bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Cybersecurity Policies in the Public Sector: Challenges and Solutions
Authors: Supriya Dhananjay Paigude
Published: January 2025

Cybersecurity in the public sector has become a critical concern as government organizations increasingly rely on digital infrastructure to manage sensitive data and deliver essential services. This paper provides a comprehensive review of current cybersecurity policies implemented in the public sector, focusing on the challenges posed by resource constraints, outdated technologies, policy fragmentation, and the growing sophistication of cyber threats. The review also highlights significant policy frameworks such as the NIST Cybersecurity Framework and the European Union’s GDPR, and analyzes case studies of effective policy implementations. In response to identified challenges, this paper proposes solutions including enhanced public-private partnerships, policy standardization, workforce development, and the adoption of advanced technologies such as artificial intelligence and automation. These strategies aim to strengthen public sector cybersecurity defenses and ensure the resilience of critical services. The review concludes with recommendations for future policy reforms and the continuous adaptation of cybersecurity measures in the face of evolving threats.
Enhanced Sliding Mode Control Strategies of Modular Multilevel Converters with Advanced Computational Simulation and Optimization Techniques
Authors: Yuanyi Liu
Published: January 2025

This paper presents advanced sliding mode control (SMC) strategies aimed at enhancing the performance of Modular Multilevel Converters (MMC) by leveraging computational simulation and optimization technologies. By integrating the robust characteristics of SMC with the stability benefits of passivity-based control (PBC), the proposed method addresses challenges such as nonlinearities and system uncertainties in MMC. High-fidelity computational simulations are employed to evaluate and optimize the control strategies, utilizing algorithms tailored for current tracking, dynamic response, and robustness enhancement. The results of these simulations are compared with traditional Proportional-Integral (PI) control across key metrics, including current tracking accuracy, power quality, Total Harmonic Distortion (THD), and overall system efficiency. The findings demonstrate that the novel SMC strategies, combined with computational optimization, significantly outperform PI control, achieving superior power regulation and system stability. This work underscores the transformative potential of integrating computational methodologies with control strategies to advance MMC technology for high-reliability power systems.
IoT Security in Smart Healthcare Systems: Risk Analysis and Solutions
Authors: Suresh Limkar
Published: January 2025

The integration of the Internet of Things (IoT) in smart healthcare systems has revolutionized patient care by enabling real-time monitoring, remote diagnostics, and improved data management. However, this transformation also introduces significant security risks that compromise patient data privacy, system integrity, and device functionality. This research provides a comprehensive analysis of the security risks associated with IoT in smart healthcare environments, including device vulnerabilities, network security threats, and data privacy concerns. Through a detailed risk analysis framework, this study identifies critical threats and evaluates their potential impact on healthcare operations. Proposed solutions, such as advanced encryption techniques, secure network protocols, and regulatory compliance strategies, are discussed to mitigate these risks. Case studies of IoT security breaches and successful implementations further highlight practical challenges and effective strategies. The findings underscore the importance of robust security frameworks to safeguard healthcare systems from emerging threats. Future research should focus on developing more advanced, AI-driven security models tailored to the evolving IoT landscape in healthcare.
Innovative Coordination Control Approach for Enhancing Grid-Connected Inverters' Resilience to Voltage Unbalance
Authors: Yuanyi Liu
Published: January 2025

Voltage unbalance in electrical grids presents a substantial challenge that directly impacts the operational efficiency and reliability of grid-connected inverters, which are integral for the seamless integration of renewable energy resources. In response to this issue, this paper develops a sophisticated coordinated control strategy that harnesses the dynamic synchronization capabilities of the d-q rotating coordinate system, specifically designed to counteract grid fluctuations. This innovative approach facilitates the meticulous regulation of both active and reactive power through a synergistic integration of advanced power management techniques and adaptive tuning mechanisms, thereby significantly enhancing the system's responsiveness and overall reliability. The strategy's effectiveness in substantially improving the fault ride-through capabilities of grid-connected inverters, even under conditions of marked voltage unbalance, is rigorously validated through a series of comprehensive experimental procedures. The findings represent a pivotal advancement in the field, underscoring the novel application of dynamic synchronization techniques within the context of grid-connected inverters. By offering an advanced control solution that markedly improves the resilience and stability of grid-connected inverters, this work makes a significant contribution to the existing body of knowledge, addressing a critical gap in the literature and paving the way for future research in this area. The implications of this study are far-reaching, providing a robust framework for enhancing the integration of renewable energy sources into the power grid, thereby supporting the global transition towards sustainable energy systems.
A Precision Measuring Approach of Shaft Edge using Dual-stage Sub-pixel Detection with Lighting Compensation
Authors: Xiang Wu
Published: January 2025

