Authors - J. Silamboli, Divya Rani M S, G Dhivyasri, Nagesh H, J Ajayan, Manaswini R, Kalyandurg Rafeeq Ahmed, M Manikandan Abstract - This article uses Koch fractals to demonstrate a compact heptagonal wideband antenna for 5G mm-wave applications. This antenna is designed with defective ground planes to enhance impedance matching and employs Koch fractal geometry for wideband characteristics. In terms of performance and wideband features, fractal antennas prove to be more effective than other antennas. This antenna is designed to achieve greater gain and improved bandwidth characteristics while also minimizing interference at higher frequencies. A peak realized gain of 5dBi at 27.58 GHz is observed for the antenna, which offers a bandwidth between 26.5 and 40 GHz. Millimeter-wave integration is well suited to the proposed flexible antenna. A similar response can be found both in the measured results and in the simulated results. It is a compact antenna with a size of 9 mm by 9 mm. With a bandwidth of 7.42GHz, the VSWR for the whole frequency spectrum is less than 2 is achieved with the HFSS Tool. Experiments and simulations yield a good radiation pattern.
Authors - Priya Shirley Muller, Yadala Sucharitha, Shilpa Kottapally, Gulshodakhon Ismoilova, Lola Rakhimova, Pundru Chandra Shaker Reddy Abstract - Many scholars worry about the stock market, a key economic component. Many experts are exploring how to forecast stock prices and market trends. Previous prediction methods centered on geometric and neural network models, which have become admired. Although not typically utilized in financial time series, deep learning offers excellent learning abilities and is ideal for complex timeseries. Due to its cyclic structure, the LSTM network has long-term memory, making it perfect for financial timeseries anticipation. A new stock closing price anticipation framework yields better predictions than standard models in the study. Data pre and postprocessing use empirical-wavelet-transform (EWT) and outlier-resilient-extreme learning-machine (ORELM) models. Dropout strategy and particle swarm optimization (PSO) method optimize the mixed frame's primary component, an LSTM predictor. Each hybrid framework algorithm can maximize its functionalities to improve prediction ac-curacy. Three hard datasets are used for predicting trials to test the model. To demonstrate the framework's efficacy, some comparison models are chosen. Investigational results reveal that the hybrid approach proposed in the study has the best anticipation accuracy and may be used for stock market data investigation.
Authors - Chandana M C, Roshna Rahim, Aarathy K Sudhakaran Abstract - In the current situation where technology is getting an important part of our day-to-day life, digital money management is an important necessity and convenience. This study examines the role of digital personal finance literacy and how it influences people’s financial behaviors, decision-making, and overarching stability. It looks at how aware people are of digital financial tools, how they feel about digital versus traditional methods, and the outcomes of certain investing techniques. The research highlights how digital tools can make people financially empowered but also acknowledgeable hurdles such as lack of access and know-how. Skills, to create economically successful and knowledgeable communities and in this way fosters social inclusion. It focuses on enhancing digital finance literacy to contribute to building economically strong and sustainable communities in this dynamically evolving digital period. The study will require 156 people to gain factual results, including those from various backgrounds and locations. A structured questionnaire will be filled through a google form to collect data. The questions will cover areas such as awareness of digital financial services, changes in behavior due to these services, preference for digital or traditional platforms, and long-term economic impacts. The questionnaire will be a set of close-ended questions comprising mostly multiple-choice options, with a few examples using a Likert scale so that it can be analyzed in an easy and evident way.
Authors - Achala Fernando, Shyani Siriwardene, Salitha Dinushika Abstract - "Food Insecurity" is a widespread global challenge that significantly impacts individuals, particularly students and working professionals living away from home. This issue extends beyond mere access to food, encompassing the quality, nutrition, and hygiene of the available options. Home-based food (HBF) vendors often face difficulties in establishing and sustaining their presence in the competitive food market, further complicating consumers' access to nutritious and hygienic meals. This research aims to address these challenges through HomeCookSL, a digital platform designed to empower HBF vendors by enabling them to effectively showcase their recipes while providing users with comprehensive nutritional information and meals tailored to their dietary preferences. By facilitating informed meal decisions and promoting healthy eating habits, HomeCookSL not only enhances consumer satisfaction but also fosters the growth and sustainability of local HBF businesses, contributing to a more resilient food ecosystem.
