bluuebunny/crossref_metadata_2025 huggingface dataset:
Orell Talk for Engineering Communication: Improving Oral Fluency in Information Systems Contexts
Authors: Raya Ali
Published: February 2025

This study aimed to investigate the effects of using the educational speaking technology tool, Orell Talk, on enhancing the speaking performance of English as a Foreign Language students. A quasi-experimental research design was employed to compare the speaking performance of EFL students who used Orell Talk with those who learned to speak using conventional methods. The study gathered data through tests, questionnaires, and interviews. The findings revealed that the EFL students who learned speaking skills through Orell Talk demonstrated significantly enhanced performance in fluency, coherence, lexical resources, grammatical range and accuracy, and pronunciation compared to the control group. The study concludes that the integration of educational speaking technology tools, such as Orell Talk, can effectively improve the overall speaking proficiency of EFL learners.
An Intelligent Job Recommendation System based on Semantic Embeddings and Machine Learning
Authors: Priyanka Singla
Published: February 2025

To address the shortcomings in existing approaches of job recommendation systems, this paper proposes a novel machine-learning-based job recommendation system that performs bi-directional matching for dynamic and accurate recommendations. The proposed approach generates ideal job recommendations for a targeted Curriculum Vitae (CV) and vice versa. Unlike previous approaches, the proposed approach incorporates natural language processing (NLP) techniques to extract linguistic features such as Bag of Words (BoW), n-grams, TF-IDF, and Parts-of-Speech (PoS) tag and build a rich feature set. These features are further analyzed using semantic embeddings, enabling robust job matching. Experiments were performed to validate the performance of the proposed approach. The designed system is validated on various real-world datasets, overcoming the dataset size limitations of prior works. Due to combination of semantic embeddings, machine learning, and various similarity measures, this approach demonstrates the potential to deliver reliable, explainable, and ideal job recommendations, addressing the challenges of static and false outputs in existing systems.
Optimizing Epileptic Seizure Recognition with Machine Learning Algorithms
Authors: Sanagavarupu Sunitha
Published: February 2025

According to the WHO, Epilepsy is a significant public health issue and increases every year from 1% to 2% in all age groups. It is one of the oldest recognized neurological disorders. Early detection and proper medication reduce the risk to the person. EEG is one of the methods to identify epilepsy, The continuous monitoring of EEG signals recognizes seizures. These occur in the partial or total body or several parts of a brain for a person and it causes unconsciousness. A person who suffers from any one of the following health problems: high fever, sleepless nights, anxiety, and stress might cause epileptic seizures. Surviving with epilepsy is stressful and limited to employability. This work aims to build the best model using Machine Learning algorithms with high performance and accuracy values, by using the conventional Machine Learning algorithms like K- Nearest Neighbor, Support Vector Classifiers, Support Vector Regression, Lasso, Ridge, Decision Tree, Gradient Boost, eXtreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting, and Linear Regression. Optuna used for tuning hyperparameters in the ML models to improve the performance of a model and to obtain best results. The Kaggle data set used for training and validation purposes. In these models, according to classifiers models, all the “gradient boost” classifiers produce accuracy, precision, recall and F1-score with 1.0 values. In Regression models, the best model is “Linear Regression” with R2 score as 1.0 to detect epileptic seizures.
Facial Expression and Speech Pattern Computing using Modern Techniques of Computer Vision
Authors: Anant Kaulage
Published: February 2025

Two crucial areas of research in the field of emotional computing are recognition of facial emotions and spoken emotion. In this study, we tackle the challenge of precisely identifying and analyzing emotions from speech and facial expressions. A fascinating problem in human-computer interaction, mental health monitoring, social robots, and other fields is the detection of emotions from facial expressions and verbal inputs. By combining different modalities, we can obtain a more complete picture of emotions by utilizing the complimentary information included in facial expressions and speech patterns. Our approach succeeds in a number of crucial goals. In order to accurately extract facial features and analyze facial expressions, it first applies cutting-edge computer vision techniques. Modern speech processing algorithms are also used to extract emotional indicators from speech data and record pertinent acoustic elements. Our solution has two results in total. First of all, it permits in-the-moment emotion recognition, enabling prompt and context-sensitive reactions in programs like virtual assistants or emotion-aware systems. Second, it offers insightful information about human emotions that can help with psychological research, clinical diagnosis, and therapeutic interventions. For the extraction of facial features, the suggested method makes use of the Haar cascade classifier, a well-liked object detection algorithm. Numerous facial characteristics, including the areas around the eyes, the nose, and the lips, are represented by haar-like features. The classifier is trained using a training dataset made up of facial photos that have been labeled with the associated emotions. Preprocessing is done on the labeled dataset to separate out the Haar-like features and produce the positive and negative samples. The trained classifier can then recognize and categorize emotions from facial photos or video streams in real-time. The findings point to the potential for real-time emotion recognition in a variety of real-world settings, enabling more sympathetic and context-sensitive human-machine interfaces.
Hydroponics: Advancing Sustainable Technologies and Applications in Crop Production with a Focus on Lettuce Cultivation
Authors: Bhagyashree Ambore
Published: February 2025

