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Microplastic pollution, particularly from textile waste, has become a significant environmental concern, especially in urban runoff systems. These pollutants pose a considerable threat to water quality, aquatic life, and human health. Traditional wastewater treatment methods often fall short in addressing the complexities of microplastic contamination. This research explores the integration of advanced biodegradable polymer coatings with solar-powered textile waste treatment to reduce microplastic pollution in urban runoff systems. Biodegradable polymers, such as polylactic acid (PLA) and polyhydroxyalkanoates (PHA), are highlighted for their potential to efficiently filter microplastics while providing an eco-friendly alternative to conventional filtration technologies. By combining these materials with a small solar-powered unit, the prototype enables an off-grid, low-energy solution to treat textile wastewater in urban environments. The study includes testing the prototype in simulated urban runoff conditions with varying concentrations of microplastics, evaluating key performance indicators such as microplastic removal efficiency, energy consumption, and operational sustainability. Results demonstrate a significant reduction in microplastic concentration, indicating the effectiveness of biodegradable polymer coatings and solar-powered systems in treating urban runoff. The discussion addresses the feasibility of using local biodegradable materials, performance in real-world urban environments, and operational challenges such as maintenance and scalability. This innovative approach is compared with existing microplastic filtration methods, such as membrane filtration and adsorption, highlighting its advantages in terms of sustainability and cost-effectiveness. The findings suggest that this integrated system could offer a viable, low-cost solution for addressing microplastic pollution in urban drainage systems, with potential for widespread urban implementation.

As 6G networks promise unprecedented speeds and ultra-low latency, AI-based resource allocation plays a crucial role in optimizing network performance. This study explores AI-driven techniques for spectrum management, energy efficiency, and real-time data processing. By leveraging machine learning and deep learning models, AI enhances network adaptability, reduces congestion, and improves overall efficiency. The proposed approaches enable intelligent decision-making, dynamic resource allocation, and predictive analytics to meet the growing demands of future wireless communication. The findings highlight the potential of AI in revolutionizing 6G networks, ensuring seamless connectivity, and maximizing network capacity while minimizing power consumption. These advancements contribute to the development of more sustainable and intelligent telecommunication infrastructures.  

This study evaluates the impact of an AI-driven nutritional monitoring system in hospital settings, focusing on its effectiveness in reducing food waste and improving the accuracy of dietary assessments. Traditional food waste management and nutritional tracking methods in hospitals often suffer from inefficiencies, inaccuracies, and time constraints. In contrast, the AI-based system utilizes advanced technologies, including 3D scanners, digital scales, and image recognition, to optimize food production, minimize waste, and provide more accurate and timely nutritional assessments. The results of this study show a 31% reduction in food waste and a 40% improvement in the accuracy of nutritional assessments after implementing the AI system. This system enhances meal planning, portion control, and real-time tracking of food intake, offering personalized recommendations based on patient needs. The AI system also streamlines the nutritional assessment process, reducing labor-intensive procedures and providing real-time feedback to clinicians, which helps improve patient care and reduce errors associated with traditional methods. Furthermore, the environmental and financial implications of adopting AI technologies in healthcare are significant. The reduction in food waste not only helps lower hospital costs but also contributes to sustainability goals by reducing resource consumption, including water, land, and energy. This study underscores the potential of AI-driven systems to improve healthcare operations, support sustainability, and enhance patient outcomes. Future research should focus on expanding the application of AI in other healthcare sectors and further exploring its integration with other technologies for comprehensive healthcare solutions.

