural networks (RNN) and long-term short-term memory networks (LSTM). The methodology was developed by collecting Earthquake Data in real time using a network of IoT sensors, where historical real data from the year ( 1965 to 2021 ) was used to train the model and process this data using an optimized LSTM model with the use of activation functions (Relu,tanh , Sigmoid ) and optimization algorithms (Adam, RMSprop, SGD) . The model was developed using the ReLU activation function with the Adam optimization algorithm to improve the prediction accuracy with Learning Rate is equal to (0.001) and Epochs is equal to (200).. The experimental results showed a high efficiency of the proposed model, achieving a determination coefficient (R2). By (0.9984) with a low MSE error rate of up to (0.0003), which confirms the system's ability to accurately predict earthquakes before they occur. This research makes an important contribution to the field of forecasting natural disasters using artificial intelligence and IoT technologies. These results confirm the possibility of relying on modern technologies in the development of earthquake early warning systems, which opens up new horizons for research in the field of Natural Disaster Risk Reduction and protection of vulnerable communities.
Fusarium Wilt on Pepper, Disease Incidence and Control Potential using Trichoderma Longibrachiatum
Fusarium wilt is one of the important and major diseases that affect pepper crops in different parts of the world. The study aimed to determine the presence of wilt fungi associated with pepper crops in different areas of central Iraq and to evaluate the response of different pepper varieties to infection with the possibility of reducing infection using the biological agent Trichoderma longibrachiatum. The results of a survey of 10 areas showed the presence of Fusarium spp. on pepper in six areas covered by the survey. The fungal isolates were purified and multiplied and their pathogenicity was tested, where two isolates with the highest pathogenicity and an isolate with medium pathogenicity were selected for field tests. The pathogenic isolates were diagnosed microscopically and morphologically, and the diagnosis was confirmed molecularly, which was found to be due to the fungus Fusarium solani and registered under the accession number (PP95237) in (NCBI). The antagonism of the biological fungus Trichoderma longibrachiatum registered under the accession number MZ021580 against the isolated fungi was tested. The results showed the highest percentage of antagonism against the isolate (F3), while the antagonism ranged from (82.35) to (83.88). The pathogenicity of the pepper plant was tested for the three hybrids Carisma, Gennext and Golden Land, as the isolates F3 and F5 of the fungus Fusarium spp. gave the highest percentage of seed rot and seedling death. To evaluate the effectiveness of the biological control agents and varieties used to control Fusarium wilt disease on pepper, the results showed that T. longibrachiatum led to a reduction in the disease severity. T. longibrachiatum was superior in the field in increasing plant height, leaf area, dry weight of vegetative and root groups, fruit weight, total yield regardless the pepper variety compared to pathogen infected and in the absence of biological factor. Golden Land was superior in the field in most of the study indicators, especially in the presence of the biological fungus T. longibrachiatum.
Detection of New Strains of Echinochloa Crus-Galli Resistant to ALS-AHAS and Accase Weedicides for the First Time in Iraq
The competition between weeds and rice crop is one of the most important restrictions that limit the success of production in the world. Despite the excessive and multiple use of herbicides, weeds still one of the biggest problems that facing rice cultivation in Iraq, as both farmers, and agricultural departments complained about the challenges of controlling it particularly the herbicide-resistant strains. Current study aimed to determine the reason/s for the spread of the barnyard grass and its resistance to ALS-AHAS and ACCase herbicides. A field survey was conducted in different areas of Najaf province where rice crop is cultivated. Weeds samples were collected and diagnosed phenotypically and molecularly. The outcomes of this research showed that the geographical distribution of weeds accompanying the rice crop indicated that the highest average numerical density of the spread of weeds is 43% in the Shallal- Al-Mishkhab area, and the barnyard grass weed was occupied about 36% of the percentage of weeds in the areas included in the field survey. Phenotypic diagnosis of Echinochloa crus-galli samples also revealed the presence of the variety (var. crus-galli) for the first time in Iraq, supported by molecular diagnosis that resulted in the registration of 19 new strains that were deposited at NCBI under the serial number (OQ591719 to OQ591737) for the first time in Iraq. The phenotypic characteristics of the seeds showed that their average size was 2.82 - 3.67 mm and their average weight was 0.0012 - 0.0038 g. Anatomical characteristics showed that the average thickness of the upper epidermis of the cochineal was 146.22 µM, the lower one was 181.76 µM, and the thickness of the shell was 136.48 µM, which indicates the great ability of seeds to spread and sustain growth. The phylogenetic tree the registered strains showed a genetic affinity with the barnyard grass strains registered in the NCBI database that distinguish by its resistance to herbicides.
The Effect of Different Concentrations of Some Nanoparticles and Seaweed Extract on the Vegetative Growth of Rosmary [ Salvia Rosmarinus L.] Plant
The presented study to investigate the response of rosmary (Salvia rosmarinus L .) plants on the influence of nanoparticles (zinc and magnesium) and seaweed extract carried out in 2024 on fields in wasit university , College of Sciences , Iraq. The treatment construction was a perfectly randomized factorial experiment (5×5×3) in completely randomized design , done with three replications . The rosemary plants treated with nanoparticles (zinc oxide) with concentrations (0, 0.03 , 0.05) mg/ml and (magnesium oxide ) with concentrations (0, 0.02 , 0.04 ) mg/ml and rosmary treated with seaweed extract with concentrations (0, 50 , 100 , 150 , 200 )gm/L.Studing plant height (cm) , total chlorophyll (µmol m-2) , percentage of dry matter (%) , leaf surface area (cm2.plant-1) , nitrogen (%) , phosphorus (%) and potassium percentage .Based on results , adding nano zinc and nano magnesium and seaweed extract to the rosemary plant significantly increased all triats , regardless of vegetable characteristics or chemical content achieved wih highest concentrations using highest effects whether alone or with two interactions .
Vulnerability Analysis of Power System Considering the Multi-Scenario of Renewable Energy Output
With the wind power penetration rising in recent years, the high uncertainty of the wind power increases the vulnerability and outage risk of power systems. In view of this, we propose a vulnerability analysis method considering the topology and state of the wind power grid-connected system. Firstly, we construct a wind power prediction model named as the Kmeans_HPO_GRU_LSTM model. Secondly, we propose a vulnerability evaluation method of the wind power grid-connected system based on the resilience betweenness of power flow considering the uncertainty of the wind power. Finally, the effectiveness and accuracy of the proposed method are verified by the simulations on the IEEE-39 bus system and a regional power grid in China, respectively.
Prevalence and Factors Affected to Covid-19 Infection among Pregnant Women in Urban Area Bangkok Metropolitan, Thailand
Pregnant women were the risky group for COVID-19 infection. COVID-19 infection affected the health of pregnant women as well as the fetus. This study was a cross-sectional descriptive research aimed to seek for the prevalence and factors affected to COVID-19 infection among pregnant women in urban area Bangkok Metropolitan, Thailand. Samples were 284 pregnant women who received services at the antenatal care clinics in the hospitals and public health center affiliated to Bangkok Metropolitan during July to August 2023, retrieved via stratified sampling method. Research tools consisted of questionnaires for personal information, the prevention of COVID-19 infection in pregnant women, and mindset questionnaire on the issue of COVID-19 infection and pregnancy.Data analysis was conducted using descriptive statistics and multiple logistic regression analysis. Results showed that the prevalence of COVID-19 infection among pregnant women in urban area Bangkok Metropolitan, Thailand was equal to 52.11 percent. The factors affected to COVID-19 infection among pregnant women were having close person infected with COVID-19 (OR = 2.080; 95% CI = 1.02 - 4.23), wearing face mask (OR = 0.178; 95% CI = 0.04 - 0.77), fixed mindset (OR = 4.110; 95% CI = 2.50 - 6.75), and growth mindset (OR = 0.037; 95% CI = 0.02 - 0.09) at statistical significance level of 0.05. Research results could be the information for COVID-19 infection preventive planning in order to arrange for pregnant women care service, especially the risky group in order to prevent COVID-19 infection. Changing toward growth mindset would lower the impact on pregnant women and fetus. Moreover, it requires further planning to prevent respiratory infections in pregnant women and other groups.
Waffle Slab with Eccentric Opening Externally Strengthened by Basalt Textile Reinforced Mortar
This study investigates the behaviour of two-way waffle slabs that contain an eccentric opening. These slabs were externally strengthened by the basalt textile reinforced mortar (BTRM) technique under simply supported boundary conditions. The dimensions of the samples were (1050 ×1050 mm), and the precise topping thickness was (50 mm), while the joist dimension was (100 × 50 mm). Steel reinforcement was mesh at the top (ϕ6 mm @ 75 mm c/c), and the bottom of the joists contained (1 ϕ8 mm). The strengthening process was carried out on the ribs that were cut by square and circular opening, and the result revealed that warping the catted ribs by the basalt textile reinforced mortar technique enhanced flexural strength and significantly increased ductility.
Microstructural and Spectroscopic Analysis of Sweet Potato Starch Bioplastics Reinforced with Activated Carbon Using Scanning Electron Microscopy
The annual production of petroleum-based plastics releases hazardous gases, including carbon dioxide, which negatively impacts the environment. This study focuses on producing bioplastic using sweet potato starch as the main raw material, with activated carbon as a promising additive for enhancing starch-based biomaterials. A scanning electron microscope (SEM) was used to analyze the bioplastic samples, providing high-resolution imaging that is valuable for evaluating surface fractures, flaws, and contaminants. SEM analysis revealed that the morphological properties of the samples with and without activated carbon did not show significant differences in appearance, though voids, holes, and cracks were observed as flaws. The inclusion of activated carbon improved the bioplastics' capacity to absorb moisture and enhanced their porosity and tensile strength. By using renewable materials like sweet potato starch and additives like activated carbon, this study aims to develop sustainable alternatives to conventional plastics, reducing environmental impact and promoting eco-friendly materials in various industries.
