Search Results (423)

This study developed a Soil and Water Assessment Tool (SWAT) model for the Fuzhou River Basin in China to quantify the spatial distribution, sources, and reduction potential of total nitrogen (TN) load. We comprehensively evaluated the effectiveness of eight Best Management Practices (BMPs) and 186 combinations thereof in reducing TN load. Our analysis demonstrated that adding more BMPs did not yield proportionally additive benefits but instead led to reduced cost-effectiveness (CE) once the number of BMPs exceeded three. Targeting BMPs to Critical Source Areas (CSAs) increased CE by an average of 15.6% compared to watershed-wide application, although the environmental benefit (EB) was lower (22.0% versus 32.8% on average). We identified a critical budget threshold of 70 million CNY. Below this threshold, CSA-targeting optimized BMPs delivered the most cost-effective TN reductions (123.0 kg/10⁴ CNY per year). However, with a sufficient budget exceeding this threshold, our findings support implementing BMPs throughout the entire watershed, which maximized the TN reduction rate to over 40%. Overall, our findings highlight that spatial targeting and budget-adaptive implementation of BMPs are essential for maximizing both economic efficiency and environmental benefits, providing a practical decision approach for nutrient management in river basins. Full article

Background/Aim of Study: Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality globally. Cardiac rehabilitation (CR) plays a pivotal role in the recovery of post-acute myocardial infarction (AMI) patients. Despite evidence supporting its clinical benefits, CR remains underutilized, especially in middle-income countries like Kazakhstan. This study aimed to evaluate the clinical effectiveness and economic impact of phase II CR among patients with AMI treated at the Almaty City Cardiology Center between 2018 and 2022. Methods: A retrospective cohort study was conducted using data from 2672 AMI patients. Two cohorts were compared: those who participated in phase II CR and those who did not. Primary outcomes included changes in left ventricular ejection fraction (LVEF), rehospitalization rates, and return to active work. Results: Economic outcomes involved direct medical costs related to initial hospitalization and follow-up care. CR participants showed significant improvements in LVEF (53.7% vs. 49.0% in non-CR patients, p < 0.001). Despite these clinical benefits, there was no significant reduction in long-term treatment costs between the CR and non-CR groups. CR users had slightly higher initial treatment costs but similar cumulative costs for subsequent treatments over two years. Importantly, government funding limitations were found to hinder the full effectiveness of CR programs in Kazakhstan. Conclusions: Phase II CR improves cardiac function in AMI patients but does not reduce long-term treatment costs. The current insufficient government funding for CR limits its broader impact. Expanding CR services and increasing funding are essential to maximize its benefits within Kazakhstan’s healthcare system. Full article

This research proposes a novel multi-objective optimization framework for virtual power plants (VPPs) operating in day-ahead electricity markets. The VPP integrates diverse distributed energy resources (DERs) such as wind turbines, solar photovoltaics (PV), fuel cells (FCs), combined heat and power (CHP) systems, and microturbines (MTs), along with demand response (DR) programs and energy storage systems (ESSs). The trading model is designed to optimize the VPP’s participation in the day-ahead market by aggregating these resources to function as a single entity, thereby improving market efficiency and resource utilization. The optimization framework simultaneously minimizes operational costs, maximizes system flexibility, and enhances reliability, addressing challenges posed by renewable energy integration and market uncertainties. A new flexibility index is introduced, incorporating both the technical and economic factors of individual units within the VPP, offering a comprehensive measure of system adaptability. The model is validated on IEEE 24-bus and 118-bus systems using evolutionary algorithms, achieving significant improvements in flexibility (20% increase), cost reduction (15%), and reliability (a 30% reduction in unsupplied energy). This study advances the development of efficient and resilient power systems amid growing renewable energy penetration. Full article

For aquaculture systems, maximizing feed efficiency is a major challenge since it directly affects growth rates and economic sustainability. Feed is one of the largest costs in aquaculture, and feed waste is a significant environmental issue that requires effective management strategies. This paper suggests a novel approach for optimal fish feeding in pond aquaculture systems that integrates vision language models (VLMs), optical flow, and advanced image processing techniques to enhance feed management strategies. The system allows for the precise assessment of fish needs in connection to their feeding habits by integrating real-time data on biomass estimates and water quality conditions. By combining these data sources, the system makes informed decisions about when to activate automated feeders, optimizing feed distribution and cutting waste. A case study was conducted at a profit-driven tilapia farm where the system had been operational for over half a year. The results indicate significant improvements in feed conversion ratios (FCR) and a 28% reduction in feed waste. Our study found that, under controlled conditions, an average of 135 kg of feed was saved daily, resulting in a cost savings of approximately $1800 over the course of the study. The VLM-based fish feeding behavior recognition system proved effective in recognizing a range of feeding behaviors within a complex dataset in a series of tests conducted in a controlled pond aquaculture setting, with an F1-score of 0.95, accuracy of 92%, precision of 0.90, and recall of 0.85. Because it offers a scalable framework for enhancing aquaculture resource use and promoting sustainable practices, this study has significant implications. Our study demonstrates how combining language models and image processing could transform feeding practices, ultimately improving aquaculture’s environmental stewardship and profitability. Full article

