bluuebunny/crossref_metadata_2025 huggingface dataset:
Screening Status Gizi Dan Kelas Literasi Gizi Anak Sekolah Di SD X Kota Makassar
Authors: Mukty Ma'rifat , Jusriani Rini , Kusuma Nina , Ilyas Asmiana , Syafri Muhammad
Published: January 2025

Abstract
Nutritional status is a critical indicator in assessing children's health, growth, and development, as well as in preventing future diseases. The Body Mass Index-for-Age (BMI-for-age) is commonly used to measure children's nutritional status, encompassing categories such as undernutrition, overnutrition, and obesity. This study aims to analyze the nutritional status of primary school children based on BMI-for-age and evaluate the relationship between their nutritional knowledge and nutritional status. The study involved 58 children, with data collected through anthropometric measurements and a nutrition knowledge questionnaire. Results indicated that 65.5% of the children had a normal nutritional status, while 34.5% had an abnormal nutritional status, comprising 13 undernourished children, 5 overnourished, and 2 obese. Before the post-test, 90% of the children demonstrated good nutritional knowledge, which increased to 100% post-test. However, the high level of nutritional knowledge did not always correlate with an ideal nutritional status, highlighting the need for a more holistic approach to nutrition education and regular nutritional monitoring. These findings emphasize the importance of further interventions to comprehensively improve children's nutritional status.
Keywords: Nutritional Status, BMI-for-age, School Children, Nutrition Education
Self-Fashioning as Rapid Curation: A Case Study Through Anna Piaggi
Authors:   , Di Trocchio Paola
Published: January 2025

The thesis explores the work of Italian fashion journalist Anna Piaggi (1931-2012). Throughout her life, she was described as a work of art, living fashion painting, and walking museum. The last description derives from the way she combined collectable fine art museum quality vintage and contemporary fashion on her body, introducing the body as a site for the display and arrangement of museum objects and therefore as a new site for curatorial practice. This research takes the last expression, walking museum as its starting point, to recognise her approach to self-fashioning as a curatorial act.
Plasma Activated Matter for Improved Supercapacitor Electrode Materials
Authors:   , Barclay Mitchell
Published: January 2025

This thesis investigates the utility of plasma, the fourth state of matter, as a tool for improving portable energy storage devices such as batteries and supercapacitors. First, water is imbued with potent properties such as acidic pH and reactive chemicals, called plasma activated water, to enhance the energy storage capability of manganese dioxide supercapacitors. Plasma activated water is also used as a tool for the synthesis of nickel doped manganese dioxide cathodes which are used to assemble rechargeable zinc ion batteries. Plasma is also used for the incorporation of conductive copper into manganese oxide cathode material.
Impact of Job Stress on Mental Health and Work-Life Balance: Cross-Sectional Study
Authors: Malik Ayesha , Javaid Fatima , Gardezi Syeda Bisma Batool
Published: January 2025

This study examines the impact of job stress on university teachers from government and private universities in Multan, and how it affects their mental health and work-life balance. Data was collected from (N=140) university teachers using a convenience sampling method. The scales used were Workplace Stress scale (Rosenfeld, 1988), Psychological Wellbeing scale (Ryff et al., 2007) and Work Balance Questioner (Brough et al., 2014). The results indicated a strong negative correlation (r=-.07) between job stress and mental health, suggesting that mental health problems arise from job stress. Furthermore, there was an inverse relationship between job stress and work-life balance, with an (R2=-.02). Additionally, there was a negative correlation between mental health and work-life balance, as indicated by (R2=.08). The study also investigated gender disparities and found that female teachers exhibited a higher level of mental health (M=164.52) compared to male. The proposes implications for mental health and work life balance for university teachers.
Ludo Star Game Addiction and Aggression: Social Support and Empathy as Moderators
Authors: Mussawar Bushra , Jaffri Asmat Raza , Khan Muhammad Ali
Published: January 2025

In today's society, technology has made life more convenient, yet its addiction can lead to clinical distress and mental health challenges. The goal of the study is to find social support and empathy as moderators between Ludo Star game addiction and aggression, in young adults. The study involved 268 participants (M=22.2, SD=1.9), aged 18 to 25, using a correlational research design and purposive sampling technique, and employed tools like the Gaming Addiction Scale for Adolescents, the Buss and Perry Aggression Questionnaire, the Basic Empathy Scale, and the Social Support Scale (Short Form). Correlational analysis showed that Ludo Star game addiction is negatively and significantly associated with empathy. However, Ludo Star game addiction is positively linked to aggression and negatively linked to social support, although these connections were not statistically significant. Empathy is positively and significantly related to social support, while its connection with aggression is positive but not significant. Furthermore, aggression showed a negative but non-significant link to social support in young adults. Moderation analysis revealed that empathy significantly moderated the connection between Ludo Star addiction and aggression, while social support did not moderate this link. Gender differences indicated that men scored higher than women in aggression, addiction, and empathy, while men scored lower in social support, though these differences were not significant. The study concludes with limitations, recommendations, and implications for social welfare.
Comprehensive assessment of the properties of additives as a necessary component of the intelligent decision-making system in the proofing of steel at the ladle-furnace installation
Authors:   Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Togobytska D. M. , Povorotnia I. R. ,   Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Piptyuk V. P. ,   Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Kuksa O. V. ,   Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine
Published: January 2025

