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Search Results (4,811)

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Keywords = decision-making techniques

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23 pages, 2643 KiB  
Article
Exploring Staff Perceptions of the Management of Clinical Trials and Its Impact on Enhancing Health Service Delivery
by Emmanuel Ekundayo Sappor and Rhyddhi Chakraborty
Hospitals 2025, 2(1), 1; https://doi.org/10.3390/hospitals2010001 - 6 Jan 2025
Viewed by 90
Abstract
The role of clinical trials cannot be ignored due to its contribution to innovative treatment, therapies, and drug development in promoting quality service delivery. We investigated and explored the management aspect of clinical trials and its impact on healthcare service delivery within the [...] Read more.
The role of clinical trials cannot be ignored due to its contribution to innovative treatment, therapies, and drug development in promoting quality service delivery. We investigated and explored the management aspect of clinical trials and its impact on healthcare service delivery within the NHS. A qualitative methodology with an interpretivism approach was adopted to collect data from nine participants using a purposive sampling method in the management of clinical trials at the NHS. A semi-structured interview with open-ended questions and probing techniques conducted via Microsoft Teams was used as a data collection tool. The collected data were thematically analysed with the support of NVivo 14 software. The staffs’ perceptions were somewhat effective and highlights required improvement for better performance regarding clinical trial management at the NHS setting. The findings represent improved patient outcomes, increasing evidence-based decision making, and the development of innovative therapies and research infrastructure could be some positive impacts of the effective management of clinical trials. However, the findings show that improvement in stakeholder collaboration and communication is vital to combat the existing challenges such as regulatory hurdles and issues in participant recruitment, retention, and communication. The findings provide practical values and insight into the staff working in the management of clinical trial processes and the audiences relevant to this field. A comprehensive understanding of the proactive measures and factors that are essential for the improvement of clinical trial management has been interpreted. In the hospital’s settings, supervision and improvement of clinical trials are necessary to promote innovative therapies, research infrastructure, and quality patient care and service delivery. Full article
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24 pages, 6317 KiB  
Article
BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance
by Panagiotis Tsikas, Athanasios Chassiakos, Vasileios Papadimitropoulos and Antonios Papamanolis
Energies 2025, 18(1), 201; https://doi.org/10.3390/en18010201 - 5 Jan 2025
Viewed by 426
Abstract
The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design [...] Read more.
The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design phase. Digital tools are currently available for performing energy assessment analyses and can efficiently handle complex and technically demanding buildings. However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. In this paper, typical residential buildings are considered along with eleven factors that highly affect energy performance. A dataset of 337 instances of such parameters is developed. For each dataset, the building energy performance is estimated based on BIM analysis. Next, statistical and machine learning techniques are implemented to provide artificial models of energy performance. They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. An easy-to-use interface tool has been developed for the instantaneous calculation of the energy performance based on the independent parameter values. Full article
(This article belongs to the Special Issue Building Energy Performance Modelling and Simulation)
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39 pages, 14159 KiB  
Article
Preventive Conservation of Vernacular Adobe Architecture at Seismic Risk: The Case Study of a World Heritage Historical City
by Neda Haji Sadeghi, Hamed Azizi-Bondarabadi and Mariana Correia
Buildings 2025, 15(1), 134; https://doi.org/10.3390/buildings15010134 - 4 Jan 2025
Viewed by 339
Abstract
Heritage is strengthened through proactive actions, known as preventive conservation, that are considered before earthquakes, rather than reactive actions addressed when the emergency situation occurs. Considering that there are several regions around the world with very active seismicity, conservation interventions should guarantee human [...] Read more.