This research introduces a method with a pragmatic approach for measuring the contour of shaft workpieces. The algorithm utilizes dual-stage Canny edge detection and sub-pixel localization techniques. This study focuses on the challenges associated with edge detection in the context of back-light lighting systems, with the aim of enhancing both accuracy and stability. The proposed technique integrates adaptive threshold-based Canny edge detection, weighted fusion, and morphological algorithms in order to improve the continuity of edges. The process of identifying the location of a coarse edge is accomplished through the utilization of cubic spline interpolation and curve fitting techniques to determine sub-pixel coordinates. The RANSAC fitting algorithm is employed to estimate lines that best match a set of edge points, hence enabling the computation of accurate values for axis-type components. The error analysis and compensation under various lighting conditions were completed through experimental analysis. The method described in this study demonstrates a notable level of accuracy and reliability in the recognition of cylindrical standard parts. It exhibits the capability of precisely identifying standard parts of varying sizes, with an absolute error margin of 8 micrometers. In addition, the algorithm has anti-interference capability, and we deployed the algorithm on Nvidia's Jetson orin nano, so that the entire system design can be more compact. In conclusion, this algorithm offers a viable solution for the efficient and accurate recognition of part contours in diverse settings.
Empirical Investigation and Analysis of Learner Learning Effectiveness in Open Education Online and Offline Teaching in the Digital Age
Authors: Yongfu Liu
Published: January 2025

With the rapid development and popularization of Internet technology, offline teaching in the age of digital intelligence has gradually become an important part of the education field, which together with traditional offline teaching forms a diversified teaching model. Study aims to provide useful references for educators through comparative analysis, in order to better combine the advantages of two teaching modes and improve learners' learning outcomes. At the same time, the study focuses on the psychological anxiety of open education learners in the process of online learning. Through empirical investigation and analysis, the impact of learning anxiety on online learning effectiveness is revealed, and the relationship between anxiety and learning behavior is explored. This study conducts an in-depth comparative analysis of the learning outcomes of online and offline teaching from the perspective of open education in the digital age. The research results show that online teaching exhibits significant advantages in flexibility, resource diversity, and personalized learning. Learners can learn anytime, anywhere without being limited by time and space, and the teaching resources are rich and diverse, which can meet the personalized needs of different learners. However, online teaching also has some shortcomings, such as insufficient teacher-student interaction, high demands for learner self-discipline, and relatively weak learning atmosphere, which may to some extent affect learning outcomes.
Visual Localization and Dense Map Construction in Dynamic Scenes
Authors: Huipeng Li
Published: January 2025

The key technology for autonomous navigation of mobile devices such as robots lies in Simultaneous Localization and Mapping (SLAM) based on vision, which has become increasingly sophisticated in idealized static environments. However, in dynamic scenarios, existing instance semantic based visual SLAM methods cause over segmentation during the dynamic segmentation process, while deep learning methods increase system runtime. Therefore, this article proposes a visual SLAM scheme in dynamic scenes. The method of combining fractal dimension and epipolar constraint is used to detect dynamic regions in images, remove all feature points in the corresponding dynamic regions, construct a backend model in dynamic scenes, and achieve a more comprehensive dense point cloud map. This enables the visual SLAM system to accurately complete navigation, positioning, and obstacle avoidance functions in real time.
Evaluating Cyber Security Policies: A Global Perspective on Best Practices
Authors: Smita Chaitanya Pangarkar
Published: January 2025

This paper evaluates cybersecurity policies across various nations, highlighting global best practices to combat cyber threats. The study utilizes a comparative analysis methodology, reviewing policies from developed and developing countries, with a focus on their adaptability, effectiveness, and resilience. Data were gathered from government reports, industry white papers, and case studies. The findings indicate that countries with comprehensive, multi-layered strategies integrating legal frameworks, public-private partnerships, and education initiatives demonstrate higher resilience against cyber-attacks. The study emphasizes the importance of international collaboration and continuous policy evolution in addressing emerging threats, offering recommendations for harmonizing global cybersecurity practices.
The Future of Cyber Security Policy in the Post-Quantum Era
Authors: Bindu Ronald
Published: January 2025

