bluuebunny/crossref_metadata_2025 huggingface dataset:
Reshaping Cognition and Emotion: An Ethical Analysis of AI Anthropomorphization’s Impact on Human Psychology and Manipulation Risks
Authors: Yanmin Xu
Published: January 2025

With the rapid advancement of AI technology, the anthropomorphization of AI systems has reached unprecedented levels, enabling these systems to simulate human behaviors, emotions, and decision-making processes with remarkable accuracy. While this enhances human-computer interactions, it also raises significant psychological concerns. Research indicates that anthropomorphized AI can profoundly influence human cognition, emotions, and behavior, emphasizing the necessity to explore how these systems reshape human psychology and the potential risks of manipulation. This study investigates the psychological and social impacts of AI anthropomorphization, focusing on its effects on cognitive biases, emotional dependency, and decision-making. Utilizing a social cognitive theory framework, it examines how anthropomorphized AI evokes empathetic responses and fosters trust in AI systems. Additionally, the study addresses critical concerns related to the collection of emotional data, the potential for information manipulation, and the influence of AI on human decision-making processes. The findings reveal that anthropomorphized AI, through human-like emotional expressions and behaviors, significantly alters cognitive and emotional states. This alteration is characterized by heightened emotional resonance and dependency, making individuals more prone to unconscious guidance by AI during decision-making, thereby diminishing their autonomous decision-making abilities. Furthermore, the study highlights the increased risks of information manipulation and privacy invasion associated with AI anthropomorphization, complicating the ethical landscape of AI integration. In the realm of social behavior, anthropomorphized AI has the potential to change human social norms and interactions, possibly leading to a decline in social skills and the emergence of behavioral biases. To mitigate these risks, the study suggests enhancing the transparency and explainability of AI systems, improving public AI literacy, reinforcing ethical oversight and regulation, and promoting collaborative human-AI interaction models. These strategies aim to prevent AI from exerting undue influence over human psychology while ensuring a balanced relationship between technological advancement and human welfare. The research underscores the importance of ethical guidelines to harness the benefits of anthropomorphic AI for mental well-being while minimizing potential negative impacts, thus fostering responsible and beneficial AI integration in society.
Research on Collaborative Innovation to Boost the Development of New Quality Productivity under the Background of Big Data
Authors: Chunan Liang
Published: January 2025

Under the background of global economic integration and rapid development of technology, innovation has become an indispensable engine of economic growth and social progress. Collaborative innovation integrates the innovation resources, capabilities and information of multiple innovation subjects, and realizes the synergistic effect of innovation efficiency 1+1>2. The development of new quality productivity depends on new knowledge, new technology, artificial intelligence, big data and innovation ability, which is the product of collaborative innovation. In this study, collaborative innovation helps new quality productivity as the research object. By using the methods of literature review and big data analysis, the concepts of collaborative innovation and new quality productivity are defined, and the theoretical framework diagram and implementation path map of collaborative innovation strategy are constructed. This paper discusses the influence of collaborative innovation on the development of new quality productive forces. This paper puts forward a series of management implications to improve the productivity of new quality; empirical analysis shows that collaborative innovation not only increases the investment of R&D, shortens the time to market of new products, but also speeds up the speed of patent application, which significantly promotes the development of new productivity. It helps the development of new quality productive forces and provides empirical support for the sustainable development of economy and society.
Multi-level Fire Alarming System for Urban High-Voltage Cable Tunnel
Authors: Han Fu
Published: January 2025

At present, the research and applications about fire alarming of high-voltage cables tunnel focus on measuring various characteristics of fires through sensors such as smoke, various characteristic gases of fire, videos, etc., so that fire can be detected as soon as possible to avoid greater losses. In order to detect the fire as early as possible, it proposed to build a multi-level early alarm system. Firstly, it established an experimental platform, and found that the cable and it would emit particles with nanometer size during overheating and discharging, so, it presented a method for detecting hidden cable tunnel fire hazards based on thermal-released-particle detection, which can alarm several hours or several days before the fire occurs. Additionally, the CO sensor and other conventional sensors were also used, then a multi-level fire alarming system for high-voltage cable tunnel was introduced, by which the fire can be effectively alarmed in the whole process of fire development.
Research on the Application of Big Data Technology in Sports Core Literacy Education: Learning Models, Teaching Strategies, and Internal Relationships
Authors: Ximei Xu
Published: January 2025

