Search Results (5,531)

Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1 mm–0.2 mm. The OC’s non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications. Full article

Effective water resource management requires understanding the causal mechanisms driving water quality dynamics, yet extracting actionable insights from complex multivariate monitoring data remains a persistent challenge. This study presents CEcBaN (CCM-ECCM-Bayesian Networks), a decision-support tool that integrates Convergent Cross Mapping (CCM) for detecting dynamical coupling, Extended CCM (ECCM) for identifying temporal lags and causal directionality, and Bayesian network (BN) modeling for probabilistic scenario-based inference. The tool was designed to enable managers and researchers without programming expertise to reconstruct causal networks from routine monitoring data, distinguish direct from indirect effects, and evaluate intervention scenarios. CEcBaN was validated using four synthetic datasets with known causal structures, achieving superior specificity (0.83) and edge count accuracy (25% error) compared to Transfer Entropy (0.47 specificity, 139% error), Granger causality (0.82, 39% error), and the PC algorithm (0.83, 46% error). Application to Lake Kinneret, Israel, demonstrated the tool’s utility across three water quality challenges: (1) nitrogen cycling, where the nitrification pathway was reconstructed and seasonal stratification was identified as a key modulator (accuracy 0.931); (2) thermal dynamics, where a transition from atmosphere-driven to internally regulated heat transfer during stratification was revealed (2.1-fold increase in coupling strength); and (3) cyanobacterial bloom prediction, where prior phytoplankton community composition provided a 4–6-week early warning window (accuracy 0.846). CEcBaN advances causal inference in water resource management by making these analytical methods accessible through an intuitive interface. Full article

The dispatch planning process plays a central role in liquid transportation, where the accurate selection of trailers, ISO tanks, vehicles, and drivers determines the effectiveness, safety, and cost structure of operations. Each resource has its own technical, regulatory, and operational characteristics, and these characteristics must align with product specifications, transportation routes, loading and delivery conditions, and the current state of the fleet. The breadth of these parameters makes resource selection a highly complex task for planners, especially in environments where rapid decision-making is needed to address changing demands. This study presents a rule-based expert system designed to capture the decision-making logic of experienced professionals and apply it consistently during dispatch planning. The system incorporates 28 decision rules formulated from the collective knowledge of experts working in liquid logistics operations, including planners, industrial engineers, and senior managers. These rules enable the system to evaluate multiple resource combinations and recommend the most suitable allocation for each order. The expert system was evaluated using real operational data obtained from a leading logistics company in Turkey. Comparative results indicate that the system provides more cost-effective, efficient, and balanced dispatch plans than manual planning conducted by an experienced human planner. The system not only improves resource utilization but also reduces planning errors and variations arising from human judgment. Overall, the findings demonstrate that a rule-based expert system can serve as a reliable and scalable decision-support tool for complex dispatch planning problems in liquid transportation, offering consistent performance across different operational scenarios. Full article

Against the growing global focus on green development, government subsidies are widely recognized as a crucial policy tool to promote firms’ green transformation. In competitive markets, green investment decisions are jointly shaped by supply chain power structures, and different research and development (R&D) leadership can yield distinct policy outcomes. This study develops a Bertrand competition model of a green supply chain with one manufacturer and two competing retailers, comparing two structures: manufacturer-led R&D (SM) and retailer-led R&D (SR). We examine how these policies affect pricing decisions, product greenness, and revenues. Under the retailer-led R&D, a green cost-sharing ratio is introduced to capture the interaction between internal coordination and government support. The results show that subsidy effects depend on consumer green awareness. When green awareness is low, subsidies mainly raise prices through cost pass-through. When green awareness is high, subsidies can lower prices by stimulating demand. In addition, the interaction between subsidy intensity and cost sharing leads to non-monotonic changes in retailers’ revenues. By comparing different market structures and parameter settings, we identify the conditions under which SM or SR dominates in terms of prices, product greenness, and revenues, providing guidance for more flexible green subsidy design. Full article

