Search Results (370)

Improving the efficiency of distribution systems (DSs) through reactive power compensation using shunt capacitor banks is a widely applied practice, as it enhances the voltage profile and reduces operating costs. Owing to the nonlinear nature of DSs, heuristic algorithms—along with other optimization tools—are frequently employed to support techno-economic decision-making in DS design. In this study, we employ the neural population dynamics optimization algorithm (NPDOA), a recently developed heuristic approach inspired by brain neuroscience. The simulation and optimization model adopted in this research is based on quasi-static time-series analysis, which enables the planning problem and DS constraints to be examined from a probabilistic perspective. A comparative analysis with the genetic algorithm (GA) and the whale optimization algorithm (WOA) indicates that NPDOA provides a similar solution with comparable computational time. Specifically, the results show that NPDOA produces a solution only 0.02% higher than GA, with improvement probabilities of 27.42% and 10.94%, respectively. In comparison with WOA, NPDOA yields a solution that is 0.05% lower, with a corresponding probability of improvement of 10.76%. Furthermore, the installation of shunt capacitor banks optimized using NPDOA reduces the net present cost by 33%. Full article

Rainfall is a significant input for several engineering designs such as hydraulic structures, culverts, bridges and ducts, rainfall water sewer, and highway drainage system. The detailed statistical analysis of extreme daily rainfall of each arid environment’s region is essential to estimate the relevant input value for designing and analyzing engineering structures and agricultural planning. This paper aims to assess the best-fitting distribution to estimate the design of rainfall depth (XT) and maximum rainfall values for different return periods (2, 10, 25, 50, 100, and 150). This study used extreme daily rainfall historical data collected in period of 1970–2020, collected from four rainfall gauge stations nearby the Wadi Al-Aqiq that are selected for analysis; they are Al Faqir (J109), Umm Al Birak (J112), Madinah Munawara (M001), and Bir Al Mashi (M103). The methodology approved in this paper examined four frequency distributions, namely: GEV (Generalised Extreme Value), Gumbel, Weibull, and Pearson type III to identify the most suitable and extreme storm design depth corresponding to different return periods. The results demonstrate that GEV and Pearson Type 3 produce higher extremes values, while the Weibull method is commonly suggested in the HYFRAN-PLUS MODEL (DSS) for criterion suitability. The findings for the 100-year storm design demonstrate that extreme values generated by the Hyfran-Plus model are higher than the decision support system (DSS). All (DSS) comparative values are less than the maximum historical data from 1970–2020, except the Al Faqir station (DSS), which has a value of 79.6 mm that exceeds the historical maximum of 71 mm. This study will provide advantageous information about the study area for water resources planners, farmers, and urban engineers to assess water availability and create storage. Full article

Background: Digitalization and artificial intelligence-supported clinical decision support systems (AI-DSS), defined here as tools that generate patient-specific alerts, risk estimates, prioritization prompts, documentation suggestions, or related recommendation outputs intended to support rather than replace professional nursing judgment, can improve clinical decision-making, yet they may also amplify technostress and burnout, with downstream effects on missed nursing care and implementation readiness. Methods: We surveyed 239 registered nurses from a tertiary-care hospital in Timișoara, Romania (January–March 2025), including critical care (n = 60) and general wards (n = 179). Measures included a 15-item technostress scale, eHEALS, Maslach Burnout Inventory–Human Services Survey (MBI-HSS), Safety Attitudes Questionnaire (SAQ) teamwork and safety climate subscales, a 10-item missed nursing care inventory, and a six-item AI-DSS acceptance scale reflecting perceived usefulness, trust, and stated willingness to use such tools if available as an attitudinal readiness outcome rather than as routine observed use. Multivariable regression, exploratory mediation models, cluster analysis, and exploratory ROC analysis were performed. Results: Higher technostress was associated with higher emotional exhaustion (r = 0.52) and more missed care (r = 0.41), whereas eHealth literacy correlated with higher AI-DSS acceptance (r = 0.35) and lower technostress (r = −0.34). In adjusted models, technostress (per 10 points) was associated with higher missed care (β = 0.28, p < 0.001) (equivalent to 0.14 points per 5-point increase) and higher odds of low AI-DSS acceptance (OR = 1.38, p = 0.001), while eHealth literacy was associated with lower odds of low acceptance (OR = 0.71 per 5 points, p < 0.001). Burnout and the safety climate statistically accounted for approximately 35% of the technostress–missed care association. Three workflow phenotypes were identified, with the high-strain/low-literacy cluster showing the most missed care (3.5 ± 1.8) and the lowest AI acceptance (19.7 ± 5.2). An exploratory in-sample ROC model for intention to leave achieved an AUC of 0.82. Conclusions: Higher technostress clustered with worse nurse well-being, more care omissions, and lower AI-DSS acceptance, whereas eHealth literacy appeared protective. Interventions combining digital skills support, usability-focused redesign, and a stronger safety climate may reduce missed care and support safer AI implementation. Full article