Deficiency in energy and nutrient intake in athletes can disrupt physiological functions, hinder muscle growth, cause fatigue and illness, and result in suboptimal performance. It is crucial for athletes to carefully manage their food intake during training, competition, and recovery, while also considering eating behaviors that include perception, knowledge, and habits related to food selection and consumption. This study aims to identify the factors influencing eating behaviors in non-elite athletes with a history of macro-nutrient intake deficits in the UNESA Basketball UKM. The research employs a qualitative methodology. Data were collected from key and supporting informants selected based on inclusion criteria established by the researcher. The number of research informants was determined based on the information obtained which was deemed to have reached saturation point, resulting in 7 key informants and 8 supporting informants. The collected data were analyzed through transcription and then categorized into relevant codes and themes. The findings of this study identified two main themes influencing the eating behaviors of non-elite athletes in the UNESA Basketball UKM: internal and external factors. Internal factors, including food preferences, self-discipline, self-confidence, state of mind, gastrointestinal discomfort, body image, sleep time, past eating habits, physical activity level, nutritional knowledge, and food allergies. Meanwhile, external factors include social influence, food availability, food prices, and social media.

This study aims to examine the effect of water usage, energy consumption, carbon emissions produced, waste generated, independent Assurance, and environmental expenditures on Return On Equity. (ROE). This study provides information about entities that are socially responsible and whether their financial performance will improve or not after the implementation of corporate social and environmental activities. The independent variables in this study are water usage, energy consumption, carbon emissions produced, waste generated, independent Assurance, and environmental expenditures. ROE in this study is the dependent variable. The secondary data required for the study comes from the sustainability reports of 35 companies in 2023, and then the relationship between the independent and dependent variables is tested using EViews. The results of data management in the study indicate that there is an effect between water usage, waste generated, and environmental expenditures on ROE, thus supporting H1, H4, and H6. This condition is reversed with energy consumption, carbon emissions produced, and independent Assurance, where these three variables do not have an effect on ROE, proving that H2, H3, and H5 are not supported. The implication of this study is that management can achieve sustainable profits by disclosing activities that support environmental sustainability and using independent parties to guarantee the sustainability reports that have been created, thereby gaining more trust from stakeholders, which affects ROE.

An environmentally friendly fuel with great potential for use in transportation and industry is B40 biodiesel. It is a mixture of 60% diesel and 40% biodiesel. With an emphasis on engine performance, environmental impact, and financial incentives, the study assesses how beneficial its use can be. According to the findings, B40 biodiesel burns better and produces fewer carbon emissions compared to pure diesel because it has a higher cetane number. However, fuel consumption may increase due to the much lower energy content of biodiesel. B40 requires more maintenance to avoid hygroscopic effects, but it has been shown to improve lubrication and support heavy equipment operations in industrial environments. Implementing B40 has several benefits, including the ability to reduce greenhouse gas emissions.  

This study aims to analyze the effect of population density on electricity consumption and evaluate the potential use of solar panels as a solution to overcome energy waste in urban areas, especially on Jalan Kapten Muhammad Jamil Lubis, Medan Tembung, Medan, North Sumatra. The method used is qualitative research with a literature study approach, which involves collecting data from various written sources, such as journals, books, and research reports. The results of the study indicate that the high population density in this area contributes to inefficient energy consumption. Factors such as people's habits in using electronic devices, less than optimal energy infrastructure quality, and increased energy demand due to economic activities are the main causes of energy waste. Solar panels are identified as a potential solution to overcome these problems. This technology is able to convert solar energy into electricity efficiently, even in areas with varying weather. In addition to supporting environmental sustainability by reducing carbon emissions, solar panels also utilize empty spaces such as building roofs, making them suitable for densely populated areas such as Medan Tembung. This study concludes that the adoption of solar panels, supported by government regulations, incentives, and community education programs, can be a strategic step in creating a more efficient and environmentally friendly energy system. Further research involving field trials is recommended to support wider implementation of this technology.    

This research aims to design and develop a motion detection-based automatic light control system with a light intensity setting feature using IoT technology. The system utilizes a combination of PIR sensor, MOSFET module, and Arduino Uno microcontroller to control lighting based on the presence and position of the user. With an adaptive control algorithm, the lamp can dynamically adjust the lighting intensity to several levels as needed. Test results show that the system successfully reduces energy consumption optimally without reducing user comfort. The implementation of this technology has great potential in supporting energy efficiency in public areas, such as parks, office hallways, and sidewalks.