Research on Acidizing Fracturing Based on Matrix - Fracture Interaction Permeability Model of Coal Seam
With the normalization of deep mining, scientific challenges are increasingly complex, particularly in managing and mining deep high-gas mines. For safe and efficient operations, gas treatment is essential before coal extraction to reduce gas content and pressure to safety standards. By quantifying coal's structural characteristics and understanding gas migration behavior, the mechanisms behind increased permeability is explored. To quantify the impact of gas adsorption on crack deformation, we define an internal expansion coefficient that represents the ratio of crack deformation due to gas adsorption relative to matrix deformation. It is analyzed that crack growth is influenced by principal stress magnitude and direction. Through quantitative characterization of coal structure, we analyze gas migration characteristics within it and investigate how strong oxidizing agents enhance oxidation yield. Based on the ternary coal industry's engineering context, we conduct a technology process analysis and verify application effects for acidizing combined with fracturing stimulation techniques.
Outsourcing and Value Chain Climbing: Vertical Coopetition in Buyer-supplier Supply Chains under Demand Uncertainty When the Supplier is Powerful
We study the supplier's value chain climbing behavior in a two-level supply chain consisting of a buyer and a supplier, where the buyer outsources its production to the supplier in the first stage, and the supplier may enter the market and compete with the buyer in the second stage. We model the problem as a two-stage game, assuming the supplier is relatively powerful and sets the outsourcing price. We show how the buyer decides on the outsourcing strategy and/or in-house production quantities, and obtain three equilibria, depending on the attitudes of the buyer to the supplier's value chain climbing behavior: (1) complete outsourcing in Stage 1; (2) complete in-house production in Stage 1; and (3) partial outsourcing in Stage 1. We identify a few properties of each equilibrium and examine if outsourcing reduces production costs under various conditions. Our computational study analyzes the optimal outsourcing strategies, expected profits, and the impact of demand uncertainty. The computational results indicate that a buyer may switch from outsourcing to in-house production if product prices are high or production costs are low. Additionally, the demand distribution affects both the speed and direction of this shift.
Research on the Impact of Financing Decisions on Innovation Investment of Small and Medium-sized Enterprises
Select relevant data of small and medium - sized enterprises (SMEs) from 2016 to 2020, this paper studied the impact of different financing methods on innovation investment. The results show that equity financing and endogenous financing have promoted innovation investment of SMEs, these two financing methods are both significantly positively correlated with innovation investment. In other words, as the scale of these two types of financing expands, the investment in innovation also increases correspondingly. However, when studying the relationship between debt financing and innovation investment, indicating that debt financing inhibits innovation investment. In addition, the relationship between different types of debt and innovation investment was studied. The results show that all four types of debt financing inhibits innovation investment. Basically, when debt financing goes up, innovation investment goes down. The study shows that the most favorable financing methods for innovation are endogenous financing and equity financing, and both of these financing methods have an incentive effect on innovation.
Research Status and Prospect of Nitrogen Fixation Technology: A Brief Review
The energy consumption of nitrogen fixation industry accounts for 2% of the world. The development of new nitrogen fixation technology based on renewable energy is of great significance for realizing energy conservation and emission reduction. The synthesis of ammonia by H-B method has low energy consumption and large scale, but the reaction conditions of high temperature and high pressure with catalysts and the high requirements for the stability of the power system make it incompatible with the regional and instability properties of renewable energy such as photovoltaic and wind power. The new nitrogen fixation technology represented by plasma technology and photoelectric catalytic nitrogen fixation technology has made remarkable progresses, with the advantages of mild conditions and environment friendliness, which provides a new direction for the future development of nitrogen fixation technology. The review of nitrogen fixation technology development, e.g. photoelectric catalytic nitrogen fixation and the photoelectric catalytic nitrogen fixation are presented. The analysis of the nitrogen fixation mechanism, energy consumption are introduced, which is expected to reduce the feasibility of nitrogen fixation energy consumption. This paper systematically summarizes the research status and looks forward the future development trend. It’s proposed that the coordinated nitrogen fixation by non-equilibrium plasma and catalyst will be the key to reduce the energy consumption and improve the yield of plasma nitrogen fixation, which has a broad development prospect.
Spatial Effects of Innovation Efficiency in High-tech Industries Based on DEA-Malmquist Model
New productive forces, driven by innovation and characterized by high-tech features, are essential for promoting high-quality economic development. These forces emphasize technological innovation as their core and industrial upgrading as their direction. Based on this premise, this paper combines the upgrading of high-tech industry with scientific and technological innovation, analyzing the innovation efficiency and the spatial effect. The results reveal significant regional disparities in the innovation efficiency across 30 regions in china, with notable differences between the Beijing-Tianjin-Hebei economic region, coastal areas, and other regions. Analysis indicates that the panel Spatial Lag Model provides the best fit. Government support, economic development, and urbanization exert positive spatial effects on innovation efficiency, while technological talent, international openness, and education exhibit negative spatial effects.
Research on Whole Process of Material Supply for Converter Station’s Main Equipment Based on BIM + GIS
Considering the problems with the main equipment of the converter station, such as difficult conditions, wide involvement, complex process and difficult transportation, on the basis of BIM+ GIS visualization technology support, research is carried out on multi-source data fusion of engineering material control, state information monitoring of the whole process of material supply, multi-node dynamic control and early warning of materials. A system for the whole process of material supply of the main equipment of the converter station is constructed, which realize the 3D visual display, fine control of important material supply, and warning of material supply status. Through the research, the information tracing mechanism of the whole process of material supply is established, and it supports the monitoring and warning of the material supply, effectively improves the efficiency of engineering material management, and lays the foundation for the promotion and application of the whole process control of engineering material supply.
Thermal Decomposition and Stability of CL-20/TNT Co-crystal
2, 4, 6, 8, 10, 12-hexanitro-2, 4, 6, 8, 10, 12-hexaazaisowurtzitane, commonly referred to as CL-20, is among the most potent explosives in existence, but it can't be generally applied due to its extremely high sensitivity. However, it has been found that CL-20 can be passivated by TNT within the co-crystal structure. In the current work, many testing methods such as DSC-TG and Raman spectroscopy, etc., were utilized to study the thermally induced decomposition of the CL-20/TNT co-crystal. Results show that the CL-20/TNT co-crystal can no longer maintain over 90 ℃, but its thermal decomposition process is severely retarded by the co-crystal structure, with a sharp increase in decomposition rate at around 127 °C. The activation energies of the thermal decomposition of the co-crystal below and above 127 ℃ are 141 kJ·mol-1 and 167 kJ·mol-1, respectively. In the region from 90°C to 127°C, the intermolecular hydrogen bonds between TNT and CL-20 molecules gradually weaken and break, leading to the decomposition of the CL-20/TNT co-crystal; above 127°C, CL-20 within the co-crystal tends to recrystallize into γ-phase CL-20, accelerating the decomposition of the co-crystal. This study discovered a slow decomposition process preceding the rapid decomposition of the CL-20/TNT co-crystal, re-determined the thermal decomposition products and the decomposition temperature of this explosive, and found that the thermal stability of the co-crystal explosive is influenced by the phase transitions of its components.
The Strategic Impact of Business Intelligence Tools: A Review of Decision-Making and Ambidexterity
This study aims at exploring the use of Business Intelligence (BI) tools in supporting decision making processes at strategic, tactical and operational levels. In this study, the research data is collected from secondary sources including the academic developments, business reports, and case analysis to investigate the role of BI in enhancing the decision making, organizational ambidexterity, and strategic alignment in different business sectors. The research also shows that the use of BI tools enhances decision making accuracy and quickness, giving organizations valuable information for informed decision making. The present work also discusses how BI can help organizations to attain organizational ambidexterity, i.e., the ability to explore new markets while, at the same time, exploiting the existing ones. However, the analysis also finds that there are critical problems, including data integration, limited resources, and the search for an optimal balance between analytics and intuition. A comparison with other industries shows that while BI tools have a rather high potential, their efficiency strongly depends on the context and may require a unique approach to address the needs of a particular industry. This research work will also be useful to managers and other stakeholders in organizations in order to enhance their understanding on how to effectively integrate BI to gain competitive advantage in today’s business environment.
Comparative Analysis of Project Management Software: Functionality, Usability, and Integration for Modern Workflows
This research aims at determining the suitability of the trending PM tools; Microsoft Project, Jira, Trello, and Asana based on the functionality, security, usability and cost needs of different organisations. The study reveals that while Microsoft Project and Jira offer better-developed features, their complexity and cost make them more suitable for large companies. On the other hand, Trello and Asana have simple and easy to use interface, which is suitable for small and medium sized business and also they are less costly, but they lack the depth of features and security features which may be required by some large companies or companies in some highly regulated industries. In addition, the study reveals an increasing interest in flexible PM tools that support different methodologies and have security measures to meet the data protection challenges of cloud projects. The results of this study imply that PM tool selection criteria should be matched to particular organisational capability, weighing utility, security, and cost. The study also suggests new tendencies concerning further developments such as the demand for PM solutions that are suitable for Agile and Waterfall methodologies concurrently, as well as the need for more elastic pricing strategies
"The Impact of AI on Pharma R&D: Accelerating Innovation in Drug Developments"
The paper investigates how AI technologies, particularly machine learning, with deep learning and natural language processing, transform drug development processes. The pharmaceutical R&D sector transforms its operations through artificial intelligence technology implementation into industry operations. Pharmaceutical R&D operates more efficiently with AI implementation because they can reach drug market launches quickly with precise drug development results obtained at reduced research costs through shorter product development frameworks. This initial part highlights how conventional research development encounters increasing barriers due to high project fees and numerous unsuccessful outcomes AI intervention becomes essential. The analysis combines literature research, pharmaceutical case studies and expert professional interviews to develop findings. The research employed data derived from peer-reviewed publications and corporate reports as well as AI conference papers. The research studies how AI is implemented through three core R&D segments that concentrate on target discovery together with drug transformation operations alongside clinical trial optimization initiatives. The evaluation of drug development acceleration examines two advanced tools, which consist of AI-powered molecular modeling and predictive analytics. Pharmaceutical research development speeds quickly transform through AI systems, which improve operational effectiveness across drug discovery and development processes. AI technology has proven to reduce pharmaceutical timelines while decreasing production costs while simultaneously improving predictive abilities for decision support. Pharmaceutical R&D organizations speed up development processes with AI technology to address critical global health needs, according to research findings. AI exploitation in the pharmaceutical sector needs a complete evaluation of ethical components with regulatory structures and expert workforce deployment approaches.
