bluuebunny/crossref_metadata_2025 huggingface dataset:
Status of Labor Education of College Students in Jiangsu Province
Authors: Ma Lei , Simon Julius
Published: February 2025

Labor education plays a crucial role in the holistic development of college students, equipping them with practical skills, a strong work ethic, and social responsibility. However, this study reveals significant challenges in the current labor education system in universities in Jiangsu Province. The research, employing a quantitative approach, surveyed students across selected universities, assessing their perceptions of labor education opportunities, curriculum effectiveness, participation levels, and available resources. Findings indicate that students generally perceive labor education as insufficient in preparing them for the workforce, with concerns about curriculum alignment, career support, and practical training. Despite moderate support for career-focused and hands-on experiences, innovation and entrepreneurship education received low recognition. Additionally, disparities in career counseling and resource allocation further hinder labor education’s effectiveness. To address these issues, the study recommends strengthening university-industry collaboration, enhancing career counseling services, integrating skill-based learning, and improving faculty training. Furthermore, efforts should be made to align labor education with students’ career aspirations and provide more structured feedback mechanisms. The study underscores the necessity of a more structured and practical labor education system to meet students' evolving needs and improve their career readiness. The findings contribute valuable insights for policymakers, educators, and institutions aiming to enhance labor education policies and practices. By implementing these strategic improvements, universities in Jiangsu Province can foster a more effective labor education framework, ensuring students are better prepared for future career challenges.
Exploration of the Realistic Dilemmas and Improvement Paths in Cultivating College Students' Awareness of the Chinese National Community in the New Era
Authors: Xu Yangfei
Published: February 2025

In the context of the new era, the cultivation of college students' awareness of the Chinese national community faces multiple challenges, including the fragmentation of historical narratives, the disconnection between educational practice, and the weakening of value identification. This paper deconstructs the theoretical core of the awareness of the Chinese national community from the triple dimensions of political identity, cultural ties, and historical memory. It systematically analyzes the practical dilemmas existing in cultivating college students' awareness of the Chinese national community and proposes the establishment of a trinitarian cultivation system of "curriculum reconstruction - practical innovation - institutional coordination." Through interdisciplinary integration, embodied practice, and the reform of evaluation mechanisms, this system promotes the transformation of college students' awareness of the Chinese national community from superficial cognition to deep value internalization. It provides a theoretical paradigm and practical reference for universities to advance the solidification of the college students' awareness of the Chinese national community.
Toxic Encephalopathy Induced by Diquat Poisoning: A Case Series of Three Patients
Authors: Liu Zihan , Jiang Yabin
Published: February 2025

Diquat is the third most widely used herbicidal biocide globally, following glyphosate and paraquat, with toxicity lower than paraquat. In the United States, diquat poisoning is predominantly linked to occupational exposure (8%–44%), with a low mortality rate (<1%–3%). In China, however, diquat use has increased only recently, following the ban on paraquat, leading to limited awareness and a significantly higher fatality rate compared to developed countries. Diquat primarily affects the liver and kidneys, but it can also damage the central nervous and respiratory systems. We report three cases admitted to our hospital, each involving severe central nervous system injury following accidental diquat ingestion. These patients exhibited characteristic clinical and imaging findings associated with diquat-induced brain injury. This case series provides valuable insights and may guide prevention and management strategies for similar poisoning cases in the future.
Research on Computer Model of Seismic Performance and Shear Bearing Capacity of Prefabricated Building Wall Structure Based on BIM
Authors: Liu Ze
Published: February 2025

The Markov model limited maximum likelihood algorithm is used to analyze the image. Then, the original image block and the image block affected by noise are estimated respectively. Local adaptive regression kernel and principal component analysis were used to extract feature and cluster the image block structure. The original image block's mean value and covariance matrix were computed. Subsequently, by applying weighted averaging techniques to these matrices, the noise reduction estimates for each individual image block could be achieved. The maximum likelihood method is used to make the completely confocal non-correlation, and the Markov random field model is used to limit it. This method can recover the high frequency component of the image well. The seismic response characteristics of fabricated building steel frames are studied. The results show that each group of tests follows the structural system of "block-rib-frame column triple seismic defense system", and its failure mode is one of overall shear failure. The combination of SRC frame and inclined ribbed composite shear wall can not only significantly improve its shear performance, but also obtain better damping effect.
Probing the Link Between Critical Thinking Ability and Academic Achievement of Chinese Art Design Students
Authors: Wu Yunyi , Joson Carmina G.
Published: February 2025

This study explores the relationship between critical thinking ability and academic performance of art and design students in Chinese universities. The researchers surveyed 500 art and design students from 10 universities in nine cities in China. The research results show that students' critical thinking ability in art and design is at the average level, and students' academic achievement assessment in art and design is also at the average level, and critical thinking ability has a significant impact on the academic performance of art and design students.
Research of Frequency Multiplexing in 5G Networks
Authors: Zhang Guoyi
Published: February 2025