Background: The increasing volume of waste and stricter environmental regulations necessitate efficient waste transportation. Optimizing the specialized vehicle fleet remains a challenge due to fragmented decision-making approaches. Methods: This study develops a Decision Support System (DSS) integrating a simulation model (developed in AnyLogic) with a vehicle competitiveness assessment module (developed in Python). The simulation reproduces waste generation, collection (schedule-based and event-based), and transport logistics. An optimization experiment was conducted to minimize total logistics costs by varying fleet composition. Results: The findings indicate that the optimal fleet configuration reduced total logistics costs by 40.64% compared to the baseline; this reduction was statistically significant. Conclusions: The proposed DSS enables integrated optimization of fleet composition, demonstrating substantial potential for improving both economic and environmental performance of waste transportation systems. The modular architecture supports adaptation to diverse operational contexts. Full article

Background: Spare parts distribution networks (DNs) play a strategic role in retailers’ profitability. Among DN configuration decisions, selecting the optimal stock deployment policy—centralised, decentralised, or hybrid inventory allocation across distribution centres (DCs)—critically affects service levels and logistics costs. This decision becomes more complex with additive manufacturing (AM) as an alternative to conventional manufacturing (CM). While AM enables production with shorter lead times, its higher costs alter stock deployment cost-effectiveness. Given the complexity of joint stock deployment and manufacturing decisions, retailers require decision support systems (DSSs). Methods: To address this need, we develop a DSS through a three-step methodology: (i) a mathematical model evaluates logistics costs across different stock deployment policies and manufacturing technologies; (ii) parametric analysis tests the model across 2000 realistic scenarios; (iii) Random Forest trained on this dataset predicts optimal solutions, with SHapley Additive exPlanations (SHAP) interpreting post hoc recommendations. Results: The DSS achieves 93.4% prediction accuracy—outperforming (+16.4%) the only comparable literature DSS (77%)—while explaining recommendations. SHAP reveals that AM and CM unit costs dominate decision-making, followed by backorder costs. Conclusions: Beyond individual spare parts recommendations, the DSS provides guidelines enabling retailers to maintain cost-effective DNs aligned with evolving customer needs and to plan valuable investments in AM. Full article

This study presents a scalable decision support system (DSS) framework designed to meet the growing demands of instant data-driven decision-making environments. The architecture integrates key technologies, including Apache Kafka for parallel data streaming, a Python-based data analytics module for distributed processing, JWT-based secure user authentication, and WebSocket communication for instantaneous prediction delivery. The system performs mitochondrial localization in electron microscopy (EM) images using multiple versions of the YOLO (You Only Look Once) object detection model. The publicly available CA1 Hippocampus dataset was used for detection evaluation. Among the evaluated models, YOLOv10x achieved the highest detection performance, yielding a mean average precision (mAP) score of 95.2%. Experimental evaluations of the DSS were conducted under simulated load conditions using the Artillery tool to assess the system’s scalability and responsiveness. Empirical results indicate consistent low-latency performance across varying consumer group sizes, confirming the architecture’s ability to scale the analytics module horizontally without compromising responsiveness. These findings validate the system’s suitability for just-in-time decision support applications. In particular, the system may support clinicians in the task of mitochondrial analysis, where structural abnormalities can be indicative of pathological conditions, including cancer. By enabling early detection of such abnormalities, the proposed framework has the potential to contribute to the timely diagnosis of diseases such as cancer. The proposed study differs from existing studies by combining deep learning with real-time scalable data processing technologies, such as Kafka and WebSocket, in a web-based DSS application for mitochondria detection. Full article

