Search Results (700)

Precision agriculture increasingly requires intelligent systems capable of integrating multimodal sensing with transparent decision support to enable timely and reliable crop management. This study proposes a hybrid intelligent IoT framework integrating environmental monitoring, wearable plant physiological sensing, AI-based pest-monitoring, machine-learning-based prediction of crop physiological stress, and explainable fuzzy rule-based decision support into a unified architecture for crop stress assessment. A novel Physiological Stress Index (PSI) was developed by combining vapor pressure deficit, relative humidity, Delta-T, leaf capacitance, and relative irradiance to provide an interpretable indicator of crop physiological stress. The proposed framework was experimentally validated under real field conditions using a commercial environmental monitoring station, wearable leaf sensors, AI-enabled pest-monitoring devices, and cloud-based analytics. Correlation analysis confirmed strong relationships between PSI and the principal environmental variables (VPD: r = 0.980, Delta-T: r = 0.990, RH: r = −0.961), demonstrating the internal consistency and sensitivity of the proposed index. At the 15 min forecasting horizon, Linear Regression and Gradient Boosting demonstrated virtually identical performance: Gradient Boosting achieved a marginally lower RMSE and higher R² (RMSE = 0.0273; R² = 0.9810), whereas Linear Regression achieved a slightly lower MAE (MAE = 0.0186). At the 1 h forecasting horizon, Gradient Boosting achieved the strongest performance (R² = 0.9034), indicating increasing relevance of nonlinear modelling at longer prediction horizons. The proposed framework demonstrates the feasibility of combining multimodal sensing, machine learning, explainable artificial intelligence, and edge-enabled IoT technologies to support proactive, transparent, and intelligent precision agriculture. Full article

The quantitative measurement of TCC at tourist cities involves considerable methodological complexities. Conventional evaluation methods based on fixed numerical criteria are frequently insufficient since tourism systems are characterized by unclear information, gradual shifts, and subjective experiences. To address these methodological constraints, this study develops and implements a hybrid FAHP-weighted Fuzzy Comprehensive Evaluation (FCE) framework to estimate TCC. The framework incorporates four critical indicators, visitor density, waste generation, infrastructure load, visitor perception, reflecting the physical, environmental, infrastructural, and socio-cultural dimensions of tourism development. These indicators are assessed using a structured analytical procedure integrating fuzzification, expert-derived weighting through the fuzzy analytic hierarchy process, fuzzy comprehensive evaluation, and centroid defuzzification. This study presents a hybrid SEM–FAHP–FCE framework, which is a multi-method integrated decision model that combines SEM (structural equation modeling) for visitor perception and behavioral drivers, FAHP (fuzzy analytic hierarchy process) for expert-based indicator weighting and FCE (fuzzy comprehensive evaluation) for final carrying capacity assessment under uncertainty. This combined methodology offers a more adaptive and empirically grounded assessment of TCC by synthesizing quantitative conditions with qualitative stakeholder perceptions within a unified evaluative model. The framework is validated through empirical application to Vrindavan, Uttar Pradesh, one of India’s most visited destinations, across eight study sites and two seasonal conditions (normal and festival-peak). Primary data collection comprises 300 visitor count observation units and field data, i.e., visitor count, waste generation and infrastructure details. The defuzzified FAHP–FCE results for each site (W = 3.94, 3.33, 2.44, 1.95, 3.66, 1.87, 1.86 and 2.71) reveal that major pilgrimage nodes exhibit scores approaching the upper bound of the evaluation scale, indicating that tourism pressure has significantly exceeded carrying capacity during peak periods. This study establishes proof-of-concept for the FAHP–FCE framework as a standardized, replicable instrument for evidence-based TCC governance at tourism cities. Full article