The pressing need to adopt sustainable, climate-resilient, and resource-efficient agricultural practices has brought hydroponics—a soilless cultivation system—into the spotlight. Lettuce (Lactuca sativa), a popular leafy vegetable, is particularly suited for hydroponic farming due to its high-water content, fast growth cycle, and adaptability. This study explores various hydroponic techniques for lettuce cultivation, focusing on their potential to enhance yield and quality. Key methods such as the nutrient film technique (NFT), deep water culture (DWC), aeroponics, and others are evaluated for productivity, resource efficiency, and scalability. The integration of advanced technologies, including artificial intelligence (AI), precision agriculture, and plant growth-promoting rhizobacteria (PGPR), further strengthens the role of hydroponics as a sustainable approach to urban agriculture and global food security. Comparative analyses demonstrate that adaptive hydroponic systems deliver higher yields stability (up to 4.0 kg/m²) and superior quality while effectively addressing climate change, water scarcity with water utilization (0.8 kg/L), and soil degradation excelling in return on investment (ROI).
Leveraging Artificial Intelligence in Human-Robot Interaction to Develop Intuitive and Adaptive Systems for Enhanced Collaboration and Task Execution
Authors: Balaji Bodkhe
Published: February 2025

AI is one of the foremost imperative variables characterizing human-robot interaction (HRI). It does this by making frameworks that are basic and versatile, which makes it simpler for people and robots to work together and total assignments. Robots can see, think, and act in changing environment by utilizing AI programs, which gives them considering aptitudes comparable to people. This combination of AI and robots makes it simpler for individuals to conversation to each other and work together, which leads to more proficient and compelling collaboration. Brilliantly apparatuses that make things simpler for individuals to utilize with robots are a vital portion of utilizing AI in HRI. Robots can get it and answer to human orders and developments much obliged to Characteristic Dialect Preparing (NLP) and computer vision strategies. This makes discussion simpler and more normal. This basic interface makes contact less demanding for everybody, so individuals can effortlessly work with robots indeed on the off chance that they haven't had any preparing. Moreover, AI-driven frameworks let robots alter their behavior and tastes to coordinate those of people, which makes working together simpler. Machine Learning systems permit robots to memorize from experiences with people and alter how they act based on what they've learned. For case, when individuals work together on a task, a robot can learn from their activities and report the most excellent ways to assist individuals, which progresses the common performance and speed of the errand. AI too makes it simpler to make frameworks that are mindful of their environment and can alter how they act based on clues from their environment and the circumstance. Robots can get it and see their environment by utilizing sensors and discernment programs together. This lets them make savvy choices and alter how they act based on those choices. This understanding of its environment makes it less demanding for the robot to work with individuals in a assortment of settings.
Application of Traditional Painted Decoration and Modern Technology in Ceramic Art Design
Authors: Bai Ying
Published: February 2025

Introduction: Traditional painted decoration, with its rich cultural heritage and artistic style, plays a vital role in ceramic art design. As a medium for cultural expression, ceramics offer a platform for reinterpreting traditional motifs using modern technologies. Digital design, 3D printing, and AR/VR provide new creative tools, transforming traditional decorations in design techniques, presentation, and user engagement.
Objectives: This study explores the integration of digital design, 3D printing, and AR/VR into ceramic art to enhance the visual appeal, precision, and cultural significance of traditional motifs. It also examines how technological innovation supports the internationalization and cultural exchange of ceramic design.
Methods: Using a mixed-methods approach, the study combines literature review, expert interviews, case analysis, experiments, and quantitative research. It critically examines existing studies, gathers insights from practitioners, analyzes artworks, and evaluates market trends to understand how modern technologies influence ceramic craftsmanship and cultural heritage.
Results: Through the digital reconstruction and dynamic reinterpretation of traditional decorative motifs, ceramic art can present more diverse visual effects and cultural meanings. The precision and flexibility provided by modern production technologies allow for the realization of intricate patterns and complex forms that were previously difficult to achieve. Furthermore, the integration of technology and art fosters cross-cultural exchanges, enriching ceramic design with an international perspective and facilitating the creation of works that balance both traditional and innovative elements.
Conclusions: Digital tools enable intricate patterns and diverse visual effects, revitalizing traditional motifs. Advanced production techniques enhance design precision and flexibility. Moreover, integrating technology and art fosters cross-cultural exchanges, enriching ceramic design with both traditional and contemporary elements.
Redesign of the Digital Literacy Program for Older Adults, validating Desirable Skills with Generative Artificial Intelligence
Authors: Lamed Mendoza Lámbiz
Published: February 2025