Heritage is strengthened through proactive actions, known as preventive conservation, that are considered before earthquakes, rather than reactive actions addressed when the emergency situation occurs. Considering that there are several regions around the world with very active seismicity, conservation interventions should guarantee human safety and the improvement of the inhabitant’s living conditions while keeping alive the earthen fabric and adobe buildings, thus preserving the lives of the residents but also preserving cultural heritage in the face of earthquakes. The main aim of this paper is to define a comprehensive conservation procedure addressing the preventive conservation of vernacular adobe vaulted houses in Yazd, an Iranian World Heritage property, since 2017. The fundamental phases of this procedure, which this paper’s structure is based on, include introducing the case study and addressing the conservation objectives, the assessment of significance and value, the seismic criteria, the conservation strategies, seismic safety assessment, and decision-making on interventions. The comprehensive preventive conservation procedure presented in this paper was determined by relevant conservation criteria, which contributed to an adequate seismic-retrofitted intervention design. This conservation approach requires evaluation of the seismic performance and the buildings’ safety, through which the decision regarding intervention could be made. Accordingly, this research also dealt with the seismic safety assessment of an adobe building through numerical research work performed using the software HiStrA Ver.2022.1.6. Based on the numerical results, decisions on the need and on the extent of intervention techniques were addressed. In addition, a comparative study was performed on different seismic strengthening techniques available in the literature to define fundamental conservation criteria. In this way, there are more chances for human lives to be preserved if an earthquake occurs. Full article
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15 pages, 5155 KiB  
Article
Enhancing Transparency and Fraud Detection in Carbon Credit Markets Through Blockchain-Based Visualization Techniques
by Yun-Cheng Tsai
Electronics 2025, 14(1), 157; https://doi.org/10.3390/electronics14010157 - 2 Jan 2025
Viewed by 427
Abstract
Net-zero emission targets require transparent and efficient carbon credit trading systems. This paper introduces a blockchain-based data visualization framework to enhance decision-making in the production and logistics sectors by simplifying blockchain transaction records and identifying potential arbitrage activities. The framework integrates real-time decision [...] Read more.
Net-zero emission targets require transparent and efficient carbon credit trading systems. This paper introduces a blockchain-based data visualization framework to enhance decision-making in the production and logistics sectors by simplifying blockchain transaction records and identifying potential arbitrage activities. The framework integrates real-time decision support tools, enabling production system managers to monitor carbon offset activities, detect fraudulent behaviors, and streamline operations. This research provides actionable insights into supply chain emissions management and operational risk reduction by leveraging advanced visualization techniques. The proposed approach offers innovative solutions to address the complexities of blockchain-based carbon trading, emphasizing transparency and sustainability. Our analysis demonstrates the effectiveness of these techniques in mitigating fraud and supporting compliance with international carbon trading standards. The findings contribute to integrating advanced technologies into sustainable production systems, offering practical implications for achieving global climate change mitigation goals and fostering a more efficient and secure carbon credit market. Full article
(This article belongs to the Special Issue Advances in Blockchain Challenges)
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26 pages, 3172 KiB  
Review
Recent Advances in the Mechanisms of Quality Degradation and Control Technologies for Peanut Butter: A Literature Review
by Xinyan Liu, Xuchun Zhu, Zhaowei Han and Hongzhi Liu
Foods 2025, 14(1), 105; https://doi.org/10.3390/foods14010105 - 2 Jan 2025
Viewed by 484
Abstract
As the quality of life continues to improve globally, there is an increasing demand for nutritious and high-quality food products. Peanut butter, a widely consumed and nutritionally valuable product, must meet stringent quality standards and exhibit excellent stability to satisfy consumer expectations and [...] Read more.
As the quality of life continues to improve globally, there is an increasing demand for nutritious and high-quality food products. Peanut butter, a widely consumed and nutritionally valuable product, must meet stringent quality standards and exhibit excellent stability to satisfy consumer expectations and maintain its competitive position in the market. However, its high fat content, particularly unsaturated fatty acids, makes it highly susceptible to quality deterioration during storage. Key issues such as fat separation, lipid oxidation, and rancidity can significantly compromise its texture, flavor, and aroma, while also reducing its shelf life. Understanding the underlying mechanisms that drive these processes is essential for developing effective preservation strategies. This understanding not only aids food scientists and industry professionals in improving product quality but also enables health-conscious consumers to make informed decisions regarding the selection and storage of peanut butter. Recent research has focused on elucidating the mechanisms responsible for the quality deterioration of peanut butter, with particular attention to the intermolecular interactions among its key components. Current regulatory techniques aimed at improving peanut butter quality encompass raw material selection, advancements in processing technologies, and the incorporation of food additives. Among these innovations, plant protein nanoparticles have garnered significant attention as a promising class of green emulsifiers. These nanoparticles have demonstrated potential for stabilizing peanut butter emulsions, thereby mitigating fat separation and oxidation while aligning with the growing demand for environmentally friendly food production. Despite these advances, challenges remain in optimizing the stability and emulsifying efficiency of plant protein nanoparticles to ensure the long-term quality and stability of peanut butter. Future research should focus on improving the structural properties and functional performance of these nanoparticles to enhance their practical application as emulsifiers. Such efforts could provide valuable theoretical and practical insights into the development of stable, high-quality peanut butter, ultimately advancing the field of food science and technology. Full article
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32 pages, 2525 KiB  
Article
Cyberthreats and Security Measures in Drone-Assisted Agriculture
by Kyriaki A. Tychola and Konstantinos Rantos
Electronics 2025, 14(1), 149; https://doi.org/10.3390/electronics14010149 - 2 Jan 2025
Viewed by 262
Abstract
Nowadays, the use of Unmanned Aerial Vehicles (UAVs), or drones in agriculture for crop assessment and monitoring is a timely and important issue that concerns both researchers and farmers. Mapping agricultural land is imperative for making appropriate management decisions. As a result, the [...] Read more.
