Search Results (375)

The rapid expansion of generative artificial intelligence (AI) is transforming higher education and creating new opportunities that are associated with the development of perceived professional competencies. At the same time, the accounting profession increasingly requires graduates who can evaluate sustainability disclosures and form informed judgments regarding sustainability-related information. However, limited research has examined how AI-supported learning relates to sustainability-oriented decision-making capabilities in accounting education. Drawing on Decision Support Systems (DSS) theory and constructivist learning theory, this study examines the associations between generative AI-supported learning and students’ perceived sustainability judgment capability. Specifically, the study investigates the mediating roles of perceived critical thinking and perceived sustainability knowledge, as well as the moderating role of AI literacy. A quantitative, cross-sectional research design was employed using self-reported survey data collected from 721 accounting students, and the proposed relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that generative AI-supported learning is positively associated with students’ perceived critical thinking and perceived sustainability knowledge. In turn, both constructs show significant positive relationships with perceived sustainability judgment capability, with perceived sustainability knowledge demonstrating a stronger association. Additionally, AI literacy strengthens the relationships between generative AI-supported learning and the cognitive constructs. Importantly, the study captures students’ self-reported perceptions of their cognitive and judgment-related capabilities and does not assess objective cognitive performance or demonstrated judgment ability. The study contributes to the literature by positioning generative AI as an educational decision-support mechanism associated with perceived sustainability-oriented judgment capability through cognitive pathways, while highlighting the importance of aligning theoretical claims with perceptual measurement approaches. Full article

Background: Algorithm-based insulin dosing systems are increasingly used in hospitals and have shown the potential to efficiently and safely enable glycemic control. The goal of this study was to evaluate glycemic control using the ultralong-acting basal insulin degludec (IDeg) in combination with insulin aspart (IAsp) within an algorithm-driven electronic clinical decision support system (cDSS) in inpatients with type 2 diabetes (T2D). Methods: In this non-controlled single-arm pilot study, an electronic, algorithm-based cDSS was applied for the management of insulin treatment in an internal general ward. Thirty hospitalized patients with T2D (18 female, age 74.1 ± 10.9 years, HbA1c 72.4 ± 22.3 mmol/mol, BMI 28.6 ± 5.6 kg/m², diabetes duration 13.2 ± 11.6 years, creatinine 1.5 ± 1.2 mg/dL, length of hospital stay 9.1 ± 4.0 days) were included in the study. Capillary blood glucose (BG) was evaluated four times daily using a point-of-care device integrated into the hospital information system. In addition, all participants received a blinded continuous glucose monitoring (CGM; Abbott Freestyle Libre Pro) system. The primary endpoint was defined as the percentage of BG measurements within the target range of 3.9–7.8 mmol/L. Results: Overall, 722 BG values and 17,242 CGM data points were available. Of those, 52.2% and 55.0% were in the specified target area (3.9–7.8 mmol/L), respectively. Mean BG prior to study start was 11.9 ± 4.4 mmol/L and improved to 7.5 ± 1.9 mmol/L and 7.4 ± 1.4 mmol/L after 6 and 10 days of treatment. BG < 3.9, <3.0 and <2.2 mmol/L was 1.25%, 0.28% and 0%, respectively. Adherence to the total daily insulin dose suggested by the cDSS was 94.2%, and 99.5% of all basal and 85.3% of all bolus insulin suggestions were accepted by the nurses in charge. Basal-bolus therapy using the cDSS covered 85% of the participants’ total hospital stay. Conclusions: Glycemic control using IDeg within an algorithm-driven cDSS could effectively and safely be achieved in the hospital and was highly accepted. Full article