The advent of quantum computing presents unprecedented challenges to the existing cybersecurity framework, especially in the realm of cryptographic algorithms. Current encryption methods, such as RSA and ECC, which secure sensitive data, are vulnerable to quantum-based attacks due to the capabilities of quantum algorithms like Shor’s and Grover’s. This research paper explores the future of cybersecurity policy in the post-quantum era, analyzing the gaps in current policies and the need for quantum-resistant solutions. By examining emerging cryptographic algorithms, including lattice-based and hash-based cryptography, this study underscores the necessity for governments and industries to collaborate in developing post-quantum cryptographic standards. Furthermore, the paper addresses the economic and ethical considerations of transitioning to quantum-safe systems and provides case studies to illustrate the real-world implications of quantum threats on critical sectors such as finance, national security, and cloud computing. Ultimately, the study advocates for an adaptable, globally coordinated policy framework to ensure resilient cybersecurity in the quantum age.
Quantum Cryptography and its Implications for Secure Voting Systems
Authors: Shweta Bandhekar
Published: January 2025

Ensuring the security and integrity of voting systems is crucial for maintaining democratic processes. With the rise of cyber threats targeting electronic voting, traditional cryptographic methods are proving inadequate in safeguarding against sophisticated attacks. Quantum cryptography, particularly Quantum Key Distribution (QKD), offers a promising solution to enhance the security of voting systems by leveraging the principles of quantum mechanics. This research explores the application of quantum cryptography in secure voting, highlighting its potential to address vulnerabilities in current systems. The study examines the technological, practical, and legal challenges of implementing quantum-based voting systems, including infrastructure requirements and public acceptance. Furthermore, it assesses how quantum cryptography ensures voter privacy, prevents tampering, and provides robust protection against future quantum computing threats. While significant hurdles remain, the integration of quantum cryptographic techniques presents a transformative opportunity to create more secure and transparent voting processes. This paper outlines the roadmap for adopting quantum cryptography in voting systems and its potential impact on electoral integrity.
Cloud-Based Cognitive Image Authentication Using Deep Learning Techniques for Secure Access
Authors: Pranali Dahiwal
Published: January 2025

Cognitive image authentication leverages unique behavioural and cognitive traits for secure user authentication. Traditional text-based passwords, while prevalent, are vulnerable to security breaches due to user tendency to choose simple, easily guessed passwords. Biometric methods, particularly face recognition, have gained popularity for their enhanced security, but face challenges such as susceptibility to spoofing and limitations with age-related variations or face accessories. This study introduces a cloud-based cognitive image authentication framework that combines facial recognition with cognitive biometric features to improve security and reliability. Key contributions include optimizing facial image transmission from handheld devices to the cloud, enhancing authentication accuracy despite aging or accessories, and comparing cognitive authentication with existing methods. Experimental results demonstrate that the proposed MobileNetV2-based model achieves high accuracy and robustness in real-time and large-scale datasets, making it a suitable solution for cloud-based authentication.
Quantum Cryptography: Enhancing Security in Quantum Computing Environments
Authors: Saurabh Bhattacharya
Published: January 2025

When it comes to protecting data, quantum security is a huge step forward, especially in settings with quantum computers. The development of quantum technologies makes standard encryption methods more open to attacks from quantum computers that can use their computing power to break common encryption protocols. This essay looks into the basic ideas of quantum cryptography, with a focus on Quantum Key Distribution (QKD) as a key part of safe communication. Quantum key distribution (QKD) uses quantum physics ideas like superposition and entanglement to help two people make a shared secret key that can't be hacked. The paper talks about different QKD methods, like BB84 and E91, and looks at how they work and what security promises they offer. We look at the problems that come up with real-world uses, like flawed devices and influence from the surroundings, which can affect how accurate quantum states are.
Securing Cloud Infrastructures: A Multi-Layered Approach to Data Protection
Authors: Megha Dhotay
Published: January 2025

This paper explores a comprehensive, multi-layered approach to securing cloud infrastructures, emphasizing the critical importance of data protection in today’s digital landscape. As organizations increasingly migrate to the cloud, they face heightened risks of data breaches and cyber threats. Our proposed framework integrates various security measures, including encryption, access control, intrusion detection systems, and continuous monitoring, to create a robust defense against unauthorized access and data loss. We discuss the effectiveness of each layer in mitigating vulnerabilities and ensuring compliance with regulatory standards. Additionally, the paper highlights best practices for implementing these strategies, tailored to different organizational needs. By adopting this multi-layered approach, businesses can enhance their overall security posture, safeguard sensitive information, and maintain customer trust in cloud-based services.
IoT Security: Addressing the Challenges of Distributed Sensor Networks
Authors: Jayita Moulick
Published: January 2025