The research described in this article aims to evaluate and redesign the digital literacy program for older adults by validating desirable skills with AI. The same redesign is based on the execution of the specific objectives of this research, highlighting among them: 1) identifying the training requirements so that older adults can break the digital gaps in access and use of ICTs; 2) redesign the digital didactic management model of the digital literacy program and 3) validate the redesign proposal by incorporating feedback with artificial intelligence. The applied methodology is based on the analytical observation of three years of experience of the digital literacy program, which takes into account the virtualization factor of the learning experience, to motivate the transformation of the digital culture of older adults, taking into account The validation of the thematic axes includes comparisons made against the results of queries to information search and analysis tools based on generative artificial intelligence. The results have led to maintaining references of the experience worldwide, but also enriched with its own factors and lessons learned that make the digital literacy experience for older adults in Panama innovative.
The Viability of An Authentic Learning Material in Teaching Drug Discovery and Development Course: ADDIE Model Approach
Authors: Richie Gregorio Bayuran
Published: February 2025

The rapid and dynamic challenges posed by the Fourth Industrial Revolution are continuously reshaping the educational environment creating a difficult task for teachers in educating Generation Z to ensure they are currently aligned with the digital world. This design-based research aimed to develop authentic learning material integrated into the Pharmacy curriculum for teaching drug discovery and development using the ADDIE model. Prior knowledge, perception, practice, and utilization of multimedia played a significant role in analyzing the participants' needs, which were used as requirements for developing the video animation. The minimum standards of the video validation, with an acceptability rating of 4.00 for the quality of audio and visual components, were complied with before using the video during regular class instructions. Infranodus text network analysis was used to analyze the sentiments drawn from the use of multimedia that affect the student's learning experiences and teachers' teaching practices. The underlying themes generated were technology challenges, technology adoption, resistance to change, barriers to skill development, lack of focus, engagement, and interaction. Student performance in the post-test was statistically significantly higher than the pre-test (M=3.95, p=0.001) in three periodic examinations. Students' positive overall learning experience using comprehensive video for effective knowledge transfer was attributed to video quality, content relevance, engagement, production value, accuracy, length, creativity, audio quality, and accessibility. Consequently, a parallel study involving a control and experimental group of respondents is recommended to assess the difference in exposure to the learning material.
The Influence of Work-Life Balance on Turnover Intention is Mediated by Job Satisfaction at KAP in Indonesia
Authors: Adonia Akelois
Published: February 2025

In this study, we examine the influence of the Work-Life Balance dimensions, namely WIPL (Work Interference with Personal Life) and PLIW (Personal Life Interference with Work), and Job Satisfaction (JS) on Turnover Intention (TI) in the context of Public Accounting Firms (KAP) in Indonesia. A survey was conducted on 114 employees working in various KAPs such as EY, PWC, KPMG, Deloitte, and other KAPs. This study uses quantitative analysis with the use of SmartPLS 4.0 for primary data management and SEM model acquisition. The results show that five out of seven hypotheses are accepted. The simulation of these effects shows that WIPL significantly affects JS and TI, while PLIW only affects JS and does not affect TI. Furthermore, JS significantly affects TI and mediates the effect of PLIW on TI, but JS does not mediate the relationship between WIPL and TI. This study found that employees who feel their personal lives are interfered with by their work lives are more likely to have the intention to leave the company. This study also shown that employees will be satisfied with their work if there is a balance between work responsibilities and personal life. Therefore, this study can be used to design human resource strategies that include a comfortable wo$rking environment while still giving employees the freedom to pursue personal activities outside of work in order to increase JS and minimize TI.
Comparative Study of The Implementation of Digital Learning Media in The Merdeka Curriculum in Indonesia and Thailand
Authors: Sri Marmoah
Published: February 2025

The Merdeka curriculum is expected to improve the quality of education. Digital technology can affect learning in the Merdeka curriculum. Digital learning media is an intermediary technology that can be used for learning purposes. The aim of this study is to identify differences and similarities in the practice of implementing Merdeka curriculum digital learning technology between Indonesia and Thailand, and analyze the challenges faced. This research uses qualitative descriptive method with comparative study approach. Data was collected through literature study, observation, interviews and documentation. This research was conducted at SD Sekolah Penggerak in Surakarta City, Indonesia and Indonesian School Abroad (SILN) in Bangkok, Thailand. The results showed that the implementation of digital learning media in the Merdeka curriculum and Indonesian schools in Thailand has not much difference in concept and equipment used. The results of this study will provide a better understanding of how digital learning media is applied in the Merdeka curriculum, so that it can be used as an adjustment to improve the effectiveness of education.
Lecturer Voices: Exploring the EAP Materials Needs in Technology and Information Education
Authors: Piping Rahadianto
Published: February 2025

