bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing E-Health with Natural Language Processing: The Role of Sentiment Analysis in Modern Healthcare
Authors: He Mu
Published: January 2025

The advent of e-health systems has significantly addressed the challenges of inefficiencies, high operational costs, and limited accessibility in traditional healthcare systems. This paper explores the critical role of Natural Language Processing (NLP) in e-health systems, particularly the transformative potential of sentiment analysis in improving patient care and system efficiency. The study employs NLP techniques to analyze and interpret large volumes of healthcare data, utilizing sentiment analysis to extract emotional information from patient experiences. This information helps identify patient sentiments towards treatments and services, thereby enhancing patient satisfaction and care quality. Additionally, sentiment analysis plays a crucial role in mental health monitoring by identifying potential issues based on patients' emotional expressions. The results indicate that NLP-driven sentiment analysis can significantly improve patient satisfaction, enhance mental health monitoring, and support more effective public health strategies. By leveraging sentiment analysis, healthcare providers can better understand and respond to community needs, ultimately leading to better healthcare outcomes. This research provides important insights for the future development of e-health systems, particularly in handling unstructured data, improving data analysis accuracy, and enhancing decision support.
Comparative Analysis of Helium-Ion Microscopy and Scanning Electron Microscopy in Nanofabrication and High-Resolution Imaging
Authors: Chen Yun , Hu Yufei , Li Sixu , Wang Chenxu
Published: January 2025

With the development of science and technology, scientists' demands for fineness in fields such as semiconductors, materials, and bioimaging are increasing day by day. Traditional electron microscopes have certain limitations in observing the microstructure of substances. The helium ion microscope (HIM) has a higher imaging resolution and more refined nano-processing capabilities, playing a breakthrough role in the development of science and technology. In this paper, the literature analysis method is used to retrieve, collect, and compare different literature. Firstly, it introduces the important role of the HIM in the fields of materials and nanofabrication. Then, it introduces the development background of the HIM and specifically analyzes its imaging principle. Next, it compares it with the scanning electron microscope, analyzes the similarities and differences between the two, and points out the problem of its high cost. Moreover, it discusses the application prospects of the HIM in high-resolution imaging and high-precision nanofabrication. Finally, it is concluded that the HIM has high accuracy and precision in the field of fine-processing materials.
Logistic Regression Model to Personality Type Prediction Based on the Myers–Briggs Type Indicator
Authors: Wang Yunshang
Published: January 2025

The Myers-Briggs Type Indicator (MBTI) is a widely-used tool in psychology for determining personality types, playing a crucial role in fields like team building, communication, and personalized marketing. Despite its popularity, accurately classifying MBTI types using machine learning remains a significant challenge. This study focuses on addressing this challenge by exploring the effectiveness of logistic regression in MBTI classification tasks. Two approaches are used: four-times binary classification and multi-class classification. The findings show that while logistic regression performs exceptionally well in binary classification tasks but the accuracy is not good in multi-class classification. Additionally, combining binary classification results yields an overall accuracy that is lower than the direct multi-class classification. These results highlight the limitations of logistic regression in multi-class tasks and suggest the necessity for more advanced models. Future research should focus on improving multi-class classification accuracy, potentially through more complex architectures or hybrid models combining binary and multi-class approaches.
Gaussian Process Regression Model Based on Cross-Validation Weighted Bagging Algorithm and Its Applications
Authors: An Yongqi , Feng Yizhe
Published: January 2025

As a non-parametric regression technique widely applied across various disciplines, Gaussian Process Regression (GPR) faces certain challenges in the selection of the kernel function. To address this issue, this paper innovatively adopts a model aggregation strategy to dynamically determine the kernel function, rather than selecting a single model, thereby enhancing the model's adaptability and predictive power. Specifically, on one hand, the proposed method integrates Gaussian processes with different kernel function representations using a bagging algorithm. On the other hand, considering the varying importance of each sub-model, this paper employs cross-validation for weighted processing to balance the overfitting and underfitting issues of the predictive model. Finally, through extensive simulation experiments and real data analysis, the results demonstrate that the proposed method significantly improves the predictive capability compared to some classic predictive models, possesses better generalization performance, and can be effectively applied in various application scenarios.
Visual Object Tracking Using Deep Learning Techniques: A Comparison
Authors: Su Enzheng
Published: January 2025