In the architecture design and 3D visualization industry, rendering speed and quality are crucial factors in enhancing productivity. This study aims to compare the performance of GPU and CPU-based rendering software, focusing on three main aspects: render time, visual quality, and resource efficiency. The experiment was conducted using two leading rendering software, Corona (CPU) and Lumion GPU, with identical design objects. The results show that GPU rendering excels in render speed, although it slightly sacrifices quality detail compared to CPU rendering. Additionally, GPU is more efficient in resource usage, with lower energy consumption during longer rendering processes. Based on these findings, it is recommended that design and architecture professionals choose GPU rendering for projects that prioritize speed and time efficiency, while CPU rendering is more suitable for tasks that require higher quality and more complex details. This study provides valuable insights for the creative industry in selecting the most appropriate rendering technology to enhance their design productivity.  

This research examines the effect of bricks enhancement with phase change material (PCM) consisting of paraffin on their thermal considerations. The experimental methods were aimed at investigating the mechanisms of temperature transfer throughout the thermal rounds, mimicking diurnal fluctuations of temperature. Due to the high amount of latent heat contained in paraffin a portion of it was transferred into regular bricks with temperature probes tracing the heat processes. It is shown that enhancement of bricks with PCM reduced temperature range and controlled the rate of heat transmission more than the ordinary bricks did. It was significant that the PCM-embedded bricks did not drift in heat and were able to stock up excess heat during the day and release it at night which resulted in monumental energy efficiency and comfort in thermal standing. The conversation additionally seeks to bring out the function of paraffin in insulation which does not only save energy consumption but goes forward to maintain comfortable temperatures within the building. The study finally achieved its goal and assert that energy saving with the use of PCM enhanced bricks in construction makes it a preferable material in regards to energy and the fight against climate change.

Excessive caffeine consumption among university students can lead to negative health effects such as sleep disturbances, anxiety, and dependency. To address this issue, a workshop was conducted to introduce infused water as a healthier alternative to caffeinated beverages among students at the Faculty of Public Health, Halu Oleo University. The program aimed to increase awareness about the benefits of infused water in supporting hydration and energy levels naturally, without caffeine's side effects. The workshop utilized three methods: lectures, practical training, and informational leaflets. Lectures provided insights into the adverse effects of excessive caffeine intake and highlighted infused water's benefits, including improved hydration and detoxification. Practical training allowed participants to create infused water using natural ingredients such as lemon and cucumber, fostering hands-on engagement and encouraging immediate application of the knowledge gained. Leaflets were distributed to reinforce the material and served as a take-home guide for participants to share with others. Evaluation of the program showed a significant improvement in students' knowledge about the health risks of caffeine and their interest in adopting infused water as an alternative. Positive feedback indicated that the materials were informative and practical for daily use. Despite challenges such as limited time for practical demonstrations, the workshop successfully achieved its objectives of promoting healthier beverage choices and equipping students with practical skills to support long-term behavior change. This program demonstrates the potential of integrating educational and interactive methods to encourage healthier lifestyle habits, positioning students as agents of change within their communities.

The construction industry is one of the sectors that has a significant impact on environmental damage due to high energy consumption, exploitation of natural resources, and carbon emissions. In response to these challenges, this study aims to analyze the potential for implementing sustainable construction materials to support the development of green infrastructure. Using qualitative methods through literature studies, documentation, observation, and in-depth interviews, this study evaluates the advantages, challenges, and economic and environmental impacts of various innovative materials, such as foam glass, green wall systems, bamboo composites, eco-cement, and mycelium-based blocks. The results show that these materials are able to reduce carbon emissions, increase building energy efficiency, and offer long-term cost savings. Mycelium-based blocks, in particular, are recorded as the most effective material in reducing construction costs by up to 25% and CO₂ emissions by 250 kg per project. However, the implementation of sustainable materials in Indonesia still faces obstacles such as cost, material availability, and technical regulations. Therefore, synergy is needed between the government, academics, and industry in encouraging research, preparing technical standards, and public awareness campaigns to expand the adoption of environmentally friendly materials in the national construction sector.