The information basis for theoretical and applied metallurgy in developing solutions aimed at improving existing and developing fundamentally new technological schemes for the manufacture of high-quality metal products are problem-oriented programs, which should be based on a reliable database and models. Analysis of modern specialized computer programs indicates a significant lack of such models for multi-component melts, which are the main participants in metallurgical processes - metal, slag, additives. The main task of the work is to create a set of basic models for predicting the primary physicochemical and thermophysical properties of metal melts in order to directionally form high-quality metal and increase its competitiveness. The original concept of directional chemical bonding was chosen as the basis for modeling, the core of which is the consideration of metal melts as chemically unified systems, rather than a mechanical mixture of constituent elements and taking into account the contribution of all components, even in small concentrations. The work uses an important information component, which is a database on the properties of metallurgical melts, which is continuously updated with modern data and contains the results of our own and industrial experimental research and literature search (articles, patents, inventions, scientific developments, monographs). The significance of databases is undeniable and requires their introduction to the interdisciplinary and interuniversity level with open access, as a separate instance to promote the development of the scientific level and capabilities of scientists. Adequate mathematical models have been developed based on the integral parameters of interatomic interaction, which ensured high accuracy of the operational forecast (R^2≥0.9). The results of the research are recommended for use in scientific research and industrial conditions for the purpose of directed formation of the composition and properties of smelting products, as well as reducing energy costs, reducing defects by making operational technological decisions by integrating the developed models into automated scientific research systems and automated control systems for the technological process of steelmaking.
Features of the construction of single-tier top tuyeres for oxygen converters
Authors:   Iron and Steel Institute of Z. I. Nekrasov National Academy of Sciences of Ukraine, Yushkevych P. O.
Published: January 2025

Expected steel production in Ukraine in 2024 is projected to exceed the 2023 output by 17%. Up to 75% of global steel production is carried out using the oxygen converter process. In Ukraine, the capacity of converter production is concentrated at PJSC ArcelorMittal Kryvyi Rih, PJSC Dnipro Metallurgical Plant, and PJSC Kamet Steel. For the oxygen converter process, in addition to the converter agregat one of the most important components of the equipment is the top tuyere. In this work definition and explanation of the top tuyere operating principles are provided. Continuous development and improvement of top tuyere constructions since their inception have led to various construction features. To explain these, the concepts of nozzle group, tier, and row of an oxygen converter top tuyere are introduced in this work. The general structure of a classic top tuyere is presented. The main design features of single-nozzle and multi-nozzle top tuyeres are characterized. It is noted that, according to the cooling principle, top tuyeres with central and peripheral water supply are distinguished. A classification of top tuyere tip is provided based on the number and type of nozzles and the manufacturing method. It is noted that multi-nozzle top tuyeres with Laval nozzles in the tip with quantity from 3 to 12 are the most widely used in leading metallurgical countries, and in Ukraine, those with 4, 5, 6 nozzles in tip. According to the formulated notions for the nozzle group, tier, and row, such top tuyeres are defined as single-tier multi-nozzle, corresponding to the trend towards the development of multi-tier and multi-row top tuyere designs. It is important to note that the correct selection of the top tuyere affects a number of parameters of the converting melt process, advantages and disadvantages of the oxygen converter method compared to the open-hearth and electric steelmaking processes. It is determined that further development of new top tuyeres should be aimed at strengthening the existing and creating new advantages of the converter steelmaking process while simultaneously eliminating its drawbacks.
Pelatihan Pengolahan Instan Jahe Bersama Ibu-Ibu PKK Desa Tlogoagung Kecamatan Kembangbahu Kabupaten Lamongan
Authors: Najih Ainun , Adityawan Tofan , Shulhi Muhammad Ibda’u , Sulistyowati Sulistyowati
Published: January 2025