Frequency multiplexing techniques for 5G communications include large-scale antenna techniques and frequency division multiplexing techniques such as OFDM and GFDM. Reasonable use and development of these techniques can significantly reduce the cost of 5G technology deployment and improve the spectral efficiency. This paper firstly introduces the current status of multi-input multi-output large-scale antenna technology. This paper then describe the main challenges faced by large-scale antenna technology. Following this, this paper discuss related development and innovation directions, such as the adoption of compact and lightweight radiating devices and high-precision amplitude-phase calibration networks, which are crucial for improving the practicality and efficiency of large-scale antenna systems. Secondly, for OFDM technology, the performance of its improved f-OFDM and CP-OFDM in different situations such as BER is demonstrated. Finally, due to the limitations of OFDM, the SER performance of GFDM is investigated in this paper. To complement these discussions, this paper concludes by exploring the potential synergies between massive MIMO and advanced waveform technologies like f-OFDM and GFDM, highlighting how their integration could further enhance the performance and flexibility of 5G and future wireless communication systems.
Efficacy of different sterilants in sterilization of nodal explants of Dalbergia sissoo Roxb. from mature trees
Authors: Joshi Ila , Bisht Prabha , Sharma Shivam
Published: February 2025

One of the most important steps involved in in-vitro propagation is establishment of contamination free cultures. To overcome the problem of contamination of explants of Dalbergia sissoo Roxb. collected from mature trees present investigation was initiated and it was observed that treatment of explant with 1.10% HgCl2 for 15 minutes was found to be the best and most effective and resulted in establishment of contamination free healthy cultures of Dalbergia sissoo.
Research on economic development problems in Guangdong province based on principal component analysis and cluster analysis
Authors: Shi Man Wa Tik
Published: February 2025

Located on the southeast coast of China, Guangdong Province has a favorable geographical position and is one of the more economically developed provinces. At the same time, it has been effective in solving the problem of unbalanced regional development, but at the same time, there are some remaining problems. In this paper, 13 indicators are selected from four aspects, namely, national economy, per capita economy, foreign investment economy, and basic economy, to compare the economic development of 21 cities in Guangdong in 2022. Through the method of principal component analysis and k-mean clustering, it is found that for all cities in Guangdong Province, the best economic development is in the cities of Guangzhou and Shenzhen, followed by Dongguan and Foshan, and finally other cities in Guangdong Province such as Zhuhai, Zhongshan, and so on. Based on analyzing the raw data and providing insights into the economic development of Guangdong Province, it is hoped that it will add to the future development of Guangdong Province and contribute to the future development of Guangdong Province.
Research on Target Costing Method in Cost Control of Small and Medium-sized Enterprises
Authors: Gong Junyi
Published: February 2025

In the fierce market competition, enterprises need to improve the competitiveness of their products and optimize the product structure, and the control of the operating budget is equally important. As an important method of enterprise cost accounting, the target costing method has been widely used in many enterprises and has achieved good results. However, for many enterprises, especially small and medium-sized enterprises, due to the lack of scientific internal control and strict target cost accounting, the phenomenon of cost accounting problems also occurs frequently. Based on this, this article combines statistical research methods to deeply analyze the application of the target costing method in small and medium-sized enterprises. It proposes solutions to the above problems so that the target costing method can be better applied to the production and operation organizational structure of enterprises and help enterprises develop healthily and sustainably. This article will analyze the current practical problems of enterprises, analyze specific cases, and further demonstrate the feasibility of the target costing method, providing practical cost control methods for enterprises.
Overview of Machine Learning Bots Capable of Achieving Top-level or Superhuman Performance in 2d Competitive Games
Authors: Li Xiaoyu
Published: February 2025

With recent developments in machine learning, bots have conquered many games that were previously believed to be too difficult for computers. We evaluated 4 machine-learning bots that mastered 4 different games: AlphaGo for Go, Stockfish for chess, AlphaStar for StarCraft, and Juewu for Honor of Kings. We summarized and compared their fundamental algorithms and discovered two potential patterns: an increase in generalizability causes an increase in the amount of computation needed, and an increase in the complexity of the game environment causes a decline in the neural networks performance. To conclude, we discuss the implications of machine learning game bots on neural networks aimed at handling real-life scenarios and artificial general intelligence (AGI).
Movement Detection of Tennis Players Based on Yolo and Human Skeleton Recognition Technology
Authors: He Mengle , Fu Mingyu , Cao Deyang
Published: February 2025