Authors - Ivan Cassidy A. Cadiang, Jared L. Hechanova, Wilson M. Tan Abstract - Traditional identity verification methods pose significant challenges to user privacy and security, often requiring individuals to disclose more personal information than necessary for manual verification. As the country advances toward a digital economy, the Philippine Identification System (PhilSys) provides an opportunity to shift from physical document checks to a unified and secure digital identity framework. This paper introduces MasquerAge, an age verification system leveraging the Modular Open Source Identity Platform (MOSIP). The system utilizes MOSIP-based IDs, containing QR codes aligned with PhilSys specifications, to perform secure age verification. A web application was developed to facilitate scanning QR codes and authenticating users via MOSIP APIs. Performance testing showed the system to be generally efficient; however, cryptographic processes required by MOSIP introduced noticeable latency. The results highlight the viability of a privacy-preserving age verification system while identifying areas for optimizations and security enhancements in real-world implementations.
Authors - Efrem MBAKI LUZAYISU Abstract - The aim is to analyze the possibilities of optimizing decision-making systems in financial institutions, essentially based on a comparison of two models: centralized (corporate) BI and decentralized (business) BI under four do-mains — decision-making efficiency, data governance, agility and user satisfaction — The article evaluates the advantages and limitations of each approach. The study is based on a qualitative methodology combining semi-structured interviews and documentary analysis, applied to two major European banks. Based on concrete indicators (reporting time, adoption of BI tools, GDPR compliance, satisfaction), the article puts into perspective the trade-offs between centralization and autonomy, and proposes avenues towards hybrid architectures reconciling rigor, flexibility and innovation.
Authors - Mo Xiao, Chatchai Chatpunyakulr, Sarist Gulthawatvichai Abstract - This study investigates how psychological capital (PsyCap) influences nascent entrepreneurial behavior in China's dynamic economic environment, with a focus on passion mediation and environmental contingencies. Drawing on social cognitive theory and planned behavior frameworks, we propose an integrated model testing (1) PsyCap direct effects on passion and venture activity, (2) passion's mediating role, and (3) environmental dynamism's moderating effects. Using stratified sampling across three Chinese innovation hubs (N=387), we employ mixed methods combining SEM with fuzzy-set qualitative analysis. Findings reveal industry-specific boundary conditions where regulatory turbulence amplifies PsyCap impact in tech sectors but attenuates effects in policy-sensitive domains. The study contributes a cognition-anchored PsyCap measurement model while informing adaptive training protocols for nascent entrepreneurs facing institutional voids. Future research should examine cultural contingencies in emerging economies through longitudinal designs.
Authors - Nitin Prakash, Kajal P. Salampuriya Abstract - Significant progress has been made in integrating artificial intelligence (AI) with entrepreneurship, which has changed conventional business structures. AI is used by entrepreneurs to improve automation, predictive analytics, and decision- making, which increases operational effectiveness and fosters creativity. This abstract examines the various applications of artificial intelligence (AI) in entrepreneurship, emphasising how these applications can revolutionise strategic planning, consumer involvement, and the development of flexible and adaptable businesses. The adoption of AI technology by entrepreneurs confers a competitive advantage in navigating intricate markets and promoting sustainable growth, hence highlighting the critical role of AI in defining the trajectory of entrepreneurial endeavours.