Nowadays, the use of Unmanned Aerial Vehicles (UAVs), or drones in agriculture for crop assessment and monitoring is a timely and important issue that concerns both researchers and farmers. Mapping agricultural land is imperative for making appropriate management decisions. As a result, the necessity of this technology is increasing, given its numerous benefits. However, as with any modern and automated technology, security concerns arise from various aspects. In this paper, we discuss cyberthreats to drones, as this technology is vulnerable to attackers during data collection, storage, and usage. Although various techniques and methods have been developed to address attacks on drones, this field remains in its infancy in many respects. This paper provides a comprehensive review of the security challenges associated with the use of agricultural drones. The security issues were thoroughly analyzed, with a particular focus on cybersecurity, categorized into four distinct levels: emerging threats, sensor vulnerabilities, hardware and software attacks, and communication-related threats. Additionally, we examined the limitations and challenges posed by cyberthreats to drone systems. Full article
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22 pages, 5922 KiB  
Article
Predictive Modeling and Experimental Analysis of Cyclic Shear Behavior in Sand–Fly Ash Mixtures
by Özgür Yıldız and Ali Fırat Çabalar
Appl. Sci. 2025, 15(1), 353; https://doi.org/10.3390/app15010353 - 2 Jan 2025
Viewed by 269
Abstract
This study presents a comprehensive investigation into the cyclic shear behavior of sand–fly ash mixtures through experimental and data-driven modeling approaches. Cyclic direct shear tests were conducted on mixtures containing fly ash at 0%, 2.5%, 5%, 10%, 15%, and 20% by weight to [...] Read more.
This study presents a comprehensive investigation into the cyclic shear behavior of sand–fly ash mixtures through experimental and data-driven modeling approaches. Cyclic direct shear tests were conducted on mixtures containing fly ash at 0%, 2.5%, 5%, 10%, 15%, and 20% by weight to examine the influence of fly ash content on the shear behavior under cyclic loading conditions. The tests were carried out under a constant stress of 100 kPa to simulate field-relevant stress conditions. Results revealed that the fly ash content initially reduces shear strength at lower additive contents, but shear strength increases and reaches a maximum at 20% fly ash content. The findings highlight the trade-offs in mechanical behavior associated with varying fly ash proportions. To enhance the understanding of cyclic shear behavior, a Nonlinear Autoregressive Model with External Input (NARX) model was employed. Using data from the loading cycles as input, the NARX model was trained to predict the final shear response under cyclic conditions. The model demonstrated exceptional predictive performance, achieving a coefficient of determination (R2) of 0.99, showcasing its robustness in forecasting the cyclic shear performance based on the composition of the mixtures. The insights derived from this research underscore the potential of incorporating fly ash in sand mixtures for soil stabilization in geotechnical engineering. Furthermore, the integration of advanced machine learning techniques such as NARX models offers a powerful tool for predicting the behavior of soil mixtures, facilitating more effective and data-driven decision-making in geotechnical applications. Evidently, this study not only advances the understanding of cyclic shear behavior in fly ash–sand mixtures but also provides a framework for employing data-driven methodologies to address complex geotechnical challenges. Full article
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15 pages, 714 KiB  
Article
Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data Integration
by Maryam Abbasi, Paulo Váz, José Silva and Pedro Martins
Sustainability 2025, 17(1), 256; https://doi.org/10.3390/su17010256 - 2 Jan 2025
Viewed by 302
Abstract
The efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and [...] Read more.
The efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R2 of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environmental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance. Full article
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34 pages, 1009 KiB  
Article
A Review of Environmental Perception Technology Based on Multi-Sensor Information Fusion in Autonomous Driving
by Boquan Yang, Jixiong Li and Ting Zeng
World Electr. Veh. J. 2025, 16(1), 20; https://doi.org/10.3390/wevj16010020 - 2 Jan 2025
Viewed by 538
Abstract
Environmental perception is a key technology for autonomous driving, enabling vehicles to analyze and interpret their surroundings in real time to ensure safe navigation and decision-making. Multi-sensor information fusion, which integrates data from different sensors, has become an important approach to overcome the [...] Read more.
Environmental perception is a key technology for autonomous driving, enabling vehicles to analyze and interpret their surroundings in real time to ensure safe navigation and decision-making. Multi-sensor information fusion, which integrates data from different sensors, has become an important approach to overcome the limitations of individual sensors. Each sensor has unique advantages. However, its own limitations, such as sensitivity to lighting, weather, and range, require fusion methods to provide a more comprehensive and accurate understanding of the environment. This paper describes multi-sensor information fusion techniques for autonomous driving environmental perception. Various fusion levels, including data-level, feature-level, and decision-level fusion, are explored, highlighting how these methods can improve the accuracy and reliability of perception tasks such as object detection, tracking, localization, and scene segmentation. In addition, this paper explores the critical role of sensor calibration, focusing on methods to align data in a unified reference frame to improve fusion results. Finally, this paper discusses recent advances, especially the application of machine learning in sensor fusion, and highlights the challenges and future research directions required to further enhance the environmental perception of autonomous systems. This study provides a comprehensive review of multi-sensor fusion technology and deeply analyzes the advantages and challenges of different fusion methods, providing a valuable reference and guidance for the field of autonomous driving. Full article
(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)
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23 pages, 3855 KiB  
Article
Harnessing the Power of an Integrated Artificial Intelligence Model for Enhancing Reliable and Efficient Dental Healthcare Systems
by Samar M. Nour, Reem Salah Shehab, Samar A. Said and Islam Tharwat Abdel Halim
Appl. Syst. Innov. 2025, 8(1), 7; https://doi.org/10.3390/asi8010007 - 2 Jan 2025
Viewed by 542
Abstract
Nowadays, efficient dental healthcare systems are considered significant for upholding oral health. Also, the ability to utilize artificial intelligence for evaluating complex data implies that dental X-ray image recognition is a critical mechanism to enhance dental disease detection. Consequently, integrating deep learning algorithms [...] Read more.
Nowadays, efficient dental healthcare systems are considered significant for upholding oral health. Also, the ability to utilize artificial intelligence for evaluating complex data implies that dental X-ray image recognition is a critical mechanism to enhance dental disease detection. Consequently, integrating deep learning algorithms into dental healthcare systems is considered a promising approach for enhancing the reliability and efficiency of diagnostic processes. In this context, an integrated artificial intelligence model is proposed to enhance model performance and interpretability. The basic idea of the proposed model is to augment the deep learning approach with Ensemble methods to improve the accuracy and robustness of dental healthcare. In the proposed model, a Non-Maximum Suppression (NMS) ensembled technique is employed to improve the accuracy of predictions along with combining outputs from multiple single models (YOLO8 and RT-DETR) to make a final decision. Experimental results on real-world datasets show that the proposed model gives high accuracy in miscellaneous dental diseases. The results show that the proposed model achieves 18% time reductions as well as 30% improvements in accuracy compared with other competitive deep learning algorithms. In addition, the effectiveness of the proposed integrated model, achieved 74% mAP50 and 58% mAP50-90, outperforming existing models. Furthermore, the proposed model grants a high degree of system reliability. Full article
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26 pages, 7805 KiB  
Review
Acoustic Emission Technique for Battery Health Monitoring: Comprehensive Literature Review
by Eliška Sedláčková, Anna Pražanová, Zbyněk Plachý, Nikola Klusoňová, Vaclav Knap and Karel Dušek
Batteries 2025, 11(1), 14; https://doi.org/10.3390/batteries11010014 - 1 Jan 2025
Viewed by 392
Abstract
The rapid adoption of electric vehicles (EVs) has increased the demand for efficient methods to assess the state of health (SoH) of lithium-ion batteries (LIBs). Accurate and prompt evaluations are essential for safety, battery life extension, and performance optimization. While traditional techniques such [...] Read more.