EAP or English for Academic Purposes is a specialized English language learning targeted to specific students according to the field pursued and achieved an academic goal in the result. Similarly, in EAP teaching and learning activity, the materials or books are indispensable. Currently in Indonesia, the availability of teaching materials in the form of EAP books, especially for the Department of Education in Technology and Information, is quite limited and lecturers must arrange by their own. Consequently, this becomes a crucial issue in the teaching and learning process in the classroom. In this article, the researcher attempts to explore lecturers’ voices and perceptions regarding the EAP materials for Technology and Information Education. The data was gathered through interviews on the needs of EAP lecturers regarding EAP teaching materials themselves. The descriptive data were analyzed qualitatively using the semi-structured interview. In qualitative inquiries it sums of three aspects of lecturers’ voices concluding the EAP materials challenge, the features in EAP materials project and lecturers want in EAP materials for the future.
Animated Comics with Genyo Integration: A Digitized Approach to Learning El Filibusterismo Among Grade 10 Students
Authors: Ajie C. Dagohoy
Published: February 2025

Several studies have shown a notable discrepancy between the academic level of learners and their comprehension of various literary works in both the English and Filipino languages. Therefore, educators must consistently enhance their teaching methodologies and curricula. To address these problems, the researcher conducted a study that specifically examined the application of an intervention utilizing animated comics and Diwa Publishing's Genyo Learning Management System. This paper used a quasi-experimental research design to compare the respondents' scores before and after the experiment. As a result, the researcher discovered that using animated comics, aided by the learning management system (LMS), successfully improved students' understanding of literature. The significant disparity in student performance between the intervention and control groups confirms this finding. Nevertheless, it is essential to acknowledge that while this approach to instruction was successful, conventional instructional approaches also influenced the students' knowledge acquisition. These findings indicate that both modern and traditional teaching approaches can yield positive outcomes in the classroom when they enable appropriate instruction and/or discussion.
Forecasting Solid Waste Generation in Rodriguez, Rizal Using Artificial Neural Network (Ann) and Regression Analysis: An Input to Municipality’s Solid Waste Management Plan
Authors: Jose D. Elveña Jr.
Published: February 2025

Accurate and reliable forecasting of metropolitan solid devastate is extremely significant for an effective solid plan for waste management. Every local government is having a constant review of measure in the implementation of its waste management program to ensure its continuous viability and importance. The aim of this paper is to recognize the several influential variables and expand an effective model for authentic forecasting of ‘MSW generation’ and offer strategic recommendations that will improve several practices that are associated with waste management practices and several policy making in Rodriguez, Rizal.Solid waste collection of the municipality from 2010-2022, the population, different households, the GDP, commercial establishments and services (CES), and the tourist arrivalfrom 2010 to 2022 were gathered. Two forecasting methods, the “Artificial Neural Network” (ANN) and the multivariable simple regression with the use of Principal Component Analysis (PCA) have been tested that is for their ability for predicting the annual waste production within the municipality. Among the five components, population, Household, and Commercial Establishments have the highest eigenvalues and it account for almost 86% of the total variance in the original data. Furthermore, these components, present the lowestp-values; to which regression model was developed. Artificial Neural Network (ANN) model was has been established through using Multilayer-perceptron Neural Network. The same factors with normalized importance were identified, the Population, Household and Commercial Establishments. Result showed that “ANN” that is Artificial Neural Network has outperformed regression analysis in predicting the solid waste generation having less quantity in terms of the root RMSE. It was also including “mean error” (ME). At the same time, it also include “mean absolute deviation” (MAD) along with “mean percentage error” (MPE). It was in terms of mean “absolute percentage error” (MAPE).A strategic measure has been recommended to enhance waste management practices and policy making of the municipality of Rodriguez that will improve the accuracy as well as effectiveness of its waste management practices and also reduce environmental impacts.
Going Above and Beyond: Revolutionising Thai Secondary Schools Education via MOOC Course Development
Authors: Piyaluk Potiwan
Published: February 2025