This study aims to develop a monitoring model for the action states of athletes (such as standing, moving, and striking) during tennis matches. The model is based on the YOLO (You Only Look Once) architecture and is trained with the 3dResNet50 to achieve automatic recognition of athletes actions. Additionally, we utilized the MediaPipe model to recognize the human skeletal structure, further enhancing the accuracy of action recognition. Tested on real tennis match video data, the model demonstrated efficient action recognition capabilities and good real-time performance, providing strong technical support for sports training and competition analysis. This research not only extends the application of computer vision in the field of sports but also lays a foundation for further advancements in motion analysis technology.
Receptivity of flow in a square lid driven cavity to implicit numerical excitations for high-fidelity HPC
Authors: Sengupta Tapan K. , Prakash Abhinav , Sengupta Aditi , Vajjala Suman K.
Published: February 2025

This study aims to investigate the effects of implicit numerical excitation on the receptivity of flow inside a square lid-driven cavity (LDC) leading to bifurcation and instability for a fixed (257 × 257) grid with different temporal resolutions via the solution of the Navier-Stokes equation. Computational results have been provided showing the flow dynamics of the LDC problem as explained with a time series at a representative point near the top corner of the cavity at (0.95, 0.95) for supercritical Reynolds numbers with respect to the bifurcation phenomenon by lowering the time step. As the accuracy of numerical methods plays a vital role in capturing the dynamics at different Reynolds numbers, this vortex-dominated flow is explained for bifurcation and instability. We propose this as a benchmark problem for the direct numerical simulation (DNS) and for machine learning (ML) of fluid flow that will lead to efficient ML algorithms and an understanding of flow receptivity, instability, and transition by DNS.
Navigating the Transition to Higher Education: Understanding the Experience among First-Year University Students
Authors: Osafo Abigail Boatemaa , Ohene-Darko Sophia , Agormedah Edmond Kwesi , Omane-Adjekum Charles
Published: February 2025

The transition from high school to university is a pivotal period in students’ educational journey and personal development. This study assesses the transitional experience of first-year university students through the lens of Schlossberg’s Transition Theory. A quantitative survey design was employed, and data was collected from 614 first-year university students. Descriptive (mean and standard deviation) and inferential (independent sample t-test and One-way ANOVA) statistics were employed to analyse the data. The results showed that first-year students experienced significant high transitional challenges, including academic, social, financial, and emotional difficulties. There were no statistically significant differences in transitional experience (academic, social, financial and emotional difficulties) based on gender and age. However, a significant difference was found in social challenges based on age category, with students aged 20-25 years experiencing more severe social challenges than those above 25 years. The study’s findings supported the transition theory, highlighting the importance of considering the interplay of individual and environmental factors in facilitating a smooth transition to university. Implications for practice and recommendations for future research are discussed.
Best Practices for Ensuring High-Performance Cloud Systems in Airline Management
Authors: Kumar Hemanth
Published: February 2025

Cloud computing is revolutionizing airline management by providing scalable, reliable, and cost-effective solutions. Its adoption streamlines operations, enhances passenger experiences, and enables real-time decision-making. However, achieving high performance requires strategic implementation of best practices, addressing challenges, and leveraging emerging technologies. This paper explores these practices, supported by case studies, while highlighting the benefits and challenges of adopting cloud systems in airline management.
Teaching Reform and Practice of "Modern Signal Processing" in the Intelligent Era
Authors: He Fang , Jia Weimin , Zhao Jianwei , Pan Lei , Jiang Nan
Published: February 2025

With the rapid development of technology, modern signal processing has been widely applied in various fields such as communication, image processing, medical imaging, and bio-informatics. For graduate students majoring in electronic information engineering, communication engineering, and related fields, mastering the theories and techniques of modern signal processing is particularly important. However, the traditional "Modern Signal Processing" course presents challenges such as abstract content, complex mathematical derivations, and high difficulty in understanding for students. Therefore, this paper aims to explore the teaching reform and practice of the "Modern Signal Processing" course in the intelligent era. By optimizing teaching content, innovating teaching methods, and introducing diversified evaluation mechanisms, the goal is to enhance students' learning interest and practical abilities to meet the needs of the times.
Movilización y abstención en unas elecciones anticipadas y estivales
Authors:   Universidad Pablo de Olavide, Trujillo José Manuel , García-Hípola Giselle ,   Universidad de Granada, Colomé Gabriel ,   Universidad Autónoma de Barcelona
Published: February 2025