Amid rapid urbanization in China, widespread gated residential districts have created physical and visual isolation from surrounding nature, undermining environmental benefits and daily accessibility. The emergence of a twenty-first-century “sharing” paradigm reshapes how buildings and landscapes are used and experienced, opening new opportunities for diversified sharing between communities and natural systems. Yet, despite mature research on city-scale landscape sharing, micro-scale tools to balance sharing versus exclusive route allocation—and to operationalize cross-system sharing-route design—remain limited. This study examines nature-integrated community design through the Landscape Route Sharing Ratio (LRSR), a metric derived from the Length and Density of Sharing Landscape Route (L_s/D_s), the Length and Density of Non-shared Landscape Route (L_ns/D_ns). It analyzes eight cases using a mixed-methods approach (field surveys, spatial mapping, planning-document review and quantitative measurement), and identifies five core cross-system features through typological analysis: extension to surrounding landscapes (ENL), cross-boundary landscape axes (CBLA), multi-scale hierarchy (MSH), multi-elevation systems (MES), and non-motorized priority (NMP). This study demonstrates that higher LRSR values significantly enhance landscape integration and pedestrian experiences. By establishing actionable target ranges (0.50–0.70), the research provides a practical decision-support tool for nature-integrated community design, advancing the methodological understanding of how shared routes foster ecological and social vitality in contemporary urban environments. The framework effectively bridges the gap between quantification with design guidance for nature-integrated communities. Full article

Background/Objectives: In exercise science and sports medicine, the potential use of large language models for generating personalized exercise programs is being explored. However, the practical applicability of AI-generated exercise prescriptions has not yet been sufficiently validated, particularly in complex clinical contexts. This study aimed to evaluate their practical utility under expert supervision. Methods: Exercise prescription outputs generated by a large language model (Gemini 2.5, Google LLC) were analyzed using clinical cases incorporating complex exercise-related considerations. Three levels of prompt structuring were applied. Experts evaluated the outputs using a structured rubric assessing safety, feasibility, guideline alignment, and personalization. Inter-expert agreement was assessed using intraclass correlation coefficients (ICC), and expert-specific internal consistency was evaluated using Cronbach’s alpha. Results: AI-generated exercise prescriptions demonstrated a certain level of structural completeness. However, inter-expert agreement was low (ICC (2,3) = 0.139), whereas expert-specific internal consistency was high (Cronbach’s alpha > 0.92). Prompt structuring from Stage 1 to Stage 2 was associated with improved mean scores in safety and guideline alignment. Additional structuring did not consistently yield further improvements. Conclusions: AI-generated exercise prescriptions may have practical potential as supportive decision-making tools when expert involvement is assumed. Nonetheless, expert judgments did not converge toward a single evaluative standard, reflecting the inherently expert-dependent nature of exercise prescription. Full article

Background: Modern supply chains (SC) are increasingly difficult to manage as they become more complex and interconnected. This encourages companies to rely more on real-time data analysis and analytical tools on operational processes. This study aims to develop and evaluate a Supply Chain Wave Report for a non-food retail that represents goods movement across logistics stages as a continuous analytical flow. Methods: Proposed framework integrates multiple operational phases—Booked Orders, Main Transit, On-Carriage, Warehouse Operations, Store Delivery, and Sales—into a unified monitoring structure. This model can combine operational data with advanced analytics, including Artificial Intelligence-, cloud computing-, and Internet of Things-based technologies. Through cloud-based data infrastructures, System enables data integration and near real-time visibility across organizational functions, allowing continuous monitoring through key performance indicators and predictive simulations. Results: This framework enables dynamic performance of supply chain management and generates real-time signals as goods move across logistics network. This enables managers to detect irregularities earlier and respond before operational deviations propagate further along the chain. Wave-based monitoring approach highlights interdependence between SC stages and illustrates how small disruptions may propagate over time, potentially contributing to effects like bullwhip effect. Conclusions: Findings suggest that a cloud-enabled wave analytics framework can enhance coordination, reduce information gaps, and support informed decision-making in retail. Full article