Authors - Ioseb Gabelaia Abstract - In the digital attention economy, brands are increasingly utilizing diverse content strategies, including user-generated content (UGC), brand-generated content (BGC), and AI-generated content (AIGC), to engage consumers and shape their brand perception. This research investigates the psychological mechanisms that influence consumer engagement with these three content forms, utilizing cognitive processing theory (CPT). The research insights are based on a survey of 291 respondents, which measured engagement behaviors, emotional responses, and perceived authenticity. Moreover, 17 marketing professionals, through semi-structured interviews, provided insights into content reception, trust dynamics, and platform-specific interaction patterns. Results indicate that UGC produced the highest levels of perceived authenticity and emotional resonance, positively influencing engagement and brand trust. BGC was associated with consistent brand identity but yielded moderate emotional impact. AIGC, while efficient and scalable, generated uncertain responses, valued for personalization yet questioned for credibility. Thematic analysis of the interviews underscored the growing strategic integration of AIGC in marketing while also highlighting consumer sensitivity to transparency and ethical content sourcing. This research contributes to the knowledge of digital marketing by examining how psychological responses to different content origins influence digital brand engagement. Moreover, the practical implications highlight the need for content hybridization strategies that strike a balance between authenticity, control, and scalability.
Authors - Kajal P. Salampuriya, Deepak S. Sharma Abstract - Agriculture forms the foundation of the global food industry, ensuring food security and contributing significantly to economies worldwide. The agriculture contributes half of the india’s population it is the backbone of the nation’s economy. Agriculture encomposses of different activities like crop cultivation, livestrock breeding, poultry farming, cattle husbandry and aquaculture. This paper explores the interrelation between agriculture, the food industry, and international trade, highlighting the challenges such as climate change, supply chain disruptions, and trade barriers. It also investigates opportunities for growth through technological advancements, sustainable practices, and global market expansion. This study concludes by emphasizing policy recommendations for enhancing agricultural productivity and export capabilities to meet the growing global food demand. With a diverse climate supporting cultivation of various crops, India ranks among the world’s top producers of agricultural and food products in financial year 2021-22, the agricultural sector grew at an estimate rate of 3.9%, exceeded the previous year’s growth rate 3.6% respectively. Globalization has amplified the necessity for the countries to actively engage in international trade, merging the national market into a competative global economy. Rapid technological advancements and scientific progress continue to redefine agricultural practices, including machinization and chemical innovations, it helps to increase productivity and international competitiveness. This article highlights the challenging role of agriculture in economy and the growing importance of technical advancements, globalization, trade and the different opportunities.
Authors - Sayeda Rahnuma Akthar, Fatima Ibrahim, Muhammad Rezaul Islam, Marzan Binte Hasan, Md. Ataur Rahman, Zinia Hafiz, Farzana Sadia, Mahady Hasan, M. Rokonuzzaman Abstract - This study explores the challenges faced by Bangladeshi software Small and Medium-sized Enterprises (SMEs) in adopting ISO/IEC 26550 for Software Process Improvement (SPI). Data collected from 12 SMEs revealed uneven implementation across key process areas, with high familiarity in domain requirements (up to 95\%) but limited engagement in testing and validation practices. To address these gaps, the study proposes a lightweight PDCA (Plan–Do–Check–Act) framework tailored to the unique resource constraints of SMEs. Un-like traditional models such as CMMI or ISO/IEC 12207, the proposed approach emphasizes simplicity, cost-effectiveness, and role-based adaptability, making it more practical for SMEs in emerging economies. The framework supports scalable and gradual process improvement, enhances quality assurance, and facilitates stronger alignment with ISO/IEC 26550 in resource-limited environments.
Authors - Sushmitha, Vijayalaxmi Abstract - This paper provides prediction of air quality using computational intelligence techniques. The study employs various Computational Intelligence techniques to evaluate the data associated with environmental pollution and hence to predict the air quality focusing on PM2.5 and PM10 concentrations. The performance of each model is assessed with standard evaluation metrics. Through a series of simulations, it is observed that the results suggest that computational intelligence techniques play an essential role in improving the accuracy of air quality prediction models.