The cultivation of core literacy is crucial in discipline teaching, and its effective implementation requires the integration of advanced technologies, particularly big data. This paper focuses on the application of big data technology in the teaching and learning of sports core literacy, emphasizing the use of computational methods to promote the transformation of knowledge and skills. The concept of sports core literacy is explored through the lens of big data platforms, which are utilized to create dynamic learning environments that facilitate the integration of sports knowledge, health knowledge, and sports moral knowledge. The paper proposes a big data-driven learning model that focuses on knowledge acquisition, skill development, and the transformation of knowledge and ability. By analyzing the relationship between these three dimensions—sports ability, health behavior, and sports morality—it highlights how big data technologies can track, analyze, and promote the development of students’ physical and cognitive capabilities. Furthermore, the paper presents teaching strategies based on hierarchical cultivation and personalized learning, aiming to address the common challenges in sports education, such as students’ lack of engagement, insufficient practice, and limited physical ability.
Research on Rumor Monger Motivation Based on Psychological Projection Perspective and Big Data Analysis
Authors: Yong Xu
Published: January 2025

Exploring the motivation of online rumor public opinion transmission is helpful for the management department to accurately detect and control rumor public opinion through different types of rumor transmission characteristics. Psychological projection theory is used to explore the projective relationship between rumor propagation motivation and behavior. Firstly, the projection process of rumor propagation is analyzed. Secondly, using Maslow's Hierarchy of Needs Theory to analyze the relationship between the psychology of rumor dissemination and user needs. Thirdly, explore the internal mechanism between different psychology and motivation of network users. Finally, the causal relationship between different behaviors and motivations is analyzed and the public motivations and demands implied behind the behaviors are interpreted. It is found that the communication psychology of users is categorized into a total of seven areas such as anxiety, panic, altruism, and self-interest. The network users' motivation of rumor spreading can be divided into four types: information acquisition motivation, self-actualization motivation, emotional catharsis motivation and entertainment and social motivation. Network user behavior can be divided into three types: spectator participation, discourse participation and communication participation. The results show that using the psychology of rumor users to reverse analyze their behavioral motivations is helpful for the management department to grasp the characteristics of different types of rumor propagation more accurately, and further improve the efficiency and effect of rumor detection and management.
Dilemma and Countermeasures on Whole Mechanization of Rice Production by Raising Seedling with Plastic Membrance Covering in Mountainous Areas
Authors: Ruoqi Bai
Published: January 2025

That is the basis of the whole mechanized production of rice with plastic membrance covering in mountainous areas to raise strong seedlings suitable for mechanical transplanting.This paper,based on annual report data of agricultural machinery equipment from 2012 to 2023 for 12 consecutive years in Wenzhou,Zhejiang,China,studies on the current status,dilemma and countermeasures of whole mechanized production of rice in Wenzhou mountainous areas,by combining theoretical research,data analysis and typical research.The result shows that the mechanization level of rice production in Wenzhou mountainous areas is significantly lower than that in the plain areas,and the progress has been slow over the past decade.In 2023, the comprehensive mechanization level of rice in Taishun and Wencheng, which are pure mountainous areas,was 74.53% and 74.45%,respectively,lower than the average level(89.32%) of Wenzhou,and far lower than Rui'an(92.81%) and Yueqing(93.23%),which are plain in mainly  areas. From 2012 to 2023, the level of agricultural mechanization in rice tillage in mountainous areas of Wenzhou improved rapidly, but the progress of agricultural mechanization in transplanting, harvesting and other field management was very slow. In 2023, the rate of machine tillage for rice in Wenzhou reached 99.9%, and that in Taishun and Wencheng, two counties of pure mountainous areas, also reached 100% and 99.5%, respectively, which basically realized the mechanization of rice tillage farming. However, in 2023, the rate of machine transplanting for rice in Taishun and Wencheng was only 28.59% and 25.72%, while the average level of Wenzhou reached 67.78%; and the rate of machine harvesting in Taishun and Wencheng was 86.5% and 89.8% respectively, lower than the average level of Wenzhou (96.8%). At present, the fertilization of rice in Wenzhou mountainous areas depends on manual operation, and the level of mechanical equipment in plant protection link is also not high.From the perspectives of geography, industry, policy, equipment and technology, this paper deeply analyses the dilemma encountered in the mechanization of rice production in Wenzhou mountainous areas, and puts forward some accurate countermeasures and suggestions.This study shows that the low rice transplanting rate is the bottleneck of the whole mechanization of rice production in mountainous areas.And it greatly improve the level of rice mechanical transplanting,combined with the use of plastic membrance cover to cultivate the strong seedlings.By the promotion and application of advanced agricultural machinery and tools,it can realize the whole mechanization of rice cropping in mountainous areas.
The Influence of Design at Food Festivalscape on Visitor Behavioral Intentions Based on Big Data Analysis
Authors: Hongqi Zhou
Published: January 2025