A Feasibility Study on the Development of Inbound Tourism Based on Confucian Culture and Its Effect on Environment Protection in the Downstream Areas of Yellow River
The Yellow River flows through northern China and forms a fertile Huabei Plain in its downstream areas, where the psychological stability brought by farming, idyllic scenery and the stable social order makes people more gentle and polite, giving birth to the profound Confucian culture. Many foreign tourists admired Confucian culture, which leads to the will of sightseeing and experiencing Confucian culture at the birthplace of Confucian culture. If those foreign tourists can be guided to visit cultural cities and countryside scenery in the downstream areas of the Yellow River, and feel the charm of Confucian culture there, it will stimulate the inbound tourism boom and promote the economic development of that region. In addition, with more income of inbound tourism and the development of economy, the local government can provide more fund on environmental protection. Therefore, a survey has been carried out on the foreign tourists’ cognition of Confucian culture and their willingness to visit China’s Confucian cultural scenic spots, and a judgment based on this has been made: It is feasible to guide foreign tourists to visit downstream areas of the Yellow River full of Confucian cultural resources, and inbound tourism is beneficial to the environment protection.
Sports Tourism and Environmental Protection a Study on Symbiotic Path and Sustainable Development
With the rapid development of global sports tourism, its potential impact on ecological environment has attracted the attention of society and scholars. This paper deeply explores the harmonious symbiosis path between sports tourism and environmental protection, and carefully analyzes the mechanism of sports tourism on ecological environment. Through the comprehensive use of literature review, typical case analysis and field investigation and other methods, the core value of ecological sports tourism is highlighted. Advocate the deep integration of environmental protection concepts in the development and construction process of sports tourism, aiming to explore a win-win way that can promote the prosperity of sports tourism and effectively protect the ecological environment. Therefore, this paper puts forward a series of feasible paths and strategies to achieve the harmonious development of sports tourism and environmental protection.
Construction of a Situation Awareness Influencing Factor System for Manned Submersibles in Dynamic Complex Underwater Operations
To bolster the marine economy and strengthen maritime power, China's manned submersible program faces the challenge of managing the inherent complexity of underwater operations and the need for synchronized device interactions. This paper presents a novel situational awareness information system designed specifically for these complex environments. This system integrates multiple factors such as equipment status, task requirements, environmental variables, and human behavior to provide a holistic view. Subsequently, the system effectively organizes and categorizes this diverse information into a structured taxonomy. Furthermore, by utilizing the Analytic Network Process (ANP), the information elements are prioritized, offering a nuanced analysis that underscores the system's critical role in enhancing decision-making capabilities for divers and improving the efficiency and safety of underwater operations. Ultimately, the effectiveness of this system is demonstrated, showcasing its superiority in managing the complexities of manned submersible operations.
Research on the Comprehensive Test Method for the Characteristics of Generator De-Excitation Switch
This paper proposes a comprehensive test method to meet the multi-functional testing requirements of de-excitation switch, which can simplify the complex test process and greatly reduce the cost of testing. A comprehensive test topology was designed based on the requirements of multiple test projects, and the key parameters were intended. The test circuit generates a constant test current based on the supercapacitor, Through the shunt and the precision recording module, the method realizes the synchronous recording of the test current and the test voltage, and can accurately record the action state and action time of each break of the degaussing switch, so as to realize the switching action and synchronization performance test of the de-excitation switch. A multi-functional comprehensive test circuit simulation model was built for verification, which showed the feasibility of the circuit design. A multi-functional comprehensive test device was built for experiments, and the experimental results show that the comprehensive test device can accurately test the contact resistance of the break, accurately test the action time of the switch, and test the synchronous performance of the break, which can be used efficiently and flexibly in the on-site maintenance work.
Research on the Multi-dimensional Impact of Novel Tactile Film Materials on the Gaming Experience of Online Game Players
Driven by the advent of the digital intelligence era, online games have revolutionized the gaming landscape by immersing players in hyper-realistic digital worlds. This immersion is facilitated through the integration of advanced digital technologies, highly realistic simulations, and innovative tactile film materials. Consequently, the gaming experience has transcended mere sensory stimulation, evolving into a comprehensive aesthetic enjoyment that engages players on multiple levels. During this transformative process, the identities of players, the spatial dimensions of the game roles, and the modalities of experiential engagement have undergone significant shifts and reconstructions. Collectively, these changes have coalesced to form the distinctive digital aesthetic attributes of online games. Focusing on these attributes, this article delineates its research questions across three pivotal dimensions: the reconstruction of player identities, the generative reconfiguration of game spaces, and the aesthetic recalibration of the gaming experience. The reconstruction of identity primarily manifests the fluidity and transformation of identity precipitated by the innovation of the cyborg body. Meanwhile, the reconstruction of the game space delineates the interplay within the virtual-real-supra-real fields. The game experience, in turn, underscores the interrelationship between the physical body and sensory perceptions of game participants. Interpreting these issues elucidates the identity flux between game participants and game characters, clarifies the metamorphosis of game scenarios, and re-examines the relationships of "people and objects," "people and space," and "people and society" in the digital age, thereby underscoring its significant meaning and value. This is crucial for further expanding the theoretical cognitive boundaries of "human-machine relationships" and catalyzing human "chain-like" thinking regarding the real world, virtual spaces, and the symbiotic relationship between humans and machines.
Energy Method for Analyzing the Large Deformation of Geogrid Cushions above Cylindrical Soil Cavities
In regions prone to soil cavity development, geogrid-reinforced embankments must consider the impact of foundation settlement. This study models the geogrid cushion as a thin circular elastic plate, with the embankment's self-weight, vehicular traffic loads, and the frictional forces at the reinforcement-soil interface all translated into external loads. Utilizing the Pasternak model to simulate the foundation, we adhere to the principle of large deflection in thin plates to account for three types of strain energy and the work done by external loads, thereby deriving analytical expressions for the deformation of the geogrid cushion. We analysed the impact of embankment height, reinforcement-soil interface parameters, modulus of subgrade reaction, and the shear modulus of the foundation soil on the deflection of the reinforced-cushion. The findings reveal that when the embankment height surpasses the cavity diameter, its influence on differential settlement in anchorage areas becomes negligible. As the tangential resistance coefficient at the reinforcement-soil interface increases, its effect on differential settlement in the subsidence area progressively diminishes. The modulus of subgrade reaction influences differential settlement in anchorage areas but not in the collapsed area. The taller the embankment, the more significant the impact of the modulus of subgrade reaction in the collapsed area. When the embankment height and the thickness of the Pasternak shear layer are considerable, and the modulus of subgrade reaction is low, the effect of the foundation shear modulus on the deflection of the reinforced-cushion must be considered.
Exploration and Innovation of a Competition-Integrated Teaching Model for Vehicle Engineering Based on the OBE-CDIO Approach and Artificial Intelligence
The rapid advancement of Industry 4.0, intelligent manufacturing, and the new energy vehicle industry has raised higher demands for vehicle engineering talent, particularly in multidisciplinary integration, practical skills, and innovation. However, traditional teaching models exhibit significant shortcomings, such as a disconnect between theory and practice and inadequate cultivation of innovation capabilities. To address these challenges, this study proposes a teaching reform based on case-based teaching and the OBE-CDIO model. By integrating outcome-based education (OBE) with the conceive-design-implement-operate (CDIO) approach and incorporating academic competitions, the reform establishes a multi-level teaching system that deeply integrates theory and practice. Furthermore, artificial intelligence (AI) is employed to support curriculum design, competition analysis, and personalized student feedback, ensuring a dynamic alignment with industry needs. Following implementation, the completion rate of interdisciplinary graduation projects exceeded 80%, academic competition awards increased by 45%, and the employment rate of students rose from 88% to 96%, with some research outcomes adopted by enterprises. The results demonstrate the model's effectiveness in enhancing students' comprehensive abilities and fostering innovative engineering talent, providing practical insights for engineering education reform.
How Does Empathic Leadership Link To Safety Behaviors: The Mediating Role of Belongingness
In contemporary healthcare, particularly among nursing staff, there is a notable incidence of occupational accidents linked to safety participation. This research investigated the relationship between empathetic leadership and two key aspects of employee safety behavior—safety involvement and adherence—considering a sense of belonging as an intermediary factor. Data was gathered from 372 nurses across various hospitals over a two-week period. The findings strongly upheld our hypotheses. Empathetic leadership was found to have a positive association with belongingness. Furthermore, belongingness was predictive of both safety adherence and involvement, and it functioned as a mediator in the link between empathetic leadership and safety-related behaviors.. Additionally, our analysis explored the moderating role of social support. The outcomes reveal that to foster safety behaviors, managers in industries that are sensitive to safety, such as the nursing profession, should focus on psychological aspects within the workplace. The study further discusses the implications of these findings for safety interventions and future research.
Forecasting Sales Trends Using Time Series Analysis: A Comparative Study Of Traditional And Machine Learning Models
This paper aims to analyze the merits and demerits of using traditional time series models and the more sophisticated machine learning approaches in sales forecasting with the view of determining their effectiveness in different conditions. These models include the ARIMA and Exponential smoothing models which are popular because they are easy to understand and easy to compute and as such are useful in the short term forecasting of data that has a clear seasonal pattern. But these models are not very effective with non-linear and complex data structures. On the other hand, XGBoost, LightGBM, and DeepAR models are found to outperform other models in terms of accuracy for high dimensional and highly volatile data. These models are complex and computationally intensive, and the interpretability of these models is also relatively low; however, they offer better flexibility and improved prediction for dynamic forecasting. The use of hybrid models which incorporates both the conventional and the machine learning models is identified to be as being effective for organizations that want to achieve high prediction, model transparency and computational speed. This study offers implications for model selection based on data complexity, forecast horizon, and business needs to enrich the literature on data-driven decision making in sales forecasting.