Desa Tlogoagung berada di Kecamatan Kembangbahu, Kabupaten Lamongan. Jenis pertanian yang dihasilkan sangat melimpah mulai dari padi, jagung, kacang hijau, kacang kedelai, hingga tanaman obat seperti jahe, kunyit, dan sere. Komoditas yang dihasilkan tersebut masih belum banyak diolah menjadi produk yang mempunyai nilai jual lebih tinggi. Oleh karena itu dibutuhkan upaya untuk mengatasi permasalahan tersebut, salah satunya yaitu dengan melaksanakan kegiatan workshop pengolahan instan jahe bersama ibu-ibu PKK Desa Tlogoagung sehingga menjadi produk olahan yang menarik dan mempunyai nilai ekonomi yang tinggi. Kegiatan ini menggunakan metode penyampaian penjelasan tentang pengolahan instan jahe sekaligus praktik secara langsung dalam pembuatan instan jahe. Tujuan dari kegiatan ini yaitu untuk memberikan pengetahuan dan pelatihan tentang cara pembuatan instan jahe. Output dari kegiatan workshop yang telah berlangsung yaitu masyarakat Desa Tlogoagung mahir dalam mengolah jahe menjadi instan jahe, sehingga dapat digunakan sebagai modal untuk membuat produk unggulan Desa Tlogoagung. Harapannya dapat memberikan tambahan ilmu dan keahlian bagi masyarakat sekitar dalam menghasilkan produk olahan dari jahe yaitu berupa instan jahe yang memiliki nilai ekonomi lebih tinggi. Sehingga bisa meningkatkan penghasilan ekonomi warga sekitar.
Peningkatan Kreativitas Berbahasa Melalui Kegiatan Pentas Kreasi Bahasa dan Sastra pada Siswa SMP Negeri 15 Surabaya
Authors: Fernanda Niar Martin , Husada Syafa , Mubaroh Ana Sirotul , Adil Ahmad Khimas , Khotimah Khusnul
Published: January 2025

Indonesian is the language used daily by Indonesian people and functions as the official language in formal activities. This language is also the main medium in the world of education and is taught at all levels of school, from kindergarten, elementary school, middle school, high school, to university. One important aspect in mastering Indonesian is language skills, which can be measured through various training and competitions. This activity encourages teaching assistance students to organize an event to commemorate Language and Literature Month at SMP Negeri 15 Surabaya, where they carry out their duties at Trunojoyo Madura University. The aim of this competition is to increase the language creativity of SMPN 15 Surabaya students while providing meaningful and useful experiences through creative performances. The implementation of this activity is carried out through several stages, namely the preparation stage and the implementation stage. The categories of language and literary creation performances that are held include chain poetry performances, storytelling, poetry musicals, stand up comedy, monologues, recital of rhymes and orations. This performance category is designed to increase students' language creativity and literacy interest, especially in the field of Indonesian.
Relationship between integration, readiness and innovative product performance in a Brazilian supply chain
Authors: Guerra Rodrigo Marques de Almeida , Peixe Roberto Nascimento , Alberto Marcus Frantz , Zavarise Ana Paula Brum
Published: January 2025

There are few studies addressing the mediating effect of readiness in supply chains in developing countries. We use the theory of dynamic capabilities to investigate the impact of integration (INT) on readiness (REA) and on innovative product performance (IPP) and to examine the mediating effect of REA on INT and IPP. We conducted a survey with a sample of 213 supply chain (SC) managers of machinery and equipment for transport and lifting heavy loads in Brazil. To this end, we used structural equation modeling to test the hypotheses and PROCESS macro to confirm the indirect effect. The empirical results indicate a significant effect between INT and REA and REA and IPP. However, the indirect impact of REA was compromised. The literature review demonstrates that the microfoundations of dynamic capabilities (Sensing, Seizing and Reconfiguration) strengthen supply chain links, particularly between INT, REA and IPP. This article helps managers understand the functioning of SC routines and operations. To this end, they should develop strategies that strengthen SC ties with INT, REA and IPP to face future crises. This paper advances the findings on integration, readiness, and innovative product performance in an SC. The findings provide insightful implications for managers to improve their strategies. In doing so, we theorize how the microfoundations of dynamic capabilities support the efficiency of supply chain operations.
Impact of knowledge sharing, IT support, and innovation on supply chain sustainability with uncertainty moderation
Authors: Purnama Chamdan , Rahmah Mirhamida , Fatmah Dinda , Rahmah Yusriyah , Rahmah Zakiyah Zulfa , Hasani Syaiful
Published: January 2025