This study aims toward developing and implementing a Massive Open Online Course (MOOC) learning model specifically for Thai secondary schools in order to address challenges in relation to online learning with the intention to create a tailored curriculum, content, and online lessons which comply with learners’ needs in the changing landscape of teaching and learning. The research involved 70 school representatives from the Secondary Educational Service Area Office of the Mahasarakham prefecture with their consented participation in interviews and questionnaires for data collection. The data was then analysed via content analysis and descriptive statistics. The key findings of the study deduce that the students have their own learning devices, with 80% having internet access from home. Students are actively engaged in online learning, showing positive participation and effective use of teacher programs. Teachers provide strong support and recommend online resources, indicating moderate views towards the MOOC implementation. Recommendations from the study include limiting MOOC course content to 12 hours, with 35% delivered through video media in 10-minute segments. Teacher development guidelines must emphasise classroom management, teaching skills, and digital proficiency. Overall, this research contributes to the advancement of MOOC learning models in Thai secondary education, aiding in facilitating the shift towards digital teaching and learning methods.
Intelligent Tutoring Systems as A Solution to Emotional and Academic Challenges in Higher Education: Analysis of a Survey from Instituto Politécnico Nacional
Authors: Gloria J. Martínez Bejarano
Published: February 2025

The use of artificial intelligence in education has opened new possibilities for personalizing teaching and providing comprehensive support to students. This study investigated the emotional and tutoring needs of students at the Instituto Politécnico Nacional, employing data mining techniques to analyze survey results. The methodology allowed the identification of key patterns related to academic motivations, emotional needs, and access to tutoring, offering a comprehensive view of the challenges faced by students. The findings reveal that 47.7% of students experience emotional problems, 70.4% are unaware of available tutoring figures, and 93.3% have not received any tutorial support. Additionally, 45.7% consider psychological support necessary, and 72.8% would request tutoring help.
Given this situation, the implementation of Intelligent Tutoring Systems (ITS) is proposed as a viable solution. These systems, with their ability to personalize learning, provide instant feedback, and continuously monitor progress, offer not only academic support but also tools to manage emotions such as stress and anxiety. The study presents concrete proposals for implementing ITS in higher education, demonstrating how these systems can enhance the student experience by integrating academic and emotional development through artificial intelligence.
Research Trends on Job Satisfaction and Employee Turnover Intention: A Bibliometric Analysis
Authors: Fidaa Nabilah
Published: February 2025

Using citation analysis, co-citation analysis, and co-word analysis, this bibliometric study investigates research patterns related to work satisfaction and employee turnover intention. In order to objectively follow and analyze research trends in the subject, the study includes a quantitative assessment of bibliographic information, including publications, citations, authors, and institutions. The analysis showed variations in the number of journals produced year, with a significant rise in 2021 and a subsequent fall in 2015. In addition, the study determined the most referenced authors and works in the area, offering insightful information about current knowledge gaps and potential future research avenues concerning job satisfaction and employee turnover intention. The findings of this analysis offer descriptive answers to research questions and contribute to the understanding of factors influencing employee job satisfaction and turnover intention. This paper serves as a valuable resource for scholars and researchers interested in bibliometric analysis and the field of job satisfaction and employee turnover intention.
Evaluating Public Engagement and Interactivity in the Context of Digital Media Management and Emerging Information Threats
Authors: Mykola Rusyniuk
Published: February 2025

Introduction: Digital media have become the main channels of communication. Information threats like fake news and cyber attacks can change public opinion. Assessing these threats will help identify risks and develop protection strategies.
Objectives: The study aims to develop recommendations for improving interactivity, increasing media literacy, and protecting against information threats by analysing and evaluating media education and security policies and strategies.
Methods: The methods used in the study were literature analysis, synthesis and analysis, observation, and generalisation.
Results: As a result of the study, a review of the regulatory and legal framework for information security in Ukraine was conducted. Methodological approaches to assessing public activity in digital media are identified. The study of public interaction with the media is based on analysing quantitative and qualitative indicators of user activity on Facebook and Instagram. The activity analysis on the leading media platforms (Telegram, Facebook, Instagram, Twitter) in Ukraine in 2022-2024 has revealed the main trends and problems in information consumption. Based on the study's results, six key recommendations have been formulated to increase interactivity, media literacy and protection against information threats in Ukraine, considering current challenges and threats.
Conclusions: The findings can help develop policies to improve information security and media education. It was found that digital media have expanded opportunities for public participation by facilitating access to information. Necessary measures have been developed to increase media literacy and information security to protect against threats and improve public debate.
The Role of Communication and Information Dissemination in Healthcare: Assessing Its Impact on Promoting a Healthy Lifestyle
Authors: Andrii Burachyk
Published: February 2025