Intelligent Control Console System based on Deep Learning: Key Path and Warning Mechanism Optimization for Improving CNC Machining Efficiency
The increasing complexity of modern CNC (Computer Numerical Control) machining processes necessitates advanced systems to enhance operational efficiency and precision. This paper presents an intelligent control console system based on deep learning, designed to optimize key machining paths and warning mechanisms, ultimately improving CNC machining efficiency. The proposed system integrates reinforcement learning and deep neural networks to dynamically adjust machining parameters such as cutting speed, feed rate, and tool selection, ensuring optimal performance under varying operational conditions. By leveraging real-time sensor data, the system continuously monitors tool wear, machine health, and process performance, providing early fault detection and predictive maintenance capabilities. Moreover, it incorporates an optimized warning mechanism that alerts operators about potential failures, tool malfunctions, or deviations from expected performance, enabling timely interventions. The effectiveness of the system is demonstrated through its ability to reduce downtime, enhance tool life, and improve product quality by fine-tuning machining parameters based on continuous feedback. Real-time adaptive adjustments of parameters and multi-objective optimization for machining time, energy consumption, and tool wear further contribute to the system's operational efficiency. Additionally, the integration of IoT sensors with deep learning models enhances the system’s predictive capabilities, ensuring high levels of accuracy and decision-making support for CNC operators. This paper presents a novel approach to intelligent CNC machining control, providing significant contributions to the development of smart manufacturing systems.
Synthesis and Molecular Docking Studies of New 1,2,3-Triazole Derivatives Bearing Cyclohexa-2,4-Dienone for Potential Antivirus Activity
In this study, many new 1,4-disubstituted-1,2,3-triazole derivatives with a cyclohexa-2,4-dienone ring system are synthesized and their molecular docking analysis is conducted. Under basic circumstances, a series of 1,4-disubstituted-1,2,3-triazole derivatives 3a-e were reacted with ethyl acetoacetate to produce the target compounds 4a-e. FT-IR, 1H-NMR, and 13C-NMR spectroscopies were used to identify compounds 4a–e. The resulting compounds 4a–e were tested on two chosen proteins, 7dpp and 8cx9, as possible antiviral agents using in-silico molecular docking simulations. Comparing the newly synthesized compounds 4a–e to three popular antiviral drugs, the results showed that all of them had a strong binding affinity with the target proteins.
The Honeycombed Damage Effect on UHPFRC Strengthened Short Column under Eccentric Load
This research study aims to investigate the behavior of reinforced concrete columns with a honeycomb-damaged ratio under the effect of eccentric loading at the end of the effective length of the column. The experimental analyses were conducted on nine columns with a total height of 950 mm and a cross-section area of (120*120) mm to evaluate the impact of various factors on column behavior. The total columns had two corbel heads for applying the loads. All column specimens were tested under eccentric load until failure with an eccentricity ratio equal to (10/12). These factors include the defect ratio (35% and 70%) as a part of the specimen cross-sectional area at (L/4), strengthening of UHPFRC jacket thickness (15 and 30) mm and the side of strengthening (laminates 2-sides and full casting 4-sides). The honeycomb defect has been identified via foam slicing as an indicator of specimen failure. Furthermore, the bonding procedure between the new and old layers included roughening the old surface by grinding, followed by the application of epoxy resin for adhesion. The honeycombed specimens were repaired with UHPFRC before the test. According to the results of the tests, the application of the UHPFRC jacket to strengthen concrete columns resulted in improvements within the areas of ultimate load resistance, stiffness, energy absorption, and ductility. Additionally, the honeycomb-damaged ratio of the column specimens, the thickness of the UHPFRC jacket, and the side of strengthening all had a role in determining the amount of these increases. All of the column specimens failed when subjected to various levels of load. Depending on the experimental results, the gain in strength, stiffness, ductility, toughness, failure mode, and load versus displacement were recorded, illustrated and discussed.
Customers' Reported Satisfaction with Bank Services is mediated by Perceived Ease of Use, which Influences Intention to Use (Case Study: Credit Institution)
The purpose of this research was to determine how perceived ease of use at Credit Institution affected intention to use, with reported enjoyment of use acting as a mediating factor. Data was gathered using a five-point Likert scale in a questionnaire created by the researcher. Three techniques—content validity, construct validity, and convergent validity—were applied to evaluate the questionnaire's validity. Reliability was also assessed using Cronbach's alpha coefficient and composite reliability. Combination dependability is measured as values more than 0.7 and less than 0.95. As a result, it is also verified that the questions are reliable overall. The 400 consumers who received the questionnaire at random comprise the statistical population of this study, which also includes all Credit Institution clients. For data analysis, PLS software and the structural equation modeling technique have been employed. The relationship was corroborated by the results, which indicated that the perceived ease of use had a 0.39 effect on the perceived enjoyment of usage. The association is validated, as seen by the 0.48 influence of perceived ease of use on perceived intention to use. Additionally, it has been verified that reported satisfaction of usage influences perceived use intention.
Responding to Delinquent Children in Afghanistan's Criminal Policy
There may be various methods and different criteria to deal with children's offending, which are defined based on the approaches of the criminal policy of the countries. This article aims to explain the approach of Afghanistan's penal policy towards the responding to delinquent children, which has been investigated using descriptive-analytical method. the legislators have mostly used protective and corrective actions and measures, considering the principle of minimum punishment, protecting the rights and interests of children, and also paying attention to the adverse effects of widespread criminalization and punishment. The measures foreseen in the criminal laws have been established by observing important criteria such as being corrective, dynamism and adaptability, and favorability to the accused, which are completely supportive. On the other hand, by using their legal powers, judicial officers can take into account the above criteria in determining the punishment and how to apply it to the accused and take appropriate actions according to the personal case of the accused. That Afghanistan's criminal policy against children's offending has a unified and moderate approach, and is more focused on rehabilitation of the offender, rather than merely punishing him/her.
Dynamic Analysis of Drill String and Wellbore in Ultra Short Radius Wells Based on Abaqus
Economic and efficient ultra-short radius horizontal drilling technology is an effective technology to increase the production of old oilfields. However, the fatigue breakage of the drilling string and wellbore and the coupling vibration between the drilling string and wellbore are the critical problems encountered in its engineering applications. We took the drilling string and wellbore of an experimental simulation of ultra-short radius as the object of research, set up the mechanical and mathematical model with strong adaptability, and applied theories to analyze the force condition of the drilling string and ultra-short radius wellbore. Consequently, a 3D computational model representing the drilling string and wellbore was built. The dynamic simulation software Abaqus was utilized to simulate the working force state of the ultra-short radius wellbore and drilling string, analyze the force generated by mutual contact movement under various operating conditions, and investigate the drilling string's dynamics and stability during the tripping process. The results reveal that the drill string's rotating speed influences its tripping ability: more intense the speed, greater the decrease in axial force. Thus, the rotational speed can be changed, and the lubrication between the wellbore and drill string can be improved to reduce the force between the drill string and wellbore, which is conducive to the entry of the drill string.
Big Data-Driven Resilience Research Among Rural Children in Underdeveloped Areas: A Perspective of Spiritual Social Work Intervention
Big data technology has become an important tool to promote the localization of social work. Social work intervention driven by big data is characterized by strong timeliness and professionalism. In this context, rural children face multiple difficulties in their growth process, which has a profound impact on their academic performance and life development. Resilience, as an individual's coping strategy in the face of stress and adversity, is of great significance to the healthy growth of rural children. Based on the perspective of spiritual social work theory, through field research in a primary school in Y County, H Province, this study interprets the resilience patterns of 20 rural children and conducts social work intervention. It is found that intervention in group work can help promote the healthy transformation of their resilience patterns, and the intervention techniques in spiritual social work can help improve the resilience level of rural children. Big data analysis provides a new perspective and logic for the intervention research of rural children's resilience. In addition, this study attempts to discuss the social support system of rural children and proposes a three-level system architecture of "distant guidance - element support - value-driven".
A Residual-Self-Attention Fusion Network for Identification and Classification of Apparent Defects in Fair-faced Concrete
This paper proposes a fair-faced concrete surface defect classification method based on a residual-self-attention deep fusion network (RSAFuser), aiming to solve the issues of low accuracy and high time consumption in the task. The method uses an "Expert Voting"-like feature extraction approach to identify surface defects of fair-faced concrete, such as Pores, Surface Contamination, Cracks, and Repairs. The residual expert network handles small-area local point defects, such as Pores and Repairs, while the self-attention expert network processes larger-area global linear and block-shaped defects, like cracks and Surface Contamination, extracting and reflecting the overall features of the input image from different perspectives. The fused network not only retains the advantages of the residual network in local information recognition and mitigating network degradation, but also uses the self-attention network for block processing and effectively captures global long-distance information through weight calculation. Experimental results show that compared to ResNet and Swin-Transformer, RSAFuser improves accuracy by 19.19% and 21.51%, respectively. Experimental results validate the effectiveness and accuracy of the proposed method in the fair-faced concrete defect classification task.
The Impact of Artificial Intelligence for Computer Graphics on Animation Ontology Cognition and Its Future Development
Animation belongs to a thing closely combined with technology, and its ontology cognition will be affected by technological innovation. The combination of artificial intelligence and computer graphics, referred to as AICG, will have some significant impact on animation ontology cognition, enhance the importance of animation technology ontology, empower animation content expansion and process, and promote technological innovation and interdisciplinary research. Based on this, based on AICG, this paper analyzes the application of AICG in animation creation, constructs PFNN model, intelligently generates animation actions, and describes the principle and main structure of PFNN action generation model. Finally, it shows the future development trend of AICG participating in animation creation, and promotes the deeper integration of AICG technology and animation creation.
Research on Key Nodes Identification in Intermodal Transportation Network Considering Node Strength
Identifying key nodes in the coal rail-water intermodal transportation network is crucial for national energy supply, but traditional identification methods often overlook the critical factor of node strength. This paper introduced, for the first time, a Grey Relation Analysis based Importance Evaluation method considering Node Strength (GRAIE-NS), which combined local indicators of node degree with global indicators of node efficiency to achieve precise quantification of node importance. To verify the effectiveness of the GRAIE-NS method, an analysis was conducted using the actual operational network of coal rail-water intermodal transportation in China, comparing this method with classic key node identification approaches. Finally, a sensitivity analysis was performed to assess the impact of variations in the weights of initial importance and node strength on network invulnerability. The results indicate that the proposed method demonstrates high accuracy, particularly when the weight of initial importance is significant, as the failure of identified key nodes can have a substantial impact on the network.