The rapid adoption of electric vehicles (EVs) has increased the demand for efficient methods to assess the state of health (SoH) of lithium-ion batteries (LIBs). Accurate and prompt evaluations are essential for safety, battery life extension, and performance optimization. While traditional techniques such as electrochemical impedance spectroscopy (EIS) are commonly used to monitor battery degradation, acoustic emission (AE) analysis is emerging as a promising complementary method. AE’s sensitivity to mechanical changes within the battery structure offers significant advantages, including speed and non-destructive assessment, enabling evaluations without disassembly. This capability is particularly beneficial for diagnosing second-life batteries and streamlining decision-making regarding the management of used batteries. Moreover, AE enhances diagnostics by facilitating early detection of potential issues, optimizing maintenance, and improving the reliability and longevity of battery systems. Importantly, AE is a non-destructive technique and belongs to the passive method category, as it does not introduce any external energy into the system but instead detects naturally occurring acoustic signals during the battery’s operation. Integrating AE with other analytical techniques can create a comprehensive tool for continuous battery condition monitoring and predictive maintenance, which is crucial in applications where battery reliability is vital, such as in EVs and energy storage systems. This review not only examines the potential of AE techniques in battery health monitoring but also underscores the need for further research and adoption of these techniques, encouraging the academic community and industry professionals to explore and implement these methods. Full article
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26 pages, 546 KiB  
Article
Human-Centered AI for Migrant Integration Through LLM and RAG Optimization
by Dagoberto Castellanos-Nieves and Luis García-Forte
Appl. Sci. 2025, 15(1), 325; https://doi.org/10.3390/app15010325 - 31 Dec 2024
Viewed by 449
Abstract
The enhancement of mechanisms to protect the rights of migrants and refugees within the European Union represents a critical area for human-centered artificial intelligence (HCAI). Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to shape [...] Read more.
The enhancement of mechanisms to protect the rights of migrants and refugees within the European Union represents a critical area for human-centered artificial intelligence (HCAI). Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to shape technology in ways which better serve human needs, particularly for disadvantaged groups. Large language models (LLMs) and retrieval-augmented generation (RAG) offer significant potential to bridging gaps for vulnerable populations, including immigrants, refugees, and individuals with disabilities. Implementing solutions based on these technologies involves critical factors which influence the pursuit of approaches aligning with humanitarian interests. This study presents a proof of concept utilizing the open LLM model LLAMA 3 and a linguistic corpus comprising legislative, regulatory, and assistance information from various European Union agencies concerning migrants. We evaluate generative metrics, energy efficiency metrics, and metrics for assessing contextually appropriate and non-discriminatory responses. Our proposal involves the optimal tuning of key hyperparameters for LLMs and RAG through multi-criteria decision-making (MCDM) methods to ensure the solutions are fair, equitable, and non-discriminatory. The optimal configurations resulted in a 20.1% reduction in carbon emissions, along with an 11.3% decrease in the metrics associated with bias. The findings suggest that by employing the appropriate methodologies and techniques, it is feasible to implement HCAI systems based on LLMs and RAG without undermining the social integration of vulnerable populations. Full article
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24 pages, 4904 KiB  
Article
Deep Learning-Based Home Energy Management Incorporating Vehicle-to-Home and Home-to-Vehicle Technologies for Renewable Integration
by Marwan Mahmoud and Sami Ben Slama
Energies 2025, 18(1), 129; https://doi.org/10.3390/en18010129 - 31 Dec 2024
Viewed by 280
Abstract
Smart cities embody a transformative approach to modernizing urban infrastructure and harness the power of deep learning (DL) and Vehicle-to-Home (V2H) technology to redefine home energy management. Neural network-based Q-learning algorithms optimize the scheduling of household appliances and the management of energy storage [...] Read more.