Introduction: The author describes the main trends in professional interaction between a doctor and a patient and outlines the meaning of the terms "communication in medicine", "communication model", and "communication tactics". The article formulates the main communication tactics in the doctor-patient dialog, the development and implementation of which will help to improve the results of medical care and disease prevention.
Methods: In this study were employed analysis and synthesis methods, comparative and contrastive method, method of forecasting and modeling of situations, generalization method.
Results: The features of the doctor-patient communication model are described in detail. It is proved that this model involves adherence to the ethical principles of communication and knowledge of the psychological features of this type of professional interaction. The doctor's communication with the patient is realized at two levels: linguistic and extralinguistic. It has been established that the modification of the communication model between doctor and patient depends on three areas of interaction: treatment, prevention, and rehabilitation.
Conclusions: Today, one of the most optimal doctor-patient communication models is involving the latter in making a joint medical decision, which reduces the patient's anxiety. This model involves the patient in a dialog and shares responsibility for the treatment process between the doctor and the patient. The prospects for further research include studying ethical issues in medical communication and improving communication tactics in doctor-patient dialog.
Intelligent Surveillance System for Real-Time Detection of Anomalous Activities in Video Streams
Authors: Aarya Gadar
Published: February 2025

The increasing complexity of surveillance structures necessitates advanced techniques for monitoring big volumes of video statistics. This record opinions the utility of convolutional neural networks (CNN) and deep getting to know techniques for detecting suspicious pastime in video streams. The technique involves preliminary video statistics processing to extract key features, observed by training a CNN version to distinguish between normal and abnormal behaviours through recognizing spatial and temporal patterns within the video frames. techniques consisting of transfer gaining knowledge of and statistics augmentation are employed to enhance the model's generality and robustness. The effectiveness of this approach was validated via various checks, including experiments on datasets like u.s.a. Pedestrian and road, where the CNN-primarily based technique established high accuracy and go back quotes in figuring out suspicious activities. The scalability and actual-time processing abilities of the version make it adaptable to various tracking environments. These findings are great for the advancement of the surveillance era, offering a dependable technique for real-time detection of suspicious sports in video streams. The proposed CNN-based approach is promising for bolstering safety in public spaces, transportation structures, and important infrastructure, thus contributing to better safety features.
The Impact of Safety Culture on Safety Behavior in University Chemistry Laboratories
Authors: Xing Gao
Published: February 2025

This study aims to explore the relationships between safety culture constructs and safety behaviors, and to analyze the mediating roles of safety awareness, behavioral attitudes, and subjective norms. We collected data from 499 university laboratory personnel through a combination of online and offline methods, and processed the data using descriptive statistical analysis, reliability and validity analysis, and structural equation modeling.esults show that safety values, safety attitudes, and safety perceptions significantly impact safety behavior, both directly and indirectly, with safety attitudes acting as a key mediator in translating safety culture into behavior. Safety awareness also plays a crucial role in enhancing safety behavior. Notably, the study reveals that subjective norms have a less significant role than anticipated in shaping safety behaviors, suggesting that while external social factors are relevant, internal attitudes and awareness are more influential in a laboratory setting. These findings highlight the importance of fostering a safety culture through both individual attitudes and organizational strategies, emphasizing the need for comprehensive safety management systems in university laboratories.
Exploring E-Commerce Marketing Logistics for Customer Satisfaction: A Configurational Approach Using fsQCA
Authors: Vipin Vihari Ram Tripathi
Published: February 2025

The rapid growth of e-commerce in India has revolutionized the retail landscape, offering customers unprecedented convenience, choice, and on-click accessibility. However, this growth has also introduced significant challenges for businesses, particularly in meeting customer expectations regarding marketing logistics (ML) dimensions. This study aims to explore the influence of ML dimensions — product accessibility, delivery charge, ecological packaging, reverse logistics, and responsive mechanisms — on customer satisfaction (CS) among Indian e-shoppers, by adopting the Stimulus-Organism-Response (S-O-R) framework and leveraging fuzzy-set Qualitative Comparative Analysis (fsQCA). This research provides a novel perspective on the contingent and interactional effects of ML dimensions on CS. The findings reveal six unique configurations of ML dimensions that maximize CS, underscoring the configurational nature of causality in the e-commerce domain. This study contributes to theory by deepening insights into the interplay between logistics factors and customer behavior. It also offers practical implications for e-commerce firms, equipping them with actionable strategies to enhance CS through sustainable logistics approaches. The study highlights the importance of adopting robust approaches to address the dynamic needs of digitally driven markets like India.
Impact of Digital Economy on Living Standards and Equality in Rural and Urban Areas of China
Authors: Huimin Shao
Published: February 2025