Amid accelerating climate change, natural resource depletion, and rising consumer demand for sustainable, health-promoting products, circular agriculture has emerged as a strategic pathway for reconciling agricultural productivity with environmental and social sustainability; however, empirical evidence on how circular production, health-product innovation, and experiential tourism can be jointly evaluated within a single model remains limited. This study aims to propose a multidimensional value assessment framework for circular agriculture through a case study of the Tam Dao Mushroom Cooperative (TDMC) in Phu Tho Province, Vietnam, centered on the cooperative’s Cordyceps militaris-based production system, nanotechnology-enabled health-product processing, most notably the Nano Cordy Milk functional beverage and Science, Technology, Engineering, and Mathematics (STEM)-oriented experiential tourism activities, comprising guided production tours, hands-on cultivation and processing demonstrations, and on-site product tastings offered to school and university groups, domestic and international tourists, and visiting farmers and cooperatives. The research integrates a case study approach with the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) to determine the priority weights of value dimensions based on the evaluations of seven experts. The findings identify five key dimensions of value generated by the circular agricultural model: economic value, environmental value, social value, educational value, and tourism value. The Fuzzy-AHP results indicate that economic value has the highest priority weight (Best Non-fuzzy Performance, BNP = 0.4050), followed by environmental value (0.2715), social value (0.1307), educational value (0.1313), and tourism value (0.0621). The study demonstrates that the circular agriculture model not only generates economic benefits but also simultaneously creates significant environmental, social, educational, and tourism values. These findings contribute to the theoretical foundation for assessing the multidimensional value of circular agriculture while providing practical references and policy implications for the sustainable development of circular agricultural models. Full article

Digital transformation in agricultural production requires automated approaches for monitoring, risk assessment, and decision support under uncertainty. This study proposes an automated risk assessment and control framework for feed production in digital agroengineering systems. The framework is designed for the quantitative evaluation of producer and consumer risks arising during the control of key feed-quality parameters, using the case of feed production for cattle in the OHMK agricultural holding. The proposed approach integrates probabilistic modeling, simulation-based risk estimation, fuzzy logic, expert evaluation, and a multi-agent representation of agroengineering processes. A three-dimensional risk model is developed to represent producer risk, consumer risk, and actuarial risk as interconnected components of a digital control environment. In addition, a fuzzy model is introduced to assess the robustness and digital maturity of management functions, including organization, planning, motivation, and control. Computer experiments based on statistical data for crude protein content in silage demonstrate that control risks depend nonlinearly on measurement uncertainty, parameter variability, and normative thresholds. In the analyzed single-indicator case study, the arithmetic mean of crude protein content in silage was 7.5% of dry matter, the standard deviation was 0.5, and the Weibull approximation parameters were α = 1.0, β = 2.5, and γ = 6.0. Under the most sensitive normative threshold scenario, producer risk increased to approximately 25%, while consumer risk showed a lower but nonlinear increase with measurement uncertainty. The results show that producer risk may reach significant levels when measurement uncertainty becomes comparable with the variability of the controlled parameter. The proposed framework can serve as a computational basis for future automated monitoring, risk-aware control, and decision-support systems in Industry 4.0-oriented agricultural production. Full article

Remote sensing change detection technology is widely used in land-use monitoring, urban planning, and disaster assessment. However, during imaging and transmission, bi-temporal remote sensing images are vulnerable to Gaussian noise, which makes it difficult for change detection algorithms to distinguish truly changed areas from noise-affected regions. To address this issue, this study proposes an unsupervised Gaussian-noise-robust change detection algorithm, termed FRIH-SEEDSAM. The proposed method first applies the Fast and Robust Fuzzy C-Means (FRFCM) algorithm to perform noise-resistant fuzzy clustering on bi-temporal remote sensing images. To establish reliable correspondences between the clustering results, the Integrated Region Matching (IRM) algorithm is introduced to construct weighted matching relationships while reducing the influence of abnormal memberships. The change intensity of spatially corresponding pixels is then calculated to generate a more stable change intensity map. Subsequently, the change intensity map is input into the Hybrid Conditional Random Field (HCRF) to infer pixel-level change labels, where the object potential function is constructed from the segmentation results of the Superpixels Extracted via Energy-Driven Sampling (SEEDS)-guided Segment Anything Model (SEEDSAM), which uses the centroids of the SEEDS superpixel regions as point prompts for the SAM, thereby enhancing change-label consistency within the same changed object region. The experimental results show that the FRIH-SEEDSAM algorithm maintains stable change detection performance across different datasets and under varying Gaussian noise levels. It outperforms the comparison algorithms in terms of several accuracy evaluation indicators, including Kappa and F1. Furthermore, even when the Gaussian noise variance increases to 0.05, Kappa remains at 0.8 or above on multiple dataset images. Full article