Visual object tracking is one of the important topics in computer vision: given a target object in the first frame of a video, follow this target object on subsequent frames. Its applications are found in autonomous driving, human-computer interaction, military operations, and game development. However interesting these applications may be, the implementation remains very challenging because occlusions happen among other factors like scale variations or rotations that require a tracker to be robust and generalize well to give accurate results. In this paper, we provide an extensive review of visual object tracking methods divided into three primary frameworks: correlation filter-based trackers, Siamese network-based trackers, and Transformer-based trackers. We provide insights into a total of 12 state-of-the-art techniques for each category through large-scale experimentation on various benchmark data sets, aiming to reveal the research frontier of the visual object tracking field and point out some potentially useful directions for future investigations.
Exploring the Future of AI: An In-Depth Analysis of the 2024 AI Index Report
Authors: Huang Huang
Published: January 2025

Artificial Intelligence (AI) has rapidly developed across multiple fields, for example, in the medical field, various diseases are prevented, diagnosed, and classified. In the financial field, the trend of some stocks is predicted. In the education field, teachers are assisted in teaching and grading homework. This paper provides a comprehensive analysis of the “2024 AI Index Report,” which offers a detailed account of the current state of AI, including technological advancements, public perceptions, geopolitical dynamics, and trends in responsible AI development. The paper aims to present a nuanced understanding of these aspects to inform future research directions and policy-making efforts. By exploring key areas such as AI’s impact on various sectors, the potential risks and benefits, and the global competition in AI development, this study serves as a reference for stakeholders in the field of AI. Furthermore, the paper delves into the ethical considerations surrounding AI and discusses the implications of these trends for society and industry at large.
Design and Optimization of High-Voltage MOSFETs
Authors: Wang Chuanjie
Published: January 2025

Silicon carbide (SiC) power devices have gained significant attention due to their broad applications in high voltage, high frequency, and high temperature environments. Traditional silicon (Si) power devices, although well-established, face performance limitations that SiC devices can overcome. High-voltage 4H-SiC MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) devices are essential for the advancement of power electronics, and this work focuses on their design and optimization. First, a brief summary of the state of research on high-voltage 4H-SiC MOSFET devices is given, highlighting advancements and ongoing efforts in this field. Finally, the study delves into the design optimization of 4H-SiC MOSFET devices at high voltages, describing the vital design parameters and innovative techniques such as multi-zone modulation field limiting ring technology. The findings from this research provide valuable insights and practical guidance to improve SiC MOSFET devices' reliability and effectiveness, building upon the discussed design parameters and innovative techniques. This study significantly contributes to the manufacturing and utilization of electrical devices, promoting more efficient and reliable power conversion solutions in various high-demand sectors.
Comparative Study of Vision-Based and LiDAR-Based Object Detection Algorithms for Autonomous Driving
Authors: Chen Wenxiao , Gao Zihan
Published: January 2025

Autonomous vehicles achieve motion perception and path planning through object recognition. With the integration of deep learning into object detection, numerous practical and innovative studies have emerged in this field. Considering both hardware costs and recognition accuracy, object detection algorithms can be categorized into vision-based and Light Detection and Ranging (LiDAR)-based approaches. This paper reviews classic literature and cutting-edge technologies of both methods, detailing how vision-based approaches utilize computer vision to implement the You Only Look Once (YOLO) series and Faster Region-Convolutional Neural Network (Faster-RCNN). It also explains LiDAR-based approaches, focusing on classic models of point cloud and image fusion, including early fusion, deep fusion, and late fusion architectures. The advantages and limitations of both methods are compared, with experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset showing that vision-based single-stage detection algorithms perform excellently, while the LiDAR-based Cascade Bi-directional Fusion algorithm stands out. The paper concludes with promising research directions, highlighting that continuous hardware advancements and constant updates in object detection algorithms are bringing us closer to fully realizing the goal of autonomous driving.
An Overview Of The Effects Of ESG On The Financial Performance Of Companies From Various Angles
Authors: Li Yongxuan
Published: January 2025

An overview of the relationship between corporate financial performance (CFP) and environmental, social, and governance (ESG) concerns is given in this study from three angles: based on different industries, the impact of ESG dimensions, and the national development level. By reviewing and synthesizing the existing literature to examine how ESG performance affects a company's financial performance from various aspects. The analysis considers different ESG components, industry sectors, and the development of the country to provide an understanding of this relationship. The findings indicate that even though financial performance often benefits from ESG, the nature and degree of this association can vary significantly depending on the situation. This review will help the companies analyze their input cost on the ESG performance.
The Application of Machine Learning in the Field of Network Security
Authors: Tang Yangming
Published: January 2025