Digital economy is one the most crucial phenomena of the 21st century affecting social and economic development indicators such as income levels and equality. This study aims to investigate how digital economy impacts urban-rural income levels and equality in China. This study employs a fixed effects model to estimate this relationship from 2011 to 2020 in the whole urban and rural areas as well as in the western, central, and eastern regions of China. The results show a positive relationship between the digital economy and living standards in urban and rural regions. In addition, the results reveal that the digital economy narrows the rural-urban gap since this effect is more considerable in rural areas than the urban ones. More specifically, the findings indicate that the digital economy improves equality among various rural areas, due to its higher impact on the western rural area which is the least-developed region in China. Therefore, digitalization improves both social and economic development indicators like living standards and equality. The findings of this research suggest policymakers develop digital structures, especially in rural and less-developed areas particularly the western region, to not only improve equality in these specific areas but also increase economic activities throughout the region.
Fusion of Vision Transformer, Inception-V3 and ResNet50 for Efficient Eye Disease Detection
Authors: Prashant Raut
Published: February 2025

The rising prevalence of retinal disorders globally is a major challenge for the identification and classification of retinal diseases in the medical system. Timely diagnosis and treatment of disorders like glaucoma, DR, and macular degeneration are essential for preventing irreversible vision loss. Better patient outcomes and less strain on healthcare systems can result from the significant improvement in the precision and effectiveness of identifying a variety of ocular diseases by the utilization of Artificial Intelligence (AI), Machine Learning (ML) algorithms, and modern imaging technologies. The Vision Transfer (ViT) Algorithm, a novel method for the accurate identification and categorization of ocular disorders, is presented in this work. With standard datasets, our algorithm performs exceptionally well in classifying various eye diseases. Through the integration of sophisticated image processing methods with ML, the ViT Algorithm demonstrates strong performance in differentiating between various eye conditions. The outcomes demonstrate how well it works to increase diagnostic precision and make quick interventions possible. By providing an efficient method for reliable and rapid disease identification, this research significantly advances the field of ocular healthcare and improves patient outcomes.
An Introspective Study on Attention-Based Transfer Learning in CNNs for Alzheimer's Disease Detection
Authors: J. Arumugam
Published: February 2025

Introduction: Deep learning (DL) algorithms have demonstrated remarkable advancements in the field of medical image analysis, particularly in the classification of Alzheimer’s Disease (AD). Despite these advancements, a significant challenge remains in acquiring the extensively annotated image datasets required to effectively train DL models. Attention mechanisms have emerged as powerful tools in DL, enabling models to prioritize critical regions within data and extract essential features. This focus not only enhances training efficiency but also improves overall classification performance. In this study, convolutional neural networks (CNNs) are utilized as the foundational architecture for AD classification tasks. To further enhance their performance, the Convolutional Block Attention Module (CBAM) is integrated into the CNN framework. CBAM is a lightweight and versatile attention mechanism that can be easily incorporated into any CNN architecture with minimal computational cost. By emphasizing important spatial and channel-wise features, CBAM significantly improves the feature extraction capability of CNNs. Building on this concept, an enhanced version of CBAM, referred to as enCBAM, is proposed. EnCBAM optimizes the generation of output feature maps, further improving the discriminative power of CNN architectures. In this work, the pre-trained VGG-16 network is employed as the base CNN model. When combined with enCBAM, the resulting architecture, referred to as EnCNN, achieves a substantial boost in classification performance. Specifically, EnCNN attains an impressive classification accuracy of 95.06%, outperforming its standalone counterpart and demonstrating the effectiveness of the enhanced attention mechanism.
Apple Leaf Disease Identification: A Hybrid Multi-Scale Deep Learning Model
Authors: Anupam Bonkra
Published: February 2025

Agricultural diseases have an impact on agricultural production and provide the agricultural sector with a number of significant problems. If any prior knowledge about the diseases and environmental conditions is anticipated, these difficulties can be overcome. Artificial Intelligence and computer vision-based methods are being used to this end in order to enhance the food chain. This study thoroughly examines customized models of deep learning for automating the diagnosis of apple leaf diseases. We evaluated SOTA CNN architectures, including our proprietary CNN, ResNet50, EfficientNetB3, and hybrid models, using the Apple Leaf Disease Dataset. Every model demonstrated remarkable performance, with validation accuracies surpassing 96%. Whereas the custom EfficientNetB3 model attained 100% accuracy, the custom ResNet50 model only managed 99%. These results entail that deep learning could be able to identify cedar apple rust, fire blight, apple scab, and powdery mildew on apple leaves. We assessed the models' disease classification recital using measures including recall, accuracy, and F1-score. This Study results reveal that the models performed exceptionally well on all criteria, indicating that they could correctly identify apple leaves that were healthy or ill. To sum up, our research improves automated agricultural disease detection systems and gives farmers powerful tools to lessen the burden of disease in apple orchards. By facilitating early disease identification and proactive control, the incorporation of deep learning algorithms into agricultural methods has the potential to guarantee sustainable production of apples.
Automatic Fake News Identification: A Hybrid CNN-LSTM-Logistic Regression Approach
Authors: Pummy Dhiman
Published: February 2025