This study analyzes the relationships between knowledge sharing culture, information technology support, and process innovation on supply chain sustainability in Indonesia's digital printing SMEs, with market uncertainty as a moderating variable. Employing an explanatory approach, the research utilizes a cross-sectional survey involving 225 SME owners. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that knowledge sharing culture has a significant positive impact on both process innovation and supply chain sustainability. Conversely, information technology support negatively affects these two variables. Process innovation positively contributes to supply chain sustainability, albeit with a small effect. Market uncertainty strengthens supply chain sustainability but weakens the relationship between process innovation and sustainability. The mediating role of knowledge sharing culture through process innovation highlights a critical pathway for enhancing supply chain sustainability. This study offers theoretical and practical implications regarding the importance of knowledge sharing culture and process innovation, alongside challenges in technology adoption. The study's limitations include its cross-sectional approach and focus on digital printing SMEs in Indonesia. Future research is recommended to adopt a longitudinal approach to explore dynamic changes in this context. The novelty of this study lies in its integrated understanding of factors influencing supply chain sustainability amid market uncertainty.
Research on Optimization of Tower Crane Position in High rise Buildings Based on Neural Networks
Authors: Ming Yao
Published: January 2025

In order to improve efficiency, the tower crane scheme for high-rise buildings should consider the reach distance of the crane, which directly affects the capacity of the crane and is directly related to the operating cost of the crane. This article proposes a neural network-based method for optimizing the position of high-rise building tower cranes to improve operational efficiency. The case study results indicate that this method can accurately estimate the capacity of the tower crane, automatically perform the result comparison process, improve the efficiency of the tower crane position optimization process, and reduce operating costs. This optimization model can be used to consider the operation plan of tower cranes or to design building layout planning when changing the trailer position during on-site construction.
Big Data Analysis of News Toolization Evolution Based on Computer Network Security Algorithms
Authors: Shengxuan Lin
Published: January 2025

In order to explore the inevitability of the evolution of the instrumentalization of data news, this paper combines the big data technology to study the inevitability of the evolution of the instrumentalization of news in the intelligent information age. Based on the MPTCP aggregate throughput model, this paper proposes an MPTCP throughput-aware traffic allocation and data scheduling algorithm (MPTCP-GA). Among them, the traffic allocation algorithm maximizes the MPTCP aggregate throughput through reasonable traffic allocation, and the data scheduling algorithm performs reasonable data distribution according to the results of traffic allocation. Moreover, this paper compares the proposed MPTCP-GA algorithm with CMT-QA and MPTCP round-robin scheduling algorithm through NS3 network simulator. The experimental research shows that the news big data system proposed in this paper can effectively improve the news data processing effect, which also verifies that big data tools have certain performance in news applications, and the evolution of data news tools is inevitable.
Construction of Information Security Model for Talent Technology Training Based on Improved Machine Learning Algorithms
Authors: Weixue Han
Published: January 2025

In order to improve the training quality of technical and skilled talents, this paper combines big data technology and machine learning technology to study the training of skilled talents, proposes corresponding data mining algorithms based on the characteristics of technical talents data, and combines machine learning algorithms to improve the data processing process. Moreover, this paper combines the improved algorithm to construct technical and skilled talents training and evaluation model, analyzes the system structure framework and basic logical structure, and obtains the system function structure. After building an intelligent system, this paper explores the actual effects of the training model of technical and skilled talents based on big data and machine learning. Finally, this paper combines simulation experiments to verify the system separately. Through data analysis, it can be known that the method and system proposed in this paper have a certain feasibility.
A Model for Muscle Injury Recovery after Training based on Computer Biomimetic Technology
Authors: XiaoFeng Hu
Published: January 2025

In order to improve the recovery effect of muscle injury after high-intensity training of rope skipping, this paper combines computer bionics technology to study the muscle injury recovery after high-intensity training of rope skipping. Moreover, this paper explains the working principle of the bionic muscle by combining the bionic muscle model proposed in this paper, and analyzes several stress situations of the bionic muscle during work. In addition, the relationship between the pressure difference between the two ends of the piston and the velocity of the damping rod during the movement of the damping rod is obtained by analyzing the liquid flow in the damping hole on the piston. The simulation study shows that the method proposed in this paper can effectively improve the training injury recognition and recovery effect of high intensity training muscle model in rope skipping.
Research on the Model of Safety Factors Related to Outdoor Sports for College Students Based on Big Data Analysis
Authors: Jingxue Zhang
Published: January 2025

Introduction: The existence and development of College Students' outdoor sports activities, the setting of sports events and the layout of sports industry must depend on the environment and be restricted by environmental factors.
Objectives: This paper tries to explore the external factors affecting teenagers' sports participation by using ecosystem theory from the perspective of teenagers' sports participation.
Methods: Starting with the environmental factors of College Students' outdoor sports, this paper combs and analyzes the relevant literature on the environmental factors affecting outdoor sports, and constructs the environmental evaluation elements of the development of outdoor sports.
Results: The results show that in order to promote teenagers' sports participation and improve their physical health level, we need to systematically think and understand various environmental factors affecting teenagers' sports participation from the perspective of development, and provide a good ecological environment
Conclusions: The results of this paper provide a theoretical reference for the analysis of environmental influencing factors of College Students' outdoor sports and the research on the sustainability of outdoor sports.
Utilizing Artificial Intelligence for Business Activity to Ensure Safe and Effective Practices
Authors: Yupei Du
Published: January 2025