Este capítulo se dedica al examen de la movilización y la abstención en dicha convocatoria anticipada y veraniega de elecciones legislativas, siguiendo la estela de otros trabajos centrados en estas dimensiones del voto que se encuentran incluidos en las monografías anteriores de la colección que engloba a este libro (en particular, sobre elecciones generales, véase: Font y Mateos, 2007; Lavezzolo, Riera y Santana-Leitner, 2010; Cazorla y Ortega, 2018; Urdánoz y Urquizu, 2020; y Trujillo, García-Hípola y Colomé, 2023). Se pone de nuevo el foco en la participación electoral con el objetivo de aportar evidencias empíricas que ayuden a situar y comprender unos procesos que alumbraron, otra vez, un escenario novedoso de competición partidista –que, aunque viene a mantener algunos elementos que caracterizan a la política española en la última década, incluye nuevos matices, tal y como dan mayor cuenta otros capítulos–. En concreto, este trabajo se centra en los datos relativos al Congreso de los Diputados (en adelante, Congreso) dividiéndose en su estructura formal en otros tres apartados además de esta introducción. A continuación, en el que sigue a este, se realiza una exploración descriptiva de los resultados agregados y se analizan las tendencias históricas, comparadas y de los principales niveles territoriales. Seguidamente, en el tercero, se indaga en las bases individuales de la participación a través de la exploración descriptiva y explicativa del estudio poselectoral del Centro de Investigaciones Sociológicas (CIS). Y, por último, en el punto restante, se añaden algunas reflexiones finales a modo de conclusión.
The effect of transfer pricing, thin capitalization, and foreign ownership on tax avoidance using tax haven utilization as moderation
Authors: Jazmi Variant Zulfadhli , Masripah Masripah
Published: February 2025

This quantitative study analyzes the impact of Transfer Pricing, Thin Capitalization, and Foreign Ownership on Tax Avoidance, with Tax Haven Utilization as a moderating variable. Data is sourced from annual reports and financial statements of multinational companies listed on the Indonesia Stock Exchange from 2021–2023. Using purposive sampling, the study includes 63 companies, totaling 189 observations. Panel data regression analysis was conducted with STATA 17 at significance levels of 5%, 10%, and 25%. Results show that Transfer Pricing, Thin Capitalization, and Foreign Ownership do not significantly affect Tax Avoidance. However, Tax Haven Utilization significantly strengthens the relationship between Thin Capitalization and Tax Avoidance but does not moderate the effects of Transfer Pricing or Foreign Ownership. This suggests that tax haven jurisdictions primarily influence the impact of capital structure on tax avoidance rather than ownership or pricing policies. This study enhances understanding of Tax Avoidance determinants in multinational firms in Indonesia. Findings provide valuable insights for academics and practitioners in improving tax oversight strategies. Future research should explore additional factors such as corporate tax compliance and international tax regulations.
AI Based Predictive Maintenance for Vehicles
Authors:   2 Electronics and Communication Engineering, Sri Shakthi Institute of Engineering and Technology, India, R    Dr.Jamuna
Published: February 2025

Predictive maintenance is a critical aspect of ensuring the longevity and optimal performance of vehicles. This paper presents a novel approach for vehicle health monitoring through the integration of Artificial Intelligence techniques and the Internet of Things (IoT) for predictive maintenance. The system utilizes a (RPI) platform combined with an OBD2 interface, using an ELM327 Bluetooth device to retrieve real-time vehicle data. The AI model processes these data inputs to predict potential vehicle failures and recommend maintenance actions, reducing downtime and improving overall efficiency. By leveraging machine learning algorithms, the system analyzes various vehicle parameters such as engine performance, fuel efficiency, and emission levels to generate actionable insights. This intelligent system enables early detection of faults, thus lowering repair costs, enhancing safety, and improving vehicle reliability. The proposed system is a cost-effective and scalable solution for vehicle maintenance, making it a valuable tool for both individual vehicle owners and fleet management operations.

Keywords: RPI Raspberry Pi, OBD2-Onboard Diagnosis Device
Flood Prediction using Machine Learning
Authors:   1Head of the Department, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamilnadu, India,, Sengaliappan Dr. M.
Published: February 2025

Due to urbanization and climate change, flooding has increased in frequency and severity, upsetting lives and seriously damaging property. Flood Susceptibility Modeling (FSM), which employs sophisticated machine learning approaches, helps identify flood-prone locations and the elements that contribute to these risks in order to solve this problem. This study explores hybrid FSM models that integrate the Index of Entropy (IOE) with Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) to offer a dependable approach for flood prediction and prevention. To assess the predictive power and correlations between influencing elements, the study started with feature selection and multicollinearity analysis. The relationship between several flood-causing components and their total effect on flooding was measured by IOE. Weighted inputs from these findings were used to train the hybrid models. Metrics like the Area Under the Curve (AUC) and other statistical indicators were used to evaluate the models in order to ensure correctness and reliability. The standalone DT model performed the worst (77.0%), while the hybrid DT-IOE model had the best prediction accuracy (87.1%), followed by SVM-IOE and RF-IOE. These findings show that prediction accuracy is increased when machine learning and statistical techniques are combined. 21% of the study region is extremely sensitive to floods, according to the final susceptibility maps, underscoring the major impact of human-induced factors including land-use changes and urban growth. By enhancing feature analysis and prediction accuracy, generative AI significantly enhanced model performance. The significance of hybrid machine learning approaches in developing efficient flood risk management plans is highlighted by this study, which also supports disaster resilience and sustainable urban design.

