The global energy crisis highlights the need for energy efficiency in the management of the electricity sector. One method to contribute to electrical energy efficiency in buildings is to develop appropriate prediction models. This research seeks to optimize the use of electrical energy by using an ensemble neural network approach, combining LSTM, GRU, and RNN models, to estimate reactive energy consumption. This study utilizes energy measurement data for apartment buildings in Jakarta, which includes consumption data during peak and off-peak periods, as well as reactive energy consumption. This methodology involves the use of ensemble neural network models—LSTM, GRU, RNN with Differentiable Architecture Search (DARTS) initiation—to build adaptive prediction models capable of generalizing across various data conditions. These findings demonstrate that ensemble neural network models with Differentiable Architecture Search Initiation (DARTS) achieve more accurate predictions compared to individual LSTM, GRU, and RNN models in estimating energy consumption. Correlation analysis shows a significant relationship between reactive energy consumption and peak/off-peak load More efficient and sustainable energy in apartment buildings is expected to reduce operational costs by scheduling the operation of large reactive power-consuming equipment, increasing energy efficiency, and mitigating environmental impacts through the application of renewable energy sources.
Local governments have a significant and strategic position in development because they are at the site/field level that is directl=y related to the community. In reality, each local government organization has a program in the same place, so there is no program coordination between local government organizations. Environmentally aware village governance emphasizes the role of local governments in sustainable and environmentally aware development. The analytical framework examines the relationship between four independent variables [X1] Program, [X2] Environmental Governance, [X3] Market Access, [X4] Institutional Collaboration, with dependent variables [Y1] Sustainable Development and [Y2] Collaborative Governance-Pentahelix. This study focuses on the significant role of effective institutional collaboration in achieving sustainable development and good governance. To optimize sustainable development and governance in forest landscapes, a holistic approach that combines strong institutional collaboration and environmental governance is essential. In addition, strong environmental policies at the village level must support mangrove forest conservation, which is essential for maintaining coastal biodiversity. The results of the study indicate that institutional collaboration and environmental governance are significant key factors in achieving sustainable development and good governance, while market access and programs do not have a significant impact on sustainable development. Institutional collaboration directly contributes to sustainable development and good governance. Consequently, this study shows that strong and effective environmental governance is needed for environmental management and sustainable development in the village. Thus, this study shows that to achieve success in environmental management and sustainable development in the village, strong environmental governance and effective institutional collaboration are needed.
As a critical component of mechanical transmission systems, gears play a vital role in ensuring industrial production runs smoothly. Undetected gear failures can lead to mechanical breakdowns, production interruptions, and even safety hazards. Therefore, an efficient gear fault detection method is essential for maintaining industrial continuity and safety. This paper proposes a hybrid model that integrates convolutional neural networks (CNN) and support vector machines (SVM) for gear fault detection. The model leverages CNNs to automatically extract key features from vibration signals, while SVMs enhance classification accuracy, resulting in a high-precision fault diagnosis system. On a publicly available gear fault dataset, the proposed model achieved an impressive accuracy of 0.9922, significantly outperforming single-classifier models. Moreover, the model exhibits a short training time, demonstrating its computational efficiency. This research provides an effective and automated approach to gear fault detection, offering significant potential for industrial applications.
The transition from outdated biomass boiler systems to modern, efficient district heating technologies represents a critical pathway toward sustainable energy production. In this study, the replacement of obsolete solid biomass-fueled boilers with a new wood chip-based heating system in the district heating plant of Pale, Bosnia and Herzegovina, was analyzed under real-world operational conditions. Historical operational data, including annual fuel consumption, were obtained directly from the facility. The degree-day method was applied to evaluate the thermal efficiency of the former heating system and to estimate the annual fuel demand for the newly installed wood chip-based infrastructure. A key component of this transition involves the reliability and efficiency of the wood chip supply chain. Therefore, the logistical feasibility of securing a continuous, local, and renewable wood chip fuel source was examined, including the assessment of storage capacity and supply chain resilience. Furthermore, a scenario-based simulation was conducted to project the cost of heat production under varying fuel price conditions and market dynamics. Through this integrated approach, a replicable methodology was proposed for replacing legacy biomass heating systems with environmentally sustainable, economically viable district heating technologies based on locally sourced wood chips. The findings offer a practical roadmap for municipalities aiming to achieve energy transition targets through the adoption of locally available renewable energy sources, with particular emphasis on operational feasibility, fuel logistics, and cost-effectiveness.