Nowadays, as the technology of AI is becoming more and applying in more fields, there rises some doubt about the safety of using AI. This paper explores the application of machine learning in the field of network security. The introduction provides an overview of machine learning and its importance in ensuring the security of networks. It also highlights the challenges and opportunities when implementing machine learning techniques in network security. The second section delves into the various machine learning techniques used in network security. Supervised learning is discussed concerning intrusion detection, while unsupervised learning is explored for anomaly detection. Reinforcement learning is also examined for secure routing purposes. The third section focuses on the applications of machine learning in intrusion detection. Deep learning, natural language processing, and graph-based machine learning are explored as methods for detecting intrusions. In conclusion, this paper summarizes the key findings of the research conducted. It highlights the implications for future research and provides recommendations for practitioners in the field of network security. Overall, machine learning has proven to be a valuable tool in enhancing the security of networks, but further research and development are needed to address the challenges and fully exploit its potential.
Predicting the Rise in California Home Prices and Factors Affecting
Authors: Bai Yufan
Published: January 2025

California’s real estate market has been in the spotlight for its unique geography and economic advantages, and home prices have been volatile. Therefore, accurate prediction of house price increases is of great importance to home buyers, investors and policy makers. This paper utilizes the California house price dataset, combines macroeconomic indicators and micro property attributes, and analyzes house price prediction through random forest, decision tree and neural network models. The study results show that the Random Forest model performs the best in predicting house prices with an R²value of 0.817210, which is significantly higher than the other models at 0.817210. the influence of R²leads to a slightly poorer neural network performance (CNN). Further through visualization studies and feature importance analysis median income, longitude, latitude, and distance from the sea are the key factors affecting the house price in California. House prices are strongly positively correlated with median income, while distance from the sea is negatively correlated with house prices. These key factors can also directly affect future home buying trends and house price direction. The research in this paper provides a strong reference for homebuyers, investors, and policymakers to help them make more informed decisions in the real estate market and promote a smooth and healthy market development.
Overview of Mainstream Face Recognition Methods
Authors: Wu Zehao
Published: January 2025

Human face is one of the most important features of human beings. In the information age, the face has personal information and emotional expression, social communication, public safety and other extremely important attributes. Due to the characteristics of inconspicuous facial features and small differences, traditional face recognition by manual training leads to low efficiency and high error rate. Based on the advantages of computer automation, face recognition technology has tended to be efficient, accurate and intelligent. Face recognition in a broad sense includes four stages: portrait image preprocessing, eigenface display, data training and data production. The use of CNN neural network can deal with complex abstract facial features. OpenCV open-source function libraries and Python simple and efficient machine language have advantages. This paper will focus on CNN deep neural network face recognition principle and OpenCV face recognition process two mainstream methods, as well as their defects comparison and conclusions.
Enhancing Spam Email Detection with Machine Learning: A Comparative Study of Logistic Regression and Naive Bayes Using Apache Spark
Authors: Ye Zhaoyang
Published: January 2025

The spread of spam emails presents serious problems for both email security and user experience. This research aims to develop an effective spam email classification system utilizing machine learning techniques, specifically Logistic Regression and Naive Bayes, within the Apache Spark framework. The methodology encompasses a thorough preprocessing of the Enron email dataset. This process involves several critical steps: text cleaning to remove irrelevant information, tokenization to break down the text into individual words, removal of stop words to eliminate common but uninformative words, and text feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) to quantify the importance of terms within the dataset. The study is conducted on a subset of the Enron email dataset, comprising 11,029 emails, with 2,996 labeled as spam. Experimental results demonstrate that the Naive Bayes model outperforms Logistic Regression, achieving higher accuracy and F1 score. This finding underscores the robustness of Naive Bayes in spam email classification, highlighting its potential for enhancing email security by effectively filtering spam.
Application and Analysis in Machine Learning Algorithms for Financial Fraud Detection
Authors: Hu Lebin , Liu Xinyang , Luan Changwen
Published: January 2025

This study provides a comprehensive examination of machine learning models and deep learning algorithms for detecting financial fraud. We begin by introducing the basic concepts of commonly used algorithms, discussing their strengths and limitations in identifying fraudulent activities. The focus is on the unique capabilities of deep learning models, particularly Graph Neural Networks (GNNs), in handling large-scale and high-dimensional data, and their proficiency in enhancing fraud detection accuracy and efficiency through automated feature extraction. Furthermore, the study evaluates and contrasts the efficacy of various models in real-world scenarios, highlighting their performance in improving recall, precision, and overall robustness. We anticipate future advancements in fraud detection technology, including dynamic graph modeling and cross-domain transfer learning. Through detailed analysis and empirical validation, this study not only enhances our understanding of existing technologies but also offers valuable insights and guidance for research and practice in the field of financial security. The empirical results demonstrate that deep learning models, especially the Residual-Layered-Camouflage Graph Neural Network (RLC-GNN) model, significantly outperform traditional methods in terms of accuracy, recall, area under the curve (AUC) and F1 scores, indicating a promising direction for future developments in financial fraud detection.
Microwave Radiometer for Detecting Internal Temperature of Biological Tissue
Authors: Qin Ruiqi
Published: January 2025