Keywords: Human-induced factors, Flood occurrences, Flood susceptibility modeling (FSM), and hybrid models Artificial Intelligence (ML), Natural Causes Remote Observation.
An Inexpensive and Effective Ventilator for use in Emergencies
Authors:   Department of Electronics and Communication Engineering, 2 Priyadarshini Bhagwati College Of Engineering, Nagpur., Thakare Dr.Amita
Published: February 2025

A low-cost ventilator is made in compliance with the US Food and Drug Administration's (FDA) emergency usage guidelines. In addition to drastically reducing the time from strategy to clinical deployment and improving reliability, this straightforward method eliminates the requirement for several components that are necessary for standard ventilators while still producing life-saving ventilation.

Key Words:  Arduino, ventilator, bag valve mask , pneumatic, COVID-19
CROP STRUCTURE AND PRODUCTIVITY OF WINTER WHEAT VARIETIES WHEN GROWN FOR THE PLANNED YIELD
Authors:   Samara State Agrarian University, Vasin Vasily G. , Vasin Alexander V. ,   Samara State Agrarian University, Fadeev Sergey V. ,   Samara State Agrarian University, Fadeeva Elena S. ,   Samara State Agrarian University
Published: February 2025

The purpose of the research is to increase the productivity of winter wheat varieties when grown to the planned yield. Recently, the use of micronutrients in agriculture has become widespread. An important place is given to stimulating preporations in the form of liquid mineral fertilizers. The use of MEGAMIX micronutrient mixtures reduces the defi-ciency of trace elements in plants and stimulates the assimilation of introduced micronutrients. The most important of them are iron, copper, zinc, manganese, cobalt, molybdenum, boron. The objectives of the research are to assess the structure of the crop, to assess the yield of winter wheat varieties and to identify the effectiveness of the use of micro-fertilizing mixtures of MEGAMIX for vegetation in the conditions of the forest-steppe of the Middle Volga region. The three-factor experiment consists of applying fertilizers for a planned yield of 4.5 t/ha, for a planned yield of 8.5 t/ha (factor A); winter wheat varieties: Svetoch, Scepter, Yuca, Grom (factor B); systems for processing crops with MEGAMIX preparation: without treatment (control), MEGA-MIX PRO (1 l/ha) in the tillering phase, MEGAMIX NI-TROGEN (1 l/ha) in the tube exit phase, MEGAMIX SULFUR (1 l/ha) in the flag leaf phase (factor C). The experiment was conducted according to the generally accepted methodology of B. A. Dospekhov. The use of fertilizers in combi-nation with stimulating preparations for vegetation increases the yield of products. Winter wheat varieties showed a good result – the yield was higher than planned. The maximum yield is the Yuca variety – 61.20 c/ha when applying fertilizers for a planned yield of 4.5 t/ha and 94.20 c/ha for a planned yield of 8.5 t/ha.
Study of Machine Learning Algorithms: A Historical Perspective
Authors: Ghosh Sushmita
Published: February 2025

ML stands for machine learning, a groundbreaking technological paradigm that has transformed the way we approach diverse areas of society from clinical diagnostics to algorithmic trading, from personalized media delivery to self-driving cars. In this article, we will look into the history of ML algorithms — in the order of their chronology. This can aid in providing an extensive perspective of how various ML techniques evolved, the theoretical underpinnings that strengthen them and the potential of forthcoming research in this branch of computational intelligence. Tracing this history helps us to appreciate how the interplay between theoretical innovations, the expanding availability of computational resources, and the demands of complex real-world problems have shaped the field of ML. From a few decades ago to today, this evolution tells the story of how ML has become such an influential driver of much of the technology that we see today. Lastly, the article discusses the interaction among improvements in algorithm design, computational infrastructure, and challenges brought by data tasks, paving the way for more advances in the years to come.
Demosaicking of Colour Filter Array (CFA) Data via Levenberg-Marquardt Optimization Method
Authors: C. R Rao. , S. K Mandal
Published: February 2025

Demosaicking is the procedure of re-constructing full colour information at each pixel from a CCD sensor, which captures only one colour component red or green or blue per pixel. Various demosaicking techniques are employed to perform this reconstruction. However, many studies overlook a detailed analysis of image quality, particularly with regard to artifacts that may appear in the edges and textures of the images. These drawbacks in the existing methods, the re-constructed image seems to be of poor in quality with less PSNR values. To address the limitations of existing methods, this paper proposes a new demosaicking technique. The technique proposed is designed with two major stages (i) CFA demosaicking (ii) Enhancement by Levenberg-Marquardt technique. The given input CFA image green, red, blue pixel values are interpolated to extract the full-colour image. This full-colour image seems to be less quality and has more distortion in the edge and texture of the image. So the output full colour image is enhanced by the Levenberg-Marquardt technique. Hence, the image can be demosaicked more effectively by reaching higher PSNR and MSE ratio compared to the conventional demosaicking algorithms. The comparison of results shows that the proposed technique extracts high-quality demosaicked images than the state-of-the-art methods, in terms of PSNR.
The Development of Training Package for Enhancing Transformational Leadership of Thai Buddhist University Students
Authors: Ramangkura Thanet , Ponathong Chakrit , Charoensuk Onuma
Published: February 2025