Background: Warfarin pharmacogenomics is critical due to its narrow therapeutic index and significant interpatient variability. While machine learning (ML) can predict anticoagulation control status (ACS), its “black-box” nature limits clinical translatability. Explainable Artificial Intelligence (XAI) addresses this by providing interpretable insights. This study applied ML and XAI to a warfarin pharmacogenomic dataset to predict poor ACS and explain model decisions. Methods: A post hoc analysis was conducted on a cross-sectional dataset of 232 patients receiving warfarin for ≥6 months. Data included age, gender, interacting drugs, SAMe-TT2R2 score, and genotypes for CYP2C9, VKORC1, and CYP4F2. Poor ACS was defined as time in therapeutic range (TTR) < 70%. The dataset was split into training (70%) and testing (30%) cohorts. Three models, Random Forest, XGBoost, and Logistic Regression, were developed and evaluated using AUC-ROC, sensitivity, and specificity. XAI techniques, including permutation importance and SHapley Additive exPlanations (SHAP), were employed for global and local interpretability. Results: Of 232 patients, 141 (60.8%) had poor ACS. XGBoost and Random Forest demonstrated comparable predictive accuracy (AUC-ROC: 0.67), outperforming Logistic Regression. Sensitivity was 0.83 and 0.79 for XGBoost and Random Forest, respectively. However, specificity was modest for both ensemble methods (Random Forest: 0.48; XGBoost: 0.41) and extremely low for Logistic Regression (0.04), indicating poor discrimination, particularly for identifying patients with adequate anticoagulation control. Globally, important predictors included the age, SAMe-TT2R2 score, CYP2C9 (*2/*2), female gender, and VKORC1 (C/T). XAI revealed predictions were primarily driven by VKORC1, CYP4F2, SAMe-TT2R2 scores, and drug interactions. Concordance between XAI predictions and actual ACS was 78% for adequate and 88.6% for poor ACS. SHAP analysis showed VKORC1 provided a stable risk signal (mean absolute SHAP: 1.44 ± 0.49 in concordant cases), while CYP2C9 was a high-variance, high-impact driver of discordance (mean SHAP: 3.44 ± 3.79 in discordant cases). Conclusions: ML models, particularly ensemble methods, show modest ability to predict poor warfarin control with limited ability to correctly identify patients with adequate control from our dataset. XAI transforms these models into interpretable tools, with SHAP analysis attributing predictions to specific genetic and clinical features. While predictive accuracy remains modest, this approach enhances transparency and provides a foundation for generating hypotheses that may ultimately support clinical decision-making in pharmacogenomic-guided warfarin therapy. Full article

This study examines the use of artificial intelligence (AI) in organizational decision-making processes (DMPs) related to investments in renewable energy sources (RESs). The research addresses the gap between AI’s technological capabilities and its actual application in investment practice. An empirical two-stage survey was conducted in 2025, and a comparative analysis was conducted to assess the stability of attitudes toward AI adoption. The findings indicate a low level of practical implementation of AI tools in investment decision-making, despite a clear perception of their potential usefulness, particularly for risk analysis and improving decision objectivity. Organizations tend to perceive AI primarily as analytical support rather than an autonomous decision-making mechanism. The results also reveal a persistent level of uncertainty and hesitation associated with trust in AI systems. Comparative analysis confirms that these attitudes remain stable across research stages, suggesting structural rather than temporary barriers to adoption. This study demonstrates that limited adoption of AI in renewable energy investment decisions results mainly from organizational readiness and trust-related factors rather than technological constraints. The paper contributes empirical evidence on the behavioral and organizational determinants of AI implementation in the context of sustainable energy transition. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

Upper extremity (UE) pain and pathology are prevalent among manual wheelchair users (MWUs) due to repetitive loading demands, highlighting the need for tools to identify high-risk tasks and inform injury prevention. This study investigated the feasibility of classifying activity intensity for wheelchair-related tasks using wearable sensors and supervised machine learning. Twenty-four MWUs with chronic spinal cord injury completed a standardized mobility course and simulated activities of daily living while UE electromyography (EMG) and inertial measurement unit (IMU) data were collected. Signals segmented into 3, 5, and 10 s windows, and time- and frequency-domain features were extracted and labeled as low, moderate, or high intensity. Multiple classification algorithms were evaluated using subject-dependent and subject-independent cross-validation, and dimensionality reduction was explored to assess class separability. Subject-dependent analyses demonstrated performance above chance but below 75% accuracy, with decision tree models demonstrating superior performance, particularly when trained on data segmented into 5 s windows. IMU features outperformed EMG features, but combining signal types enhanced performance. Subject-independent analyses revealed similar overall accuracy across signal types, but decreased high-intensity classification for EMG data, indicating subject dependency. Findings support the potential of wearable sensor-based machine learning with population-specific findings for activity intensity classification in MWUs, while highlighting challenges related to inter-subject variability for injury risk prediction. Full article

This paper presents a design research study that develops a comprehensive conceptual framework for an integrated digital twin system for architectural heritage. The framework aims to explore mechanisms for real-time monitoring and the coupled regulation of structural health, microclimatic conditions, and energy performance. In the context of the ongoing global warming emergency, this framework supports climate adaptation strategies for heritage sites. It enables a fully coordinated operational process encompassing real-time sensing, predictive analysis, coupled control, and decision support. In the structural dimension, the framework is designed to utilise sensors to monitor and warn against cracks, settlement, and deformation, whilst integrating models to analyse stress conditions. In the microclimate dimension, the study envisages predicting and adjusting HVAC and lighting systems based on environmental parameters and footfall monitoring data via algorithms, with the aim of balancing occupant comfort with humidity control and mould prevention. Regarding energy, the framework optimises equipment operation through smart metering and algorithms and we propose a modelling tool for the quantitative assessment of energy-saving retrofit effects. Furthermore, the framework incorporates the establishment of an open-access dataset covering structural, microclimate, and energy use data, providing data standards and a foundation for subsequent empirical research. Full article