This work seeks to understand the transformative role of Artificial Intelligence in business practices to enhance safety and efficiency at operations. Using a mixed-method approach, the study integrates primary data collected from interviews and surveys with information gathered from scholarly articles and reports from industries. According to the research results, it has been possible to reduce equipment failures by 30%, hazard prediction accuracy attained is 85%, and safety compliance by AI monitoring systems by 20%. Operational efficiency reveals a 25% decrease in inefficiencies, an 18% reduction in energy consumption, and a 15% increase in customer satisfaction from AI-powered solutions. The study underscores the potential of AI in driving safer, effective, and ethical practices towards creating workable frameworks for sustainable business operations.
Research on Empowering Urban Rail Transit Security with Intelligent Recognition of Individual Extreme Behavior
Authors: Liu Qi
Published: January 2025

Introduction: Individual extreme crime is one of the important risks of urban rail transit, and accurately identifying individual extreme behavior can effectively enhance the ability of police department to keep the safety of urban rail transit. At present, the early warning of individual extreme crimes generally adopts the integral method, which heavily relies on the experience of the police to assign values and cannot objectively and accurately describe the relevant factors that affect individual extreme behavior.
Objectives: This paper plans to propose a fuzzy reasoning warning method for identifying individual extreme behavior, providing a reference for public security organs to improve the security level of urban rail transit.
Methods: This studies a fuzzy processing method that conforms to the objective facts of individual extreme behavior, using a membership function to represent fuzziness and objectively describe the characteristics of individual extreme behavior and its influencing factors.
Results: This paper overcomes the limitation of relying heavily on individual police experience by extracting features from a large amount of case data to establish a membership function.
Conclusions: This paper's reasoning is based on accurate description of natural language, and the reasoning process overcomes subjectivity. The reasoning results contain more information, which can effectively improve the public security organs' ability to accurately identify individual extreme behavior and warning level.
Clothing Design System based on Intelligent Image Rendering Technology
Authors: Yidan Zhang
Published: January 2025

In order to improve the color expression effect of clothing design, this paper combines the intelligent image rendering technology to construct the clothing design system. Moreover, this paper studies the algorithm idea of the rendering algorithm and its neural network structure, and uses the shader file to simulate the forward propagation of the neural network framework to perform pixel-by-pixel parallel computing. In addition, in this paper, the scene environment information and the parameter set obtained by training are multiplied by a series of matrix vectors to calculate the ambient light occlusion results. Finally, this paper studies the image rendering process through simulation to improve the effect of clothing design. The simulation results show that the clothing design system based on intelligent image rendering technology proposed in this paper has good results.
Artificial Intelligence Applications in Fraud Detection and Prevention: Emerging Opportunities
Authors: Nasser Abdullah Alsulayhim
Published: January 2025

In today's digital landscape, technology plays a central role in nearly every aspect of business, including supply chain management, manufacturing, sales, marketing, and finance. However, the increasing reliance on digitisation has made organisations across various sectors more vulnerable to fraud. As businesses adopt technology to enhance efficiency, their exposure to these risks grows, necessitating the protection of intellectual property, business data, consumer information, and more. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising tools for detecting and preventing fraud. This article explores the potential of AI and ML to collaborate with both supervised and unsupervised systems to better address security risks. By analysing financial transactions, customer behaviour, and real-time traffic, these technologies can detect anomalies and raise alerts for suspected fraud. This study investigates the fraud detection and prevention capabilities of AI applications in the e-commerce, healthcare, and tourism sectors. Data is collected and analysed to provide meaningful insights into the managerial factors influencing various AI applications in fraud detection and prevention. The analysis of different AI applications and software, focusing on their technological models, key features, and industry use, demonstrates that tech developers have successfully integrated fraud monitoring and detection systems. Furthermore, these applications could be adapted for use in other sectors to address critical security infrastructure gaps. The survey results also strongly indicate that while organisational strategy, structure, resources, and trust support the implementation of AI, broader environmental factors such as organisational culture may significantly affect the effectiveness of AI in fraud detection and prevention.
Low-altitude Emergency Strategy Using Ad-hoc Radio Relay eVTOLs in Karst Topography and Game Analysis
Authors: Tao Li, Yingjun Du, Zemin Zhang
Published: January 2025