Research on the Construction of Fault Prediction Model for Flexible DC Converter Valve Submodule Network Based on Deep Learning
Along with the wide use of DC power network in the modern electric power system, the fault of the sub-module network of converter valve has become a key factor which influences the stability of the power system. For the purpose of forecasting these failures and enhancing the stability of the power system, a new model of the deep learning fault forecast is presented in this paper, which is based on the Transformer and GCN. This model combines the global time series modeling capability of Transformer with the topological relationship modeling capability of GCN, and can simultaneously process the time series data of converter valve submodule and the spatial topological structure information between modules, thereby enhancing the accuracy of fault prediction. Experimental results show that this model has significant advantages over traditional machine learning methods (SVM, RF) and other deep learning models (LSTM, CNN + LSTM). The Transformer + GCN model achieved 96.2%, 94.7% and 95.4% in accuracy, recall and F1 score respectively, which greatly surpassed the performance of traditional models. It is proved that the combination of time series and topology characteristics of the deep learning model can increase the precision and robustness of the fault forecast.
Techno-Economic Assessment of Wind Energy with Battery Storage and Membrane-Based Air Filtration for Cleaner and More Efficient Power in Naama
This study provides a detailed techno-economic assessment of a wind energy system integrated with battery storage, specifically designed for the Naâma region of Algeria. Conducted at a small scale using a mini wind turbine, the results were scaled to model the performance of a wind farm comprising multiple turbines. The analysis combines experimental measurements, refined simulations, and economic evaluations to optimize system performance and assess its feasibility for large-scale deployment. The technical findings highlight the seasonal wind patterns modeled using the Weibull distribution, revealing a characteristic wind speed of 8 m/s, with peak energy production during spring and fall. The hybrid system achieved an average seasonal efficiency of 85%, supported by optimized battery storage of 100–120 kWh, ensuring a reliable energy supply during surplus and deficit periods. A key innovation in this study is the integration of membrane-based filtration technology to mitigate the disper- sion of PM10 and PM2.5 particles around wind turbines. Simulations using Computational Fluid Dynamics (CFD) demonstrate that membrane filtration reduces PM10 concentration by up to 65% within 20 meters of the turbine. The results further indicate that filtration is most effective at low wind speeds, where natural dispersion is mini- mal. At higher wind speeds, wind turbulence naturally reduces PM10 concentration, making filtration less critical beyond 50 meters. The efficiency of filtration systems decreases with distance, emphasizing the need for strategic membrane placement near turbine bases. Moreover, higher-efficiency membranes (80%) significantly outperform lower-efficiency solutions (50%) in reducing particulate pollution. Economic evaluations demonstrate the system’s viability, with a scaled-up configuration of 20 turbines achiev- ing a net present value (NPV) exceeding 11 million DZD ($82,090) and a payback period of 7 years under a tariff of 4 DZD/kWh. Sensitivity analyses emphasize the critical role of energy tariffs, identifying profitability thresholds at 2.5 DZD/kWh. The additional analysis of air filtration systems suggests potential maintenance cost reductions by mitigating particle-induced wear on turbine components. This study not only validates the technical and economic feasibility of wind energy systems in Naâma but also demonstrates the potential environmental benefits of membrane filtration in wind farms. The findings provide a scalable framework for optimizing wind energy production while ensuring sustainable environmental management. Future research should focus on field validation of membrane filtration, alternative aerodynamic strategies for pollution control, and large-scale deployment models to further enhance system performance and adaptability.
The Impact of Government Subsidies on the Alliance Mechanism Between Microgrids and Shared Energy Storage Under A Three-Party Evolutionary Game
Aggregating distributed renewable energy (RE), flexible loads, and energy storage (ES) in microgrids (MGs) is a feasible option for optimizing energy structures and facilitating the low-carbon transformation of power systems. The shared energy storage (SES) model, as an emerging business model, can reduce the resource waste issues caused by the ES configuration of individual MGs, and connect multiple MGs to achieve energy sharing among different MGs. However, the cooperation between MGs and SES is influenced by government subsidies. The transition to a sustainable energy system requires a strong policy and regulatory framework to support the deployment of RE MGs and ES systems. This paper addresses the cooperation between MGs and SES. Firstly, considering the limited rationality of participants, it proposes an evolutionary game approach to analyze the impact of government subsidies on their alliance mechanism. Secondly, it demonstrates the stability of the equilibrium points in the aforementioned game model, discusses the equilibrium stability of the system under different gaming scenarios and their influencing factors, and further analyzes the dynamic behavioral characteristics of trilateral evolution. Finally, the impact of different initial strategy states, subsidy costs, penalty amounts, and profit statuses on the behavior of each party is discussed. The results confirm the validity of the proposed model.
The Multi-Objective Robust Optimization of Molding Process Parameters for Particles from Tobacco Stems and Wood Chips
Considering the stochastic uncertainty of three parameters (molding temperature, moisture content of raw materials and molding pressure) in the process of particles from tobacco stems and wood chips, the analytical robust optimal design of two quality properties (particle density and radial resistance pressure) is carried out. Based on the quadratic regression equation between performance and parameters, and the improved analytic hierarchy process (AHP) is used to deal with multi-objective. Taking the minimum variance of performance fluctuation as the objective, the optimal combination of process parameters is obtained by interior-point algorithm. Comparing with deterministic optimization, the variance of particle density is reduced by 8.45%, and the variance of radial resistance pressure is reduced by 29.76%, which has a good reference value to the actual production of biomass fuel.
Characteristics and Evolutionary Laws of The Factor Structure of China’s Entire Industry
Based on clarifying the boundaries of production factors such as labor, land, technology, and capital, this study constructs an industrial factor structure measurement model using an extended C-D production function and input-output analysis. Using panel data of regional total output and factor inputs from 31 Chinese provinces during 2003-2020, along with input-output tables from 2005-2020, the study measures China's overall industrial factor structure at average social production levels from 2005 to 2020. Analysis of China's industrial factor structure from 2005 to 2020 reveals strengthened labor dominance, weakened technological contribution, land contribution shifting from negative to positive, and overall stable capital contribution. The findings indicate that the substitution and complementary effects among factors are multi-dimensional, dynamic, and non-linear. These interactions drive the dynamic rebalancing of factor structure, while technological innovation emerges as the driving force for China's high-quality sustainable economic development. Based on these research conclusions, the study proposes policy implications including: strengthening support policies for technological innovation, developing innovative factor allocation and industrial upgrading policies, and introducing policies for innovative international technological cooperation and competition. These suggestions aim to provide reference for formulating China's high-quality sustainable economic development strategy. The analysis demonstrates that the substitution and complementary relationships between factors are characterized by multi-dimensional, dynamic, and non-linear properties, driving the dynamic rebalancing of factor structure. Furthermore, technological innovation has become the source of power for achieving China's high-quality sustainable economic development.
The General Solution of Coupled Riccati Equations Based on Nonlinear Superposition
Due to the fact that the Riccati equations are nonlinear equations, it is difficult to obtain its analytical solution by using commonly used elementary integration methods. We discuss the calculation of general solution for a class of coupled Riccati equations. Firstly, the nonlinear superposition theorem of ordinary differential equations is given; Then the general solution expression and properties of coupled Riccati equations are obtained on the basis of known particular solutions; Finally, a special type of Riccati equations are calculated by the nonlinear superposition formula, which shows that the nonlinear superposition is effective for the integration of nonlinear equations. Nonlinear superposition method has universal significance in constructing new composite exact solutions for nonlinear evolution equations.
Urban Traffic Optimization and Parking Demand Analysis Utilizing Big Data
With the popularization of motor vehicles, transportation convenience has brought significant improvements to people's lives and travel, but the ensuing urban road traffic congestion affects residents' daily lives. Therefore, this paper utilizes big data statistics provided by monitoring devices, including vehicle passage times, travel directions, intersections, etc. Using Excel, we identified data trends for analysis, established corresponding mathematical models, optimized constraint conditions, and employed Python software to obtain the total daily traffic volume. We also analyzed the frequency of vehicle license plate appearances and grasped the traffic volume at road intersections during different time periods and in different directions. Based on this, we optimized the traffic signal settings at intersections and estimated the number of parking spaces needed for the scenic areas during the May Day holiday. This series of measures effectively achieved the rational allocation of road resources, significantly improved residents' travel efficiency, and contributed to promoting the high-quality development of urban transportation construction.
Research on the Application of Generative AI in Vocational Education Practice Teaching
With the rapid development of artificial intelligence technology, its potential in the field of vocational education has attracted widespread attention from both the academic and educational communities. This study is based on the current state of practical teaching in vocational education and delves into the potential applications of Generative AI in terms of teaching model innovation, generation of educational resources, and enhancement of learning outcomes. The paper employs an empirical research method, constructing an interactive teaching experimental platform, and inviting 300 vocational school students and 30 professional teachers to participate in practical teaching activities to evaluate the effect of Generative AI teaching systems in improving students' vocational skills and theoretical knowledge. The results show that the application of Generative AI technology significantly increases student motivation and participation, and the experimental group outperforms the control group in both hands-on skills and theoretical examinations. In terms of teacher workload, the generation of teaching content and simulated scenarios with the aid of Generative AI saved a considerable amount of preparation time, allowing teachers to focus more on classroom interaction and personalized instruction. Furthermore, the study proposes a series of innovative strategies for the application of Generative AI in education, including the construction of personalized learning roadmaps, the automatic generation of simulated training scenarios, and the design of intelligent assessment and feedback systems. The implementation of these strategies not only promotes the improvement of vocational education quality but also lays the groundwork for deeper development of educational informatization and intelligence. The research also offers specific recommendations for the widespread application of Generative AI in vocational education and looks ahead to possible challenges and future research directions.
Research on the Coupling Coordination and Impact Mechanism between Digital Inclusive Finance and Regional Innovation Capability in the Yangtze River Economic Belt
This paper analyzes panel data from 2013 to 2022 covering 11 provinces and cities within the Yangtze River Economic Belt. It establishes a holistic assessment framework for Digital Inclusive Finance (DIF) and Regional Innovation Capability (RIC), calculates RIC using the entropy method, and explores the coupling coordination degree between DIF and RIC using the coupling coordination model. In the end, the paper examines the influencing mechanism between the two variables using the Spatial Autoregressive Model (SAR). The study reveals that from 2013 to 2022, DIF and RIC in the upper, central, and lower regions of the Yangtze River Economic Belt exhibited steady growth, though with significant regional disparities and varying development rates. Most cities in this Belt are experiencing a steady ascending trajectory in the coupling coordination between DIF and RIC. However, most provinces are categorized within the range of poorly coordinated and primarily coordinated levels of coupling. The economic foundation, education level, information development level, and foreign trade level positively influence the coupling coordination between DIF and RIC, while the intensity of technological support and the level of industrial structure hinder their coupled coordinated development.