Festival activities are new leisure tourism consumption scenarios with huge development potential. With the rapid development of big data technology, the service scenario design of food festivals also faces new opportunities and challenges. This study takes the Zhuanghang Mutton Festival in Shanghai as a case and focuses on the impact of the service scenario design of food festivals on tourists' behavioral intentions under the background of big data. Service scenario design covers multiple aspects such as the physical environment, spatial layout, and atmosphere creation of food festivals. Through literature review and big data analysis of network texts, the study extracts five analytical dimensions of food festival scenarios (atmosphere, layout/design, food, entertainment, service attitude), and combines them with questionnaire surveys to explore how these dimensions affect tourists' emotions, satisfaction, and then influence tourists' behavioral intentions. With the support of big data, food festivals can understand tourists' needs and preferences more accurately, so as to carry out personalized service scenario design. The study suggests that festival managers should attach importance to the atmosphere, food, and entertainment dimensions of festival scenarios, create richer and more diversified activity experiences, create a comfortable atmosphere and improve food quality, so as to better promote tourists to have positive emotional experiences, and further improve tourists' satisfaction and the benign development of festival behavioral intentions.
A Study on Pivot Sentences Acquisition of International Students based on Natural Language Processing Technologies
Authors: Wenwen Cheng
Published: January 2025

In the context of the rapid development of globalized education, the demand for Chinese language learning among international students continues to rise. As a special and complex sentence structure in Chinese, the acquisition of "pivot sentences" is of great importance. Traditional acquisition research methods often rely on manual annotation and empirical analysis, which are difficult to handle large-scale data and are subject to a high degree of subjectivity. With the rapid advancement of natural language processing (NLP) technologies, new opportunities have emerged for research in this field. This paper utilizes various techniques from NLP, such as pre-trained language models (e.g., BERT, GPT) and grammar error detection methods based on Bayesian classification algorithms, to automatically analyze the production of pivot sentences by international students. These technologies enable precise identification of grammatical errors, syntactic structure anomalies, and inappropriate word usage in serial verb constructions. By deeply exploring the underlying causes of error patterns, this research provides a comprehensive and systematic understanding of the acquisition process of pivot sentences by international students, offering strong support for targeted teaching and promoting the advancement of Chinese as a second language education to a new level.
Effect of Using Geogrid on Some Properties of Concrete
Authors: Rawa Shakir Muwashee
Published: January 2025

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
Influence of Austempering Heat Treatment on Microstructure and Mechanical Properties of AISI 9255 High Silicon Steel
Authors: Maheshwar A. Hipparag
Published: January 2025

The present work is mainly centered on austempering of high silicon steel at different austempering conditions and its influence on microstructure and mechanical properties of AISI 9255 high silicon steel. The tensile specimens machined as per the ASTM standard were initially austenitized at three levels of temperatures namely 850oC, 900oC and 950oC for 30 minutes.Austempering was done in a molten salt bath at different levels of temperatures namely 280oC, 320oC, 360oC and 400oC for 90 minutes.
X-ray diffractometer, scanning electron microscope and optical microscope were used to characterize all heat-treated specimens. At lower austempering temperatures namely 280°C and 320°C the samples exhibit lower bainitic microstructure while the higher temperatures such as 360°C and 400°C exhibited upper bainitic microstructure. The specimen austempered at 280oC for 90 minutes after austenitizing at 850oC for 30 minutes showed highest UTS of 1680 MPa with a percentage elongation of 8.7 percent.
Overview of Light Trapping Technology in Solar Cells Photovoltaic Devices: Modelling and Simulation
Authors: Hussein Hashim Zaidan
Published: January 2025

The study concentrated on solar-powered thin-film batteries and how to combine plasmonic nanoparticles and antagonistic-thinking coatings to address the problem of light-catching. The models showed reasonable gains in effectiveness testing and cost reductions. While concentrating on creating feasible innovative textiles, the study again highlights the need for expert validation of the constructed models.
A Novel Computational Model of Insulin-Glucose Chaotic System to representβ-Cells Effects, Type 1 Diabetes Mellitus (T1DM) and Hyperglycemia
Authors: Ahmed M. Ali Ali
Published: January 2025