The low-altitude economy (LAE) represents a novel economic framework centered around low-altitude aviation activities. Electric Vertical Take-Off and Landing vehicles (eVTOLs) are emerging as the key instruments for LAE due to their quiet operation, scalability, environmental friendliness, efficiency, and autonomous capabilities. In regions with complex karst topography, such as the ASEAN border areas, leveraging eVTOL networks with radio relays can significantly enhance communication quality, logistics efficiency, emergency response, and tourism experiences, thereby offering a transformative development for regional LAE. We investigate the coverage of wireless base stations along a section of the Hechi Muluojia Canyon coastline in Guangxi, China, identifying challenges in achieving high-quality wireless communications. In response, we propose the Low-altitude Emergency Strategy (LES), which employs ad-hoc radio relay eVTOLs (ARR eVTOLs) in conjunction with GSOs to deftly counteract radio fading and interference, thereby improving communication accessibility and quality. To generalize the issue, we develop a differential evolutionary game model of LES, examining various game scenarios where participants face negative net gains and disregard costs. This study offers a theoretical contribution to addressing wireless network security challenges within the LES framework.
Research on the Construction of an Intelligent Safety Assurance System for Building Construction and Accident Prevention Strategiesand Network Security Technology
Authors: Xingang Yang
Published: January 2025

As society's technology evolves, especially with advancements in big data and artificial intelligence, management systems across various industries need continuous innovation.To effectively prevent accidents in the construction industry, this paper relies on current laws, regulations, and standards to deeply research strategies for building a safety assurance system. Through analyzing \"Supervision Penalty\" documents and accident investigation reports from the Ministry of Housing and Urban-Rural Development over the past three years, it has been determined that hidden dangers, inspections, training and education, systems, supervision, technical briefings, investment, and emergency rescue are the eight major factors leading to accidents. Based on these findings, we have constructed six sub-systems within the safety assurance framework, encompassing risk classification and control, hidden danger identification and treatment, safety inspections, safety education and training, systemic guarantees, oversight, and technological and financial support. These sub-systems collectively form the construction safety guarantee system. Verified through questionnaire surveys, this system receives an average rating of nearly 8 points, demonstrating its application value in reducing production safety risks. Finally, targeted prevention measures based on each sub-system are proposed for common injury types such as falls from height, struck-by incidents, machinery injuries, and collapses. This further highlights the system's characteristics of being straightforward, easy to operate, and highly effective in preventing accidents.
Research on the Tightness Diagnosisand Network Security Risk Protection of Slot Wedge Tightness for Generator Wall-Climbing Robot Based on Multi-Scale Frequency Band Energy Entropyand CNN-LSTM
Authors: Jie Bai
Published: January 2025

During its operation, the generator stator is prone to vibration as a result of the joint effect of powerful centrifugal force and significant electromagnetic stress. This phenomenon may potentially give rise to significant incidents when the slot wedge is found to be loose. For an extended period, the detection of a loose slot wedge in a generator has necessitated the removal of the end cover and extraction of the rotor, followed by tapping and inspection by a designated professional. This particular process has been noted to demand a substantial quantity of human and material resources, thereby leading to a rise in operational expenditures. The present study showcases the creation of a space-efficient inspection robot, engineered to access the air gap of a sizable generator stator without the necessity of rotor pumping. The robot is furnished with a percussive head, allowing for the assessment of slot wedge looseness. Additionally, this study puts forward a technique for extracting the characteristics of slot wedge tightness using multi-scale frequency band energy entropy,which proficiently dissects the energy dispersion of the sound signal. Subsequently, a CNN-LSTM algorithm is employed to identify the faults of the extracted features. It is contrasted with a CNN and LSTM algorithms under identical experimental circumstances. The outcomes signify that the method proposed in this study exhibits greater precision.
The Application of Computer Technology in College Students' Mental Health Education and Psychological State Recognition and Early Warning
Authors: Mingwei Li
Published: January 2025

To address the challenge of traditional questionnaires failing to accurately reflect individuals' psychological states, this paper proposes the integration of computer technology into mental health education for college students to identify psychological states and issue timely warnings. The study adopts an improved pyramid optical flow algorithm combined with a micro-expression recognition algorithm based on three-dimensional convolutional neural networks (3D-CNN). This combination further enhances the technical robustness. First, preprocessing algorithms are used to crop facial images; then, the pyramid optical flow algorithm is employed to extract facial features from the images, capturing dynamic characteristics of micro-expressions. Based on these steps, 3D-CNN is used to train the feature data, achieving efficient recognition of micro-expressions. Experimental results show that the micro-expression recognition accuracy on the CASME dataset reached 89.1%, with an F1 score of 0.6742, outperforming other traditional methods. The proposed algorithm significantly reduces model training parameters and computation time while exhibiting stronger feature learning capabilities. By accurately identifying students' psychological states, it provides objective data support for mental health education in colleges and, more importantly, offers an effective solution for early warning of psychological problems.
Enhancing Interaction Design through User Behavior Naturalization with AI-Powered Recommendation Systems
Authors: Liping Liu
Published: January 2025