Data Mining and Professional Development: Research Hotspots and Trends of Digital Literacy of Rural Teachers
Along with information technology iterative update, and the rapid penetration into various fields. Improving rural teachers 'digital literacy has become the core element to realize urban-rural education equity. The digital literacy of rural teachers has been paid more and more attention by the academic circle and society. With the help of visualization analysis software Cite Space6.3.1, bibliometric analysis was carried out on the research results of rural teachers 'digital literacy in the past ten years (2014 to 2023). Based on Popper's three world theory and the "second-order science" paradigm, The evolution and progress of the theme of rural teachers 'digital literacy research were deeply analyzed by using Price's formula. The number of published papers shows the characteristics of stages, the distribution of research authors and research institutions is wide, most of them are normal universities, and cooperative research has gradually become a development trend. The research focuses on the topics of data mining, technical support and ascension path of rural teachers' digital literacy. It also reflects the research trends of rural teachers' digital professional development, digital training, and enhancement of digital resource integration ability. Future research should further strengthen cooperation, focus on digital literacy goals, improve rural teachers' ability of data thinking and data mining, improve the training system, pay attention to professional development, and build an overall improvement framework for rural teachers 'digital literacy.
Dry-Bulb Temperature-Modulated Curing’s Impact on the Carbon and Nitrogen Metabolism of Tobacco Leaves in Clean-Energy Bulk Curing Barns
The optimized curing environment in bulk curing barns can enhance the carbon and nitrogen metabolism of tobacco leaves. The impact of dry-bulb temperature (DBT) treatments during the yellowing stage of the tobacco curing (CK: constant yellowing DBT at 38 °C; T1: DBT fluctuation range ±1 °C, frequency of 3 hours; T2: DBT fluctuation range ±1.5°C, frequency of 5 hours) on the carbon and nitrogen metabolism of tobacco leaves was evaluated. (1) After the yellowing stage, the T1 treatment led to lower levels of starch, total sugars, and soluble proteins, but a higher accumulation of free amino acids in tobacco leaves. (2) Despite differences in magnitudes during tobacco curing, carbon and nitrogen metabolism-related enzymatic activities showed similar trends among different treatments. (3) Treated by T1 method, the expression levels of genes encoding starch enzymes (AMY, BMY-1, and BMY-3) in tobacco leaves rose sharply, while the expression levels of genes encoding soluble starch synthases (SSS-1) and pyrophosphorylase (AGPase-1 and AGPase-2) were at their lowest among all treatments during the same period. In addition, the expression levels of genes involved in nitrogen transfer (GS1-3) were higher, whereas the expression levels of genes related to nitrogen assimilation (NR and GS2) were lower. Finally, based on the constant yellowing DBT at 38 °C, the DBT treatment with T1 can promote thorough degradation and transformation of starch and protein, together with the effective accumulation of free amino acids in tobacco leaves. In this way, chemical composition of tobacco leaves can be better optimized, which vastly affects tobacco leaf quality and reduces ineffective curing energy consumption.
Machine Vision-Based Straightness Measurement of Pressed Joints
To address the issues of complexity, time consumption, and low detection accuracy in traditional straightness detection methods for pressed joints, a non-contact measurement method based on machine vision is proposed. The main approach is to rotate the joint and acquire images at different angles. Bilateral filtering is used to denoise the images. Considering the potential reflections and simple background of the joint, color space conversion and local binary patterns are introduced to capture local texture features. Through feature stitching and principal component analysis to adapt the Grab Cut algorithm, the joint region is segmented. The weighted average method is used to grayscale the extracted image, and after Canny edge detection, edge connection, and filling algorithm, the edge contour image of the joint is obtained. According to the contour information and the straightness measurement method proposed in this paper, the center coordinates of each cross-section of the joint are obtained, and then the least squares method is used to evaluate the straightness error of the joint to judge the bending condition of the joint. Finally, comparative experiments show that the detection system has high detection accuracy and reliable detection results.
Research Progress and Trend Analysis of Eco-Museums from a CiteSpace Perspective
As an important approach to achieving sustainable development, eco-museums have attracted widespread attention in recent years. This study employs CiteSpace to conduct a visual analysis of eco-museum research literature from 2011 to 2025 in the Web of Science Core Collection, unveiling the research progress and development trends in this field.The findings indicate that eco-museum research has gradually evolved from an initial focus on cultural heritage preservation into a broader interdisciplinary framework, incorporating environmental sustainability, community engagement, and digital innovation.This study contributes to a deeper understanding of the current state and trends of eco-museums, identifies gaps in existing research, and provides directional guidance for future studies. It aims to facilitate further exploration of eco-museums in areas such as policy-making, heritage conservation, community governance, and ecological sustainability
Sustainable Water Management in Agriculture: Assessing the Effectiveness of Irrigation Systems and their Impact on Crop Productivity and Environmental Health
The study examines irrigation system efficiency through analysing crop productiveness with environmental outcome assessment among drip, sprinkler and surface irrigation techniques. Water scarcity and environmental degradation pose significant challenges to global agriculture. The successful preservation of water resources is necessary for providing a reliable food system alongside protecting natural systems. The analysts aim to define irrigation methods that combine high agricultural performance with sustainable practices. The research design combines quantitative and qualitative data collection methods through its mixed method approach. Scientists obtain crop yield and water-use efficiency information through experimental studies conducted at numerous irrigation systems. The analysis includes an evaluation of soil quality with groundwater depletion and energy consumption as environmental impact measurements. The adoption of irrigation systems receives qualitative assessment through online survey conducted among expert farmers in the field of agriculture. The Advanced irrigation techniques installed as drip and sprinkler systems enhance both water conservation and yield production levels more than rudimentary surface water irrigation methods. Widespread adoption remains limited due to installation expenses with implementation barriers between farmers and new technologies. The study proves the necessity of implementing policy measures with economic rewards and farmer training initiatives for promoting sustainable irrigation solutions
Study on Ammonia Copper Nanocomposite Thermal Performance in Cavity Nanochannels under Electric Field
Enhancing the performance of thermal exchange systems remains a significant challenge for technical professionals and system architects. To address this, introducing nanoscale particles into primary fluids has emerged as a promising passive approach, offering enhanced thermal conductivity properties and enabling more compact heating and cooling systems. This study utilizes molecular-scale simulation techniques to examine the thermal transport characteristics of copper nanoparticles interacting with ammonia refrigerant in a nanoconfined system. The findings reveal that as the frequency of the applied electric field rises from 0.11/ps to 0.51/ps, the time required for phase change extends from 2 to 3 nanoseconds, while heat transfer efficiency drops from 0.76 W/mK to 0.72 W/mK. Furthermore, the study explores the impact of different structural designs of cavities—closed, round, and box-shaped—on the microscopic and heat-related properties of the simulated samples. Among these, the cubic cavity demonstrates the maximum heat transfer efficiency, reaching 0.80 W/mK. The analysis also considers different atomic cavity types, including Hermitian, spherical, and cubic structures. The extent and duration of the phase change are 65%, 66% and 68%, and 2.88 nanoseconds, 2.87 nanoseconds and 2.85 nanoseconds, respectively
Artificial Intelligence and Economic Growth in China: An Empirical Analysis Using Vector Autoregression
With the rapid development of technology, artificial intelligence (AI) has become a key force driving the new round of technological revolution and industrial transformation, profoundly impacting the global economic landscape. This study aims to reveal the dynamic relationship among China's Gross Domestic Product (LNGDP), effective invention patents in high-tech industries (LNNVI), and R&D expenditure in high-tech industries (LNRDE) through empirical analysis, in order to provide valuable references for policymakers to promote high-quality economic development in China. The study adopts the Vector Autoregression (VAR) model to systematically analyze the public data released by the National Bureau of Statistics of China. LNGDP is selected as the indicator of economic growth, LNNVI and LNRDE as the indicators reflecting the development of the AI industry. Statistical methods such as variance decomposition and impulse response function are used to explore the interactions among these variables. The study finds that China's R&D expenditure in high-tech industries increased from 29.213 billion yuan in 2004 to 650.77 billion yuan in 2022, an increase of about 22 times. The number of effective invention patents increased from 4,535 in 2004 to 809,824 in 2022, an increase of about 178 times. There is a significant positive correlation among LNGDP, LNNVI, and LNRDE, indicating that economic growth and the development of high-tech industries are mutually reinforcing. There is a strong positive correlation between R&D investment and the number of effective invention patents, and the increase in R&D investment directly drives the output of innovation results. The impulse response of LNGDP to LNNVI and LNRDE changes over time, showing the long-term cumulative effect of R&D investment and patent output on economic growth. This study reveals the close link between economic growth and high-tech industries, especially the AI industry. Specifically, economic growth promotes innovation activities and R&D investment in high-tech industries, and the increase in R&D investment further drives patent output, which ultimately has a positive impact on economic growth. Furthermore, the study also finds the long-term cumulative effect of R&D investment and patent output on economic growth. Based on these findings, the study proposes policy recommendations, including increasing support for high-tech industries, strengthening intellectual property protection, improving the efficiency of R&D investment conversion, optimizing the industrial structure layout, enhancing talent cultivation and introduction, promoting international science and technology cooperation and exchange, and focusing on long-term effects, to jointly promote China's economic development towards high-quality growth.
The Relation of Parenting Style and Meaning in Life among Village Middle School Students: The Chain Mediating Effects of Psychological Resilience and Perceived Social Support
Meaning in life serves as a crucial indicator of adolescents' mental health. To investigate the connection between parenting style and this sense of meaning, along with the impacts of psychological resilience and perceived social support, we recruited 2121 junior high school students aged 12-16 from 24 schools in southern China. These students self-reported on their parenting style, sense of meaning in life, psychological resilience, and level of perceived social support. The results showed psychological resilience and perceived social support have chain mediating role between warm parenting styles and meaning in life. However psychological resilience and perceived social support have masking effects between parental rejection and meaning in life. Under the influence of psychological resilience and perceived social support, both emotionally warm parenting styles and rejecting parenting styles significantly positively predicted the sense of meaning in life. Therefore, although different parenting styles of parents have diverse impacts on life meaning of middle school students, individuals' adaptability to the environment and good understanding of social support would improve the negative effects of poor parenting styles in a certain extent.