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.
Dynamic Path Planning for Cooperative Robots Based on the Hybrid SA-PSO Algorithm
Authors: Qian Wang
Published: January 2025

Addressing the issues of particle swarm optimization (PSO) in robot path planning, such as getting trapped in local optima, improper parameter settings, and low computational efficiency, this paper proposes a novel hybrid optimization strategy that combines PSO with simulated annealing (SA). This hybrid algorithm integrates the advantages of both algorithms, leveraging the rapid convergence ability of PSO and the global search capability of SA to provide a more efficient, flexible, and robust solution. It is particularly suitable for complex, dynamic, and multi-objective scenarios and can be used to solve the path planning problem of cooperative robots in complex and changing environments. Through simulation experiments, this article has verified the effectiveness of the algorithm. The results show that, compared to traditional PSO and SA algorithms, this algorithm demonstrates higher performance and efficiency in planning the trajectory of a robot in a complex dynamic environment.
Application of Intelligent Technology in Electromechanical Engineering System
Authors: Xiaohu Zeng
Published: January 2025

Introduction: With the rapid development of science and technology, the systems in various industrial fields are becoming more and more complex and huge, and the degree of intelligence and integration is constantly improving.
Objectives: Combined with several theoretical branches of intelligent control research, this paper analyzes the current application of intelligent control in electromechanical control system from the aspects of construction machinery, mechanical manufacturing, robot technology and industrial process.
Methods: This paper also analyzes the research hotspot and focus of intelligent electromechanical fault diagnosis. This paper presents a fault diagnosis method of electromechanical actuator based on LVQ neural network. At the same time, this paper designs and improves the LVQ neural network learning algorithm and network parameters, and improves the classification effect of LVQ neural network. In this paper, a mechanical fault diagnosis method based on GA-SVM algorithm is proposed, and the real value coded genetic algorithm is used to improve the parameter optimization of support vector machine.
Results: Experiments show that this method improves the classification speed of support vector machine. Finally, the effectiveness and superiority of the two fault diagnosis methods designed in this paper are verified.
Conclusions: By summarizing the application status of intelligent control, this paper puts forward some development countermeasures to further develop intelligent control.
Innovation of Green Low-Carbon Management of Enterprises Based on the Concept of Sustainable Development
Authors: Zihao Hu
Published: January 2025

Introduction: As the world's second largest economy, its economic development, social construction and cultural innovation have become the focus of the world. At the same time, behind the rapid economic development and rising output value, green environmental protection technology is relatively backward, and the traditional "three high and one low" development mode undoubtedly brings enormous pressure to natural resources and environmental protection.
Objectives: This paper constructs a triple difference model based on the quasi natural experiment of China's low carbon pilot city policy.
Methods: This paper examines the impact of the concept of sustainable development of low carbon pilot cities on green technology innovation of high carbon emission enterprises in pilot cities.
Results: The test results show that the low carbon pilot city policy is not a stumbling block to the green development of high carbon emission enterprises. The low carbon pilot city policy has significantly improved the green technology innovation level of high carbon emission enterprises in the pilot cities. The policy of low carbon pilot cities has significantly improved the green technology innovation of high carbon emission enterprises in the eastern and western pilot cities. Therefore, the concept of sustainable development can promote the research and innovation of green and low-carbon management of enterprises.
Conclusions: This paper enriches the relevant research on the evaluation of green low-carbon management effect of enterprises under the concept of sustainable development.
Sustainable Development of Tourism Economy under Agricultural Ecological Environment
Authors: Hongyan Zhang
Published: January 2025

Introduction: Tourism industry is a sunrise industry with broad prospects. The integration of tourism economy and sustainable development under agricultural ecological environment is crucial to the development of ecological economy. This is also an important way to optimise the regional economic and industrial structure and promote the construction of ecological civilization.
Objectives: Based on this, this paper chooses the ecological benefit road in the agricultural tourism planning and design. Starting from environmental protection, this paper combs the internal relationship between natural resources and agricultural production environment, agricultural tourism environment.
Methods: On the premise of not destroying the original ecological environment, this paper creates a natural ecological sightseeing agricultural park, which is conducive to the sustainable use of resources and environment. The test results show that the agricultural tourism consumer groups mainly focus on appreciation, understanding, experience and agricultural natural environment. Ecological harmony can help the sustainable development of agricultural tourism economy.
Results: The model proposed in this paper is helpful to promote the harmonious development of economic, ecological and social benefits.
Conclusions: In this study, the CVM method is used to evaluate the value of public goods with both use value and non use value. The author hopes to provide a basis for macro policy formulation by discussing the practical application and effect of the CVM method in this paper
Research on Thermal Management System of Advanced Proton Exchange Membrane Fuel Cells in Fuel Cells
Authors: Donglai Wu
Published: January 2025