Small- and medium-sized enterprises (SMEs) increasingly use artificial intelligence (AI)-based customer tools and social media marketing to compete in digital markets. However, prior research has not fully explained how customer-facing digital interaction and strategic customer sensing are converted into internal organisational capabilities or how alternative combinations of capabilities lead to business sustainability. In this study, we develop and test a sequential mixed-method framework for Thai SMEs. In Phase I, we applied the Interval Type-2 Fuzzy Delphi Method (IT2FDM) with 21 experts to validate 43 observed variables. In Phase II, we analysed 659 Thai SME responses using Partial Least Squares Structural Equation Modelling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The PLS-SEM measurement assessment showed that service innovation and internal learning formed a consolidated service innovation–internal learning capability (SILC) construct, with 42 indicators retained in the final measurement model. The structural model supported all hypothesised paths: AI chatbot usage, social media marketing, and customer value anticipation were positively associated with SILC; SILC was positively associated with external learning, business performance, and business sustainability; external learning was positively associated with business performance; and business performance was positively associated with business sustainability. The fsQCA results showed that no single present or absent/low condition was necessary for business sustainability and identified three sufficient pathways, with SILC and business performance present across all primary configurations. One pathway further showed that strong SILC, external learning, and business performance could support business sustainability even when AI chatbot usage, social media marketing, and customer value anticipation were weak or absent. The findings advance SME digital transformation and sustainability research by demonstrating capability conversion, integrated innovation–learning transformation, and multiple compensatory pathways to business sustainability. Full article

Accurately assessing the fatigue levels of air traffic controllers is crucial for reducing human errors in ATC and ensuring the safe and orderly operation of the civil aviation transportation system. In remote tower scenarios, air traffic controllers’ work environments and task interaction modes differ significantly from those in traditional towers, and traditional fatigue detection approaches relying on physiological monitoring can cause intrusive disruptions to ATC operations. To overcome these limitations, this study proposes a scenario-based, non-invasive assessment framework for accurate and low-interference fatigue recognition. Taking three key scenario elements (traffic load, main operation screen brightness, and core work area illuminance) as the basis for measuring fatigue, the framework bridges the mapping from scenario elements to fatigue status, thereby enabling the transition of assessment inputs from physiological metrics to scenario features. In this mapping, fatigue labels are determined using a fusion strategy. Specifically, objective fatigue labels are derived from optimal wave features extracted from electroencephalogram data using one-way analysis of variance (OW-ANOVA), which are then fused with subjective labels based on the Karolinska Sleepiness Scale (KSS) self-reports through fuzzy C-means (FCM) clustering. Ultimately, a hybrid intelligent classification model integrating the Gannet optimization algorithm (GOA) and random forest (RF) is constructed to perform the primary assessment task. The experimental results indicate that the proposed framework achieves a recognition accuracy of 95.00%, outperforming six other commonly used classification or combination models. Ablation experiments and robustness tests validate the effectiveness of the fused labeling strategy and GOA modules, as well as the method’s excellent stability in resisting data noise. Furthermore, feature interpretability analysis reveals the quantitative influence of the three core fatigue drivers used. The research findings confirm the feasibility of non-invasive fatigue assessment for remote tower controllers leveraging scenario-based elements, which can offer intelligent decision support for controller shift scheduling, visual environment optimization, and targeted safety interventions. Full article

This study presents an experimental validation of an adaptive fuzzy logic controller (AFLC) for solar-driven proton exchange membrane (PEM) hydrogen production under dynamic operating conditions. The proposed solar-driven PEM hydrogen production system was accurately modeled and investigated under various operating conditions using the MATLAB/Simulink simulation platform. In order to evaluate the effectiveness of the proposed controller, the AFLC strategy was experimentally investigated using a dSPACE DS1104 platform and compared with conventional Proportional–Integral (PI) and Fuzzy Logic Controller (FLC) approaches in terms of tracking accuracy, transient response, overshoot suppression, current ripple minimization, and overall hydrogen production stability. Under step-change solar irradiance conditions, the proposed AFLC exhibited superior MPPT performance, achieving improvements of 34.32% and 84.26% in power-tracking accuracy compared with the conventional FLC and PI controllers, respectively. The developed control architecture employs real-time feedback from electrolysis to regulate the duty cycle of the DC–DC converter controlled by maximum power point tracking (MPPT), ensuring precise power delivery to the PEM electrolysis despite fluctuating solar irradiance. Additionally, the solar hydrogen production system’s performance indices were computed. These indices are intended to assess the viability of the AFLC in comparison to the PI and FLC under the same solar irradiance conditions. Full article