This study examines Responsible Decision-Making (RADM) in AI-enabled sustainability within tertiary education under conditions of uncertainty and complex interdependence. Conventional analytical approaches are limited in such settings because they typically explain behavioural relationships without adequately modelling uncertainty. To address this limitation, the study proposes an AI-driven Decision Support System (DSS) based on a hybrid probabilistic framework integrating PLS-SEM with Bayesian Network (BN) inference. The framework combines structural analysis with probabilistic reasoning in a unified, interpretable system capable of modelling conditional dependencies among decision variables. Data were collected from 713 academic leaders in tertiary education institutions in Saudi Arabia. The model examines the effects of AI-Driven Sustainable Value (AISV), Responsible AI Ease of Use (RAIU), Institutional Sustainability Support (ISS), Ethical Leadership Norms (ELN), Responsible AI Competence (RAC), and AI Risk and Hallucination Awareness (ARHA) on Responsible Decision-Making and Sustainability Impact Performance (GGIP). The results indicate that ELN and ARHA have significant positive effects on RADM, while AISV and RAIU also contribute positively to decision quality. In contrast, ISS and RAC do not demonstrate significant direct effects on RADM. However, ISS shows indirect effects through contextual and cognitive pathways. The findings further suggest that awareness of uncertainty and AI-related risks plays a more influential role in decision quality than technical competence alone. The model demonstrates strong explanatory power (R² = 0.64) and acceptable predictive capability (R² = 0.48). Bayesian inference further indicates that sustainability outcomes improve under favourable institutional and cognitive conditions. Overall, the framework provides an interpretable and scalable DSS that supports scenario-based evaluation and probabilistic decision analysis under uncertainty. The findings are specific to the institutional context examined in this study. Although the framework may have relevance to other organisational environments characterised by uncertainty and complex decision structures, no external or cross-contextual validation was conducted. Therefore, the findings should be interpreted with appropriate contextual caution. Full article

The use of satellite products for the identification of landslide-prone areas and zones affected by subsidence represents a research field in continuous evolution, thanks to the possibility of integrating radar data in multiple ways. Such information can be used as a static feature, as a criterion for the selection of landslide-absence samples, or as a true dynamic input. This work adopts the latter perspective, proposing an integrated framework of backscatter analysis and SBAS-InSAR analysis for the identification and characterization of landslide-affected areas. GRD images were preprocessed and analyzed through Google Earth Engine, from which temporal backscatter descriptors useful for highlighting instability signals were extracted. These were then combined with the results of the SBAS-InSAR technique. The integration of the two components allows the synergistic combination of different information derived from satellite products together with data characterizing the territory, improving the ability to identify areas subject to instability. The results, obtained over a portion of territory in Southern Italy, show that the inclusion of dynamic Sentinel-1 data significantly improves the identification of susceptibility areas. The synergistic use of dynamic SAR information allows the model to move beyond static or single-source susceptibility mapping, providing an updatable framework that supports near-real-time monitoring. The outputs are integrated into a 3D/4D WebGIS with Decision Support System (DSS) connotation, which further enhances the practical applicability of the methodology by enabling the real-time visualization and interpretation of the results. Full article

Despite the growing importance of Environmental, Social, and Governance (ESG) performance in shaping brand perception and consumer trust, limited empirical evidence exists on how ESG indicators translate into measurable digital consumer engagement outcomes, particularly in ethically driven markets such as the vegan food sector. This study addresses this gap by examining how ESG performance translates into digitally observable consumer engagement and frames this relationship as a strategic performance measurement and decision-support problem. Building on the sector’s reliance on ethical positioning, trust, and online visibility, we integrate ESG indicators with digital marketing and web analytics metrics (e.g., traffic and engagement proxies) for a panel of five leading vegan food firms [Nestlé SA (Vevey, Switzerland), Kellanova (Chicago, IL, USA), Beyond Meat Inc. (El Segundo, CA, USA), Danone SA (Paris, France), and Conagra Brands Inc. (Chicago, IL, USA)], using data from the Semrush web analytics platform and the Eikon Refinitiv ESG database for the period January–December 2024. We employ a mixed-method design combining descriptive analytics with correlation analysis and simple linear regression to estimate the direction and strength of ESG–digital performance links, and we extend inference through Fuzzy Cognitive Mapping (FCM) using the MentalModeler platform to simulate “what-if” scenarios that support managerial foresight under digital uncertainty. Results indicate that stronger ESG profiles are associated with more favorable digital outcomes, with specific ESG mechanisms (e.g., human-capital and environmental initiatives) aligning with deeper engagement signals. The FCM scenarios further suggest that coordinated ESG improvements can amplify digital traction and reinforce sustainable brand growth. The proposed framework contributes to strategic management by operationalizing an ESG-enabled digital performance measurement system and a lightweight Decision Support System (DSS) that can guide resource allocation, KPI monitoring, and risk-aware positioning in sustainability-oriented markets. Full article

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