This paper takes proton exchange membrane fuel cell system as the research object. The working mechanism of each component in the system is also studied. A mathematical model of dynamic thermal management of reactor based on the theory of functional conservation is proposed. This project proposes an optimal quadratic programming algorithm based on Lyapunov function, and solves it with iterative algorithm. By using MATLAB/Simulink simulation analysis method, the calculation model of reactor voltage, cathode, proton exchange membrane, anode temperature and other parameters is established. Simulink/Simscape modeling software was used to simulate it. These two models are integrated to form a comprehensive battery simulation model. The operating characteristics of the fuel cell were studied under various failure modes. The simulation results of PEMFC are in agreement with the experimental results. The results show that the method is reasonable. Through the simulation of its failure process, people can understand its failure mechanism more clearly. The research results of this paper have certain guiding significance for the fault diagnosis of the motor.
Research on the Regulation of Organization Performance and Heat Treatment Process of Low-Temperature Lightweight Steel
Authors: Jinxuan Ha
Published: January 2025

Ni-system low-temperature steels have been widely used in the past 50 years, and the Ni-free low-temperature steels independently developed in China still have problems such as low yield strength, poor low-temperature toughness, and anisotropic properties. In this paper, based on the compositional design idea of ultra-low carbon, high manganese, and medium aluminum, a Fe-Mn-AI system lightweight cryogenic steel with high strength and good plasticity, as well as good low-temperature toughness, was developed. The regulation technology of the organization and properties of the experimental steel was mastered through the study of the heat deformation behavior as well as the heat treatment process, and the plastic deformation mechanism and the strong toughening mechanism of the experimental steel at room temperature and at one 1960C were revealed. Compared with Fe-Mn-Al austenitic steels, Fe-Mn-AI duplex steels exhibit a series of anomalous thermal deformation characteristics. During hot deformation, both work hardening and dynamic softening occur preferentially in 6 a ferrite. The dynamic restitution of ferrite in the early stage of hot deformation caused a yield-like behavior on the rheological curve. An intrinsic model of transient stresses in Fe-Mn-A1 duplex steels was developed. The model is empirically calculated to have a high prediction accuracy.
Research on the Evaluation Model of Fruit Tree Leaf Area Index Based on LiDAR
Authors: Liangfu Zhou
Published: January 2025

In the process of variable application, acquiring canopy structure parameters of fruit trees is crucial. Traditional manual measurement of these parameters is time-consuming and not conducive to online acquisition. This study presents a method for measuring leaf area index of fruit trees using LiDAR. Six simulated fruit trees with varying leaf area indexes were tested, and real-time collection of canopy data was conducted. The point cloud side view of scanned fruit trees was compared with the contour of simulated fruit trees. When the operation speed was below 1.5 m/s, The point cloud side view of fruit trees closely matched the actual fruit tree contour at less than 1m/s operation speed. It was observed that the point cloud obtained under the same leaf area index was inversely correlated with the operating speed. Mathematical models were developed using Origin to fit the relationships among operation speed, point cloud data, and leaf area index, yielding a coefficient of determination of 0.93. This demonstrates the feasibility of analyzing fruit tree leaf area index using point cloud data. In field tests, the maximum relative error was 12.5% under orchard 0peration speeds (0.5-1 m/s). This method enables real-time measurement of canopy leaf area index, providing technical and model support for variable spray decision-making in orchards.
Using Big Data to Analyze the Processing Technology, Chemical Composition, and Pharmacological Effects of Paeoniae Radix Alba
Authors: Caiqin Xiang
Published: January 2025