The aims of this study were to: 1) assess the current and expected conditions of transformational leadership of Thai Buddhist university students; 2) develop and examine the effectiveness of training package to enhance the transformational leadership of Thai Buddhist university students, and 3) study the effectiveness of the training package. This research involved 478 informants and representative samples, including executives, instructors, students, experts, and stakeholders. The research instruments were semi-structured interviews and opinion questionnaires to assess the need for transformational leadership in the current and expected conditions. These were measured using a 5-level Likert rating scale. The current condition had a reliability of 0.949, the expected condition had a reliability of 0.950, and the overall reliability was 0.974. The quality assessment form of the training package included focus group discussions, the training package, the transformational leadership assessment form, the training participation behavior assessment form, and the satisfaction assessment form. The statistics used for data analysis were frequency, percentage, mean score, standard deviation, the priority index of needs (PNI modified), and dependent t-test. The research revealed that 1) The assessment of the needs of transformational leadership of Thai Buddhist university students found that consideration of individuality was the most needed aspect (PNI modified equal to .3441), followed by intellectual stimulation (PNI modified equal to .3440), inspiration (PNI modified equal to .3398), and ideological influence (PNI modified equal to .3364), respectively. 2) The creation of the training package thethrough an online group discussion process (Zoom) showed that all activities were appropriate. 3) The study of the effectiveness of the training package found that the average score of transformational leadership of the students was significantly higher than before training at a statistical level of .05. Additionally, the average scores for behavior and participation in training and satisfaction with the training were significantly higher than the specified criterion (3.51) at a statistical significance level of .05.
The School-Based Professional Development for Teachers’ STEM Teaching: A Focus on Contextualized STEM Education in the Phuket Education Sandbox (Southern Regions of Thailand)
Authors: Chatmaneerungcharoen Siriwan
Published: February 2025

This research aims to develop a professional development model for science teachers using a school-based approach to enhance STEM literacy. The model leverages outdoor STEM education resources in southern Thailand. The study focuses on primary-level science and project-based teachers in Phuket Province, selected through purposive sampling from schools participating in an educational sandbox project with support from school administrators. The research employed a mixed-methods approach to design a professional development program emphasizing outdoor STEM activities and incorporating local Phuket contexts. Data collection involved STEM literacy assessments, lesson plan evaluations, classroom observations, and focus group interviews. Quantitative data were analyzed statistically, while qualitative data underwent content analysis over a one-year period. The project began in 2023 with a needs assessment, tool development, and a draft model, followed by implementation in 2024. Findings highlight that school-based coaching and ongoing lesson design workshops, core elements of the program, improved teachers’ understanding of STEM integration. Teachers designed learning activities rooted in local contexts and engineering design principles, fostering interdisciplinary connections within a STEM framework. This approach enabled them to integrate STEM concepts into primary education while emphasizing continuous assessment of student outcomes and competencies. Despite the program’s success, challenges remain, particularly in allocating time for curriculum implementation and securing stronger administrative support. Nevertheless, the program advanced STEM literacy by using local contexts as a platform, aligning with community-based education goals and enhancing primary-level STEM education practices.
A Flexible and Transparent Wearable Antenna Developed with Liquid Dielectric and Polymer
Authors: Sayem Abu Sadat Md.
Published: February 2025

This article proposes the design and realisation of a conformal and transparent antenna compatible for wearable ap- plications. This new antenna utilises water, transparent polymer and conductive fabric for its design and realisation. The topology of the fabricated antenna is deformable and has broadside radiation pattern, thus, suitable for on-body applications which is validated through experimental investigations. The basic antenna comprises a monopole radiator backed by a dielectric reflector which directs the omnidirectional radiation of the monopole to broadside. The notable features of this new antenna are its excellent optical transparency, conformality, good bandwidth, gain and efficiency and the utilisation of easily accessible and bio- friendly materials. The center resonance frequency of the antenna is 2.45 GHz ISM band with 3.42 dB peak gain and 51% efficiency. The usability of the antenna for wearable applications is checked by on-phantom measurements, bending tests and investigating specific absorption rate (SAR). Due to its amazing optical transparency, structural deformability, radiation characteristics, size and ease of fabrication, this antenna is a promising candidate for flexible transparent electronic systems, including wearable body-area networks.
Credit Card Churn Prediction: An Analytical and Model-Driven Study
Authors: R Viswadhanush B
Published: February 2025

This study compares the performance of three machine learning models namely Logistic Regression, Naive Bayes, and K-Nearest Neighbors (KNN) in forecasting customer churn within financial institutions. Predicting customer churn is important for banks and the financial sector because once firms develop targeted interventions to improve customer satisfaction and loyalty, they can be kept for the long run. A set of performance metrics like accuracy, precision, recall, F1 score, and area under the curve were used in the analysis for the better comparison of the prediction capabilities of models.