This paper explores the critical security challenges associated with Internet of Things (IoT) in distributed sensor networks. As IoT devices proliferate across various sectors, their interconnected nature introduces significant vulnerabilities, making them prime targets for cyberattacks. We identify key security issues, including data integrity, confidentiality, and device authentication, which are exacerbated by the constraints of sensor networks, such as limited processing power and energy resources. Our research evaluates existing security frameworks and protocols, highlighting their effectiveness and shortcomings in real-world applications. Furthermore, we propose a multi-layered security architecture designed to enhance resilience against potential threats while maintaining system efficiency.
Quantum Key Distribution Protocols: A Review of Security Enhancements
Authors: Sanjay P.Pande
Published: January 2025

Quantum Key Distribution (QKD) has emerged as a revolutionary technology for secure communication, leveraging the principles of quantum mechanics to ensure the inviolability of cryptographic keys. Conventional QKD protocols often rely on centralized systems, which present challenges related to scalability, trust, and single points of failure. This review explores the security enhancements introduced by adopting a decentralized approach to QKD protocols. We analyze various decentralized QKD frameworks that eliminate the need for trusted third parties, leveraging blockchain technology and other distributed systems to enhance security, transparency, and robustness. In addition to assessing their performance, we examine how these decentralized models mitigate vulnerabilities such as key interception, node compromise, and denial-of-service attacks, which are prevalent in traditional centralized systems. Our review highlights advancements in the integration of decentralized QKD with classical cryptographic techniques, offering a comprehensive view of its potential to revolutionize secure communications in critical sectors. The decentralized approach to QKD represents a promising evolution towards more secure, scalable, and resilient quantum cryptography systems.
Blockchain-Based IoT Security Framework for Critical Infrastructures
Authors: Vijaya Vijaykumar Pangave
Published: January 2025

This paper presents a comprehensive Blockchain-Based IoT Security Framework tailored for critical infrastructures. As IoT devices proliferate, they expose vulnerabilities that can jeopardize essential services. Our framework leverages blockchain technology to enhance data integrity, authentication, and access control across interconnected IoT networks. By integrating smart contracts, the framework automates security protocols, ensuring real-time responses to potential threats. We analyze key components, including decentralized identity management, consensus mechanisms, and cryptographic techniques, to establish a robust security posture. This innovative approach not only fortifies the resilience of critical infrastructures but also promotes trust among stakeholders.
A Hybrid Approach for Feature Extraction of Multi-Class Dataset in IDS
Authors: Jhansi Rani Mettu
Published: January 2025

Intrusion Detection Systems (IDS) are very important for keeping networks safe from online dangers, especially when there are a lot of different classes of data and duplicate features that can slow things down. This work presents a new way to improve the performance of IDS by mixing the K-Best and Random Forest Importance methods for feature extraction. Before Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and the suggested blend method were used on the IoT-23 dataset, features were normalized and labelled. The combination method picked out important factors like flow time, packet length, protocol type, and response numbers, which led to better classification results. We used an 80-20 split for train and test to check how well three models (XGBoost, Random Forest, and Naive Bayes) worked. Comparative research showed that the combined method was better, as it achieved 99% accuracy and big gains in precision, memory, and F1-score measures. In particular, XGBoost proved to be the best model, showing impressive speed with its mixed feature set. PCA, LDA, and ICA, on the other hand, gave average results. This shows how important it is to combine different feature selection methods. The results show that the mix method can deal with feature duplication and improve IDS performance, which makes it a good choice for real-world use. To make this method even better, more study could look into how it works with bigger datasets and more models.
Application of Modernization Platform in Container Cloud Platform Management and Practice
Authors: Shanshan Pan
Published: January 2025

With the rapid development of industrial internet platforms, enterprises' demand for cloud-native architectures has significantly increased to meet the requirements of large-scale business applications for flexibility and scalability. However, many enterprises face the challenge of lacking standardized and normalized container cloud platforms during the process of digital transformation. This paper proposes and introduces an application modernization platform based on Kubernetes, aiming to provide enterprises with key functions such as efficient cluster management, automated application building and release, and multi-tenant support, thereby significantly improving the efficiency of enterprises in building and running modern applications using Kubernetes. Through in-depth analysis of the platform's overall architecture design, core technologies and implementation methods, as well as specific implementation and test results of system functions, combined with practical application cases, this paper comprehensively demonstrates the application effects and significant advantages of the platform in practice.
Research on Innovative Methods and Implementation Paths of Ship Target Detection in Deep Learning
Authors: Kaizhi Li
Published: January 2025

