The growing concern about air pollution, driven by its severe impact on public health and the environment, has emphasized the need for comprehensive studies on its distribution. This study addresses the spatial location of atmospheric pollutants in Lima, Peru, with the objective of identifying patterns and areas of concentration. Advanced geospatial analysis techniques such as Stirling and Kriging algorithms were used, developing the study in five phases: data acquisition with quality control from National Service of Meteorology and Hydrology of Peru (SENAMHI), analysis of topographic and climatic parameters, interpolation of contaminant concentrations up to ten thousand meters of altitude, geospatial interpolation with Kriging, and creation and validation of the contaminant dispersion model. The results reveal that accurate and reliable data acquisition allowed measurement of key pollutants such as PM10, PM2.5, SO₂, NO₂, CO and O₃. The integration of topographic and climatic data was crucial to model the dispersion of contaminants. Vertical interpolation with Stirling showed a reduction in concentrations with altitude, while interpolation with Kriging provided accurate estimates at unsampled locations. The dispersion model developed demonstrated high precision, identifying priority areas for environmental management. In conclusion, the combination of advanced monitoring and geospatial modeling techniques provides a comprehensive understanding of pollutant distribution patterns in Lima, laying a solid foundation for effective mitigation measures and environmental policies, improving air quality and protecting public health.

The amount of information produced about any item or user has reached a very staggering level. Not only the volume of data, the velocity of data has reached an unprecedented magnitude. For any information retrieval or information processing system to work efficiently, it should be able to process massive amounts of data in real-time. Modern systems face a lot of challenges in managing data with high volume and velocity, especially when these systems are required to generate accurate predictions in a timely fashion. The most efficient way to ensure that modern information retrieval systems can adapt to the current volume and velocity of data is to implement them over a parallel and distributed environment. In this paper, we put forward a method for enhancing the scalability and performance of recommender systems in big data environments. By using the Euclidean distance to calculate the cosine similarity we introduce a technique which is efficient in parallelizing the algorithm for distributed environments. Thereby improving the computational efficiency and scalability of the recommender system. This enables such systems to manage large datasets with high accuracy and speed. With the help of parallel processing, our method can assist modern information retrieval systems keep up with the pace of ever-growing demands of data velocity and volume, ensuring real-time performance and robust scalability.

The industrial sector often generates wastewaters contaminated with various pollutants, contingent upon the industry type such as textile, food, petroleum, tannery and others. These pollutants pose a real threat to public health and the environment, so their removal is necessary to minimize their harmful effects. Many treatment methods are used to remove these pollutants by physical, chemical and biological techniques. Among these methods, the membrane separation process is the most efficient and less cost This review addresses the types of industrial water treatment methods, membrane filtration systems, and how to overcome the challenges facing the membrane technology. The main disadvantage of membrane process, which cause a decrease in membrane performance and increase the maintenance cost, is fouling problems. Many strategies can be employed to minimize fouling, such as grafting polymers with hydrophilic additives, applying hydrophilic coatings, using negatively charged membranes to decrease the adsorption rate of organic matter and microbial attachment, or utilizing plasma treatment to enhance surface charge or hydrophobicity. The addition of hydrophilic additives is more effective than the other methods because of its flexibility and reliability.

A bibliometric analysis was conducted to explore the research trends and emerging hotspots in the application of health monitoring technologies within nursing. Literature spanning from January 2021 to January 2025 was retrieved from the Web of Science Core Collection (WOSCC), and CiteSpace software was employed to analyze and visualize research outputs, institutional contributions, author collaborations, high-frequency keywords, and the evolution of keyword clusters over time. A total of 425 articles were identified, revealing a stable global publication output. The United States emerged as the leading contributor, with 138 articles, followed by China with 47. Prominent keywords such as "care," "management," and "remote patient monitoring (RPM)" were found to be indicative of current research foci. Analysis indicates a shift towards home-based care, smartphone integration, digital health solutions, and wearable devices, particularly in managing clinical conditions such as cardiovascular disease (CVD), cancer, and diabetes. The prevailing research trends highlight the importance of remote monitoring and nursing care within home settings, with an increasing emphasis on chronic diseases. Despite the growth in research activity, uneven international development and limited collaborative efforts, primarily within research teams, present challenges to the field’s progress. It is suggested that future research should focus on fostering international collaboration between academic, healthcare, and engineering sectors to ensure that monitoring technologies align with clinical needs. Moreover, the establishment of international regulations was recommended to standardize production processes, enhance product reliability, and facilitate the broader application of these technologies in nursing practice.