This study aims to explore the integration of user behavior naturalization into AI-driven recommendation systems to enhance recommendation accuracy and user experience. By incorporating user behavior modeling, recommendation strategy optimization, and dynamic interaction design, the study addresses issues such as static interfaces, recommendation mismatches, and insufficient interpretability. A mixed-methods approach was employed, using real-world datasets like MovieLens and Amazon Product Reviews to evaluate quantitative metrics such as precision, recall, click-through rate (CTR), and diversity, as well as qualitative user feedback on usability and satisfaction. The experimental results show a 15% improvement in recommendation precision, a 25% increase in CTR, and higher user engagement due to dynamic content adaptation. The findings suggest that user behavior naturalization can effectively optimize personalized recommendation systems, with significant potential applications in fields such as e-commerce, entertainment, and education.
Algorithmic Enhancements and Empirical Study on an Intelligent Control Platform Using Deep Reinforcement Learning for Adaptive Scheduling and Real-Time Fault Prediction in Five-Axis CNC Machine Tools
Authors: Xin Ma
Published: January 2025

This paper introduces a comprehensive intelligent control platform developed to optimize the performance and operational reliability of five-axis CNC machine tools. The platform integrates advanced deep reinforcement learning (DRL) algorithms with real-time operational data to address two critical challenges in modern industrial automation: adaptive scheduling and real-time fault prediction. The adaptive scheduling component employs DRL to dynamically adjust machining task priorities and resource allocation, ensuring minimal idle time, reduced operational delays, and enhanced productivity. By continuously learning from machine data, the system adapts to varying operational conditions and optimizes task execution to achieve superior manufacturing outcomes.
Simultaneously, the real-time fault prediction module leverages DRL’s capacity for pattern recognition and decision-making to detect and predict potential system anomalies before they escalate into critical failures. This predictive capability not only minimizes machine downtime but also significantly reduces maintenance costs and extends the lifespan of the equipment. The proposed platform offers a dual advantage of optimizing production efficiency and enhancing system reliability, making it highly suitable for deployment in high-precision manufacturing environments.
To validate the effectiveness of the proposed framework, extensive empirical studies were conducted using real-world operational data from five-axis CNC machine tools. The results demonstrated significant improvements in task scheduling efficiency, fault detection accuracy, machining precision, and overall system performance when compared to conventional approaches. Key performance metrics, including downtime reduction, fault prediction accuracy, and machining throughput, were enhanced, highlighting the transformative potential of DRL-based intelligent control systems.
This work represents a significant advancement in the application of machine learning to industrial automation and smart manufacturing. It underscores the importance of integrating intelligent algorithms into modern manufacturing systems to achieve operational excellence and foster innovation. The insights gained from this research pave the way for further exploration of DRL applications in adaptive control, predictive maintenance, and other domains within the manufacturing sector.
Research on Path Planning of Computer Fraud and Security Teacher Quality Improvement based on Deep Reinforcement Learning
Authors: Mingjun Shi
Published: January 2025

With the rapid development and popularization of technology, the demand for path planning is also increasing. This paper explores how to optimize and plan the professional development path of teachers in computer fraud prevention education and network security teaching using deep reinforcement learning (DRL) technology. As cybercrime techniques become increasingly complex, higher demands are placed on teachers' knowledge updates and skill development. To solve the convergence speed problem caused by the complex network structure and fixed reward value of the classic deep reinforcement learning algorithms, this paper proposes an improved DQN path planning algorithm and a PER-ERD3QN path planning algorithm. The improved DQN path planning algorithm first embeds a multi-layer perceptron (MLP) into DQN, accelerating the iterative update of the neural network; secondly, this paper introduces a cubic function as a dynamic reward function, returning corresponding reward values based on the state of the object (i.e., its position) after taking an action; finally, it sets an adaptive action selection mechanism, so that the object can rely more on the reward function to choose actions in the later stage of path planning, thus avoiding the probability of randomly choosing actions being too high and missing better choices. For the problem of slow network update speed caused by excessive Q value estimation and insufficient efficient data utilization in the experience pool of the ERDQN path planning algorithm, the PER-ERD3QN path planning algorithm first combines ERDQN with dual Q networks and competitive networks to solve the problem of excessive Q value estimation; secondly, it introduces a priority experience replay mechanism to better utilize the high-quality data in the experience pool. The experimental results show that the PER-ERD3QN path planning algorithm has higher levels in average scores and average reward values, so its network update speed has also been significantly improved. This research not only helps to enhance teachers' professional knowledge and technical application ability in the field of network security, but also provides scientific methodological support for educational institutions to ensure that they can keep up with changing security threats. Furthermore, this study emphasizes the importance of interdisciplinary cooperation and encourages experts from computer science, education, and psychology to work.
Post-Quantum Cryptography: Preparing for the Quantum Threat
Authors: Shashikala Gurpur
Published: January 2025