Microwave radiometers are gaining increasing attention for their ability to measure the temperature inside biological tissues, a capability that is of great significance in medical applications such as cancer treatment and burn therapy. However, there is still a research gap in their precise application in these areas. This study aims to explore the feasibility of using microwave radiometers to detect subcutaneous tissue temperature by analyzing low-frequency microwave signals. A miniature radiometric sensor was systematically experimented with in the study to evaluate its sensitivity, stability, and accuracy in real-time temperature monitoring. The results showed that the radiometer was able to accurately track subcutaneous temperature changes within a small error range, confirming its applicability in non-invasive medical applications. In particular, this study highlighted the effectiveness of the radiometer in microwave ablation therapy and biomedical thermoacoustic imaging, demonstrating its potential to improve diagnostic accuracy and therapeutic efficacy. Microwave radiometers have shown significant results in cancer treatment through microwave ablation therapy, image-guided precision therapy, and minimally invasive treatment. In addition, its application in biomedical microwave thermoacoustic imaging shows great potential to provide high-resolution and high-contrast images for cancer diagnosis and brain imaging. The research of microwave radiometers in the biomedical field has important practical significance and broad development prospects, which will have a positive impact on human health and medical care.
Research on Image Recognition Applications Driven by Artificial Intelligence
Authors: Xiang Yuyun
Published: January 2025

With the rapid development of artificial intelligence and machine learning technologies, image recognition technology has become a key force driving progress in many fields. Deep learning and machine learning algorithms have made remarkable progress in image recognition. Therefore, this article deeply explores the role of image recognition technology in three aspects: autonomous driving, medical imaging and face recognition. This article concludes that autonomous driving technology mainly relies on artificial intelligence and machine learning. It perceives the environment in real time through sensors and perception modules, and formulates driving strategies through decision-making and control modules to ensure the safe driving of vehicles. In medical image analysis, image recognition technology is used for feature extraction, image segmentation, and classification to assist doctors in disease diagnosis and treatment planning. In face recognition technology, through the three stages of face detection, face alignment and final recognition, individual identities are confirmed to improve the accuracy and efficiency of recognition. In conclusion, image recognition technology plays a vital role in the above fields, improving the intelligence and reliability of the system.
Analysis and Comparison of Length Measurement Scenarios from the Solar System to the Galaxy
Authors: Xue Yuhan
Published: January 2025

As a matter of fact, astronomical research depends heavily on distance measurement between the Earth and celestial objects, so it’s crucial to employ effective method along with advanced instrument for distance measurement. This study talks about the three most commonly used strategies to measure distance in space. Stellar parallax, based on trigonometry and the concept of stereoscopic vision, is the most conventional method to measure the distances within the Milky Way. Cepheid Variables, with their consistent period-luminosity relationship, become useful when measuring distances to nearby galaxies. Type 1a supernova that worked as standard candles can be used to measure distance that parallax and Cepheid variables cannot achieve. According to the analysis, these three methods each plays an important role in the cosmic distance ladder and excels in different distance ranges. Even though more accurate techniques are still needed for a more propound exploration of the universe, these methods become a foundation for future advancement in astronomical distance measurement.
Research on the Impact of Virtual Reality Games on Cognitive Abilities
Authors: Shi Lihang
Published: January 2025

This paper presents an extensive overview of the current research progress of Virtual Reality (VR) games in the realm of cognitive abilities. With a particular emphasis on their utilization in cognitive rehabilitation and enhancement, it explores their profound effects. Various studies have established that VR games notably influence the mitigation of cognitive decline among older adults. This is primarily achieved by augmenting participation and compliance with rehabilitation programs. These games are designed to target specific cognitive areas and dynamically adjust difficulty levels to ensure maximum training efficiency. Moreover, VR games have exhibited positive impacts on cognitive enhancement in healthy individuals, leading to improvements in areas such as attention, memory, and problem-solving abilities. Despite these advancements, there are still challenges in standardizing evaluation methods and making VR games accessible to a wider range of users. Consequently, future research efforts should be directed towards the creation of universal assessment tools and the optimization of VR game designs, with the ultimate goal of fully harnessing their potential in cognitive rehabilitation and enhancement.
Introduction and Analysis of Exoplanet Detection Techniques
Authors: Zhao Xiaoxi
Published: January 2025