In order to improve the recognition accuracy of ship targets in SAR images, this paper adopts a data-driven deep learning ship detection scheme, and attempts to use YOLOv5, YOLOv7, and YOLOv7-X to recognize ship targets in the HRSID dataset and a self-constructed HRSID dataset enhanced for small targets, and finally presents them in a visual form. While recognizing images, it is also possible to recognize ship targets in videos and real-time cameras. This article mainly explores in depth from four aspects: selection and reinforcement of datasets, selection of deep learning algorithms, Python scripts and visual interfaces for assisting model training. This article aims to simplify the repetitive operations in the experimental process and further enhance the convenience of detection. Relevant Python scripts and visual interfaces have been designed and written to achieve auxiliary functions such as batch and random addition of small targets in images, stacking of real boxes and recognition boxes of test samples, and improving the efficiency of ship detection. The experimental results in the article show that using YOLOv7-X and the enhanced HRSID dataset can achieve a recognition accuracy of 92.53%, and also have good recognition effects on small targets and ship targets in complex backgrounds such as ports. Compared with traditional ship detection methods, there is a significant improvement in recognition accuracy.
Educational Inclusion and Its Difficulties in the Teaching-Learning Process
Authors: René Oswaldo Gómez Macías
Published: January 2025

Educational inclusion has a higher meaning than a technique that must be applied in school environments, this implies a commitment and responsibility by all actors of the educational system and at all levels; this study aims to analyze the difficulties faced by educational inclusion in the teaching-learning process. As a methodology, the qualitative-quantitative approach was used, through a Likert scale questionnaire that groups the barriers to educational inclusion into 5 dimensions, which was applied to the teaching population of a rural public school, determining the reliability of the instrument using Cronbach's Alpha; interviews with experts in school inclusion were also used to discuss the results. These indicate that the difficulties that always oppose inclusion in this rural school are not caused by the actions of teachers, but by exogenous factors such as material resources that promote learning, especially in subjects such as natural sciences. It is concluded that the difficulties faced by educational inclusion in the teaching-learning process of students in a rural school come from the ministerial management of education that administers rural schools, the scarce budget for resources and materials, the number of teachers assigned, the limited infrastructure, however, the teaching vocation promotes their self-training to educate inclusively.
Implementation of Artificial Intelligence as an Educational Resource
Authors: Letty Adelaida Pin Alcívar
Published: January 2025

Artificial intelligence is a discipline that currently plays an important role in society, taking over multiple scenarios, it is projected as an invention that aims to mimic the skills of human beings. In educational environments, it has managed to enter with relevance, it is shown as an alternative to reduce the time of teachers and have a better monitoring of students. The purpose of this article is to analyze the implementation of artificial intelligence in the educational system of the Ernesto Vera Cedeño Educational Unit of Rocafuerte canton, Manabí, Ecuador. The research used a non-experimental descriptive methodology, with a mixed approach, taking as an instrument the survey that was applied to 30 teachers, who were selected through a non-probabilistic sampling where the inclusion criteria consisted of: being a volunteer and having more than 2 years in the educational institution Ernesto Vera Cedeño, finally a review of scientific documents was made, such as: texts, book chapters, articles, theses and documents with reliability information. The results reveal that artificial intelligence is applied in many aspects of the educational environment, providing virtual tutoring, individualized feedback, computing, student tracking, detailed reports, simpler planning, preparation and grading of questionnaires to a greater number of students, and enhancing creativity through robotics, among others. Likewise, it is important to take into account ethical considerations in order not to fall into dependence or loss of interaction skills.
Logistic Model for Predicting Traffic Fines: A Statistical Approach to Demographic and Behavioral Factors
Authors: Hernán Javier Guzmán Murillo
Published: January 2025

This study uses a logistic regression model to analyze the factors influencing the likelihood of receiving traffic fines within a year. The dichotomous dependent variable (yes/no) was modeled based on independent variables such as age, previous accidents, rest and vacation, active breaks, and general intensity. The results indicate that lack of rest, previous accidents, and absence of active breaks significantly increase the likelihood of receiving fines, while age and general intensity act as protective factors. The model demonstrated robust fit (AUC = 0.9101) and a Pseudo R-squared between 45.6% and 61.2%, showcasing its predictive capability. These findings can inform strategies to reduce traffic violations through interventions in driving habits and workplace wellness.
The Role of Physiotherapy in Enhancing Neuroplasticity
Authors: Mymounah Bdyri Albadri
Published: January 2025