The separation of butadiene 1,3 from pyrolysis products is a critical step in the petrochemical industry, as butadiene is a key raw material for producing synthetic rubber and other polymers. This study presents a detailed model-based analysis of the separation process, focusing on optimizing operational parameters to maximize butadiene recovery, enhance product purity, and reduce energy consumption. The simulation was conducted using Aspen Plus, evaluating critical variables such as the solvent-to-feed ratio, reflux ratio, number of column stages, and energy integration between distillation units. The simulation results indicated that an optimal solvent-to-feed ratio of 1.5:1 and a reflux ratio of 4.2:1 in the extractive distillation column provided the highest separation efficiency. Under these conditions, the recovery rate of butadiene 1,3 reached 98%, with a final product purity of 99.5%. Furthermore, this study revealed that increasing the number of theoretical stages in the distillation column improved the separation process without significantly increasing energy demand. Energy integration, specifically through heat recovery between the primary distillation and extractive distillation columns, led to a 12% reduction in total energy consumption. These findings demonstrate the importance of fine-tuning operational parameters to achieve high separation efficiency and product quality while minimizing energy use. This model-based analysis provides valuable insights into the design and optimization of industrial-scale butadiene separation processes, offering strategies to reduce operational costs and improve sustainability in production. The methodology and results can serve as a basis for further improvements in similar separation processes across the petrochemical industry. Full article

Cognitive Odor Source Localization (OSL) strategies are reliable search strategies for turbulent environments, where chemical cues are sparse and intermittent. These methods estimate a probabilistic belief over the source location using Bayesian inference and guide the searching movement by evaluating expected entropy reduction at candidate new positions. By maximizing expected information gain, agents make informed decisions rather than simply reacting to sensor readings. However, computing entropy reductions is computationally expensive, making real-time implementation challenging for resource-constrained platforms. Interestingly, search trajectories produced by cognitive algorithms often resemble those of small insects, suggesting that informative movement patterns might be replicated using simpler, bio-inspired searching strategies. This work investigates that possibility by analysing spatial distribution of entropy reductions across the entire search area. Rather than focusing on searching algorithms and local decisions, the analysis maps information gain over the full environment, identifying consistent high-gain regions that may serve as navigational cues. Results show that these regions often emerge near the source and along plume borders and that expected entropy reduction is strongly influenced by prior belief shape and sensor observations. This global perspective enables identification of spatial patterns and high-gain regions that remain hidden when analysis is restricted to local neighborhoods. These insights enable synthesis of hybrid search strategies that preserve cognitive effectiveness while significantly reducing computational cost. Full article

Water scarcity in Jordan is intensifying, creating an urgent need for innovative approaches to maximize the use of nonconventional water resources, such as greywater treatment and reuse. This study presents a detailed analysis of the suitability of nature-based solutions (NbSs) for greywater treatment, with a focus on the application of horizontal flow constructed wetlands (HFCWs). Two systems were implemented to treat greywater generated from mosques located in Az-Zarqa Governorate, a dry region in Jordan. Following several months of operation, monitoring, and evaluation, the systems demonstrated high removal efficiencies: turbidity (>87%), total suspended solids (TSS) (>96%), chemical oxygen demand (COD) (>91%), and five-day biological oxygen demand (BOD₅) (>85%). The eight-square-meter HFCW units successfully produced one cubic meter of treated greywater per day, meeting Jordanian standards for reclaimed greywater (JS 1776:2013) for use in irrigating food crops, including those consumed raw. The system achieved a 70% reduction in water consumption compared to the same period in the year prior to its implementation. These results demonstrate the potential of constructed wetlands (CWs) as effective, low-cost, and sustainable NbSs for decentralized greywater treatment and reuse in water-scarce regions. Full article