Competition in the Banking Industry, Corporate Strategy, and Reshaping of Global Value Chain Position: An Empirical Study Using Chinese Industrial Enterprises as Samples
This research investigates how competitive dynamics in China's financial sector influence firms' global value chain (GVC) positioning, utilizing branch distribution data from prefecture-level cities to construct two key competition metrics: the Herfindahl-Hirschman Index (HHI) and the four-firm concentration ratio (CR4). Empirical results demonstrate a non-linear association between financial market competition and corporate GVC status, where early-stage suppressive effects transition into positive impacts as competition intensifies. These empirical outcomes maintain statistical significance after incorporating control variables including regional financial development indicators, fiscal health measures, and temporal markers of financial crises. Building upon the benchmark regression results, the study identifies cities where the impact of banking competition on enterprise GVC positioning exceeds the critical threshold. Mechanism analysis demonstrates that the banking industry supports enterprises' GVC advancement through two key channels: facilitating technological innovation and promoting service-orientated manufacturing transformation, and correcting financial resource misallocation while fostering the development of medium- and high-tech enterprises. Furthermore, moderation effect analysis reveals a complementary relationship between fintech and banking competition, whereas financial scale exhibits a substitution effect with banking competition. This research provides significant insights for optimising China's banking market structure, enhancing the international competitiveness of export enterprises, and fostering synergistic development between the financial sector and the real economy.
Design of a Fall Detection Product Based on Arduino
The article proposes a design scheme for a fall detection product. The design utilizes a MAX6050 three-axis gyroscope sensor and a two-axis angular velocity sensor for fall detection. At the same time, a vector machine model is established, combining the advantages of both the threshold method and machine learning to reduce the probability of misjudging falls. The hardware of this system consists of five components: a triaxial acceleration data acquisition module, a biaxial angular velocity data acquisition module, a data analysis and processing module, a GPS positioning module, and a wireless data transmission module. The triaxial acceleration data acquisition module and the biaxial angular velocity data acquisition module collect human acceleration and angular velocity data, which are further processed through a threshold algorithm and a support vector machine model to effectively distinguish between daily activities and falling behaviors, thereby reducing the probability of false fall detections. Simultaneously, the system sends the location information of the injured person via data transmission, achieving a closed loop of fall detection and rescue. Experimental results demonstrate that this design can accurately detect fall behaviors, deploy an airbag within 1 second, and promptly send location information, providing a new approach for the research on anti-fall products.
Research on the Prediction Method of MOV Deterioration State based on Principal Component Analysis and Grid Search-Optimized Support Vector Machine Regression Algorithm
Background: Accurate diagnosis of faults in metal oxide varistors (MOV) is crucial for the safe operation of power systems, and the deterioration of MOV under continuous pulse impacts can be more severe. To effectively improve the fault diagnosis rate, this paper proposes a fault diagnosis algorithm based on Principal Component Analysis (PCA) and Grid Search-optimized Support Vector Regression (GS-SVR).Objective, The objective of this study is to propose an effective fault diagnosis algorithm that accurately predicts the fault state of MOV under single and continuous pulse impacts, while reducing the correlation between indication indicators through dimensionality reduction. Method: The proposed experiment involves conducting a comparative test on MOV with different time intervals between impacts, on the order of 10 seconds. The data collected from this experiment, with a time resolution of 10 seconds, will be subjected to dimensionality reduction using PCA to reduce the correlation between the original indicators. Finally, the GS-SVR model will be employed to analyze and predict the effects of single and continuous pulse impacts on MOV. Results: Experimental results demonstrate that the GS-SVR model achieves a mean square error of less than 0.00057 in predicting single pulse impacts and still exhibits certain effectiveness for irregular pulse impacts, such as continuous pulses. Conclusion: The proposed fault diagnosis algorithm based on PCA and GS-SVR can effectively improve the fault diagnosis rate of MOV, and accurately predict the fault state of MOV under single and continuous impulse shock. This is of great significance to the safe operation of power system.
Effect of soil amendment (Perlite) on some physical characteristics and Zea mays L productivity .
This study investigates the effects of perlite amendments (0%, 2.5%, and 5%) on soil hydraulic properties and maize (Zea mays L.) productivity under drip irrigation. A field experiment was conducted in Mosul, Iraq. Using a randomized complete block design (RCBD) on silty loam soil texture during the summer of 2023. Soil hydraulic properties were analyzed using the Van Genuchten model, which revealed that the 5% perlite treatment (P2) significantly improved moisture retention, reducing the value of parameter n to 1.32 and increasing water holding capacity. Results showed that P2 enhanced soil moisture content (36.58% compared with control 31.99%), reduced bulk density (1.11 Mg m⁻³ compared with the control. 1.40 Mg m⁻³), and increased total porosity (58.0% vs. 47.0%). Additionally, P2 revealed the highest saturated hydraulic conductivity (2.55 cm hr⁻¹) and infiltration rate (2.76 cm hr⁻¹), leading to a 14.4% increase in maize yield 10.3 ton ha⁻¹ compared to 9.0 ton ha⁻¹ in the control. These results determined perlite’s potential to enhance water use efficiency and crop productivity. The study concludes that perlite is improving agricultural practices in water-scarce environments, offering practical solutions for enhancing food security in drought areas. Future research should explore the long-term effects of perlite on soil health and economic feasibility
An improved prediction model for residential user response potential based on DTW-K-medoids double-layer clustering and Bi-LSTM
As demand response (DR) participants gradually loosen up, the number of new users participating in DR will continue to increase. Due to the small electricity consumption and wide distribution of residential users, they cannot participate in DR individually and need to be represented by load aggregators. However, for users who haven’t participated in DR before, the lack of historical response data makes it more difficult for load aggregators to predict their response potential. In this context, a response potential prediction model was constructed for such users. Firstly, based on the user's electricity consumption load and energy-saving awareness as the clustering criteria for the upper and lower layers, the DTW-K-medoids model is used to perform double-layer clustering on users, grouping users with similar electricity consumption behaviors and concepts into one group. Then, based on the clustering results, the electricity consumption data of residential users who have participated in DR among various types of users are used as the training set, and the Bi-LSTM algorithm is used to sequentially construct a response potential prediction model for each type of user. Finally, case analysis shows that the proposed model can effectively solve the problem of low prediction accuracy of response potential for users who haven’t participated in DR, and further reduce the assessment cost of load aggregators.
Research on Blended Teaching of Mobile Robot Path Planning Technology Course in the Context of Emerging Engineering Education
With the rise of emerging engineering education and the rapid development of artificial intelligence technology, mobile robot path planning technology has become an important research direction in the field of intelligent robotics. To meet the new demands of enterprises for robotics professionals, higher education institutions are actively exploring reforms in teaching models. This paper focuses on the research of a blended teaching model for mobile robot path planning technology. Building on traditional classroom teaching, this study introduces project-based teaching, combining online resource learning with offline practical operations to enable students to deeply understand and master fundamental path planning knowledge. Taking the RRT algorithm as an example, after explaining its basic principles, students are guided to improve the RRT algorithm by incorporating ideas from the artificial potential field algorithm, aiming to enhance the efficiency and quality of path planning. This blended teaching model not only strengthens students' theoretical knowledge but also hones their practical skills and engineering awareness, effectively meeting the training requirements for robotics professionals in the context of emerging engineering education.
Analysis on the Family Participation Model and Effect of Agricultural Research and Study Activities for Preschool Children
Agricultural research and study activities provide a unique opportunity to engage preschool children in hands-on learning experiences that enhance their cognitive, social, and motor skills. This study analyzes the family participation model in such activities and its impact on the developmental progress of preschoolers. Through qualitative and quantitative assessments, we explore the role of parental involvement in fostering curiosity, environmental awareness, and foundational agricultural knowledge. Our findings indicate that structured family participation significantly improves children's engagement and learning outcomes, emphasizing the necessity for integrated agricultural education programs at the preschool level. The Family Participation Model plays a crucial role in early childhood education, especially in agricultural research and study activities. This model integrates parents, caregivers, and communities into children's learning experiences, fostering hands-on engagement, intergenerational knowledge transfer, and experiential learning. This paper explores the structure of the Family Participation Model, its implementation in agricultural education, and its impact on preschool children's cognitive, social, and environmental awareness. The findings highlight how active family involvement enhances children's learning, strengthens community ties, and promotes sustainable agricultural practices from an early age.
Research on the Development of Computer Application Software based on Artificial Intelligence Technology
This paper explores the application of artificial intelligence technologies to software development processes. The research explores how machine learning techniques enhance software design, automated coding aids programmer productivity, and AI-enabled testing improves quality assurance. Case studies in the healthcare, finance, retail, and enterprise sectors provide examples of AI's transformative impact on applications in each of those sectors. The research identifies significant technical issues, including data quality issues, computing power limits, and difficulties in interfacing with legacy systems. The article also discusses ethical issues regarding privacy, comprehending decisions, and compliance with regulations. In the future, emerging AI technolo
Resident Perceptions and Destination Image: A Longitudinal Study of Overtourism Management in World Heritage Sites
This research explores the residents’ attitude and destination image as factors in the management of overtourism in World Heritage Sites. In this study, the dynamic aspect of these relations is examined using a longitudinal perspective in order to determine how they affect sustainable management. The paper focuses on the effects of overtourism and examine the Social Exchange Theory as well as the Co-created Destination Image framework. In the context of Machu Picchu and Angkor, the study shows that there are good practices in managing visitor restrictions and community upliftment measures. Implementation of the study suggests that managing overtourism is best done using both a ‘top-down’ approach involving legislation and a ‘grass root’ approach involving the locals. The study adds value to the existing literature of the role of resident perceptions in constructing the image of a destination throughout the stages of tourism development by providing suitable recommendations to heritage site managers and policymakers in order to achieve site conservation and improve the quality of life of residents and the quality of tourism experience.