Neurological rehabilitation through physiotherapy plays a crucial role in restoring function and improving quality of life for individuals with neurological conditions. This comprehensive review examines the fundamental principles and various therapeutic approaches used in neurological rehabilitation, with a particular focus on enhancing neuroplasticity. The paper explores multiple physiotherapy techniques, including motor learning strategies, constraint-induced movement therapy, functional electrical stimulation, robotic-assisted therapy, virtual reality interventions, and aquatic therapy. These interventions are discussed within the framework of an interdisciplinary, evidence-based approach to patient care. The review emphasizes the importance of personalized treatment plans that consider individual patient needs while leveraging the brain's natural capacity for reorganization through neuroplasticity. By analyzing current research and clinical practices, this paper demonstrates the significant impact of physiotherapy interventions on functional outcomes, motor recovery, and overall rehabilitation success in patients with neurological conditions such as stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, and Parkinson's disease.
Investigating How Organizational Commitment Influences Adherence to Healthcare Laws and Regulations in the Ministry of Health, Makkah Region, Saudi Arabia
Authors: Abdulrahman Faisal
Published: January 2025

Background: Healthcare professionals’ organizational commitment plays a significant role in compliance with healthcare laws and regulations. Understanding this relationship is critical for improving organizational performance in the Ministry of Health (MOH), Makkah region, Saudi Arabia.
Objective: This study aims to examine how the three dimensions of organizational commitment—affective, continuance, and normative—affect healthcare professionals' adherence to laws and regulations.
Method: A quantitative, cross-sectional research design was utilized. Data were collected via a structured survey from 266 healthcare professionals across various roles, including physicians, nurses, allied health professionals, and administrative staff. The Organizational Commitment Questionnaire (OCQ) was used to assess the three dimensions of organizational commitment, while adherence to healthcare laws and regulations was measured through self-reported compliance behaviors. Descriptive statistics, correlation analysis, and multiple regression models were employed to analyze the data.
Results: The study found that 82% of participants exhibited moderate to high levels of organizational commitment, with 76% showing strong adherence to healthcare regulations. Affective commitment (r = 0.68, p < 0.01) demonstrated the strongest positive correlation with adherence. Furthermore, 64% of participants with high affective commitment reported better regulatory compliance, while 52% of those with high normative commitment followed laws more consistently. Continuance commitment showed a weaker correlation (r = 0.34, p < 0.05) with compliance.
Conclusions: The study highlights the importance of fostering affective commitment among healthcare professionals to improve adherence to regulations. Interventions targeting organizational commitment dimensions can enhance regulatory compliance.
Factors Influencing Decision Making among Ambulance Nurses and Emergency Staff in Emergency Care Situations
Authors: Abdulaziz Saeed Alhammad Alshahrani
Published: January 2025

The decision-making process of ambulance nurses plays a critical role in ensuring effective emergency care and patient outcomes. This review explores the factors influencing the decision-making of ambulance nurses during emergency situations. The decision-making process in such settings is influenced by cognitive and emotional theories, which play vital roles in the way nurses process information and make decisions under time pressure. Patient-related factors, organizational factors, and personal factors also significantly impact decision-making. The complex interplay between these factors often results in a multi-faceted decision process, which can lead to improved or detrimental patient outcomes. By investigating these factors, this study aims to provide insights into how decision-making frameworks can be developed and improved, especially in high-stress emergency situations. Future research directions include integrating technological advancements to support decision-making processes and the continuous development of training frameworks.
A Novel AC-DC Hybrid Distribution Network Topology and its Characterization Analysis
Authors: Shengyong YE
Published: January 2025

With the extensive access of distributed new energy and the continuous growth of new loads, AC/DC hybrid distribution network has become a research hotspot of new distribution network. The choice of topology is very important for the construction of AC/DC hybrid distribution network. Accordingly, this paper proposes a new AC/DC hybrid distribution network topology based on the actual needs of a demonstration project. The structure combines the advantages of annular structure and radial structure, which can provide a more efficient and stable construction scheme for AC and DC power supply systems with multiple voltage levels and multiple bus structures. In this paper, various characteristics of the structure are studied through simulation analysis. The results show that the topology distribution network can quickly recover to a stable working state in the face of voltage changes caused by source-load switching and can effectively cope with the fluctuation of photovoltaic output in the system due to environmental changes, which in turn leads to voltage fluctuations. In addition, the distribution network of this structure has a high power transmission efficiency.
Research on Identification and Analysis of Extreme Behavior Risks in High-Speed Railway Stations’ Key Areas
Authors: Sheng Li
Published: January 2025