Paeoniae Radix Alba, as a commonly used menstrual regulation and hemostatic drug in clinical practice, has a wide range of uses and a large amount of usage. The processing technology and products of the traditional Chinese medicine Paeoniae Radix Alba have been used since ancient times. Currently, the processed products of Paeoniae Radix Alba include wine Paeoniae Radix Alba, fried Paeoniae Radix Alba, earth Paeoniae Radix Alba, vinegar Paeoniae Radix Alba, etc. Different processed products of Paeoniae Radix Alba have different clinical purposes. Compared with their raw products, the processed products of Paeoniae Radix Alba have certain differences in chemical composition and therapeutic effects. Therefore, it is particularly important to choose the appropriate variety of Paeoniae Radix Alba in clinical application; The chemical components contained in Paeoniae Radix Alba determine its different pharmacological effects. This article is based on big data analysis as a means, and through extensive literature research and analysis, using computer-aided functions, it is finally concluded that the main chemical components contained in Paeoniae Radix Alba include terpenes, flavonoids, tannins, and various volatile oils; Paeoniae Radix Alba has various pharmacological effects such as anticonvulsant, antiepileptic, sedative, analgesic, and anti-inflammatory. Summarize and summarize the modern processing methods, chemical components, and pharmacological effects of Paeoniae Radix Alba through literature review, in order to provide reference for in-depth research on Paeoniae Radix Alba.
Task Offloading and Collaborative Backhaul System Based on Multi-level Edge Computing in the Internet of Vehicles
Authors: Jin Lin
Published: January 2025

With the development of 5G and the Internet of Vehicles, diverse in-vehicle services continue to emerge. Computation-intensive and delay-sensitive in-vehicle tasks pose significant challenges to in-vehicle devices and represent one of the bottlenecks limiting the development of Internet of Vehicles technology. This paper proposes a Speed-Sensitive Offloading (SSO) and collaborative backhaul solution to address the problem of task offloading and result backhaul failure caused by vehicle movement, including a multi-level MEC architecture solution, speed sensitive task offloading and an MEC collaboration-based task return scheme (SSCOM). Through preliminary experimental verification, as the number of vehicles increases, the average task offloading time of all schemes shows an upward trend, but the SSCOM scheme has the smallest increase; compared with the schemes of random offloading, speed-prioritized and data-volume-prioritized offloading, the present scheme can significantly reduce the average task offloading time; collaborative backhaul can also solve the problem of result backhaul failure caused by vehicles driving out of the coverage area, etc., can improve the task backhaul success rate and MEC resource utilization rate by at least 5%.
IoT-based Underground Pipeline Intelligent Operation and Maintenance System
Authors: Yichuan Zhang
Published: January 2025

The underground pipeline network, as a crucial component of urban infrastructure, is essential for public services such as water supply, drainage, and natural gas. With the continuous development of urbanization, the scale and complexity of underground pipeline networks are also rapidly increasing. Nevertheless, the operation and maintenance methods for traditional pipeline network are inefficient and lack safety, making it urgent to introduce advanced technology to improve the management level. In this regard, an intelligent operation and maintenance system for underground pipeline networks based on the Internet of Things, which realizes real-time cyclic monitoring of pipeline operation status and data collection, is proposed in this study. The advantage of this system lies in the optimized utilization of resources, helping operations and maintenance personnel to allocate maintenance resources reasonably and reduce unnecessary maintenance costs through accurate data analysis. Meanwhile, its predictive maintenance function helps to prevent potential failures, improve the stability and reliability of the pipeline network, and timely discover safety hazards through real-time monitoring and predictive maintenance, reduce accidents, and improve the safety level of pipeline operation and maintenance.
Study on the Effect of Oxazepam Tablets Combined with Cognitive Behavioral Therapy on Foreign Language Learning Anxiety among College Students Majoring in Arts and Sports
Authors: Fang Huang
Published: January 2025

The aim of this study is to evaluate the therapeutic effect of oxazepam tablets combined with cognitive-behavioral therapy on foreign language learning anxiety among college students majoring in arts and sports. 40 college students majoring in arts and sports were randomly divided into control group and Drug group, with a total of 20 students in each group. The Self Rating Anxiety Scale (SAS) was used to evaluate the changes in foreign language learning anxiety levels among college students majoring in arts and sports. There was no statistically significant difference in SAS scores between the two groups of patients before treatment (P>0.05). After 8 weeks of Drug group treatment, the SAS scores of the Drug group were significantly lower than before treatment (t=12.706, P<0.01), while the SAS scores of the control group were significantly lower than before treatment (t=8.172, P<0.05). After different periods of treatment, except for the first week of treatment (P>0.05), the SAS scores of the Drug group were significantly lower than those of the control group, with a significant difference (P<0.05); The SAS effectiveness rate of the Drug group treatment was significantly higher than that of the control group (χ2=5.013, P<0.05). The combination of oxazepam tablets and cognitive-behavioral therapy can significantly improve anxiety behavior in foreign language learning among college students majoring in arts and sports, and reduce anxiety levels.
Analysis of Electric Energy Substitution Potential and Energy Structure in China under Dual Carbon Background
Authors: Peng Chen
Published: January 2025