Unmanned aerial vehicles (UAVs) are increasingly being used as diagnostic platforms in electricity transmission and distribution, enabling safer and faster inspections compared to manual climbing operations or manned aerial support. This article presents an implementation-oriented inspection process that integrates RGB imaging, infrared (IR) thermography and (optionally) LiDAR documentation for critical energy infrastructure and photovoltaic (PV) installations. The survey consists of two stages: a preliminary stage under controlled conditions and an operational stage in a real-world environment, limited only by UAV flight restrictions. Thermal measurements are recorded in radiometric formats and analyzed using polygon- and profile-based tools to identify temperature anomalies (hot spots) and support maintenance escalation decisions. This manuscript presents standardized sample templates for mission logs, QA/QC activities, and anomaly lists, intended to support reproducible data collection in future studies. The proposed process supports predictive maintenance by enabling repeatable inspections, archive-based trend analysis, and integration with asset management processes, while minimizing operational risk and avoiding power outages when technically feasible. Full article

Adaptive reuse has emerged to become a tool for implementing the understanding of sustainability in the domain of architectural conservation, as it encourages the continued usage of old buildings as means of reducing environmental impact, as well as preserving socio-cultural capital while generating economic income. However, in its practice, the decisions regarding granting meanings, interpretation, and preserving memories within adaptation processes are dominated by expert-driven approaches that inadequately incorporate stakeholder values or intangible heritage dimensions. To this end, this study aims to contribute to the current debate by adopting a participatory co-evaluation framework that integrates both authenticity perspectives and sustainability dimensions using Multi-Criteria Decision Analysis (MCDA) for evaluating adaptive reuse alternatives for an abandoned prefabricated wooden heritage building. Stakeholder priorities were drawn through a workshop and transformed into normalized weights using the Simos technique. Four design alternative typologies—namely, Continuity, Cultivation, Differential, and Optimization—were assessed and compared against 20 performance indicators across heritage, social, ecological, and economic criteria using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Indicator-level analyses and sensitivity tests (±10% and ±20% weight variations) were applied to confirm the robustness of rankings. The results from the best-performing alternative demonstrated the trade-offs between heritage authenticity and sustainability objectives, as well as demonstrating how combining participatory methods with quantitative evaluation can support evidence-based decision-making for adaptive reuse. The applied integrated framework helps bridge the gap between heritage theory and practice by combining authenticity, participation, and sustainability in one analytical approach, supporting evidence-based decisions for adaptive reuse. Full article

Planning capacity for wind–photovoltaic (PV)–thermal–storage systems with high renewable penetration requires models that address investment costs, operational feasibility, and strict carbon limits under uncertainty. This paper presents a two-stage robust optimization model for integrated wind–PV–thermal–storage capacity expansion that guarantees carbon compliance under worst-case renewable realizations. Unlike conventional approaches that relax carbon constraints through price penalties, we enforce the annual carbon emission cap as a hard operational constraint, ensuring candidate portfolios remain feasible even under adverse renewable conditions. To reflect practical storage design, a fixed energy-to-power (E/P) ratio couples storage energy capacity with power converter ratings, preventing unrealistic storage expansions. Renewable uncertainty is captured through a Bertsimas–Sim budgeted polyhedral set defined over representative days, balancing robustness with computational tractability. A tailored decomposition framework integrates economic dispatch and carbon-compliance verification within an outer column-and-constraint generation (C&CG) loop, simultaneously certifying worst-case operating cost and minimum achievable emissions. By exploiting strong duality, we generate two families of valid inequalities iteratively: economic cuts from the Economic subproblem (Economic-SP) and carbon-feasibility cuts from the Carbon subproblem (Carbon-SP). This dual-certification approach ensures capacity plans remain both economically optimal and carbon-compliant across all uncertainty realizations. Case studies on a realistic wind–PV–thermal–storage system demonstrate that the method produces carbon-compliant, robust capacity plans with manageable computational effort, converging in 10–15 iterations. The model explicitly captures operational coupling among renewables, thermal generation, and storage, providing a decision-support tool for low-carbon power systems under deep decarbonization targets. Full article