This study explores the potential for reducing CO₂ emissions in China’s express freight sector by promoting a modal shift from air and road transport to high-speed rail (HSR) through the implementation of a carbon tax policy. A bi-level optimization model is employed to analyze the decision-making processes of three key stakeholders: the government, HSR operators, and shippers. The government aims to maximize consumer surplus while reducing CO₂ emissions through a carbon tax policy; HSR operators seek to maximize transportation profit; and shippers select the most efficient transportation mode based on cost and service considerations. A solution algorithm combining particle swarm optimization, the CPLEX solver, and a custom convergence procedure is designed to solve the bi-level programming model and determine the optimal carbon tax rate. The findings from the Beijing–Shanghai corridor case study indicate that a well-designed carbon tax policy, when integrated with robust HSR services, can effectively encourage a modal shift towards HSR. The extent of emission reduction is influenced by both the capacity of HSR infrastructure and the stringency of the carbon tax policy. This research highlights the importance of addressing asymmetries in transportation mode preferences and market demands. The integration of carbon tax policies with HSR services not only mitigates emissions but also promotes greater symmetry and efficiency within the transportation network. Full article

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article

Predictive maintenance is essential for minimizing unplanned downtime and optimizing industrial processes. In the case of plastic injection molding machines, failures that lead to downtime, slowing production, or manufacturing defects can cause large financial losses or even endanger people and property. As industrialization advances, proactive equipment management enhances cost efficiency, reliability, and operational continuity. This study aims to detect machine anomalies as early as possible, using sensors, statistical analysis and classification models. A case study was carried out, including machine characterization and data collection. Clustering methods identified operational patterns and anomalies, classifying the machine’s behavior into distinct states, validated by company experts. Dimensionality reduction with PCA contributed to highlighting salient features and reducing noise. State classification was carried out using the resulting cluster data. Classification using XGBoost achieved the best performance among the machine learning models tested, reaching an accuracy of 83%. This approach can contribute to maximizing plastic injection machines’ availability and reducing losses due to malfunctions and downtime. Full article

This research investigates the implementation of multi-criteria decision analysis (MCDA) within the framework of the Common Agricultural Policy (CAP) for the period of 2023–2027, focusing on optimizing agricultural sustainability and profitability in Northern Greece. Using data from three farmer groups across Central and Western Macedonia, the study explores the application of MCDA models within three distinct case studies: the first optimizes a farm system focused on input minimization (Loudias), while the second and third (Ryakio and Agia Paraskevi) adopt a more comprehensive approach to farm management. More specifically, the first case focused on maximizing gross margin, minimizing variable costs, and reducing fertilizer use without targeting a reduction in water usage. By contrast, the second case study adopted a holistic approach to farm management, integrating water conservation in the Ryakio farmer group. The third included the requirement to keep arable land fallow in the Agia Paraskevi farmer group, reflecting the CAP’s new mandates. The results indicate that MCDA facilitates strategic crop selection and land changes that significantly enhance farm management efficiency and sustainability. The optimization led to more significant percentage increases in gross margin for the second (Ryakio) and third (Agia Paraskevi) case studies compared to the first, with the Agia Paraskevi group showing the most substantial improvement. Full article

The decrease in employee performance that occurs during organizational change is one of the main problems that this study attempts to address. This phenomenon, which is known as resistance to change, has been directly linked to the failure or abandonment of change initiatives when performance drops to critical levels. This study proposes an innovative approach to organizational change management based on a model that integrates real-time performance monitoring and employee reassignment to tasks. This approach contributes to improving overall system performance and stabilizing costs by achieving a reduction in resistance to change through staff training and dynamic reallocation of human resources. The method utilizes Evolutionary Dynamic Multi-Objective Optimization with the aim of both maximizing performance and minimizing costs. It incorporates the performance of employees in each task and the associated costs, enabling continuous adjustment of task assignments in accordance with temporal variability in the factors that affect the success of organizational change. Experimental simulations show that the proposed method leads to a considerable enhancement in overall system performance, cost stabilization, and a significant reduction in the risk of change abandonment. More specifically, the proposed method demonstrates an improvement in total performance from 55% to over 200% in comparison to three reference methods. Furthermore, it achieves faster recovery and a lower performance drop, especially in critical stages, providing optimized decision-making during the change process and leading to the new desired and improved state being achieved in a time that is up to 27% shorter, consequently reducing the risk of abandonment. The proposed method operates as both an optimization tool and a real-time decision support system. The continuous analysis of employee performance and cost provides actionable indications of the current state of change, allowing for timely detection and intervention. Full article