The implementation of the "electricity substitution" policy will reshape the current structure of terminal energy consumption in our country, and is a crucial approach to expeditiously achieve the dual-carbon goal. This paper conducts an analysis on the structure of terminal energy consumption in China, as well as the influencing factors of electric energy alternative development. It establishes a multiple regression model for terminal energy consumption and GDP, power investment, urbanization, and carbon emissions. Finally, it carries out a quantitative analysis on the potential amount of electric energy substitution to construct a multi-objective function with maximum substitution potential and minimum construction cost under various constraint conditions. The optimization algorithm is utilized to optimize annual energy consumption in China over the next ten years and calculate the potential for electric energy replacement during that period. This study aims to provide valuable references for government departments and support power grid enterprises in scientifically implementing electric energy replacement and dual-carbon construction.
Risk Prediction Model of Recidivism Based on Stacking Algorithm
Authors: Jia Yao
Published: January 2025

At present, the impact of AI on research in the field of social sciences is gradually attracting attention from the academic community. It has become a trend and made some progress to evaluate the risk of recidivism by quantitative means. However, actuarial assessment, dynamic risk factor assessment and other means take model driven as the basic method, and use human subjective hypothesis to fit the data with unknown distribution pattern, which reduces the objectivity, stability and accuracy of the conclusion to a certain extent, this is the bottleneck which restricting the research in this field. Based on the idea of data-driven, the author uses the dynamic tracking data of 26020 released persons within three years, combined with an ensemble learning method of Stacking, to establish a Intelligent recidivism risk prediction model. This scheme can overcome the limitations of artificial prediction method; mine the potential association relations and mapping rules in the data. At the same time, the author confirms this model is a more reliable and robust method for recidivism prediction than Random Forest model in terms of prediction accuracy, ACU area, elimination of over fitting, small sample learning and other indicators. This model achieves comprehensive performance improvement by adding multiple weak classifiers, so it will be a more efficient prediction method of recidivism based on big data in the future.
Energy Management Strategy of Fuel Cell Hybrid Electric Vehicle Based on Model Predictive Control
Authors: Bo Li
Published: January 2025

The continuous high-speed growth of automobile ownership in China has led to increasing pressure on the environment from automobile exhaust emissions, thus new energy vehicles have been favored by government policies. In new energy vehicles, Fuel Cell Hybrid Electric Vehicles (FCHEV) are considered to have great potential due to their high efficiency, smooth power output, and short fuel replenishment time. The hybrid power system is one of the important parts of FCHEV. Such system is comprised of lithium-ion batteries and fuel cells. This study combined the vehicle speed and power demand of new energy vehicles to establish a fuel cell vehicle model, longitudinal dynamics model, drive motor model, and fuel cell/lithium-ion battery model. A model predictive control method was proposed to design an energy control tactic for FCHEV. In view of the three aspects of fuel cell effectiveness, economy, and lithium battery SOC maintenance, the optimized objective of energy control was determined, and a cost function for multi-objective optimization of fuel cell hybrid power mechanisms was created. On the grounds of the rule of rolling time domain optimization, a real-time FCHEV energy control tactic was devised. Through MATLAB simulation under combined operating conditions, the efficacy of the energy management tactic presented has been validated. It could ensure that lithium-ion batteries have sufficient power reserves, and enabled fuel cells to operate stably with higher efficiency, reducing durability losses and saving more hydrogen gas.
3D Visualization Analysis of Helicopter Rescue Range Based on Cat Boost Algorithm
Authors: Ying Zhang
Published: January 2025

In the context of digital development of big data and information, the currently available aviation emergency rescue capability is not only incompatible with the national situation of frequent disasters, but also with the level of socio-economic development. This paper analyzes around the 3D visualization of helicopter rescue range based on Cat Boost algorithm. After using the big data technology combined with the typical model of complex terrain and the flight performance of the specified helicopter model, the 3D model of helicopter rescue is created in 3DSMAX, the model file is stored in the specified local directory, and the storage path is stored in the database to realize the construction and development of the 3D visualization model of helicopter rescue range. The results show that the highest elevation changes with the increase of algorithm training, and the range is 560m-750m. In the lowest elevation index, the lowest elevation changes with the increase of algorithm training, and the range is 350m-560m. The elevation difference is always maintained at about 210m, which is more favorable to helicopter rescue. This study can realize the rapid map according to the temporary flight task, which can effectively improve the preparation work before the helicopter flight, and then improve the rescue efficiency.
The Factors Influencing the Transfer of Learning Outcomes in College Students' Mental Health Courses Using Data Mining
Authors: Congcong Zhao
Published: January 2025