The study of non-Newtonian fluid dynamics within cardiovascular systems is critical for understanding the complex interactions between blood flow and arterial health. This research focuses on the application of the Finite Volume Method (FVM) to simulate non-Newtonian fluid behavior under pulsatile flow conditions, mimicking the heartbeat. The objective is to analyze the effects of varying viscosity properties and flow patterns on the development and progression of atherosclerosis. By employing computational simulations, we investigate the rheological properties of blood, characterized as a non-Newtonian fluid, and its impact on shear stress distribution and arterial wall interaction. The simulation framework incorporates advanced non-Newtonian models, including Power-law and Carreau-Yasuda models, to accurately represent blood viscosity variations. Pulsatile flow dynamics are modeled to replicate physiological conditions, providing insights into the mechanical forces exerted on arterial walls and their role in atherosclerotic plaque formation. The results highlight critical areas of high shear stress and low shear rate, which correlate with regions prone to atherosclerosis. This study's findings contribute to a deeper understanding of cardiovascular fluid mechanics and offer potential implications for medical diagnostics and treatment strategies for atherosclerosis. The application of the FVM in this context demonstrates its robustness in handling complex fluid behaviors and geometries, paving the way for more sophisticated simulations in biomedical engineering.
Groundwater in the Sudda Vagu basin, located in the Bhainsa region of Nirmal District, Telangana, serves as a critical source of water for both drinking and irrigation. To evaluate its quality and suitability, 25 groundwater samples were systematically collected during the pre-monsoon (May 2022) and post-monsoon (November 2022) periods and analyzed for major cations and anions. The concentrations of sodium (Na⁺), potassium (K⁺), carbonate (CO₃²⁻), bicarbonate (HCO₃⁻), and sulfate (SO₄²⁻) were found to remain within the permissible limits recommended by the Bureau of Indian Standards (BIS), whereas elevated levels of calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), nitrate (NO₃⁻), and fluoride (F⁻) were detected in several samples, exceeding the prescribed thresholds. The pH of the groundwater ranged from 6.5 to 8.5, indicating alkaline conditions, and was deemed generally acceptable for drinking based on BIS guidelines. Hydrochemical facies classification using the Piper trilinear diagram revealed the predominance of Ca²⁺-HCO₃⁻, Na⁺-Cl⁻, and mixed water types. Irrigation suitability was further assessed through indicators including the Sodium Adsorption Ratio (SAR), Kelly Ratio (KR), and Residual Sodium Carbonate (RSC), along with the Wilcox diagram. Pre-monsoon evaluation indicated that 12 samples were categorized under the S1C2 class (low sodium hazard–medium salinity hazard), while 13 samples were assigned to the S1C3 class (low sodium hazard–high salinity hazard). Post-monsoon analysis revealed that four samples remained in S1C2, whereas 21 shifted into S1C3. The findings indicate that the majority of samples are suitable for drinking and irrigation. Continuous monitoring and the implementation of sustainable groundwater management strategies are therefore essential to ensure water security in this region.
This study examines the influence of perceived organizational justice on employees’ turnover intention, with a focus on the mediating role of organizational dissent. It aims to identify the key factors contributing to turnover intention within the technology sector and to explore the interplay between organizational justice and organizational dissent in shaping this outcome. A quantitative approach was employed, with data gathered through surveys administered to white-collar employees working in technology companies in Istanbul. The study sample comprised 402 participants. The findings reveal an inverse relationship between perceived organizational justice and turnover intention, indicating that lower perceptions of organizational justice correlate with higher turnover intention. Additionally, organizational dissent was found to significantly impact turnover intention, with perceived organizational justice acting as a mediator in this relationship. The results underscore the critical role of organizational justice in fostering job satisfaction and employee commitment, thereby reducing turnover intention in the technology sector. These findings are consistent with existing literature on the relationship between organizational justice and turnover intention, offering valuable insights into the factors influencing employee retention in high-tech industries. The implications for organizational management are discussed, particularly in terms of the importance of promoting fairness and addressing dissent in order to retain talent within technology firms.