Introduction: High-speed rail stations serve as vital transportation hubs connecting different cities, regions, and even countries. They not only accommodate high-speed trains but also enable seamless transfers with other modes of transportation, facilitating rapid and comfortable travel for passengers. The establishment of high-speed rail stations often coincides with economic development in surrounding areas, attracting investments, businesses, and talents. However, extreme individual behaviors can pose severe destructive impacts on high-speed rail stations and their operations, including violent attacks on passengers and staff, destruction of station facilities, illegal intrusions into restricted areas or tracks, and placement of dangerous items. These behaviors threaten the lives of passengers and staff, disrupt station order, and affect the punctuality and overall efficiency of high-speed trains.
Objectives: This study focuses on the warning methods and technologies for extreme behavior in high-speed rail environments, analyzing the causes, characteristics, and prevention and control methods of extreme behavior. It points out the blind spots in the research of extreme behavior warning for high-speed rail both domestically and internationally, such as the lack of research on the aggregation, classification, and risk assessment techniques of extreme behavior in high-speed rail environments.
Methods: This study uses the R-LVC method to evaluate the extreme behavior risks of high-speed rail systems, taking into account the vulnerability and attractiveness of stations, as well as the direct consequences of extreme behavior. By using expert scoring and AHP method to obtain risk source coefficients, this study proposes a vulnerability assessment method for high-speed railway networks based on station levels to evaluate the impact of extreme behaviors on the high-speed railway system.
Results: The study identifies and assesses extreme behaviors in key areas of high-speed rail stations, calculates risk values, and demonstrates the application of risk assessment through a case study.
Conclusions: The research contributes to the identification and analysis of extreme behavior risks in high-speed rail stations, proposing risk assessment techniques and an early warning model based on the R-LVC method. It highlights the importance of considering station level, vulnerability, attractiveness, and direct consequences of extreme behaviors for comprehensive risk evaluation.
An Improved YOLOv8 Network Architecture for Enhancing Detection Accuracy of Bird Nests with Small Size and Intricate Background on Power Transmission Lines
Authors: Dan Yang
Published: January 2025

The harm caused by birds building nests on power transmission lines is enormous, greatly increasing the possibility of short circuits and damages to the transmission lines. Bird nest detection on transmission lines presents significant challenges due to the small size of nests and complex backgrounds. To address these challenges, this paper proposes an improved YOLOv8 model specifically designed for bird nest detection. The model introduces several key innovations: the inclusion of an exponential moving average mechanism in the C2f module to enhance feature extraction and improve detection performance; the development of a dynamic weighted feature fusion network to more effectively integrate contextual features, significantly reducing both parameter count and computational load, thus achieving network lightweighting; and the introduction of weight-CIOU as a bounding box loss function, which better measures the similarity between targets, accelerates convergence, and enhances detection accuracy. Experimental results demonstrate that the improved YOLOv8 model outperforms the original YOLOv8 by achieving a mAP of 96.3%. Visualization of detection results further confirms the model’s robustness in various challenging scenarios, highlighting its potential for real-world deployment in power line monitoring systems.
Research on the Optimization of Material Distribution Routes for Power Grid Technology Upgrading Projects Considering Fuzzy Time Windows
Authors: Junchao Yang
Published: January 2025

As China refines its strategy for green and ecologically sustainable development, the construction of innovative power systems that can integrate new energy generation has emerged as a central focus in contemporary power system development. This paper studies how to build an efficient distribution mechanism of power materials, focusing on optimizing the distribution route of materials in the power system. Based on this, a distribution route optimization model combining fuzzy time window for power grid technical transformation projects is proposed. The model uses multi-objective planning to minimize distribution costs and maximize the timeliness of material delivery for technical renovation projects. A fuzzy membership function processes the expected and maximum tolerance time windows, transforming them into a comprehensive satisfaction level for delivery timing. The model is solved with the elitist non-dominated sorting genetic algorithm (NSGA-II). The optimization results are analyzed, leading to recommendations for improving distribution. Arithmetic examples validate the proposed algorithm's feasibility, offering an effective solution for optimizing material distribution paths in grid technical reform projects. The findings provide valuable insights to support the safe operation of the power grid.
Numerical Simulation Study on Settlement of Surrounding Soil Caused by Sink Construction in Soft Ground Layer
Authors: Tao Deng
Published: January 2025

Sink construction is easy to trigger the surrounding surface settlement, which poses a hidden danger to road surface and buildings, and measures need to be taken to reduce the settlement. In this paper, through a sink operation of Nanniwan Power Tunnel Project, combined with numerical simulation and actual monitoring to study the impact of reinforcement treatment on surface settlement, and conduct model feasibility analysis. The results show that the surface settlement can be effectively controlled after rotary spray pile reinforcement, and it is more obvious with the increase of construction depth, the maximum settlement value is reduced by 40.2% after the first section is sunk, and 61.8% after all of them are sunk; the soil settlement after reinforcement shows a spoon-shaped change with the distance, and the in-situ soil shows a parabolic shape change. The settlement monitoring value of each point is greater than the theoretical value, the maximum is 5.3mm, 43.2% higher than the theoretical value; the minimum is 7.1mm, 9.2% higher than the theoretical value, which is within the permissible range of construction.
Fostering Creativity in Fine Arts Education Through Hybrid Learning Models
Authors: Baomin Xu
Published: January 2025