Background:Supplemental Feeding (PMT) is an activity of providing food in the form of safe and quality snacks along with other supporting activities by considering aspects of food quality and safety. There are two types of supplementary feeding, namely supplementary recovery feeding and supplementary counseling (PMT). Supplemental feeding (PMT) of local food is one of the strategies for handling nutritional problems in toddlers. The PMT activity needs to be accompanied by nutrition and health education for behavioral change, for example with support for breastfeeding, education and counseling on feeding, cleanliness and sanitation for families. Nutritional status is a state of the body as a result of food consumption and use of nutrients, where nutrients are needed by the body as a source of energy, growth and maintenance of body tissues, and regulators of body processes. Nutritional status is greatly influenced by nutritional intake. Objective: To determine the effect of providing local supplementary feeding (PMT) on the nutritional status of toddlers aged 12-59 months. Method: This type of research uses quantitative research using a pre-experimental design pretest-posttest research design. Results: Based on the results of the study, it was shown that a p-value of 0.41 (> 0.05) was obtained. Conclusion: Provision of Local Supplementary Food (PMT) Has No Relationship with Toddler Nutritional Status at UPTD Siriwini Health Center. Suggestion: It is expected that mothers who have toddlers pay more attention to the growth and development of toddlers.

Efficient energy use in household appliances has become a primary concern as awareness of environmental sustainability grows. Smart electric stoves with automatic temperature control features offer an innovative solution to enhance energy efficiency while ensuring comfort and safety during use. This study aims to design and develop a smart electric stove system based on a microcontroller with automatic temperature control algorithms. The system employs temperature sensors to detect real-time temperature changes and a Proportional-Integral-Derivative (PID) algorithm to maintain stable temperatures as set by the user. A simple and intuitive user interface is designed to facilitate operation, while the automation feature allows the system to shut off the heating element independently when the temperature exceeds a safe threshold. Test results demonstrate that the designed system can reduce energy consumption by up to 25% compared to conventional electric stoves without automatic controls. Therefore, this research significantly contributes to the development of energy-efficient and safe smart kitchen appliances.

This study aims to see the effect between energy subsidies and electricity consumption on economic growth in Indonesia. This study uses secondary time series data for the period 2014-2023 with a quantitative approach using the Eviews 12 software analysis tool. The variables in this study are energy subsidies, electricity consumption and economic growth which are analyzed by multiple linear regression methods. The results showed that energy subsidies have a positive and insignificant effect on economic growth, electricity consumption has a positive and significant effect on economic growth in Indonesia. While energy subsidies and electricity consumption simultaneously have no significant effect on economic growth in Indonesia in the 2014-2023 period.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

This study investigates optimal retrofit strategies for buildings in tropical climates, focusing on energy efficiency, thermal comfort, and indoor air quality (IAQ). Given the unique challenges of high temperatures, humidity, and energy demands in tropical regions, traditional retrofitting methods often fall short of achieving a balance between these critical factors. By employing a multi-objective optimization approach, this research identifies the most effective combination of retrofit solutions, including insulation, natural ventilation, and high-performance window treatments. The results show that the proposed retrofit strategy significantly reduces cooling energy consumption, while maintaining or improving occupant comfort and IAQ. Insulation, particularly external insulation, proved to be the most effective in reducing heat transfer, while natural ventilation strategies and advanced materials further contributed to improving thermal regulation. The study demonstrates that integrating passive and active retrofit measures, tailored specifically to tropical climates, leads to optimal building performance. The multi-objective optimization algorithm (NSGA-II) allowed for the generation of Pareto-optimal solutions, offering a set of trade-offs between energy efficiency, thermal comfort, and IAQ. These findings are particularly relevant for policymakers and building professionals seeking sustainable retrofit solutions in tropical regions. The study also highlights the importance of integrating energy efficiency and IAQ considerations in retrofit strategies to avoid compromising occupant health. Further research is recommended to explore the integration of advanced materials, such as phase change materials (PCMs), and to enhance IAQ management in retrofitted buildings, ensuring long-term sustainability and occupant well-being in tropical environments.