The review study explores contemporary Earth observation (EO) methods and the influential role of citizen science in environmental research. It emphasizes the integration of diverse datasets-such as satellite imagery, field surveys, and citizen science contributions, to enhance the precision and efficiency of Earth system studies. By combining high-resolution remote sensing technologies with ground-based observations, researchers can effectively monitor and analyze biodiversity hotspots and other critical environmental phenomena. The study highlights the crucial role of citizen science and community engagement in broadening data collection efforts and involving the public in environmental monitoring initiatives. It also acknowledges the importance of field studies and research expeditions for validating and complementing EO data. A key focus of the review is the use of open-source tools and innovative methodologies that facilitate high-quality research on constrained budgets. This approach improves accessibility and repeatability, enabling significant scientific advancements without substantial financial investments. The review showcases how leveraging these integrated technologies can advance Earth science research and overcome financial barriers, ensuring that valuable scientific contributions are achievable even with limited resources. This review provides practical insights for integrating EO techniques, field studies, and citizen science, offering guidance for conducting impactful and cost-effective environmental research.
Brain tumors represent a critical medical condition where early and accurate detection is paramount for effective treatment and improved patient outcomes. Traditional diagnostic methods relying on Magnetic Resonance Imaging (MRI) are often labor-intensive, time-consuming, and susceptible to human error, underscoring the need for more reliable and efficient approaches. In this study, a novel deep learning (DL) framework based on the Densely Connected Convolutional Network (DenseNet) architecture is proposed for the automated classification of brain tumors, aiming to enhance diagnostic precision and streamline medical image analysis. The framework incorporates adaptive filtering for noise reduction, Mask Region-based Convolutional Neural Network (Mask R-CNN) for precise tumor segmentation, and Gray Level Co-occurrence Matrix (GLCM) for robust feature extraction. The DenseNet architecture is employed to classify brain tumors into four categories: gliomas, meningiomas, pituitary tumors, and non-tumor cases. The model is trained and evaluated using the Kaggle MRI dataset, achieving a state-of-the-art classification accuracy of 96.96%. Comparative analyses demonstrate that the proposed framework outperforms traditional methods, including Back Propagation (BP), U-Net, and Recurrent Convolutional Neural Network (RCNN), in terms of sensitivity, specificity, and precision. The experimental results highlight the potential of integrating advanced DL techniques with medical image processing to significantly improve diagnostic accuracy and efficiency. This study not only provides a robust and reliable solution for brain tumor detection but also underscores the transformative impact of DL in medical imaging, offering radiologists a powerful tool for faster and more accurate diagnosis.
Alzheimer’s disease (AD), a progressive neurodegenerative disorder characterized by severe cognitive decline, necessitates early and accurate diagnosis to improve patient outcomes. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNNs), have demonstrated significant potential in medical image analysis (MIA). This study presents a robust CNN-based framework for the classification of AD using magnetic resonance imaging (MRI) data. The proposed methodology incorporates contrast stretching for image preprocessing, followed by principal component analysis (PCA) and recursive feature elimination (RFE) for feature selection, to enhance the discriminative power of the model. The framework is designed to classify MRI into four distinct categories: non-demented, very mildly demented, mildly demented, and moderately demented. Experimental validation on a comprehensive dataset reveals that the proposed approach achieves exceptional performance, with a validation accuracy of 97% and a training accuracy of 100%, alongside reduced loss and improved sensitivity. The integration of PCA and RFE is shown to effectively reduce dimensionality while retaining diagnostically critical features, thereby optimizing the model’s efficiency and interpretability. These findings underscore the potential of DL techniques to revolutionize the early detection and diagnosis of AD, offering a powerful tool for clinical decision-making and advancing the field of neuroimaging analysis. The proposed framework not only addresses the challenges of high-dimensional data but also provides a scalable and generalizable solution for the classification of neurodegenerative disorders.
Foggy road conditions present substantial challenges to road monitoring and autonomous driving systems, as existing defogging techniques often fail to accurately recover structural details, manage dense fog, and mitigate artifacts. In response, a novel defogging model is proposed, incorporating Pythagorean fuzzy aggregation, Gaussian Mixture Models (GMM), and the level-set method, aimed at overcoming these limitations. Unlike conventional methods that depend on fixed priors or oversimplified haze models, the proposed framework leverages the advantages of Pythagorean fuzzy aggregation to enhance contrast and detail restoration, GMM to estimate fog density robustly, and the level-set method for precise edge preservation. The performance of the model is quantitatively assessed, revealing a Peak Signal-to-Noise Ratio (PSNR) of up to 37.1 dB and a Structural Similarity Index (SSIM) of 0.96, which significantly outperforms existing defogging techniques. Statistical analyses further confirm the robustness of the approach, with a p-value of less than 0.001 for key performance metrics. Additionally, the model demonstrates an execution time of 0.07 seconds, indicating its suitability for real-time road monitoring applications. Qualitative assessments highlight the model's ability to restore natural road colours and maintain high structural fidelity, even under conditions of dense fog. This work provides a promising advancement over current methods, with potential applications in autonomous driving, traffic surveillance, and smart transportation systems.