The news provides important insights into current events and acts as a vital window into the world. The propagation of fake news, poses a serious problem. News that seems to present genuine but is made up is considered fake news. Such fake news can propagate inadvertently or on purpose, foment strife, and erode trust. Identifying fake news has been the focus of various studies to address this issue. To contribute in this direction, we proposed a stacking approach that combines convolutional neural networks (CNN) and long short-term memory (LSTM). We use logistic regression (LR) as a metaclassifier for final classification. We used accuracy, precision, recall, and F1-score as performance evaluation metrics on a real-world dataset. The dataset included in this study reflects a wide range of information and consists of both content from social media platforms and news items from reliable sources. We use McNemar's test to determine the statistical significance of the model's performance. The proposed hybrid approach yields impressive results: 95.19% accuracy, 95.05% precision, 95.54% recall, and 95.29% F1-score. These findings highlight the hybrid model's efficacy in correctly identifying fake news, supporting social peace and the preservation of real news.
Green Suppliers Performance Analysis in A Supply Chain Netwotk using Fuzzy Topsis and Fuzzy Vikor Techniques
Authors: Jerin Joseph
Published: February 2025

Green Supply Chain Management (GSCM) strives to decrease, if not eliminate, supply chain operations' negative environmental effects.Multi-criteria decision-making (MCDM) strategies should be utilized to evaluate suppliers' GSCM performance because GSCM includes multi-dimensional methods. In order to supply solutions to perplexing and demanding multi-attribute inquiries in a fuzzy environment, fuzzy group decision-making procedures must be produced. Based on integrated fuzzy MCDM methodologies, this research provides a methodology for assessing the GSCM performance of firms in terms of green design, green image, green transformation, green logistics, and green management system. The fuzzy DEMATEL technique is used to calculate the cause and effect relationships between GSCM dimensions. Based on this association, the fuzzy AHP approach is utilized to generate the weights of the relevant criterion.
Earnings Management Detection: The Effect of Good Corporate Governance and Executive Compensation (Comparison of Jones Model with Dechow Model)
Authors: Jeane Alfhira
Published: February 2025

The purpose of this study is to analyze the effect of the Independent Board of Commissioners, Independent Audit Committee, Managerial Ownership, and Executive Compensation on earnings management in the company, by comparing the effectiveness of the Jones Model and the Dechow Model in detecting discretionary accruals because there are inconsistencies between the two models. The population of this study are food and beverage companies listed on the Indonesia Stock Exchange (IDX) during the period 2019 to 2023. The sample selection was carried out using a purposive sampling method, namely sampling based on predetermined criteria, including companies that are consistently listed on the IDX, have complete annual reports, are not from the financial sector, and have complete data on the independent board of commissioners, independent audit committee, and executive compensation. The method used is a quantitative method that focuses on collecting and analyzing numerical data to explain certain phenomena. The results showed that Good Corporate Governance (GCG) and Executive Compensation have no significant effect on Earnings Management. This study also compares the ability of the two models to identify discretionary accruals that reflect earnings manipulation practices by management. The results of the analysis show that the Dechow Model, which modifies the Jones Model approach by considering changes in accounts receivable, provides more accurate and consistent results in detecting earnings management than the Jones Model. Therefore, companies need to strengthen governance by increasing the proportion of independent commissioners, audit committees, and managerial ownership, and designing executive compensation structures that reduce incentives to perform earnings management.
A Miniaturized Multiband Antenna with Frequency Reconfiguration for 5G and IoT Applications
Authors: Koduri Sreelakshmi
Published: February 2025

This research focuses on designing and testing a compact, multiband antenna that can switch between different frequency bands using a PIN diode. The demonstrated antenna is constructed on a 1.6 mm thick FR4 substrate and features a ground plane on the rear side. Because of its small dimensions (24 mm x 19 mm × 1.6 mm), it can be effortlessly incorporated into various kinds of RF front-end systems. The antenna presented is capable of achieving hexa/Triple band characteristics through the utilization of a PIN diode positioned between the metal strip and the F shaped monopole. The examined antenna is capable of operating in L and C bands across a range of six different frequencies when the PIN diode is inactive: 2.54 GHz, 2.64 GHz, 3.48 GHz, 4.3 GHz, 5.25 GHz, and 5.68 GHz supporting 5G and IOT services. Conversely, with the PIN diode activated, it functions in C-band across three specific frequencies: 4.3 GHz, 5.2 GHz, and 5.68 GHz supporting IOT services. The investigated antenna exhibits very small frequency ratios between two consecutive bands of the value of 1.04/1.3/1.23/1.22/1.08, respectively. The investigated design is fabricated and results of the design under investigation were compared, showing a strong correlation between the simulated and measured data.