Authors - Jaride Chama, Hajraoui Kenza, Zaidane Kaoutar, Benabdelkrim El Filali Yasmine Abstract - With technological developments and the digitalization of business processes in the banking sector, physical and virtual environments are complementing each other. Electronic channels are no longer considered as additional or less costly way of interacting with customers. Today, they represent a considerable and constantly growing part of the activities of bank institutions world-wide. The aim of this work is to assess the impact of trust in the development of fintech banking through a transnational analysis. The results of this work can be used to map the countries that studied this topic. The findings of this study reveal that, despite the fact that research in this field has been carried out in a wide variety of economic and cultural contexts (45 countries were surveyed), the previous studies agreed on the strong presence of trust as an entry barrier for the development of financial technology in the banking sector. In addition to trust, other adoption factors appear to be essential for consumers. The vulnerability resulting from digitalization in the banking sector has enabled us to formulate some suggestions for further investigations.
Authors - Md. Abbas Ali Khan, Md. Musfique Anwar, Mohammad Hanif Ali, Touhid Bhuiyan, Md. Ataur Rahman Abstract - Personalized driving assistance, security, and usage-based insurance models all depend on driver identification in intelligent transportation systems. Conventional techniques for identifying drivers primarily rely on biometric or manually engineered feature data, which may not adequately capture the nuances of unique driving actions. Using time-series driving data, including vehicle speed, steering angle, and acceleration, we present a unique contrastive learning-based method for driver identification in this study. The suggested model uses contrastive learning to compare driving sessions from the same driver to those of other drivers to learn how to distinguish between them. Rather than requiring manual feature extraction, our method automatically extracts strong, high-dimensional representations of driving behavior. Concerning driving sequences, the contrastive learning framework aims to minimize the similarity between sequences from different drivers and maximize the similarity between sequences from the same driver. When applied to situations with various driving behaviors and a small amount of labeled data, our strategy outperforms more conventional classification techniques in accuracy. The suggested model offers a scalable and effective solution for real-world applications, as demonstrated by experimental findings on driving publicly available datasets. This model also greatly increases driver identification performance. We applied seven machine learning classification models. The Random Forest Classifier obtained the highest accuracy of 93.57%.
Authors - Ronald C. Barriga, Dan Jeward Rubis, Arlene A. San Pablo, Helinor Y. Medina Abstract - Content video marketing has become vital in the world of marketing for the past few years, in which companies are into it using content creators to expand brand awareness. The use of different social media platforms makes all this possible technological advancement like 5G connectivity that makes the connection faster, the continuous and fast innovation in the electronic industry has made everything easy to access. These make content marketing more vital in establishing corporate brand awareness. Brand recall, recognition and dominance through content video marketing has a significant impact to brand awareness, where brand recall essentially a dominant contributor to Brand Awareness. Furthermore, the model Structural Equation Modeling (SEM) shows that for content video marketing to be effective, the quality of each content must be given importance with consideration also to Knowledge Opinion Leader (KOL) and the Electronic Word of Mouth (eWOM) to strengthen brand awareness. Lastly, SEM positively indicates how significant the three factors mentioned are to brand awareness. The SEM model has a very good fit for the data, explaining a large portion of the variance in brand awareness. The results suggest that to improve brand awareness, strategies that enhance brand recognition, recall, and dominance should be prioritized.
Authors - Dev Parekh, Jay Patel, Keshvi Patel, Dhaval Patel, Priteshkumar Prajapati Abstract - This paper presents CyberShieldAI, a novel framework that leverages BERT (Bidirectional Encoder Representations from Transformers) models to detect cyberbullying in online text communications. Our approach harnesses BERT’s contextual understanding capabilities to identify subtle linguistic patterns, emotional undertones, and semantic structures commonly associated with cyberbullying content. By fine-tuning BERT on carefully curated datasets of online interactions, CyberShieldAI can effectively distinguish between harmless communications and those containing bullying, harassment, or other harmful content. The framework analyzes text bidirectionally, capturing crucial contextual relationships that traditional text classification methods might overlook. Experimental evaluations demonstrate that our BERT-based system achieves significant improvements in cyberbullying detection accuracy, sensitivity, and specificity compared to conventional machine learning approaches. This paper details the system architecture, implementation methodology, dataset preparation challenges, and performance metrics that validate the effectiveness of our strategy. CyberShieldAI represents a significant advancement in automated content moderation systems, which can help create safer online environments, particularly for vulnerable user populations, such as children and adolescents.