Accurately predicting whether bank users will opt for time deposit products is critical for optimizing marketing strategies and enhancing user engagement, ultimately improving a bank’s profitability. Traditional predictive models, such as linear regression and Logistic Regression (LR), are often limited in their ability to capture the complex, time-dependent patterns in user behavior. In this study, a hybrid approach that combines Long Short-Term Memory (LSTM) neural networks and a stacked ensemble learning framework is proposed to address these limitations. Initially, LSTM models were employed to extract temporal features from two distinct bank marketing datasets, thereby capturing the sequential nature of user interactions. These extracted features were subsequently input into several base classifiers, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbour (KNN), to conduct initial classifications. The outputs of these classifiers were then integrated using a LR model for final decision-making through a stacking ensemble method. The experimental evaluation demonstrates that the proposed LSTM-stacked model outperforms traditional models in predicting user time deposits on both datasets, providing robust predictive performance. The results suggest that leveraging temporal feature extraction with LSTM and combining it with ensemble techniques yields superior prediction accuracy, thereby offering a more sophisticated solution for banks aiming to enhance their marketing efficiency.

The concept of heat commodification is proposed as a sustainable solution for global energy management, with heat being treated as a tradable commodity in an international market. In such a market, heat would be assigned a value based on factors such as available enthalpy, heat grade (temperature), and the time at which it is delivered. Heat, as the currency of this market, would allow for a decentralized and dynamic exchange system. A central heat market could be established, extending down to individual households where excess heat—such as waste heat from household appliances—could be stored and traded locally, potentially through a peer-to-peer model or a virtual marketplace. A key innovation in this system would be the development of modular heat storage solutions, analogous to gas bottles, that allow consumers to store excess heat and exchange it within the market. These “heat packets" would be rechargeable with heat, as opposed to gas, and could be traded both physically or digitally. To ensure inclusivity and sustainability, it is suggested that these heat packets be based on nature-inspired storage materials that can efficiently store renewable or waste heat with minimal environmental impact. Specifically, thermochemical storage media, such as salt, would be employed to facilitate charging and discharging processes using water as a trigger. Such solid-state storage systems would allow heat to be stored indefinitely with minimal heat loss to the environment, even in lower temperature conditions. This paradigm shift could enable the cross-continental transport of heat packets, revolutionizing the global energy market. The proposed system would also eliminate the need for electricity grids and reduce inefficiencies associated with energy conversion, as heat can be stored and utilized directly for both heating and cooling applications. Furthermore, the reliance on heat-driven refrigeration systems would obviate the need for electricity-driven heat pumps or chillers. This approach offers a potential solution to global energy challenges by facilitating a sustainable and efficient heat exchange network on a global scale.

This study investigates the optimization of wrench time to improve maintenance efficiency and reliability within a chemical processing plant. Wrench time, defined as the proportion of time spent directly performing maintenance tasks, was quantified through random observations of maintenance technicians. The findings revealed an average wrench time of 28% across the site, with variations between individual crew groups ranging from 20% to 35% and craft-specific wrench times varying from 13.3% to 45.5%. Several inefficiencies were identified, including prolonged wait times for equipment isolation, safety clearance, job planning, and parts procurement. Key contributing factors to these inefficiencies were found to include poor coordination between maintenance and production, insufficient work prioritization, inadequate adherence to schedules, a high volume of emergency tasks, and the absence of essential tools such as bills of materials (BOMs), equipment data, and troubleshooting checklists. To address these challenges, a range of improvement initiatives were implemented. These included enhancing coordination between maintenance and production by refining process steps, introducing additional planning tools for effective work prioritization, providing job aids, developing generic troubleshooting checklists, leveraging Industrial Internet of Things (IIoT) technologies, and establishing metrics to monitor progress. Early indications suggest that these initiatives have led to a reduction in maintenance backlog and gradual improvements in overall equipment effectiveness (OEE). It is anticipated that these changes will result in increased wrench time, enhanced maintenance quality and reliability, reduced downtime, and lower operational costs. For maintenance managers and engineers, the findings offer actionable insights into optimizing workflows and resource allocation, thereby contributing to the improvement of operational efficiency and reliability.