Histological changes in the testes of male Awassi sheep fed a diet containing spirulina platensis
Effect of feeding spirulina platensis (Sp. Platensis) in the diets of male Awassi sheep on some histological characteristics of the testicles, using 12 males distributed into three groups (4 males/group) distributed randomly, the feed was provided without additives to the first group and was considered a control group (T1), while the 2nd group (T2) was given concentrated feed with the addition of (80 g/kg) of concentrated feed, while the 3rd group (T3) was fed concentrated feed with the addition of 120 g/kg of concentrated feed. In order to achieve the benefit of adding Sp. platensis to the feed, histological sections of the testicles were studied after slaughtering the animals. The results showed, that feeding with Sp. platensis showed significant differences between the control group and the other two groups. Necrosis was observed in and the deposition of amyloid fluids with damage to the STs in the two treatment groups compared to the control, with the occurrence of degeneration of the interstitial tissue as well as the tunica propria. This may explain that feeding with Sp. platensis at these rates under study has It results in potential damage to the testicular tissue. To gain more knowledge, this aspect must be studied and more experiments must be conducted at different ages on male sheep.
RNN Under the Lens: Attention, Confidence, and Feature Importance
Recurrent Neural Networks (RNNs) have enjoyed considerable success in areas such as natural language processing, time-series forecasting, and sentiment analysis. Nevertheless, these models often lack clarity regarding which parts of the input sequence drive their predictions, how confident they are in those predictions, and which input features play a pivotal role. To address these gaps, we propose a unified framework that augments a baseline RNN with an attention mechanism, a confidence score module, and a feature importance estimation procedure. We evaluate our approach using the IMDB movie review dataset for sentiment classification. Empirical results show that our model not only outperforms a vanilla RNN in terms of accuracy but also produces interpretable outputs at multiple levels. We argue that such a multi-faceted approach can be easily adopted for diverse sequence-related tasks in need of greater transparency and robustness.
Current Situation, Challenges and Development Trends of Fracturing Flowback Fluids Treatment in Ordos Basin, China
Ordos Basin, a critical energy base in China, faces significant environmental challenges due to the complex composition of fracturing flowback fluids generated from hydraulic fracturing. This fluid, containing water, gases, salts, heavy metals, organic matter, and radioactive elements, is characterized by high total dissolved solid, high chemical oxygen demand, high total suspended solid, strong emulsification, and variable water quality. Current treatment methods, which integrate physical, chemical, and biological technologies, include the reinjection and reuse but are limited by issues such as the absence of oxidative viscosity reduction, lack of closed-loop filtrate treatment, unverified pollutant removal efficiency, and unclear resource recovery pathways. Future advancements aim to enhance treatment efficiency, promote resource reuse, and adopt green and intelligent technologies. Innovations like anti-fouling membrane materials, optimized microbial degradation, efficient desalination, heavy metal removal, and intelligent monitoring systems are expected to address these challenges, ensuring sustainable oil and gas development while protecting the environment.
Enhancing the effectiveness and stability of biopesticides Bacillus thuringiensis against Spodoptera frugiperda J.E Smith (Lepidoptera: Noctuidae) by lyophilization freeze-drying
The Gram-positive spore-forming bacterium Bacillus thuringiensis (Bt) Berliner produces crystal proteins as its main characteristic. The agricultural industry uses this bacteria because its insecticidal proteins established it as an effective environment-friendly biopesticide. The investigation into using freeze drying to produce the biopesticides Bacillus thurigiensis as a powder for control Spodoptera frugiperda damage on maize in Vietnam. This study assessed the stability of this product as pure biopesticides on Spodoptera frugiperda during storage. The results showed that the viability of the formulated B. thuringiensis spore-crystal complex stored at 4°C and 25°C was maintained at 22.9 × 1022 and 19.5 × 1022 CFU/g, respectively, after one month of drying storage. Furthermore, freeze-drying supported the viability of the B. thuringiensis spore-crystal complex for up to 18 months, with 10.2 × 1022 CFU/g and 1.01 x 1022 CFU/g in both temperature conditions during storage, respectively. Our study demonstrated the potential of the freeze-dried Bacillus thuringiensis spore-crystal complex powder as a more effective biopesticide. The efficiency against Spodoptera frugiperda second instar larvae showed that after 12 months during storage at two conditions, 4°C and 25°C, the B. thuringiensis spore-crystal complex powder killed 94.7% and 88% in the highest concentration of 35 mg/L. This suggests that the B. thuringiensis spore-crystal complex powder, when formulated by the freeze-drying method, could significantly improve its activity as a biopesticide under laboratory and net-house conditions. This promising result offers a bright future for pest control, with the potential for more effective and stable biopesticides.
A Tripartite Evolutionary Game Model for Power Battery Recycling Considering the Product Life Cycle Theory
The recycling of electric vehicle batteries (EVBR) not only helps mitigate environmental pollution and resource depletion but also promotes the sustainable development of industries such as electric vehicles and energy storage. In China, the EVBR industry remains in its early stages, and clarifying the interactions among key stakeholders is crucial to promoting its maturity. This paper proposes a tripartite evolutionary game model for battery recycling based on the product life cycle theory, incorporating the government, NEVMs and consumers as participants. Based on a systematic literature review, the factors and corresponding value ranges affecting stakeholders’ strategic choices were identified, and numerical simulations were conducted using MATLAB R2024b to explore stakeholders' strategic evolution and underlying mechanisms across various stages of the industry life cycle. The results show that in the early stage of EVBR industry, the government plays a leading role in guiding NEVMs to invest in blockchain traceability technology through incentive policies. As the EVBR industry develops and value creation becomes more prominent, market mechanisms gradually take effect, encouraging NEVMs and consumers to adopt a "collaborative participation" model. In the mature stage, the government's role shifts from intervention to oversight. From the results of sensitivity analysis, a moderate reward-punishment mechanism can effectively stimulate NEVM technological innovation, while excessive subsidies may impose a heavy financial burden. Enhancing recycling revenue and environmental benefits is the core driving force to stimulate the active participation of NEVMs and consumers. This study provides theoretical support for rational decision-making among stakeholders in the EVBR process and contributes to the sustainable development of the EVBR industry
Multi-Layer Cybersecurity Risk Assessment for Civil Aviation Systems: Bridging Gaps Between Policy, Technology, and Practice
The aviation field is now digitally transforming more rapidly than ever, bringing more opportunities for the sector and new vulnerabilities that traditional safety measures cannot cope with. With the attempts to integrate the civil aviation systems of today with a wide range of technological applications, including satellite-based navigation, digital traffic control, and automated passenger processing, cybersecurity has become the most vital point of aviation security. However, current cybersecurity methods in the aviation sector continue to be disintegrated, as they are concentrating on only isolated technical solutions or are compliance-focused, at the expense of holistic risk management being limited. This research introduces and verifies a civil aviation-based multi-layer cybersecurity risk assessment framework that brings back a sense of unity to policy, technology, and real-world operational practice. The paper conducts a quantitative study of 58 peer-reviewed works and legal reports published between 2010 and 2024 to identify and assess the cybersecurity risks that exist in the aviation industry and are distributed across four layers, i.e., technical, operational, human, and policy layers. The Quantitative results demonstrate that a significant part of the cyber incidents are due to technical and human failures, while the policy level deteriorates the vulnerabilities of the whole system. Thematic analysis underpins the problems, including repeated ones, such as the lack of coordination among departments in implementing security measures, the narrowness of the scope of cyber training, and the poor execution of regulations. The research offers actionable insights for regulators, airport authorities, airlines, and cybersecurity vendors. Different kinds of visual patterns, such as a PRISMA study flow diagram, a risk attribution chart, and a thematic map, provide the framework's verification and make it easier to understand from a visual perspective. The proposed implementation model outlines the strategy by which each layer can serve as a defense for the rest by working together to achieve the security goal set. The study gives practical tips to regulators, airport authorities, airlines, and cybersecurity vendors. It expresses the opinion that our cyber security defense should be based on many layers and be capable of dealing with challenges at once rather than just fixing problems when they arise. The innovation presented in the paper empowers participants to take the initiative to protect the aviation sector from digital dangers, which, consequently, is a step towards peace of mind concerning the connectedness and digitalization of the global society in the years to come.
Computer Simulation Framework for Naval Artillery: An Integrable Solution for Naval Simulation
This paper presents a novel simulation framework for naval gun systems. The framework is designed with a modular architecture comprising four main components: (1) Scenario Unit: Environment Conditions & Computer-Generated Force, (2) Sensors Unit: detection and tracking, (3) Computing Unit: focused on ballistics and trajectory calculations, interception, and stability control, and (4) I/O Unit: Data Interface for User Interface and Experience. The framework establishes a comprehensive simulation system for diverse naval gun types, encompassing multi-target tracking and engagement capabilities. It is customizable for anti-surface and anti-air warfare based on predictive firing computation methods and control techniques for gun systems and guided projectiles. Furthermore, the framework provides standardized interface that allow integration with other naval simulation systems. The primary objective is to create a flexible, reusable, and extensible platform for various anti-air and anti-surface gun simulation systems. This framework supports development, evaluation, and training in naval ship simulators while facilitating easy integration with hardware platforms such as Degrees of Freedom (DOF) for gun simulation. This research improves the accuracy and operational efficiency of modern naval gun simulation systems.
Optimizing Augmented Reality Game Rendering Pipeline Using Convolutional Neural Networks: Multi-layer Convolutional Acceleration and Texture Enhancement
This paper presents a novel approach to optimizing the augmented reality (AR) game rendering pipeline by leveraging Convolutional Neural Networks (CNNs) for real-time texture enhancement and rendering acceleration. The integration of CNNs into the Unreal Engine rendering pipeline enables the enhancement of low-resolution textures, improving visual quality without compromising performance. The CNN model employed in this study achieves significant improvements in both texture quality, as measured by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and rendering speed. The results demonstrate a reduction in rendering time by approximately 25%, while maintaining an average frame rate of 45 FPS, making the system suitable for real-time AR applications. User perception tests confirm that the CNN-enhanced method provides superior visual quality and a more engaging user experience compared to traditional methods. The proposed approach offers a promising solution to the challenges of high-resolution texture rendering and real-time performance in AR games. Future research could explore the use of advanced CNN architectures and hybrid models to further improve rendering efficiency and visual fidelity, particularly for mobile platforms.