Background/Objectives: This study aims to develop a comprehensive Clinical Decision Support System (CDSS) that integrates multi-sequence sacroiliac joint (SIJ) MRIs with rheumatological, clinical, and laboratory findings into the decision-making process for the early diagnosis of axial spondyloarthritis (axSpA), incorporating segmentation-supported explainability. Methods: Multi-sequence SIJ MRI data (T1-WI, T2-WI, STIR, and PD-WI) were analysed from 367 participants (n = 193 axSpA; n = 174 non-axSpA controls). Sequence-based classification was performed using VGG16, ResNet50, DenseNet121, and InceptionV3 models; additionally, a lightweight and parameter-efficient SacroNet architecture was developed. Slice-level probability scores were converted to patient-level scores using the Dynamic Top-K Averaging method. Image-based scores were combined with a logistic regression-based clinical risk score using weighted linear integration (0.60 image/0.40 clinical) and a conservative threshold (τ = 0.70). Grad-CAM was applied for visual interpretability. Furthermore, to support the diagnostic outcomes with precise spatial data, active inflammation in STIR and T2-WI sequences was segmented. For this purpose, the MDC-UNet model was employed and compared with baseline U-Net derivatives. Results: Sequence-specific analysis showed VGG16 performing best on T1-WI (AUC = 0.920; Accuracy = 0.878) and DenseNet121 on STIR (AUC = 0.793; Accuracy = 0.771). The SacroNet architecture provided competitive classification performance at the patient level despite its low number of parameters (~110 K). Furthermore, MDC-UNet successfully segmented active inflammation, yielding Dice scores of 0.752 (HD95: 19.25) for STIR and 0.682 (HD95: 26.21) for T2-WI. Conclusions: The findings demonstrate that patient-level decision integration based on multi-sequence MRI, when used in conjunction with clinical risk scoring and segmentation-assisted interpretability, can provide a feasible and interpretable DSS framework for the early diagnosis of axSpA. Full article

Historical small towns (HSTs) embody irreplaceable cultural heritage and territorial identity, facing depopulation, economic marginalization, and infrastructure decay. Improving their liveability and attractiveness is essential to reverse these trends and boost sustainable development. In this context, HSTs are potential drivers of circular and sustainable socio-technical systems, where the circular economy (CE) offers a framework for local sustainability. However, HSTs lack adequate sustainable CE implementation tools. This study, the culmination of the H-SMA-CE project, develops a Decision Support System (DSS) to assist local policymakers in planning CE transitions in Italian HSTs. The DSS integrates three building blocks: context analysis (metabolic flows, stakeholder networks), an intervention library with cost–benefit data, and a composite Municipal Circular Economy Index (MCEI). The tool enables users to assess baseline circularity, simulate scenarios, and identify optimal investment portfolios through multi-objective optimization. This approach allows for the simultaneous evaluation of the benefits of each sustainability aspect, i.e., environmental, economic and social. Tested on the municipality of Taurasi (Italy), an HST with a wine-based economy, the results show that balanced intervention strategies yield greater circularity improvements than single-objective approaches. The paper contributes to the discourse on digital tools for sustainability transitions, offering a replicable model for evidence-based CE governance in heritage-rich territorial contexts. Full article