This study examines the possibilities of hybrid learning models to inspire creativity in fine arts education, utilizing personalized learning technologies: Collaborative Filtering and K-Nearest Neighbors (KNN) algorithms. Hybrid learning, which blends online instruction with face-to-face interaction, offers an elastic environment that may support various learning styles and foster greater engagement by students. The research explores how such technologies can personalize learning, provide relevant content recommendations, and challenge students at a proper level to enhance their creativity and artistic expression. This study used a mixed-methods approach to determine student creativity, engagement, and satisfaction through pre-and post-assessments, as well as algorithmic evaluations of content recommendation accuracy. Results show a considerable increase in student engagement at 27%, an improvement in creativity at 15%, and satisfaction at 28% among students who followed hybrid learning compared to traditional learning methods. This study emphasizes the effectiveness of personalized pathways in fine arts education, concluding that hybrid models can enhance the development of creativity and achievement in academic performance. These findings point to the potential of embedding personalized technologies into fine arts curricula, revolutionizing art education and making it more adaptive, inclusive, and supportive of diverse student needs.
Fusion of Technology and Tradition in Indian Classical Music Pedagogy
Authors: Jiexia Wu
Published: January 2025

Indian classical music, deeply rooted in tradition, relies on the guru-shishya (teacher-student) method for transmitting its rich cultural heritage. However, the evolving digital landscape presents opportunities to augment this pedagogy through technological advancements. This study investigates the integration of technology with traditional teaching methods, evaluating its impact on the pedagogy of Indian classical music. A mixed-methods approach was employed, combining qualitative data from expert interviews and surveys with quantitative assessments of learning outcomes. The pedagogical effectiveness index (????????????) was used to compare traditional, technology-based, and hybrid learning models. Results indicated that the hybrid model, which integrates digital tools such as online resources and AI-driven personalization with traditional practices, significantly enhanced learning outcomes. Improvements were observed in improvisation skills, rhythm comprehension, and overall student engagement. While challenges such as technological literacy and infrastructure barriers were noted, the hybrid model proved effective in balancing innovation with cultural preservation. This study highlights the transformative potential of fusing technology with tradition, ensuring the accessibility, relevance, and sustainability of Indian classical music education in the modern era. The findings provide a foundation for broader applications across other traditional art forms and point to future opportunities for leveraging emerging technologies like VR and AI for deeper pedagogical impact.
Developing User-Centered Design Thinking Skills Through Collaborative Projects
Authors: Li Xiongfei
Published: January 2025

This study explores the development of User-Centered Design Thinking (UCDT) skills through collaborative projects. The objective was to assess how team-based design challenges can enhance participants' design thinking competencies, including empathy, ideation, and iterative problem-solving. A total of 50 participants, divided into 10 teams, worked on real-world design problems over 6 weeks. Quantitative data was collected through pre- and post-project assessments, while qualitative data was gathered from user feedback and weekly reflections. The results showed a significant improvement in UCDT skills, with an average increase of 36.4% in skill development. A strong positive correlation (????=0.74) was found between collaboration efficiency and skill improvement, highlighting the role of effective teamwork in fostering design thinking competencies. User feedback scores further confirmed the high quality of the solutions produced. The findings suggest that collaborative projects are an effective pedagogical tool for developing UCDT skills, offering important implications for both educational curricula and professional design practices. Future research with larger sample sizes is recommended to further validate these results.
Improving Overseas Aftermarket Aircraft Spare Parts Supply Logistics Using Simulation Modeling
Authors: Danyang Shen
Published: January 2025

Computer simulation technology not only enhances the understanding of complex systems but also provides crucial support for decision-making and design, offering an effective method for studying the supply of aftermarket aircraft spare parts (ASPs). The demand for ASPs is different from that for aftermarket general-equipment spare parts in terms of supplier quantity, supply process, and supply conditions. These differences in demand characteristics lead to changes in supply logistics. This study investigates the factors affecting the aftermarket ASP supply and proposes an overseas supply logistics model for aftermarket ASPs. A simulation model was used to reproduce the aftermarket ASP supply and compare different scenarios. The ordinary mode and full-lift-cycle coverage mode are modeled to compare their efficiency. The full-lift-cycle coverage mode was also tested to evaluate the feasibility of its supply process. The results confirm that the full-lift-cycle coverage mode improves supply logistics performance, offering benefits for the overseas supply of aftermarket ASPs for Chinese-made civil aircraft.