The exoplanet study is important for exploring life's origins in the universe. With the development of astronomical observation technology, more and more exoplanets have been discovered, and the exploration of habitable planets and the study of planetary atmospheres have become focal areas of study. In this paper, the transit and radial velocity methods for finding exoplanets are introduced and analysed in detail, by introducing the principles of these two methods and showing the calculation progress with specific planetary data. As a result, the parameters of exoplanet can be derived from the principle of doppler effect and binary system. Each method has its advantages and limitations. For radial velocity method, its limitation is that low-mass orbiting planets (planets with mass less than 10 ) cannot be detected because those planets cannot cause detectable and measurable transit signals. Also  cannot be directly measured through the radial velocity method unless the value of inclination  is obtained. For the transit method, to get full information and confirm the existence of the planet, complementary measurement is needed, such as radial velocity. Another limitation is the instability of observations. There is a maximum range of the inclination that can observe the transit on the star and nebulae in the universe may block the observing sight, both lead to a limit probability of finding the planet’s transit. So, the complementarity between the methods can make the results more comprehensive and precise. From these, it is possible to find more habitable terrestrial planets.
Study on Preparation Process and Electrical Properties of Diamond PN Junction
Authors: Liu Yufei
Published: January 2025

Diamond PN junctions exhibit exceptional properties such as high electric breakdown field, the largest bandgap and the highest thermal conductivity compared to any known materials. These unique attributes make diamond highly suitable for high-power, high-frequency and harsh-environment applications. Currently, chemical vapor deposition (CVD) and high-pressure high-temperature (HPHT) methods can be used to synthesize diamond substrates. Among these, synthetic diamond produced by CVD has garnered significant interest due to its distinctive combination of exceptional electrical and thermal properties. However, its extremely high mechanical hardness and smaller substrate size pose substantial challenges for the implementation of device technology. This paper aims to explore the distinguished properties of diamond in electronics and the preparation process of diamond PN junctions, focusing on synthesis technologies, doping issues and terminal technologies. All of these are critical for maximizing the dependability and performance of electronic devices based on diamonds. By addressing these challenges, the research seeks to forward the application of diamond in various high-performance electronic devices, highlighting its capacity to improve device durability and effieciency in harsh operating conditions. The insights provided will contribute to a deeper understanding of the material’s capabilities and the technological advancements necessary to harness its full potential in electronic applications.
Optimal line planning for multibeam line measurement based on improved algorithms
Authors: Meng Yuanke , Lei Yusheng , Kang Yikun , Zhao Yiyuan
Published: January 2025

In this paper, this paper address the challenges of multibeam line measurement in the sea by first analyzing the correlation between two-dimensional (2D) and three-dimensional (3D) models through the construction of mathematical models. Based on these mathematical models, this paper further develop ocean models and measurement models. These models aid in determining the characteristics of different sea models, as well as the properties of measurement signals under various measurement modes and positions. By employing the models constructed, this paper are able to devise optimal measurement paths and modes for specific bodies of water. Subsequently, this paper identify the best measurement methods and their corresponding limitations within those particular waters. Analyzing the optimal measurement techniques and their associated deficiencies in specific waters allows us to draw final conclusions about the measurements taken in those waters. The multibeam line model finds significant applications in numerous fields, encompassing marine topographic mapping, marine ecological research, marine resource exploration, marine environmental monitoring, and marine engineering and harbor construction. In these application areas, multibeam line measurements provide precise and detailed data support, offering critical tools for the effective understanding and management of our marine environments.
A Cantonese Restaurant Review Dataset for Aspect Category Sentiment Analysis with XLM-RoBERTa
Authors: Cai Yuxin
Published: January 2025

The volume of online consumer reviews has surged as e-commerce continues to grow, providing valuable insights for both consumers and business owners. Aspect Category Based Sentiment Analysis (ACSA) identifies sentiment polarity based on specific aspect categories in reviews. Despite extensive research in other languages, Cantonese remains underexplored due to its unique linguistic features and limited datasets. This study seeks to bridge this gap by fine-tuning a RoBERTa-based model for Aspect Category Sentiment Analysis (ACSA) on Cantonese restaurant reviews from OpenRice. A dataset is constructed by collecting and annotating 7,473 reviews based on five aspect categories: food, service, ambience, price, and timeliness. The fine-tuned XLM-RoBERTa model achieved an accuracy of 75.17%, outperforming five baseline models and demonstrating the efficacy of transformer-based models in low-resource languages. The study shows that the fine-tuned RoBERTa-based model has significant advantages in processing low-resource languages such as Cantonese, not only surpassing the baseline model in accuracy, but also providing a solid foundation for future research on Cantonese sentiment analysis. This work contributes a significant dataset and highlights potential future research directions.
Harnessing ICT Resources: Empowering Educators and Enhancing Student Skills Through Teacher Training Programs
Authors: Raza Tariq , Akhter Nasreen
Published: January 2025