Authors - Swati Kale, Jyoti Tipale, Shilpa Sonawane, Kshitij Mulay, Gaurav Patil, Nikita Khumkar Abstract - This paper presents a novel multi-modal speech analysis system that integrates deep learning architectures and “Natural Language Processing (NLP)” techniques to address the limitations of traditional speech evaluation approaches. The proposed system combines a custom “Long Short-Term Memory (LSTM)” based neural network for temporal pattern analysis, TF-IDF vectorization for keyword extraction, real-time spectrogram analysis for acoustic feature extraction, and a dynamic content fetching system from Wikipedia datasets. The system demonstrates enhanced accuracy in content relevance detection, real-time processing with minimal latency, and a scalable architecture suitable for diverse ap-plications, including educational, professional, and research contexts. The study highlights the system's ability to bridge the difference in the theoretical abilities of speech analysis and their practical implementation. The proposed methodology contributes to the advancement of speech analysis by addressing the challenges of insufficient integration between acoustic and semantic analysis, static reference materials, and limited scalability.
Authors - Md. Ataur Rahman, Sabrina Alam, Saadia Binte Alam, Mahady Hasan Abstract - Outcomes-based education (OBE) is reshaping engineering education, with an emphasis on complex engineering problem solving, with clear outcomes directly related to industry needs. This study assessed the impact of including complex engineering problem-solving in the engineering curriculum within OBE on student skills. Through qualitative and quantitative research methods, including course curriculum analysis, student feedback, questionnaires, and student achievement. We collect 3 years of data. Data collected over a three-year research period (2022, 2023, and 2024) at Independent University, Bangladesh (IUB), this study assessed the improvement in skills such as critical thinking, application of advanced tools, sustainability awareness, professional ethics, teamwork, communication, project management, lifelong learning, and entrepreneurship. There are barriers to implementation, such as resource allocation, training of faculty members, and curriculum restructuring, with evidence of positive improvements in these categories. This study strongly recommends greater application of advanced engineering problem-solving techniques in OBE to match educational outcomes to professional engineering requirements better, ultimately producing graduates more adept at solving problems in the real world.
Authors - Rohit Yadav, Rohini Bhosale, Rahul Rathod, Prashant Dhotre, Yashraj Salunkhe Patil Abstract - Phishing continues to be a prevalent threat in cybersecurity, using deceptive URLs to undermine user information and system integrity. Conventional machine learning methods, though effective when handcrafted features are used, tend to perform poorly in generalizing against more obfuscated patterns of attacks. This paper presents an effective phishing URL detection system that takes advantage of the contextual strengths of quantized Large Language Models (LLMs)—namely Mistral-7B and Mixtral-8x7B—combined with interpretable gradient boosting models like CatBoost. The LLMs are employed with prompt-based inference for semantic URL evaluation, whereas the CatBoost model employs lexical and statistical features for structured classification. Tested on a balanced dataset of more than 2.28 mil-lion URLs, the system has high accuracy and low false positive rates. SHAP-based visualizations are employed to offer transparent explanations for model decisions. The findings prove the real-world feasibility of integrating effective LLM reasoning with conventional ML in real-time phishing defense systems, particularly in resource-limited settings.
Authors - Aryan G. Kale, Kajal P. Salampuriya, Nitin Prakash Abstract - The ascent of artificial intelligence technologies, particularly the generative application of GANs, has changed the landscape of media production into a milieu that easily allows - creation of hyper-realistic yet deceptive content. While deepfakes have indeed been misused from entertaining, educative, and access provisions, their abuse causes dreadful threats to mental well-being and social harmony. This study explores the psychological effects of deepfakes, such as anxiety owing to identity theft, impersonation, trust erosion in digital media, cognitive dissonance because of the inability to tell the difference between real and fake content, and social isolation due to cyberbullying and non-consensual pornography.On the social front, deepfakes add to the pool of misinformation spreading, fake news politically entering the narratives, and a myriad of ethical dilemmas, adding to general confusion, anxiety, and distrust among the public at large in institutions. The economic designs revolve around hiring specialists to thwart deepfake manipulation and attending to its legal and psychological aftermath.To ameliorating this, the study points out a projective need for strategies of intervention. These would include developing an AI detecting tool that identifies and flags deepfakes, launching public awareness campaigns to increase digital literacy and critical thinking, instituting solid legal scaffolding with added ethical principles, and other support systems including counseling services supporting the victims of deepfake assaults.The study is primarily inclined toward discussing how integrated management approaches shall serve to address the issues faced concerning deepfakes, while the positives remain intact. Future research is necessary to explore the cultural and demographic nuances of deepfake impacts, understand the influence of the COVID- 19 pandemic on mental health in the context of deepfake exposure, and evaluate the effectiveness of emerging prevention and mitigation strategies.