The thermal performance and energy efficiency of Photovoltaic Thermal (PVT) systems were investigated through the integration of Phase Change Materials (PCMs) combined with distinct container configurations. Two types of PCMs—paraffin wax, an organic material, and Polyethylene Glycol 1000 (PEG-1000), a polymer-based alternative—were embedded within two container designs: a plain container and a baffled container. To evaluate the impact of PCM selection and container geometry on system performance, a series of numerical simulations were conducted using Computational Fluid Dynamics (CFD) in ANSYS Fluent under varying solar irradiance levels of 300, 600, 900, and 1200 W/m². The results revealed that PCM integration significantly mitigates the operating temperature of PV cells, contributing to enhanced thermal stability and electrical conversion efficiency. At the highest irradiance of 1200 W/m², the plain paraffin configuration attained a minimum cell temperature of 27.4℃ and achieved the highest electrical efficiency of 11.7%. Conversely, the baffled PEG-1000 configuration exhibited a slightly higher peak temperature of 28.1℃ with a corresponding efficiency of 11.18%. Although the baffled container promoted improved internal heat distribution, the plain configuration demonstrated superior overall thermal regulation. These findings underscore the critical influence of PCM thermal properties and container geometry on the operational sustainability of PVT systems. This study provides new insights into PCM-container coupling strategies, offering a valuable framework for the development of high-efficiency, sustainable solar energy systems.
Since renewable energy sources have an intermittent nature, forecasting strategies are increasingly important. In parallel, ports are characterized by large energy demands, especially from berthed ships. Cold ironing systems have already been proven to reduce their environmental impact by connecting ships to the electricity grid and allowing them to switch off their auxiliary engines in port. In this work, a local energy production, consisting of photovoltaic, wind turbines, and an energy storage system, is proposed to cover the energy demand of ships. In addition, an energy forecasting strategy is presented, where the solar and wind energy potential is provided by the Weather and Research Forecasting (WRF) mesoscale model. By forecasting the energy production for the following day, the storage system can be charged from the grid at night, namely in off-peak periods, reducing the pressure on the grid in on-peak periods. The methodology is tested on the port of Ancona (Italy). Results show that energy production can directly cover 54% of energy demand, and up to 70% by adding the storage system. The forecasting strategy reduces the energy withdrawn during the daytime by 24.9% and increases that during the nighttime by 18.9%, proving the effectiveness of the proposed strategy.
Despite increasing instances of fraudulent activities within the Nepalese insurance sector being periodically revealed by government bodies, regulatory authorities, and investigative journalists, a systematic academic inquiry into this issue has remained notably absent. To address this gap, an exploratory cross-sectional quantitative investigation was conducted to examine stakeholder perceptions regarding the effectiveness of fraud control mechanisms and the primary repercussions of insurance fraud on insurers in Nepal. Data were collected through a structured questionnaire administered to 200 respondents including insurance employees, policyholders, agents, insurance technicians, surveyors, and domain experts within the Pokhara Valley, selected via convenience sampling. Analytical procedures included descriptive statistics, Mann–Whitney U tests, and Kruskal–Wallis H tests. It was identified that robust legal enforcement, particularly the enactment and strict implementation of anti-fraud legislation, was perceived as the most effective control strategy. Institutional reforms, such as the establishment of a dedicated Fraud Investigation Bureau and a centralized Insurance Information Centre, were also emphasized as critical to improving the detection and monitoring of fraudulent activities. Although technology-enabled solutions, including AI-driven digital claim management and anomaly detection systems, were acknowledged for their importance, they were ranked marginally lower in perceived efficacy compared to legal and institutional measures. Fraud was reported to exert significant detrimental effects on insurers, most prominently through the erosion of public trust and social credibility. Additional impacts included claim settlement delays, reduced profitability, destabilization of share prices, and increased insurance premiums, collectively threatening both the short-term financial performance and long-term sustainability of the sector. To safeguard stakeholder interests and ensure sectoral stability, a multi-pronged anti-fraud framework has been recommended. Main recommendations include strengthening the legal framework with stringent penalties, developing a centralised fraud registry for inter-insurer information sharing, enhancing underwriting and claims verification procedures, and investing in intelligent fraud detection technologies. These findings offer empirical insights that can guide policy reform and institutional development in emerging insurance markets.
Open Access
, 