Incorporating Information and Communication Technologies (ICT) into educational settings has fundamentally transformed pedagogical practices; however, the effective implementation of these technologies within teacher training programs continues to pose significant challenges. This systematic review examines the role of ICT resources in the professional development of educators and their influence on the enhancement of student competencies, with particular attention to frameworks such as the Technological Pedagogical Content Knowledge (TPACK) and the Digital Competence Framework for Educators (Dig Comp Edu). By synthesizing findings from various studies, the review reveals emerging trends, including an increasing focus on digital literacy, critical thinking, and collaborative learning, while acknowledging obstacles such as insufficient infrastructure and limited training opportunities for teachers. The results indicate the transformative potential of innovative technologies, including artificial intelligence and virtual reality, in redefining educational methodologies. Nonetheless, the review highlights notable deficiencies, particularly the necessity for quantitative research to assess the direct effects of ICT on educational outcomes and the restricted scope of teacher training initiatives in addressing contextual challenges. This review highlights the transformative potential of ICT integration in teacher training programs and its essential role in enhancing student skills. The evidence synthesis indicates progress in developing teachers' ICT competencies but also reveals persistent challenges, such as infrastructural inadequacies and a lack of comprehensive quantitative assessments. Recommendations include the formulation of inclusive ICT policies, the expansion of teacher training programs, and the promotion of equitable access to technology in under-resourced areas. This study offers practical insights for educators, policymakers, and educational institutions, underscoring the critical importance of strategic ICT integration to prepare students for the challenges of the 21st century adequately. Future research should prioritize longitudinal studies and investigate the role of ICT in fostering inclusivity and personalized learning experiences.
Impact of Green Human Resource Management Practices on Sustainable Organizational Performance: Moderating Role of Green Transformational Leadership
Authors: Khan Aamna , Sohaib Sara
Published: January 2025

The study explores the influence of Green Human Resource Management (GHRM) practices on Sustainable Organizational Performance (SOP) within the banking sector of Pakistan, with Green Transformational Leadership (GTFL) as a moderator. Using a quantitative cross-sectional design, data was collected from 106 employees of Karachi-based private banks and analyzed using Smart PLS software. Results reveal a significant positive impact of GHRM practices, particularly green recruitment, training, and performance management, on SOP. However, GTFL showed an insignificant moderating effect on these relationships. Practical implications include fostering green practices like online banking and sustainability-focused training. This study contributes to the understanding of GHRM and SOP, with a focus on green leadership's role in developing economies.
Exploring the Role of Human Capital in Indian Oil Companies' Innovation and Entrepreneurship Initiatives
Authors: Manish Seth  , Munshi Ram  , Parinita Masih  , Praveen Kumar  , Rahul Kumar  , Salman  , Anjali Neraliya
Published: January 2025

Purpose- Within the context of Indian oil businesses, the purpose of this study is to investigate the impact that human capital management has on innovation and entrepreneurialism. Because of the growing global rivalry, innovation is becoming increasingly important for the success of organizations.Design/Methodology/Approach- The research utilized a survey design, gathering both qualitative and quantitative data from 600 employees across major Indian oil companies, including ONGC, Indian Oil Corporation, and Bharat Petroleum. Data analysis was conducted using correlation, regression, and qualitative interviews.Findings- The study found a positive correlation between human capital investment and innovation outcomes. Leadership, training, and cross-functional collaboration emerged as key factors in fostering an entrepreneurial culture. Human capital, consisting of employees’ skills, knowledge, and experience, is a critical driver of innovation.Research Implications- The findings emphasize the need for strategic human capital development to enhance innovation performance. The study also highlights areas for future research on leadership and team dynamics in innovation management.Practical Implications- The research suggests that implementing a strategic 'workforce development' plan can significantly improve innovation and entrepreneurship in the Indian oil industry.Social Implications- By enhancing human capital and fostering innovation, the oil sector can contribute to broader economic development and job creation, influencing societal progress in India.Originality/Value- This study provides a unique perspective on the relationship between human capital management and innovation in the Indian oil sector, offering valuable insights for both academics and industry practitioners.
Pengaruh Modal Sendiri Terhadap Sisa Hasil Usaha Pada Koperasi Credit Union Anggota Puskopcuina
Authors: Maria Helen Supit  , Mulyati Akib
Published: January 2025