Logistic Regression resulted as the one which performed best. It gained the highest possible accuracy of 89.6% and its recall rate was very good at 96.8%. Also, its AUC score shows high discriminative power at 0.91. Naive Bayes, gaining less accuracy by producing 86.7% yet showed good precision at 92.0%, and having good recall rates, 92.2% and thus forms another competitive selection. Its AUC score of 0.83 establishes its efficiency to differentiate churners from non-churners. KNN's accuracy was good with 89.2% along with excellent recall rate 97.6%. Moreover, AUC score at 0.86 enhances the reliability as well as performance in the role of a good prediction model for churners.

Promising results may indicate more research on such advanced techniques. More models that would improve the performance may be neural network models in this regard such as Artificial Neural Network, Feed Forward Neural Network, Multi-layer Perceptron in the sub-field of neural networks and the models applied to Deep Learning that may involve models such as Convolutional Neural Network, Recurrent Neural Networks, Long Short-Term Memory. These techniques are expected to further enhance predictive accuracy by capturing complex patterns in large datasets.

This would greatly enhance customer retention for banks and financial institutions in their implementation of machine learning models. The accurate prediction of churn will allow organizations to engage at-risk customers proactively, providing customized interventions to enhance satisfaction and loyalty. The results of this study further highlight the value of machine learning in changing customer relationship management and leading to long-term customer retention and organizational success.
Predictive Analytics and Portfolio Optimization: A Study on Mutual Fund Asset Allocation and Risk Mitigation
Authors: B Guruprasath , R Nalini
Published: February 2025

This research involves creating an efficient portfolio construction that aims to guide the retail investors about the significance of the data-driven decision-making using the analytical tool Python, especially for financial securities investments with a focus on mutual funds. A dataset comprising necessary information on nearly 625 mutual fund schemes from the dataset obtained from Kaggle has been utilized for analysis and study. The study focuses on applying the modern portfolio theory for portfolio construction proposed by Markowitz, which is a very popular financial theory, in real-world investment strategy in the case of constructing a mutual fund portfolio with an adjusted risk-return tradeoff. The methodology relies on the construction of a portfolio with modern portfolio theory concepts and predicting the possible outcomes of the portfolio with the Monte Carlo simulation technique by running the codes in Python and constructing two distinct portfolios: one with diversified and lower risk, comprising 15 mutual fund schemes for conservative investors, and another with minimum compromised risk, comprising 5 mutual fund schemes, which achieves a higher return than the previous one. The parameters taken for the choice of selecting the schemes from the data set are based on the renowned ones such as the Sharpe Ratio and Sortino Ratio. The findings reveal that the 15 schemes portfolio returns are in the range of 10% and 12% with risk levels between 1.5% and 2.5%, and the 5 schemes portfolio returns are in the range of 15% and 17% with risk levels between 4% and 4.5%. The optimum weights to be invested in each scheme to achieve maximum return at the lowest possible risk are also mentioned in proportion for both the portfolios. The findings can be interpreted in a way that the construction of a portfolio with rational decisions backed by data is more appropriate in the modern world with the availability of analytical tools such as Python for forecasting and predicting the potential return and constructing the portfolio based on that by minimizing risks with the traditional theories, which can be efficiently and easily used with technology.
A Novel Car Interior Sound Classification Method based on Multileveled Local Binary Four Patterns and Iterative ReliefF
Authors: Akbal Erhan , Dogan Sengul , Tuncer Türker
Published: February 2025

Sound classification is one of the crucial study areas in machine learning and sound forensics. However, there are limited studies on sound forensics or sound-based crime investigations in the digital forensics literature. In this work, a novel area of sound forensics is presented: car interior sound classification (CISC). The main aim of CISC is to identify a car using its interior environmental sound. A car interior sound dataset was collected using 10 car models. This CISC model includes feature generation using the local binary four pattern and one-dimensional multilevel discrete wavelet transform (DWT), iterative ReliefF-based feature selection, and classification. k-nearest neighbors (kNN) and support vector machine (SVM) were utilized as classifiers to demonstrate the general success of the proposed learning model for CISC. The accuracy rates were calculated as 93.72% ± 0.37 and 95.04% ± 0.30 with kNN and SVM, respectively. These results demonstrate the success of the proposed method.
Variation stylistique du stylème « moment » chez Emmanuel Dongala
Authors: Ntsoua Ndombo Pierre Destain , Elongo Arsène
Published: February 2025