Driven by the global rise in animal protein demand, poultry farming has evolved into a highly intensive and technically complex sector. According to the FAO, animal protein production increased by about 16% in the past decade, with poultry alone expanding by 27% and becoming the leading source of animal protein. This intensification requires rapid, complex decisions across multiple aspects of production under uncertainty and strict time constraints. This study presents the development and evaluation of a conversational decision support system (DSS) designed to support decision-making to assist poultry producers, particularly broiler producers, in addressing technical queries across five key domains: environmental control, nutrition, health, husbandry, and animal welfare. As a proof-of-concept study, the reference context is intensive broiler production, covering common floor-rearing housing settings, including environmentally controlled and mechanically ventilated houses. The system combines a large language model (LLM) with retrieval-based generation (RAG) to ground responses in a curated corpus of scientific and technical literature. Additionally, it adds a reasoning component using Paraconsistent Annotated Evidential Logic Eτ, a non-classical logic designed to handle contradictory or incomplete information. Methodologically, Logic Eτ is used as a workflow-level control mechanism to gate clarification, domain routing, and answer adequacy signaling, rather than serving only as a post hoc label on generated outputs. Evaluation was conducted by comparing system responses with expert reference answers using semantic similarity (cosine similarity with SBERT embeddings). The results indicate that the system successfully retrieves and composes relevant content, while the paraconsistent inference layer makes results easier to interpret and more reliable in the presence of conflicting or insufficient evidence. These findings suggest that the proposed architecture provides a viable foundation for explainable and reliable decision support in modern poultry production, achieving consistent reasoning under contradictory or incomplete information where conventional RAG chatbots may produce unstable guidance. Full article

Precision agriculture employs advanced technologies to enhance farm productivity and sustainability; however, selecting the most appropriate tools can be challenging for small and medium-sized farms. This study conducts a comparative analysis of ten key precision agriculture technologies (PATs): remote sensing, GPS, GIS, VRT, soil & crop sensors, DSS, UAVs/Drones, AI & ML-based precision farming, autonomous agricultural machinery, and IoT-based smart farming. The analysis employs a neutrosophic set-based multi-criteria decision-making (MCDM) framework. Domain experts evaluated ten representative technologies using a structured questionnaire based on ten critical criteria, including spatial-temporal accuracy, data acquisition latency, scalability, robustness, interoperability, environmental resilience, economic feasibility, and agro-ecological impact. A hybrid MCDM methodology was employed, integrating neutrosophic entropy and DEMATEL to construct criterion weights. Furthermore, we utilized neutrosophic DEMATEL to identify inter-criterion causal relationships. Neutrosophic TOPSIS, enhanced by a newly proposed hybrid Cosine-Jaccard similarity measure, was introduced to rank the alternatives under conditions of uncertainty. The findings reveal that IoT-based smart farming solutions achieved the highest overall score, followed by remote sensing and decision-support system (DSS) platforms. At the same time, variable-rate technology and sensor networks received lower rankings. The findings underscore the appropriateness of particular PATs for small and medium-scale farming contexts and illustrate the effectiveness of neutrosophic MCDM in addressing ambiguity and indeterminacy. The comparative insights provide direction for researchers, policymakers, and practitioners in prioritizing precision agriculture technologies and strategies to enhance sustainable practices in small and medium-scale farming. Full article

Decision-making in Urban Road Renewal is often hindered by the disconnect between static conceptual models and dynamic industry specifications. To address this, this paper proposes an ontology-based decision support framework that formally models and integrates multi-source knowledge for automated compliance checking. A domain ontology was constructed by extracting entities from 15 key industry specifications using a BERT-BiLSTM-CRF deep learning model (achieving an accuracy of 98.2%), and a rule base of over 50 Semantic Web Rule Language (SWRL) rules was formulated to enable automated reasoning. The framework’s effectiveness was validated through a multi-agent simulation of the G15 Jialiu renewal project. Results demonstrated that the system-generated optimization measures increased traffic capacity by up to 95.0% and improved the Pavement Condition Index (PCI) by 6.1%, empirically verifying the directional consistency of the decision logic. Finally, the practical feasibility was demonstrated through a Decision Support System (DSS). This research provides a novel framework for leveraging fragmented knowledge, enhancing the consistency and rationality of decision-making in smart city infrastructure management. Full article

In light of the climate protection goals of the Paris Agreement, optimizing the sustainability of planning processes is becoming increasingly important. Building Information Modeling (BIM) centralizes planning information for interdisciplinary evaluation, enabling sustainable decision-making. This paper presents a systematic review of BIM-based decision support approaches for building sustainability. Following vom Brocke’s five-phase model and the PRISMA 2020 standard, 70 studies were analyzed to identify current methods, their respective strengths and limitations, and future research needs. The findings reveal a highly dynamic but fragmented field of research. Assessment-Based Optimization and multi-criteria decision-making (MCDM) methods dominate. However, the holistic integration of ecological, economic and social indicators remains rare, with social sustainability receiving the least attention. Most approaches rely on proprietary BIM environments, while open BIM applications and interoperable data standards remain underdeveloped. Standardized data sources, such as Environmental Product Declarations (EPDs), are well established for ecological assessments, but are largely lacking for the economic and social dimensions. The review highlights the urgent need for interoperable data formats, standardized evaluation methods, and accessible databases to enable scalable and comparable BIM-based sustainability optimizations. Advancing these foundations will be essential for achieving consistent, holistic sustainability optimization in the construction industry. Full article

