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Sustainable Water Pollution Treatment: A Technological Breakthrough via Bismuth Halide Solid Solution Photocatalysts
No full textWith the rapid development of industrialization, the problem of water environment pollution is becoming increasingly serious, especially the discharge of organic pollutants such as antibiotics and dyes, which pose a serious threat to the ecosystem and human health. Traditional water treatment methods suffer from low efficiency and secondary pollution issues, while photocatalytic technology has emerged as a research hotspot due to its green and efficient characteristics. This paper shows that BiOBrxCl1-x solid solutions with varying compositions were successfully synthesized via precipitation and applied to degrade the organic pollutant tetracycline (TC). Most BiOBrxCl1-x solid solutions demonstrated enhanced photocatalytic activity over BiOBr and BiOCl, with the optimal sample achieving a 79.5% TC degradation efficiency within 30 min. The formation of solid solutions modulates the band structure and provides abundant active sites, facilitating the separation of photogenerated charge carriers. This study demonstrates significant potential for environmental remediation and is expected to advance sustainable pollution control strategies
Understanding Digital Wallet Continuance Intention: An Integrated TAM–ECT Perspective with Trust as a Serial Mediator
No full textThe rapid expansion of digital payment systems has increased the need to understand the factors that drive continued usage, particularly among small business users such as vendors, petty shop owners, and micro-retailers. Although the Technology Acceptance Model (TAM) provides a strong foundation for understanding technology adoption, post-adoption behaviour requires integrating additional constructs such as Trust and Customer Satisfaction. Therefore, the objective of this study is to examine how Perceived Ease of Use, Perceived Usefulness, Trust, and Customer Satisfaction influence Continuance Intention to Use digital payments. Data were collected from 284 business users and analysed using PLS-SEM. The findings reveal that Perceived Ease of Use, Perceived Usefulness, Trust, and Customer Satisfaction significantly predict continuance intention. Mediation results show that Trust and Customer Satisfaction partially mediate several relationships, while sequential mediation occurs only through the pathway beginning with Perceived Ease of Use. The results also indicate that Perceived Usefulness primarily affects continuance intention directly, without meaningful mediation. The novelty of this research lies in demonstrating that ease of use produces a stronger cognitive–emotional pathway than usefulness in shaping long-term digital payment usage among small business users. These findings offer practical insights for enhancing digital payment design, user experience, and trust-building strategie
Between Preservation and Revitalization: Mapping Sustainable Futures for Intangible Cultural Heritage
No full textIntangible cultural heritage serves as a bridge connecting tradition and modernity, carrying significant value for cultural identity, ecological wisdom, economic vitality, and cross-cultural exchange. However, the preservation and revitalization of intangible cultural heritage preservation currently face multiple challenges, including declining public engagement, disrupted intergenerational transmission, and an imbalance between economic benefits and cultural value. To address these issues, this paper proposes innovative pathways: integrating intangible cultural heritage into cultural tourism to enhance its visibility and experiential value, utilizing digital technologies for dynamic preservation and broad dissemination, and advancing industrial upgrading to foster a synergistic relationship between cultural preservation and economic development. These strategies aim to provide theoretical and practical references for the sustainable safeguarding and innovative development of intangible cultural heritage
Design of an Off-Grid Solar Power System for a Residential-Scale 1300 VA Application
No full textThis study presents the design and simulation of an off-grid solar power system optimized for a 1300 VA residential application in Keude Geudong Village, North Aceh, Indonesia. Motivated by the global reliance on non-renewable energy sources, this research aims to offer a sustainable and autonomous energy solution for rural households. The methodology includes detailed energy load auditing, component selection, and system performance simulation using PVSyst software. The system is configured to supply 3.5 kWh of energy per day, utilizing twelve 100 Wp monocrystalline solar panels, a 45 A MPPT charge controller, a 2000 W inverter, and six 52 Ah lithium-ion batteries, ensuring operation for up to three days without additional power input. Simulation results indicate a performance ratio of 58% and a solar fraction of 1:1, confirming the system’s capability to operate independently from the national grid. The proposed configuration demonstrates the technical and practical feasibility of residential solar electrification in rural Indonesian settings. This study offers a fully autonomous home solar solution based on actual energy usage data and site-specific solar information, unlike previous research that focused on large-scale or hybrid systems, providing a scalable model for expanding energy access in off-grid areas
VR-Based Exposure Therapy for Acrophobia: Effects of Visual Realism and Interactivity
No full textAcrophobia, the intense fear of heights, affects a significant portion of the global population. Traditional therapeutic approaches, such as Cognitive Behavioral Therapy (CBT), have been supplemented by emerging technological solutions like Virtual Reality (VR). This paper explores the role of VR-based visual imagery in treating acrophobia. We examine the use of immersive environments, integration of multi-sensory stimuli, and how images in VR are designed to elicit controlled exposure therapy outcomes. Additionally, the paper discusses the impact of realism, scaling, and interactivity of VR-generated images on patient treatment efficacy. VR-based acrophobia treatment, particularly when leveraging realistic images and immersive environments, represents a promising advancement in therapeutic techniques for phobia management
A Smile Detection for Hands-Free Selfie Capture Using Machine Learning
No full textThis paper presents a novel, real-time smile detection system designed to enable hands-free selfie capture using machine learning. The system leverages computer vision techniques and deep learning models to accurately detect smiles in live camera feeds, triggering automatic photo capture without user intervention. Built on a modular architecture utilizing OpenCV for face detection and a convolutional neural network (CNN) for smile classification, the application ensures low-latency performance suitable for mobile and embedded platforms. The system is evaluated on public datasets such as GENKI-4K and CelebA, achieving an average accuracy of 94.2% in real-world lighting and expression conditions. A lightweight, Flask-based web interface offers live preview, detection feedback, and photo gallery integration. Experimental results show that the system operates at over 15 FPS on mid-range hardware, confirming its applicability for edge devices. Future extensions include emotion-based gesture capture, multilingual voice commands, and AR filter integration. The system demonstrates the potential of machine learning to create intuitive, user-friendly photo applications with minimal manual input