Authors - Anubrata Dhar Chaudhuri, Raj Chakraborty Abstract - The hospitality sector faces increasing pressure to integrate sustainability into operations while maintaining high productivity. Green Human Re-source Management (GHRM) practices have proven to enhance employee engagement and promote eco-friendly organizational citizenship behavior. How-ever, traditional GHRM frameworks lack the digital, real-time analytics needed to respond dynamically to workforce changes and sustainability goals. This research work proposes the AI-GHRM Integration Model (AGIM), a novel conceptual framework that extends the classical Stimulus-Organism-Response (SOR) paradigm into a dynamic feedback loop powered by artificial intelligence (AI). AGIM consists of a multi-layered architecture incorporating AI-based predictive analytics for attrition and engagement forecasting, NLP-driven sentiment analysis of employee well-being, IoT-enabled automation of sustainability compliance, and privacy-preserving security mechanisms. This paper outlines the AGIM conceptual model, compares its dimensions with traditional GHRM, and presents a hypothesis matrix linking AI-powered interventions to employee and sustainability outcomes. A mixed-method research methodology is discussed, blending qualitative case studies with quantitative analytics. Use case scenarios illustrate AGIM’s implementation phases in a hotel setting. The discussion highlights theoretical contributions (extending SOR with AI feed-back) and practical implications for business information systems in “smart secure systems”. Future work includes empirical validation and expansion to other service industries.
Authors - Nerisa Thornsr, Sa-Aat Niwitpong, Suparat Niwitpong Abstract - The delta-Rayleigh distribution comprises a mixture of zero and non-negative values following the Rayleigh distribution. Percentiles are practical tools for describing skewed data and are widely used in several fields. The purpose of this study is to construct confidence intervals for different percentiles of the delta-Rayleigh distributions using generalized confidence interval (GCI), normal approximation (NA), method of variance estimates recovery (MOVER), percentile bootstrap confidence interval (PBCI), and Bayesian methods. Monte Carlo simulations were conducted to evaluate the performance of these methods using coverage probabilities and average widths. The results indicate that the GCI method outperformed others. For a real-world data application, we utilized a dataset on CO concentration to assess the performance of the presented methods.
Authors - Surya Anugrah, Sabo Hermawan, Christian Wiradendi Wolor, Eka Septariana Puspa, Windy Permata Suyono, Dwi Handarini Abstract - Indonesia’s bold leap toward digital tax reform culminated in the 2025 launch of its Core Tax Administration System (CTAS) a centralized platform aimed at streamlining taxpayer services, boosting compliance, and modernizing revenue administration. This study critically evaluates the early- stage implementation of CTAS using a dual-theoretical lens: the Technology Acceptance Model (TAM) and Institutional Theory. Drawing on government documents, user surveys, and media analysis, findings reveal a paradox while users widely acknowledge CTAS’s potential to simplify tax filing, technical glitches, complex interfaces, and poor system responsiveness undermine its usability and acceptance. Moreover, institutional misalignments such as inadequate training, low digital literacy, and resistance to change pose structural barriers to reform success. By integrating behavioral insights with systemic analysis, this research highlights the multilevel challenges of digital transformation in public finance. The paper concludes with actionable lessons for emerging economies navigating the tension between technological innovation and institutional readiness in the digital era.
