Gongcheng Kexue Yu Jishu/Advanced Engineering Science

Abstract : Contamination of water by hexavalent chromium [Cr(VI)] has garnered considerable attention because this ion is highly toxic and carcinogenic, posing serious threats to human health. In this work, we present an easy and effective method to eliminate Cr(VI) pollutants by reusing red mud. The prepared adsorbents were examined using XRD, FT-IR, FE-SEM, and XRF techniques. Red mud exhibited greater adsorption performance than synthetic goethite. Both materials are simple to operate and have the potential to be regenerated for repeated Cr(VI) removal. The key benefit of red mud lies in its wide availability and functional properties, making it a readily obtainable and efficient material for sorption studies. Therefore, this mining residue offers a practical option for treating wastewater containing hexavalent chromium..
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Abstract : Mobile Edge Computing (MEC) has emerged as a disruptive paradigm for supporting computation intensive and latency critical applications on resource constrained mobile devices. By offloading tasks to proximate edge servers, MEC reduces communication delay and energy consumption compared to conventional cloud computing. However, dynamic radio access conditions, heterogeneous server capacities, and user mobility make efficient task offloading highly challenging. Recent advances in deep learning provide promising alternatives by enabling adaptive, data driven decision making. This survey presents a comprehensive review of deep learning approaches for MEC task offloading. A novel dual taxonomy is proposed, jointly classifying existing works by learning methodology—supervised, unsupervised, reinforcement, and distributed learning—and by optimization objectives including deadline satisfaction, energy efficiency, cost reduction, and scalability. Representative neural architectures such as multilayer perceptrons, convolutional networks, recurrent models, and reinforcement learning agents are analyzed for their suitability in dynamic MEC environments. Distributed learning paradigms including federated and split learning are further examined to highlight privacy preserving and scalable edge intelligence. Key lessons and limitations are distilled, emphasizing the strengths of deep learning in feature extraction, data driven modeling, online adaptation, and distributed coordination, while acknowledging challenges such as training complexity, lack of transparency, and communication overhead. Finally, future research directions are outlined, including hybrid optimization frameworks, adaptive federated learning, explainable AI, sustainable edge intelligence, transformer based architectures, multi agent reinforcement learning, and emerging applications such as vehicular edge computing and IoT driven smart cities. By explicitly highlighting novelty and providing a forward looking roadmap, this survey advances the state of knowledge in deep learning enabled MEC offloading and serves as both a reference for established scholars and a guide for new researchers entering the field..
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Abstract : Entrepreneurship is helping drive local economic growth in the Philippines, but many new business owners find it hard to meet regulatory requirements and pick the right business locations. These issues are especially common in the food service sector, where several government agencies enforce strict legal, zoning, and health rules. Although spatial analysis and the Gravity Model are often used in urban planning, public health, and market analysis, there is still a lack of decision-support systems that combine regulatory compliance and location recommendations for entrepreneurs at the local government level. This study developed a web-based recommendation system that reviews business regulations and finds the best business locations using the Gravity Model. The system gives entrepreneurs a list of regulatory requirements based on their chosen business type and suggests sites in Janiuay, Iloilo. System Development Life Cycle (SDLC) and gathered data from municipal agencies, population and spatial records, and business guidelines. The system was evaluated using ISO 25010 Software Quality Standards. Results show that the system works very well in terms of functionality, usability, security, reliability, and performance. This proves it can help entrepreneurs with legal compliance and choosing business locations. The system also adds value to both research and practice by combining spatial data analysis with regulatory automation. It offers a tool that supports evidence-based entrepreneurship, helps local governments make decisions, and improves business planning..
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Abstract : This study developed an intelligent decision-support tool, the Student Track Recommendation System Using Data-Driven Methods, to identify the best specialization track fit for incoming third-year Information and Communications Technology students based on their theoretical and practical examination results. Realizing that the choice of specialization is an essential academic decision that often receives limited guidance and thus relies heavily on subjective judgment, this system will analyze patterns of student performance using data-driven techniques, namely collaborative filtering and content-based filtering, to develop personalized, objective recommendations. To achieve this goal, it involves data gathering, preprocessing, algorithmic modeling, system design, and implementation to ensure the system supports structured, evidence-based analysis. This systematic assessment of students' competencies will help improve academic decision-making, support the faculty and the administration in determining readiness for specialization, and foster a more transparent, data-informed guidance process. The present study aims to contribute to the field of educational technology by demonstrating the value of implementing data-driven methodologies to facilitate improved student placement and further align academic pathways with demonstrated strengths. Other variables to be used in further development include soft skills, experiential learning, and labor market insights, to further increase the precision of recommendations and the system's adaptability..
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Abstract : The Philippines' agriculture, particularly crop cultivation, plays a crucial role in the economy. Still, many farmers face poverty, with a significant portion struggling due to a lack of education and resources. This leads to the rise of middlemen who buy crops from the farmers at low prices and sell them at a much higher price. Also, even though agricultural production has improved, farmers in the country still have trouble getting goods to consumers directly, limiting their income and access to fair prices. To solve these problems, the researcher developed a web-based farmer-to-consumer marketplace designed to facilitate direct transactions between farmers and consumers, limiting the involvement of middlemen. It applied an enhanced item-based k-nearest neighbor (k-NN) algorithm with weighted metrics (ratings, purchases, and searches) to generate personalized product recommendations for consumers to help them make better decisions in terms of product reducing decision fatigue and giving them direct access to fresh crops..
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