Blockchain and cryptocurrencies have changed the way we transact and interact in the digital age. However, the rapid advancement of these technologies has resulted in major environmental impacts. Efficient implementation of blockchain requires the use of large amounts of energy and computing power, with consensus algorithms as the foundation. The purpose of this study is to investigate the environmental implications of blockchain and cryptocurrency implementation, as well as initiatives to mitigate these issues. The type of research is a library study (library research) using a qualitative method, namely by combining, collecting information or previous scientific papers on relevant topics. Along with the growing popularity of cryptocurrencies, the continuous mining process often results in large energy consumption and carbon emissions, sparking concerns about their long-term viability and environmental impact. Based on the results of previous research and research sources, the author found a solution to the problem of high energy consumption from the use of blockchain, namely: Proof of Stake (PoS), Proof of Authority (PoA), Sidechains and Layer-2 Solutions, Hardware Optimization, Implementation of Consensus Algorithms Based on RUST.

Blockchain and cryptocurrencies have changed the way we transact and interact in the digital age. However, the rapid advancement of these technologies has resulted in major environmental impacts. Efficient implementation of blockchain requires the use of large amounts of energy and computing power, with consensus algorithms as the foundation. The purpose of this study is to investigate the environmental implications of blockchain and cryptocurrency implementation, as well as initiatives to mitigate these issues. The type of research is a library study (library research) using a qualitative method, namely by combining, collecting information or previous scientific papers on relevant topics. Along with the growing popularity of cryptocurrencies, the continuous mining process often results in large energy consumption and carbon emissions, sparking concerns about their long-term viability and environmental impact. Based on the results of previous research and research sources, the author found a solution to the problem of high energy consumption from the use of blockchain, namely: Proof of Stake (PoS), Proof of Authority (PoA), Sidechains and Layer-2 Solutions, Hardware Optimization, Implementation of Consensus Algorithms Based on RUST.

The global clean water crisis is exacerbated by significant losses in water distribution networks (WDNs), resulting in inefficient use of both water and energy resources. Traditional methods of leak detection and pressure management often fail to address these inefficiencies, leading to substantial water wastage and high operational costs. This research aims to design a sustainable, smart water distribution system using advanced technologies such as Machine Learning (ML) for leak detection and automated pressure control. The system employs real-time monitoring through IoT sensors, which continuously gather data on water pressure, flow rates, and other critical parameters. This data is analyzed using various ML algorithms, including supervised and unsupervised learning models, to detect anomalies indicative of leaks. Additionally, the system integrates automated pressure control mechanisms that dynamically adjust pressure to prevent over-pressurization, reducing both water loss and energy consumption. By combining leak detection and pressure control, the proposed system offers a more efficient, sustainable solution to water resource management compared to traditional methods. The expected outcomes include a significant reduction in water loss, enhanced energy efficiency, and improved water service quality. However, the implementation of such a system in rural or small-town infrastructure faces challenges, including sensor maintenance, algorithm reliability, and regulatory issues. A cost-benefit analysis suggests that while the initial investment in smart technologies may be high, the long-term savings in water and energy costs outweigh these costs. This study underscores the potential of ML-based systems in enhancing water conservation, operational efficiency, and sustainability in water management.