Salah satu indikator utama dalam mengukur perkembangan koperasi dari tahun ke tahun adalah dengan melihat besarnya Sisa Hasil Usaha (SHU). Penelitian ini bertujuan untuk mengetahui sejauh mana pengaruh modal sendiri terhadap Sisa Hasil Usaha (SHU) pada Koperasi Credit Union anggota Pusat Koperasi Credit Union Indonesia (PUSKOPCUINA). Metode yang digunakan dalam penelitian ini adalah pendekatan kuantitatif dengan mengandalkan data yang diperoleh dari laporan keuangan, seperti Neraca dan Laporan Laba Rugi tahun 2023. Untuk menganalisis data, digunakan teknik analisis regresi linier sederhana. Berdasarkan hasil analisis, pengaruh modal sendiri terhadap Sisa Hasil Usaha (SHU) menunjukkan bahwa data terdistribusi secara normal dengan koefisien R sebesar 0,894, yang berarti Modal Sendiri memiliki pengaruh terhadap SHU sebesar 89,4%, sementara 10,6% sisanya dipengaruhi oleh faktor lainnya. Hasil uji hipotesis menunjukkan bahwa terdapat hubungan yang signifikan antara modal sendiri dan Sisa Hasil Usaha (SHU).
Transformasi Penggunaan Fitur Workspace Google Dalam Meningkatkan Efektivitas Pengelolaan Administrasi Nagari
Authors: Flomina G Katrina , Rosman Edwar , Amnur Hidra , Arsyah Ulya Ilhami
Published: January 2025

Nagari di Solok Selatan masih banyak yang belum mengoptimalkan penggunaan teknologi informasi dalam pengelolaan administrasinya. Hal ini menyebabkan prosesnya menjadi kurang efektif dan efisien. Penggunaan fitur Workspace Google dapat membantu Nagari dalam meningkatkan efektivitas pengelolaan administrasi. Tujuan kegiatan ini adalah untuk Meningkatkan pengetahuan dan keterampilan aparatur Nagari dalam menggunakan fitur Workspace Google. Meningkatkan efektivitas dan efisiensi pengelolaan administrasi Nagari. Meningkatkan kualitas pelayanan publik di Nagari. Metode yang digunakan adalah pelatihan, pendampingan, dan monitoring. Target pengabdian ini adalah Aparatur Nagari memiliki pengetahuan dan keterampilan yang baik dalam menggunakan fitur Workspace Google. Proses pengelolaan administrasi Nagari menjadi lebih efektif dan efisien. Kualitas pelayanan publik di Nagari meningkat. Pengelolaan administrasi Nagari yang efektif dan efisien menjadi kunci kemajuan sebuah Nagari.
A study on the effects of different training methods on the biomechanical characteristics of sprinting in young track and field athletes
Authors: Lin Qiusheng , Chen Yuansheng
Published: January 2025

In order to improve the level of sprinting, we should not only rely on scientific and technological progress, but also have scientific training methods. Based on this, this paper mainly starts from the perspective of sports biomechanics, and studies the influence of two training methods of “weight loss running platform” training and flat running training on sprint biomechanical characteristics of young track and field athletes. The research results show that: The movement curves of the main joints of the lower limbs in the training of “weight loss running platform” have the same basic characteristics as that of flat running. The safety protection of “weight loss running platform” can increase the density of athletes’ high-speed training, but athletes will produce involuntary technical adaptation phenomenon in the training of “weight loss running platform”, and there are some different technical characteristics.
Research on the correlation between campus big data and the mental health status of college students with a focus on biological implications
Authors: Peng Yiwen , Qin Wanyi , Qin Min
Published: January 2025

This study aims to explore the potential association between multidimensional big data and the mental health status of college students, with a particular focus on biological implications. The goal is to utilize big data to achieve more accurate mental health services and timely psychological crisis interventions while addressing the shortcomings of traditional psychological assessments. We collect relevant big data from all undergraduate students at College J of a university. Through data preprocessing and quantitative transformation, students are identified as research subjects. Six representative data categories are selected, including academic achievement records, Second Classroom Report Card, access control records, leave records, daily consumption, and Internet usage hours. Pearson’s correlation analysis is employed to assess the correlation between these data and psychometric scores, establishing a link to physiological and biochemical markers of mental health, such as stress-related hormones. To further validate and deepen this finding, we select the three categories of data with the highest correlation coefficients and conduct grey relational analysis (GRA) on 20 students exhibiting psychological abnormalities. The results indicate that all six categories of big data analyzed are correlated with college students’ mental health, with the strongest correlation found between the Second Classroom Report Card and students’ mental health status. This finding highlights the biological implications of mental health on academic performance and overall well-being. The “Second Classroom Report Card” serves as a quantitative reflection of the comprehensive qualities of college students in areas such as moral, intellectual, physical, aesthetic, and labor education. Its implementation provides a novel approach for applying big data technology and methods in evaluating the mental health levels of college students, emphasizing the physiological and biochemical factors that may influence their mental well-being.
MACHINE LEARNING IN MELANOMA DETECTION ANALYZING THE ROLE OF FEATURE ENGINEERING IN SKIN LESION CLASSIFICATION
Authors: Manisha Bandari , Raju Dr. GVS
Published: January 2025