Authors - Krishna Saurabh Soni, Pranshi Talati, Jash Mandani, Dweepna Garg, Parth Goel Abstract - Recruitment has become a lot more complicated with a surge in the number of applicants for each job. Traditional resume screening methods are time-consuming and subjective—and that leads to inefficiencies and biases in candidate selection. What we need is a more efficient, fair and accurate way to evaluate resumes. That’s where a new resume screening system comes in. By using Retrieval-Augmented Generation (RAG) with the Gemini API, we can improve the accuracy, efficiency and fairness of resume evaluation. That system pulls out the most relevant information from resumes, compares a candidate’s skills to the job description and gives you a selection probability score-complete with reasoning. We combined Natural Language Processing (NLP), machine learning and a resume dataset from Kaggle to refine the evaluation process. The experiments show the model can identify missing skills, match candidate qualifications to job requirements and give job seekers valuable insights. Integrating RAG into the hiring process can make a real difference to recruitment outcomes-and reduce bias and manual workload.
Authors - Naina Kokate, Supriya Gorde, Rashmi Kale, Sayali Mulik, Kashmira Naik, Kartik Mundwadkar, Atharva Navale, Piyush Nakade Abstract - The rapid increase in portable electronic device usage has intensified the demand for sustainable and self-sufficient energy solutions. KineCharge addresses this need by innovatively harvesting kinetic energy generated during cycling and converting it into usable electrical power. The system uses a DC motor directly connected to a bicycle wheel, which produces electricity as the wheel rotates. This generated electrical energy is regulated through a diode to ensure proper current flow and is managed via a battery charging module. The harvested energy can be stored in a rechargeable battery for later use or accessed immediately through a USB output to charge smartphones and other portable devices. This design offers an environmentally friendly and costeffective alternative to traditional power sources, particularly benefiting cyclists, outdoor enthusiasts, and individuals in remote or off-grid locations. KineCharge promotes clean energy utilization while providing a practical means to keep devices powered during travel. By leveraging mechanical energy from cycling, it reduces reliance on conventional electrical grids and supports the adoption of sustainable energy practices. Additionally, the project serves as an educational tool that demonstrates principles of energy conversion and renewable power generation. Designed for easy installation on most bicycles, KineCharge offers a portable and green energy solution adaptable to a variety of users. It exemplifies innovative thinking in renewable energy and contributes to fostering energy independence and environmental sustainability in everyday life.
Authors - Surya Anugrah, Rochma Sudiati, Eka Septariana Puspa, Windy Permata Suyono, Sabo Hermawan, Septi Nurmalita Abstract - Carbon tax policies have been widely adopted as instruments for climate change mitigation; however, their implementation in developing countries continues to face significant structural and political challenges. This study aims to analyze the influence of per capita carbon emissions and tax revenue on carbon tax rates in ten developing countries during the period 2021-2023. Utilizing a panel data regression approach with fixed effects models, this research examines the causal relationship between environmental and fiscal factors and the determination of carbon tax rates. The estimation results reveal that per capita carbon emissions have a negative and statistically significant effect on carbon tax rates, with a coefficient of -6.09 (p0.05), suggesting that fiscal considerations are not yet a primary determinant in the design of carbon tax policies in developing economies. The policy implications of this study highlight the need for more progressive and integrated carbon tax reforms aligned with sustainable development agendas, particularly in support of Sustainable Development Goal (SDG) 13. This study contributes novel empirical evidence to the green fiscal literature in developing countries and offers strategic recommendations for policymakers to optimize the role of carbon taxation in global emissions reduction efforts.