Machine Learning Based Detection for Compromised Accounts on Social Media Networks
No full textThe proliferation of social networking platforms has led to a corresponding increase in the frequency and sophistication of cyberattacks targeting user accounts. Compromised accounts can be used to spread misinformation, launch phishing attacks, and steal personal information. This paper presents a novel approach to detecting compromised accounts on social networks. Our method leverages a combination of behavioral and linguistic features to identify anomalous activity that may indicate account compromise. Behavioral features include changes in posting frequency, interaction patterns, and location data. We employ machine learning algorithms to train models that can accurately classify accounts as compromised or legitimate based on these features. Our experiments demonstrate the effectiveness of our approach in detecting compromised accounts with high precision and recall. Furthermore, we explore the potential of incorporating graph-based techniques to analyze the social network structure surrounding compromised accounts. By examining the relationships between compromised accounts and their associated nodes, we can identify potential propagation paths and take proactive measures to mitigate the spread of malicious activit
A Data-Driven and Modular Flask-Based Architecture for Secure and Intelligent Programming Education Powered by LLMs
No full textIn this paper, the research is about a modular, AI data-driven programming education platform developed using the Flask web framework and integrated with the LLaMA 2 large language model (LLM) to deliver dynamic, personalized learning experiences. The system combines real-time question generation, contextual feedback, and secure code execution through Docker containerization to ensure safe and isolated code evaluation across multiple programming languages, including Python, C, and C++. Architecture supports adaptive learning by analyzing user submissions and providing feedback on syntax, logic, efficiency, and coding style. Performance evaluation demonstrates that the system maintains optimal response times and throughput for up to 70 concurrent users, with CPU usage remaining below 80% and average response times under 300 ms. Beyond this threshold, resource utilization increases, and error rates rise, highlighting the need for future load balancing and optimization strategies. User testing further confirms high learner engagement and effectiveness, with over 85% of participants reporting improved understanding and satisfaction with real-time AI feedback. The platform’s modular design enables seamless integration of future enhancements, including support for additional languages, learning management system (LMS) interoperability, and gamification features. These results validate the proposed system as a secure, scalable, and intelligent solution for next-generation programming education
Effect of Indirect Taxes on Consumer Goods Prices in Nigeria
No full textWith an emphasis on Value Added Tax (VAT), Excise Tax (ET), and Import Tariffs (IT), this study examines how indirect taxes affect the price of consumer products in Nigeria. The survey aims at evaluating the influence of VAT rates, ET rates, and import tariffs on the prices of consumer goods in Nigeria. By analysing secondary data from government reports, industry reports, and previous studies, the research explores the extent to which these indirect taxes contribute to inflationary pressures and consumer prices in the country. Using a quantitative approach and regression analysis, the inquiry explores the interaction between tax rates and consumer prices. It concludes that there is a statistically significant positive correlation between VAT rates and the prices of consumer goods, with VAT having a greater effect than the other taxes. The effect of Excise Tax on prices is positive but weaker, while Import Tariffs also show a positive, though less pronounced, influence on prices. These findings suggest that VAT is the most significant contributor to higher consumer prices in Nigeria, while Excise Tax and Import Tariffs also contribute but to a lesser extent. The study recommends that policymakers carefully consider the impacts of indirect taxes on consumer prices and inflation when making tax policy decisions. In particular, VAT rates should be examined in relation to their impact on consumer affordability and economic stability. Further research is also recommended to determine the long-term implications of indirect taxation on the broader Nigerian economy and to explore potential strategies for mitigating negative effects on consumers
Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends
No full textIn the backdrop of increasing sustainability awareness among Indian consumers, this study explores the role of machine learning (ML) in optimizing green influencer marketing strategies to drive eco-conscious purchasing. While eco-friendly consumer behavior and influencer marketing have gained traction, there remains limited empirical evidence on how ML-enabled recommendation systems can enhance green influencer effectiveness in India. Employing a quantitative research design, data were gathered through surveys of 1,500 users of an Indian e-commerce platform. Respondents provided insights on their interactions with green influencers, their perceptions of influencer authenticity and transparency, and the impact of ML-driven recommendations on purchase intent. Factor and correlation analyses examined the relationships among perceived authenticity, consumer trust, and purchase behavior. Findings reveal that influencer trustworthiness, particularly authenticity and transparency, significantly drives consumer engagement with green products. Most respondents expressed willingness to purchase green products when the messaging was authentic and well-targeted. Moreover, ML algorithms were instrumental in identifying top-performing influencers, segmenting audiences by green preferences, and personalizing recommendations, which enhanced engagement and conversion rates. Positive correlations were observed between influencer authenticity, trust, and purchase intention. This study fills a regional gap by offering India-specific, empirical evidence on the synergy between ML-driven marketing and green consumer behavior. Its practical implications are twofold: marketers can leverage these insights to enhance influencer selection and recommendation strategies, while policymakers and researchers gain a data-informed perspective to promote sustainable marketing practices. The study demonstrates that ML-augmented green influencer marketing can effectively elevate sustainability and commercial performance within the Indian e-commerce context