Smart cities embody a transformative approach to modernizing urban infrastructure and harness the power of deep learning (DL) and Vehicle-to-Home (V2H) technology to redefine home energy management. Neural network-based Q-learning algorithms optimize the scheduling of household appliances and the management of energy storage systems, including batteries, to maximize energy efficiency. Data preprocessing techniques, such as normalization, standardization, and missing value imputation, are applied to ensure that the data used for decision making are accurate and reliable. V2H technology allows for efficient energy exchange between electric vehicles (EVs) and homes, enabling EVs to act as both energy storage and supply sources, thus improving overall energy consumption and reducing reliance on the grid. Real-time data from photovoltaic (PV) systems are integrated, providing valuable inputs that further refine energy management decisions and align them with current solar energy availability. The system also incorporates battery storage (BS), which is critical in optimizing energy usage during peak demand periods and providing backup power during grid outages, enhancing energy reliability and sustainability. By utilizing data from a Tunisian weather database, smart cities significantly reduce electricity costs compared to traditional energy management methods, such as Dynamic Programming (DP), Rule-Based Systems, and Genetic Algorithms. The system’s performance is validated through robust AI models, performance metrics, and simulation scenarios, which test the system’s effectiveness under various energy demand patterns and changing weather conditions. These simulations demonstrate the system’s ability to adapt to different operational environments. Full article
(This article belongs to the Special Issue Advances and Optimization of Electric Energy System—2nd Edition)
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17 pages, 2353 KiB  
Article
Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement
by Weidong He, Xiaojing Yin, Yubo Shao, Dianxin Chen, Jianglong Mi and Yang Jiao
Mathematics 2025, 13(1), 123; https://doi.org/10.3390/math13010123 - 31 Dec 2024
Viewed by 307
Abstract
As a critical component of the engine, the failure of aviation fuel pumps can lead to serious safety accidents, necessitating the development of effective maintenance programs. Fault Tree Analysis (FTA) has a clear structure and strong interpretability in maintenance decision making. However, it [...] Read more.
As a critical component of the engine, the failure of aviation fuel pumps can lead to serious safety accidents, necessitating the development of effective maintenance programs. Fault Tree Analysis (FTA) has a clear structure and strong interpretability in maintenance decision making. However, it heavily relies on expert knowledge, which is subject to uncertainty and incoherence. Therefore, this paper proposes the NOA (Nutcracker Optimization Algorithm)–GNN (Graph Neural Network) model to enhance the accuracy and robustness of FTA by mitigating the uncertainty and inconsistency in expert knowledge. The NOA algorithm efficiently searches the solution space to identify globally optimal solutions. An FTA-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) maintenance decision-making framework has also been developed. By integrating FTA with TOPSIS, the proposed method provides a comprehensive and systematic approach that combines qualitative and quantitative analyses, thereby improving the effectiveness and reliability of maintenance decision making. Full article
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25 pages, 2417 KiB  
Article
Analytical Techniques for Supporting Hospital Case Mix Planning Encompassing Forced Adjustments, Comparisons, and Scoring
by Robert L. Burdett, Paul Corry, David Cook and Prasad Yarlagadda
Healthcare 2025, 13(1), 47; https://doi.org/10.3390/healthcare13010047 - 30 Dec 2024
Viewed by 260
Abstract
Background/Objectives: This article presents analytical techniques and a decision support tool to aid in hospital capacity assessment and case mix planning (CMP). To date, no similar techniques have been provided in the literature. Methods: Initially, an optimization model is proposed to [...] Read more.
Background/Objectives: This article presents analytical techniques and a decision support tool to aid in hospital capacity assessment and case mix planning (CMP). To date, no similar techniques have been provided in the literature. Methods: Initially, an optimization model is proposed to analyze the impact of making a specific change to an existing case mix, identifying how patient types should be adjusted proportionately to varying levels of hospital resource availability. Subsequently, multi-objective decision-making techniques are introduced to compare and critique competing case mix solutions. Results: The proposed techniques are embedded seamlessly within an Excel Visual Basic for Applications (VBA) personal decision support tool (PDST), for performing informative quantitative assessments of hospital capacity. The PDST reports informative metrics of difference and reports the impact of case mix modifications on the other types of patients present. Conclusions: The techniques developed in this article provide a bridge between theory and practice that is currently missing and provides further situational awareness around hospital capacity. Full article
(This article belongs to the Section Health Informatics and Big Data)
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