The assessment of grid-connected systems depends on their cost efficiency, reliability, and greenhouse gas (GHG) reduction potential. This study presents a multi-objective optimization framework for designing a grid-connected photovoltaic (PV) and battery energy storage (BES) system integrated with an electric vehicle (EV) for a household in Riyadh, Saudi Arabia. The framework aims to minimize the Cost of Energy (COE) and Loss of Power Supply Probability (LPSP) while maximizing the Renewable Energy Fraction (REF). Additionally, GHG emissions are evaluated as a result of these objectives. The EV operates in Vehicle-to-Home (V2H) mode, enhancing system flexibility and energy management. The optimization process employs two advanced metaheuristic techniques, Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Harris Hawks Optimization (MOHHO), to identify Pareto front solutions. Fuzzy logic is then applied to determine a balanced compromise among the economically optimal (minimum COE), renewable energy-oriented (maximum REF), and environmentally optimal (minimum GHG emissions) solutions. Simulation results show that the proposed system achieves a COE of USD 0.0554/kWh, a LPSP of 1.96%, and an REF of 92.55%. Although the COE is slightly higher than that of the grid, the system provides significant environmental and renewable energy benefits. This study highlights the potential of integrating dynamic EV management and advanced optimization techniques to enhance the performance of grid-connected systems. The findings demonstrate the effectiveness of combining Pareto-based optimization with fuzzy logic to achieve balanced solutions addressing economic, environmental, and renewable energy objectives, paving the way for sustainable energy systems in urban households. Full article

As the global carbon neutrality process accelerates, the proportion of distributed power sources such as wind power and photovoltaic power continues to increase. This transformation, while promoting the development of clean energy, also brings about the issue of new energy consumption. As wind and solar distributed generation rapidly expands into modern power grids, consumption issues become increasingly prominent. In this paper, a robust optimal scheduling method considering multiple uncertainties is proposed for community microgrids containing multiple renewable energy sources based on potential games. Firstly, the flexible loads of community microgrids are quantitatively classified into four categories, namely critical base loads, shiftable loads, power-adjustable loads, and dispersible loads, and a stochastic model is established for the wind power and load power; secondly, the user’s comprehensive electricity consumption satisfaction is included in the operator’s scheduling considerations, and the user’s demand is quantified by constructing a comprehensive satisfaction function that includes comfort indicators and economic indicators. Further, the flexible load-response expectation uncertainty and renewable generation uncertainty model are used to establish a robust optimization uncertainty set. This set portrays the worst-case scenario. Based on this, a two-stage robust optimization framework is designed: with the dual objectives of minimizing operator cost and maximizing user satisfaction, a potential game model is introduced to achieve a Nash equilibrium between the interests of the operator and the users, and solved by a column and constraint generation algorithm. Finally, the rationality and effectiveness of the proposed method are verified through examples, and the results show that after optimization, the cost dropped from CNY 2843.5 to CNY 1730.8, a reduction of 39.1%, but the user satisfaction with electricity usage increased to over 98%. Full article

This research presents a novel methodology for lightweighting and cost reduction of components with high structural demands by integrating advanced design and manufacturing techniques. Specifically, it combines topology optimization (TO) with additive manufacturing (AM), also known as 3D printing. Unlike conventional approaches, the proposed method first determines the optimal geometry using an artificially stiff material, and only then evaluates real materials for structural and manufacturing feasibility. This design-first, material-second strategy enables broader material screening and maximizes weight reduction without compromising performance. The proposed workflow is applied to the design of a turbofan air intake—an aeronautical component operating under supersonic conditions—addressing both structural integrity and manufacturing feasibility. Three materials from distinct classes are assessed: two metallic alloys (aluminum alloy 6061 and titanium alloy, Ti6Al4V) and a high-performance polymer (polyetheretherketone, PEEK). This last option is preliminarily discarded after being analyzed for this specific application. Finite element (FE) simulations are used to evaluate the mechanical behavior of the optimized geometries, including bird-strike conditions. Among the evaluated manufacturing techniques, Selective Laser Melting (SLM) is identified as the most suitable for the metallic materials selected, providing an effective balance between performance, manufacturability, and aerospace compliance. This study illustrates the potential of TO–AM synergy as a sustainable and efficient design approach for next-generation aerospace components. Simulation results demonstrate a weight reduction of up to 71% while preserving critical functional regions and maintaining structural integrity in Al 6061 and Ti6Al4V cases, under the diverse loading conditions typical of real flight scenarios, while PEEK remains an attractive option for uses where mechanical demands are less stringent. Full article