Le présent article étudie la variation stylistique du stylème « moment » chez Emmanuel Dongala. Il fait l’exploitation du corpus issu de quatre romans de cet auteur. Notre objectif vise à démontrer que « moment » a non seulement plusieurs constructions discursives différentes, mais ces constructions obéissent notamment à plusieurs effets stylistiques. Dans l’application des approches pragmatique et stylistique, nos résultats ont prouvé que « moment » a plusieurs constructions et cela produit des effets discursifs, puisque chaque structure syntaxique renvoie à une signification des effets stylistiques chez Emmanuel Dongala portant sur des connotations linguistiques et de la motivation langagière.
Pragmatics of Symbolism in Chrétien De Troyes’s Tristan and Iseult1
Authors: MOUTATI-KABOU Sylviède Nephtali
Published: February 2025

This study proposes to find out the practical meaning of symbols in Tristan and Iseult, written by Chrétien de Troyes. It starts from the observation that the author codifies his work by the means of symbolism and that each symbol does not only summarize or hide a vast reality that sometimes escapes the reader; but also takes on a situational meaning which matches the context in which it appears; hence the hypothesis: the reading of a symbol is done in accordance with the environment to which it belongs. Thus, this study, which requires a rapprochement with biblical stories, revolves around the human body, nature and colors as specific elements of analysis.
Pengembangan Media Pop Up Book pada Materi Sistem Tata Surya Berorientasi pada Keterampilan Ilmiah bagi Siswa Kelas VI Sekolah Dasar
Authors: Dewi Indriany  , Karimatus Saidah  , Muhamad Basori
Published: February 2025

This study is based on the results of observations made at SDN Lirboyo 1 Kediri City, teachers in delivering material, especially the Solar System, only use the lecture method and student books. Many students have difficulty in explaining and mentioning the constituent members of the Solar System. student difficulties are seen when students are asked questions only silent, and the value at the end of learning is far from the Minimum Completeness Criteria (KKM). The formulation of the problem developed is how the process, practical validity and effectiveness of pop up book media on solar system material for grade VI elementary school students in its use as media during learning. This research uses the Research and Development (R&D) research method with the ADDIE model (Analyze, Design, Develop, Implementation, and Evaluation). The subjects of this study were teachers and students of grade VI SDN Lirboyo 1 Kediri City. There are two instruments used in this study, namely using questionnaires and tests. Questionnaires are used to determine the validity and practicality of pop up book media, while tests are used to determine the effectiveness of pop up book media. The results of the pop up book media development research are as follows: (1) The results of the pop-up book media validity trial were carried out by media experts and material experts and received a percentage of 90% very valid or can be used without revision. (2) The results of the pop-up book media practicality trial in the limited trial were carried out by teachers by 92% and from students by 95%, then the results of the pop-up book media practicality in the broad trial were carried out by students by 93.5%. The percentage of scores from teachers and student responses is in the range of 81.00% - 100.00% which can be categorised as very interesting/valid. (3) The results of the effectiveness of pop-up book media obtained from the results of pretest and post test scores and getting a percentage of 81.65% are in the vulnerable range of 81.00% - 100.00% declared very effective can be used without improvement.
Music History and Nostalgia
Authors:   Teacher, Department of "Traditional Singing and Folk Instruments", Faculty of Music Studies, Gulistan State University, Uzbekistan, Tairjanovna Masharipova Gulbakhor
Published: February 2025

This scientific article analyzes the complex and multifaceted relationship between the history of music and nostalgia. The research aims to study the process of formation of the feeling of nostalgia in the context of the development of music throughout historical periods, its cultural heritage and impact on social changes. The article analyzes the role of music in awakening personal and collective memories, the psychological and social aspects of the phenomenon of nostalgia, as well as the continuity and change of musical traditions in modern society. The results of the research, conducted on the basis of empirical data, historical sources and theoretical approaches, serve to further understand the interaction between music and nostalgia.
S. Esenin and Erkin Vohidov: Linguo-Cultural Features of the Concept of Homeland and Native Land
Authors:   Senior lecturer of the Jizzakh branch of the National University of Uzbekistan named after Mirzo Ulugbek, Uzbekistan, M.N. Ernazarova
Published: February 2025

In literature, the concepts of homeland and native land are closely intertwined with national identity and historical memory. The works of Sergey Esenin and Erkin Vohidov provide a profound analysis of the linguo-cultural manifestations of these two concepts. Esenin's poetry vividly portrays the unique landscapes of Russian rural life and its culture, while Vohidov glorifies the spirit of independence and cultural heritage of the Uzbek people. This article examines the linguistic and cultural aspects of the concepts of homeland and native land in the works of these poets.