Authors - Navya H, Ananthavalli Thangam Abstract - Education is an important tool in empowering people with the required knowledge and influencing their perspective and attitude for transforming the society. This assumes more importance in the context of the goal of lifelong learning. Achieving goals require behaviour change at multiple levels. This study is an attempt to assess the impact of education on the perspectives of people in promoting eco-friendly behaviour. Quality education is invariably connected with certain goals such as reducing poverty and promoting gender equality. As we drive towards the targets of SDGs, a closer look at the intricate relation of education with the goals of zero hunger, gender equality and poverty reduction points towards the urgency in using education not merely as a goal in itself but a catalyst for attaining the desired goals. Equipped with the necessary skills, education provides opportunities for employment. Coupled with the awareness of sustainable development, the goals of economic advancement can be attained with less damage to the environment. Inclusive education with provision of equal opportunities for women leads to gender equality and participation of women in progress. A cross-sectional survey was taken to assess the perspective of the role of education as a catalyst to promote the SDGs. The results strongly indicated the need to provide quality education with the right strategy and policy interventions. There are economic and social challenges to overcome. The role of institutions is to be emphasized. Concerted efforts by academicians, corporates and the government can strengthen the pillars of economy, environment and equity.
Authors - Tran Phuong Duy, Nguyen Gia Tuan Anh Abstract - This study aims to evaluate the trend of stock price fluctuations in the Vietnamese banking industry in the period of 2019-2023 by integrating real-world data with quantitative models and modern machine learning techniques. Data is collected from reputable sources such as HOSE, Vietstock and CafeF, including financial indicators (EPS, ROE, P/E, ROA), macroeconomic factors (operating interest rates, exchange rates, foreign cash flows) and market sentiment index (Vietnam VIX). The applied analytical models include linear regression, logistic regression, GARCH, VAR, VaR and Random Forest. The research results show that the VIX index has a clear impact on bank stock prices, and the proposed system is capable of supporting investors in determining the appropriate trading time, classifying stocks according to risk and expected return. The article not only provides a quantitative perspective but also provides an effective visualization tool, contributing to improving the quality of investment decisions in the Vietnamese stock market.
Authors - Usha Sadhani, Melita Simoes, A M Kadakol Abstract - In today’s dynamic work environment, employee attrition is a crucial challenge for organizations that are impacted by demographic and job-related factors. This study examines the role of gender in attrition across different job roles and encompasses a multi-method approach taking into consideration 1,470 employees. The analysis found that there is no significant influence of gender in the attrition rates. Job satisfaction and tenure of an employee within an organization emanated as stronger predictors of attrition than gender, aligning with the established theories that highlighted the preference of job-related factors over demographic characteristics that helps in the retention of an employee. The logistic regression model showed a statistically significant result but was not very effective at predicting who leaves, highlighting the complexity of attrition and the role of factors not included in the study. Overall, the findings suggest that gender alone is insufficient to explain attrition patterns, highlighting the importance of contextual factors such as job role and employee satisfaction. This research contributes to the ongoing dialogue on diversity and retention by demonstrating that effective talent management should consider the nuanced interactions between job characteristics and individual experiences rather than relying on broad demographic generalizations.
Authors - Priyanka S. Dhore, Michael Savariapitchai, Rohini Chavhan Abstract - Repeatedly, being burnt out, constitutes a severe health issue, affecting employee welfare, and negatively impacting the long-term sustainability of health organizations. It revolves around different types of management styles that don't burn an employee out, but instead create an environment conducive to work, given health institutions. Exploring the work of four different dimensions of leadership: transformational, ethical, health-promoting, and resilient; would be towards reducing burning out and improving employee well-being, as well as creating a positive organizational culture. The study adopted a quantitative approach wherein close-ended survey questionnaires were used to collect data from a sample of 101 healthcare employees. Results revealed that empathic, supportive, and team-focused behavior of leaders has considerable impact on the burnout and job satisfaction levels of workers. In addition, interventions such transformational and health-promoting leadership have been found to have a positive influence on organizational health, supportive policies, and retention of employees, among others. Workplace leadership praise the productivity and balance. Leadership helps to focus Tasks. It encourages the distribution of workloads. It sets up mental health projects and it backs employee welfare. Healthcare leadership requires listening to concerns raised by employees, creating a work-life balance, and enforcing fair policies to prevent burnout. There is evidence in favor of having an enlightened discussion for healthcare leaders that improves the environment balancing conditions between burnout-prevention and better performance of the organization.