Skin cancer is one of the most common types of cancer in people. Doctors usually find it through visual inspections, starting with a clinical screening, then followed by a biopsy and lab tests. We can improve the prediction of skin cancer and determine if it is melanoma or benign by using automated systems that categorize images of skin lesions. This is made possible by machine learning and artificial intelligence (AI).) approaches. This chapter describes Spotting skin cancer early can make a big difference in treatment and recovery! using Spark and a deep neural network. To find the best algorithm for skin cancer prediction, a comparison study of the several algorithms currently in use has also been conducted. Based on findings gathered from several iterations, skin lesion photographs might be categorized using a CNN approach. Then, many transfer learning models were employed for fine-tuning, This study focuses on several advanced image recognition models, specifically Resnet50, InceptionV3, and Inception Resnet, to improve how we analyze skin lesions. One of the key contributions of this research is the use of a technique called ESRGAN to enhance images before they are processed by these models. By applying this preprocessing step, we aimed to boost the accuracy of the results.We tested multiple models, including our customized model and a standard CNN, to see how well they performed. Interestingly, both the standard pre-trained model and our own produced similar results, indicating that our approach is effective. The effectiveness of our method was shown through simulations using the ISIC 2020 skin lesion dataset, a well-known collection of images used for this kind of research. We found that our CNN model achieved an impressive accuracy of 89.2%, showcasing the potential of our approach in helping to improve skin lesion analysis.
The issue of surrogate motherhood in Poland: a coherent analysis of the branches of Polish law
Authors: Wróbel Agata
Published: January 2025

The article was devoted to the analysis of the surrogacy phenomenon in Polish law. The author evaluates the regulation of the phenomenon from the perspective of various legal branches. The analysis is directed at identifying problems with the regulation of the surrogacy phenomenon. For the Polish legislator has not yet attempted to regulate surrogacy. Thus, following one path of interpretation, it can be assumed that everything that is not forbidden is allowed. However, the situation becomes more complicated at the level of surrogacy contracting. The analysis is intended to polemicize over the interlocking fields that, if regulation is made, will also require amendment.
Considerations Regarding the Model of Polish Preparatory Proceedings
Authors: Bojanowski Tomasz
Published: January 2025

The subject of this article is the analysis of selected issues of the Polish model of preparatory proceedings. The author begins his considerations by highlighting historical and model issues, whereafter he discusses the goals of preparatory proceedings resulting from the Code of Criminal Procedure 1997. The next segment analysed is the forms and phases of preparatory proceedings. Further on, the article discusses the prosecutor‘s supervision of preparatory proceedings and the role of the judicial factor in this phase of preparatory proceedings. The last substantive subsection deals with the options for the termination of preparatory proceedings. It concludes with a final assessment of the model—divided into its advantages and disadvantages. In addition, the author derives de lege fereneda conclusions, which would contribute to the improvement of the current model of Polish preparatory proceedings.
The Recognition of a Foreign Adoption : The Human Rights Principles and Croatian Reality
Authors: Drventić Barišin Martina
Published: January 2025

Any person crossing a border wants to have his or her civil status recognised in the host country. Reasons of a personal nature primarily drive such an endeavour. Recognising one’s personal status may also play a significant role in exercising many other rights. When a host state refuses to recognise the personal status or family ties already enjoyed in the territory of another state, this may constitute a breach of the individual’s right to respect for private and family life and be contrary to the standard on the prohibition of discrimination. The difficulties arising in cross-border status recognition mainly stem from the pluralism of national legal systems. The Republic of Croatia has ratified many international documents whose provisions guarantee the right to personal status and has been bound by the EU’s acquis communautaire. The national law, dispersed in several acts, has regulated the mere recognition of personal status acquired abroad. This research starts with an overview of the national legal regulation of cross-border recognition of status in the Republic of Croatia, focusing on recognition of the adoption established abroad. The research puts the national legal framework into the context of the human rights principles derived from the international and EU legal framework.
Torture in respect to imprisonment: A Croatian perspective
Authors: Gašparić Asea
Published: January 2025

The discussion of strengthening prisoners’ rights is rooted in human rights principles and the recognition of inherent dignity. Historically, torture was widely accepted, but enlightenment ideals led to its condemnation. International efforts and universal legal documents shaped global attitudes towards the prohibition of torture. This comprehensive analysis explores the safeguarding of individuals deprived of liberty, primarily from an international perspective, with a focus on the European Convention on Human Rights and its pivotal Article 3, which explicitly prohibits torture and inhumane or degrading treatment. The examination encompasses material and procedural obligations imposed on states, providing a nuanced understanding of the fundamental rights tied to human dignity and physical integrity. Significantly, the study delves into the jurisprudence of the European Court of Human Rights, emphasising cases involving Croatia and revealing persistent shortcomings in prison conditions.