This work aims to utilize data mining techniques to analyze the factors influencing the transfer of learning outcomes in college students' mental health courses. It explores the role of self-interpretation in indirectly promoting the transfer of learning outcomes to daily life situations by influencing course satisfaction. Based on social cognitive theory and self-determination theory, a theoretical model is developed to examine the relationships between self-interpretation, course satisfaction, and the transfer of learning outcomes. The work utilizes a questionnaire survey method, randomly selecting 250 university students from multiple domestic universities as the sample. Data are collected using a self-interpretation scale, a course satisfaction questionnaire, and a learning outcomes transfer assessment tool. Statistical software is used for data analysis, including descriptive statistics, correlation analysis, and path analysis to test the hypothesized relationships. The results show that self-interpretation is significantly positively correlated with both course satisfaction and the transfer of learning outcomes. Furthermore, self-interpretation plays a significant mediating role between course satisfaction and the transfer of learning outcomes. Course satisfaction promotes the transfer of learning outcomes through both direct and indirect pathways (by enhancing self-interpretation). Additionally, factors such as gender, age, and academic background are found to moderate these relationships to some extent. This work provides empirical evidence for the design and teaching strategies of mental health courses, emphasizing the importance of cultivating positive self-interpretation skills. The work also offers strategic guidance for educators to enhance course satisfaction and promote knowledge transfer through data mining techniques.
The Ideological Education Narrative of Short Video Culture by Digital Intelligence
Authors: Zhuo Qu
Published: January 2025

The current application of digital intelligence and numeracy technologies in education is marked by personalized learning, data-driven insights, and remote access. These technologies enhance student engagement, facilitate customized learning paths, and bridge educational gaps. The current landscape of education is being reshaped by the pervasive influence of digital intelligence and numeracy technologies. Classrooms are becoming more interactive, with personalized learning plans tailored to each student's needs. Data analytics provides teachers with actionable insights into student progress, enabling targeted interventions. Additionally, remote learning platforms have ensured continuous education amidst disruptions, fostering resilience in the educational system. As we look towards the future, the integration of technologies in education promises to deepen further. With the rapid advancements in digital technologies, particularly artificial intelligence (AI), big data, and cloud computing, Short video culture has proliferated at an unprecedented pace, with TikTok and WeChat short videos being the most representative. Their rise and development have profoundly impacted various domains of social life, such as altering modes of entertainment, communication, product marketing, and innovative income distribution models. Universities, as crucial bastions of knowledge dissemination, have also been notably affected by short video culture. Through short video culture, university students engage in online shopping, information dissemination, and new business models that integrate advertising and accelerated development, thereby influencing their cognitive frameworks and presenting significant challenges to ideological education. This paper analyzes the content, characteristics, and functions of short video culture. By conducting an empirical investigation into ideological education among university students within the context of short video culture, it examines students’ perceptions of short video culture and reveals the negative impacts on their thoughts and cognition, presenting both opportunities and formidable challenges for ideological education.
Risk Mitigation Strategy for Power Grid Construction Accidents Based on Knowledge Graph Representation Learning
Authors: Wushuang Gui
Published: January 2025

In order to solve the problems of lack of accident relation, unstable response results and insufficient diversity in power grid construction accident risk handling, a method of power grid construction accident risk handling based on knowledge graph representation learning was proposed. First of all, for enhancing the correlation between accidents, the power grid construction accident report was taken as the data source, and the information graph structure was constructed according to the accident elements and the relationship between the elements through knowledge graph technology. Secondly, for improving the retrieval speed and accuracy, Bert model and GraphSAGE model were used to complete the knowledge graph representation learning and the accident feature vector was obtained. Finally, for having more comprehensive knowledge coverage, the accident case database was established to compare and match the new accident knowledge map. The risk control and emergency treatment of power grid construction accidents we carried out. The results show that the method combined with GraphSAGE model has better processing effect on knowledge graph, the stability of output data is improved by 12.3%, and it can make rapid response, forming an efficient accident risk processing system. It is concluded that the designed method can significantly improve the efficiency of power grid construction accident handling, and provide reference and support for power grid construction accident risk handling.