The complexity of internal control in commercial banks continues to increase, and relevant reports exhibit notable lag and template issues. In response to the demand to transform unstructured disclosures into actionable insights, this study proposes an “augmented Business Intelligence (BI) framework” that integrates a text-based internal control quality assessment system, a dual-validation process, and the resulting Intelligent Internal Control Decision Support System (IIC-DSS). By combining large language models and neural-symbolic models of regulatory prototypes, a quality evaluation system for internal control based on complex text is constructed using a mixed probability mechanism to reduce interference from defensive disclosures. A dual validation process is designed with Partial Least Squares Structural Equation Modeling (PLS-SEM). PLS-SEM verification confirms the construct validity of this evaluation system, while XGBoost verification indicates that internal control quality has incremental predictive ability for asset quality deterioration. The IIC-DSS uses SHapley Additive exPlanations (SHAP) to explain XGBoost outputs, quantifying the marginal contribution of each control factor to the predicted risk. Overall, this study advances internal-control measurement by establishing a neural-symbolic, text-to-indicator representation within an augmented BI architecture and empirically demonstrating its utility in improving predictive power for bank asset quality deterioration and in enhancing decision transparency via explainable AI. Full article

Climate change has intensified wildfire risk, creating an urgent need for integrated, data-driven tools that connect forest operations with fuel-reduction strategies. This paper introduces a real-time IoT-enabled Decision Support System (DSS) that unifies wood traceability with optimization–simulation planning for biomass collection and processing. The system captures detailed operational data from harvesting, transportation, and processing through IoT devices and industry formats, enabling the continuous monitoring of wood flows and precise estimation of biomass residues that directly contribute to wildfire fuel loads. The DSS transforms these real-time streams into actionable planning outputs through an optimization–simulation module that generates efficient biomass harvesting and processing schedules while evaluating their robustness under wildfire-related constraints. By linking wood traceability with biomass logistics, the system provides the missing operational bridge between forest management decisions and wildfire-risk mitigation. Results show that the DSS not only improves operational efficiency but also enhances resilience by supporting risk-aware planning, prioritizing high-exposure areas, and reducing the accumulation of hazardous biomass. These insights demonstrate how digital traceability and robust planning can work together to lower ignition potential while maintaining service levels and operational continuity. Overall, this work presents a practical and scalable solution that strengthens forest supply chain resilience and provides a new pathway for integrating wildfire-risk mitigation into everyday operational planning. Full article

The forest-to-bioenergy supply chain is significantly vulnerable to natural disruptions, including wildfires, heavy snowfall, and windstorms. The increased occurrence of these disruptive events has caused severe challenges in forest biomass harvesting and transportation processes, which are difficult to manage. With the need to support decision-makers in designing resilient supply chains (SCs), we propose a Decision Support System (DSS) combining a two-stage stochastic programming framework with various flexibility mechanisms, such as dynamic network reconfiguration and operations postponement. The DSS incorporates an AI-based methodology to identify the most appropriate datasets and resilience metrics, capturing different supply chain dimensions (supply, demand, and operations). This integrated framework supports the selection of effective resilience-enhancing strategies to mitigate large-scale disruptions, with a particular focus on wildfires. The proposed approach is applied in a real case study in Portugal, where the most significant risk factor is wildfires. We perform computational studies and sensitivity analysis to evaluate the applicability and performance of the model and to drive managerial insights. The results show that adopting the model solutions can significantly reduce supply chain logistics and operational costs under more severe disruptive scenarios. Moreover, the results indicate up to a 60% increase in the tons of forest residues that can be removed and processed. Full article