Post-quantum cryptography is essential for safeguarding digital communications against the emerging threat posed by quantum computers, which can potentially break traditional cryptographic algorithms. This paper explores the vulnerabilities of current cryptographic systems to quantum attacks and examines the development of post-quantum algorithms designed to resist such threats. We analyze various candidate cryptographic schemes, including lattice-based, code-based, and multivariate polynomial cryptography, highlighting their theoretical foundations and practical implementations. Additionally, the paper discusses the challenges of transitioning to post-quantum standards, including key management and integration with existing systems.
Exploring the Role of Blockchain in Preventing Cyber Fraud in Financial Systems
Authors: Rupali Gangarde
Published: January 2025

Blockchain technology plays a pivotal role in enhancing the security of financial systems, providing a robust framework to prevent cyber fraud. As cyber threats in financial transactions escalate, blockchain's decentralized and tamper-resistant nature offers an innovative solution for fraud mitigation. By leveraging distributed ledger technology (DLT), blockchain ensures transparency, traceability, and immutability in transactions, significantly reducing the risk of unauthorized alterations or manipulations. Smart contracts, a feature of blockchain, automate and secure transactions, minimizing human error and preventing malicious interventions. Additionally, consensus mechanisms like proof-of-work and proof-of-stake enhance security by requiring agreement from multiple nodes before validating a transaction, thus eliminating the risk of single points of failure. Financial institutions adopting blockchain can secure payment processing, authenticate identities, and prevent fraudulent activities such as double-spending or phishing attacks. Blockchain also ensures compliance with regulatory standards through real-time auditing and secure data sharing between financial entities. However, despite its advantages, challenges like scalability and regulatory acceptance remain. This paper explores the potential of blockchain in preventing cyber fraud within financial systems, highlighting its impact on security, trust, and fraud detection, while addressing existing challenges in adoption and implementation.
Research on Anomaly Detection in Microservice Based on Graph Neural Networks
Authors: Shuai Fang
Published: January 2025

With the development of the information and innovation industry, microservice architecture has become mainstream in software development due to its faster delivery, better scalability, and greater independence. However, microservice architecture also faces some operational challenges. The large number of services, complex dependencies, and varying resource demands make traditional log inspection or threshold configuration methods often inadequate for anomaly detection, making it difficult for operations personnel to quickly locate the root cause of failures. To address the difficulty of pinpointing fault nodes in microservice systems, this paper proposes the GraphSAGE with Attention algorithm based on graph neural networks. This algorithm combines the neighbor node sampling method of GraphSAGE with an improved attention mechanism to effectively locate anomalies in real-time using abnormal data. Compared to traditional methods, GraphSAGE with Attention does not require manual intervention and can locate anomalies by periodically collecting operational data that reflect fault characteristics. Additionally, GraphSAGE with Attention achieves better fault localization with lower resource usage while meeting real-time requirements. In this pa- per, we constructed a dataset of fault call chains using fault injection and simulated user calls on the Kubernetes microservice platform and tested the accuracy of the GraphSAGE with Attention algorithm in various anomaly and scenarios. Experimental results show that this algorithm achieves the best performance across multiple metrics. Therefore, the algorithm is of great significance for fault root location and has practical application value.
An Improved YOLOv8 Fish Identification Based on PConv and Attention Mechanism
Authors: Aimin Lu
Published: January 2025

Accurate and effective identification of fish species is crucial for the processing and transportation of fish products. It enables efficient tracking and management of fish products, making it an essential aspect of the industry. Traditional methods of fish identification usually require expert knowledge within a particular domain. However, these methods can struggle to capture complex fish characteristics, particularly under varying lighting and angle conditions. In the article, an effective recognition algorithm named YOLO-FD is proposed. First, a novel feature extraction module has replaced the C2f module in YOLOv8, which effectively reduces the amount of model parameter calculations. The Efficient Multi-Scale Attention (EMA) applied in this study adeptly preserves spatial and channel information, fostering inter-regional interaction and enhancing feature extraction within the backbone network. The loss function in YOLOv8 was improved to address the sample imbalance problem. The YOLO-FD as an effective fish recognition algorithm addresses challenges faced by traditional methods simultaneously enhancing identification accuracy and offering lightweight improvement.