The Middle Route of the South-to-North Water Diversion Project (SNWD-MR) serves as a strategic infrastructure critical to safeguarding water security in Northern China. Traversing complex geographical units, the project is perpetually exposed to long-term risks of land subsidence. Conventional Interferometric Synthetic Aperture Radar (InSAR) monitoring is hampered by waterbody isolation, causing spatial discontinuities in the retrieved deformation fields; furthermore, relying solely on deformation metrics fails to comprehensively quantify multidimensional risks. To address these issues, this study proposes an integrated assessment framework that couples time-series InSAR observations with the Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation (AHP-FCE) model. To specifically mitigate the challenge of waterbody isolation, we developed a connectivity-aware multiscale down-sampling phase unwrapping strategy. By exploiting cross-canal bridges to construct a spatial connection network, a highly accurate, spatiotemporally continuous deformation field across the entire alignment was successfully reconstructed. Using the derived deformation field as the core dynamic indicator, an AHP-FCE model integrating hydrogeological features and human perturbations was constructed. A complementary evaluation process comprising sensitivity analysis and an internal physical consistency assessment was subsequently implemented. The results demonstrate that (1) the proposed algorithm effectively resolves the spatial discontinuity issue of the cross-canal deformation fields, reducing the deformation-velocity RMSE from 7.9 to 5.7 mm/y, corresponding to an approximately 27.8% reduction in RMSE relative to the traditional Minimum Cost Flow (MCF) method; (2) land subsidence along the alignment exhibits prominent spatial heterogeneity, with the northern Henan and southern Hebei sections identified as very-high-risk zones; and (3) InSAR deformation magnitude and the groundwater elevation indicator emerge as the most influential factors in the modeled risk distribution. Overall, this study expands conventional deformation monitoring into a systematic, quantitative risk assessment framework, thereby providing scientific insights and theoretical support for the early warning of geo-hazards and the smart operation and maintenance of large-scale water diversion projects. Full article

This paper presents the design, control, and validation of a solar photovoltaic (PV)-powered DC microgrid (MG) integrated with a battery energy storage system (BESS), which was studied at laboratory scale as a step towards remote electrification in resource-constrained regions. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based maximum power point tracking (MPPT) algorithm is implemented to maximise solar energy extraction under varying irradiance. An Adaptive Power Management (APM) framework is proposed to maintain DC bus stability when the BESS is unavailable to support the bus—a condition that may arise from battery degradation, sensor or communication failures, converter malfunctions, protection trips, or physical damage. In this work, BESS unavailability is represented at the system level as the withdrawal of BESS support; the individual fault mechanisms that may cause it are not separately modelled. The APM operates across three hierarchical layers—monitoring, decision, and control—and reuses only the voltage and current measurements already present in the MG, requiring no additional sensing. The system is evaluated under three operating scenarios: (i) intermittent renewable generation; (ii) varying load demand; (iii) stochastic fluctuations in both irradiance and load. During BESS unavailability, the APM activates prioritised adaptive load shedding or PV generation curtailment as appropriate, preserving critical loads and preventing DC bus overvoltage. In the scenarios studied, the APM reduces worst-case voltage sag from 35.9% to 2.4% and worst-case swell from 53.51% to 0.14%, while maintaining BESS State of Charge (SOC) within 20%–80% during normal operation. Compared with the conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, the ANFIS-based MPPT achieves a mean point-wise tracking and conversion efficiency of 99.46%, a 1.78% improvement and a 0.86% improvement, respectively, which were corroborated by independent energy-based assessments (1.76% and 0.92%), with voltage deviations of 2.34% and oscillations of only 0.57 V peak-to-peak. Lyapunov-based analysis establishes asymptotic stability of the DC bus voltage in the BESS-regulated operating modes under stated assumptions. The proposed control strategies are validated through MATLAB/Simulink (R2025b) simulations and laboratory-scale experimental results, with the latter demonstrating coordinated PV–BESS–converter operation and bus voltage regulation. Full article

Cultural heritage sites are affected by environmental and anthropogenic pressures that require decision-analysis methods capable of combining heterogeneous datasets while accounting for uncertainty. Multi-Criteria Decision Analysis (MCDA), particularly when integrated with Geographic Information Systems (GIS) and Earth Observation (EO) data, is widely used in geospatial analysis involving multiple, often conflicting criteria. This review examines the evolution, application domains, and methodological challenges of MCDA in cultural heritage risk assessment. The literature indicates a predominant reliance on weighting-based methods, especially the Analytic Hierarchy Process (AHP) combined with GIS-based weighted overlay techniques, while uncertainty treatment, temporal monitoring, validation, and multi-threat applications remain limited. Three illustrative applications show that asset-level, regional susceptibility, and historic-urban frameworks address complementary decision needs but differ in their data, expertise, and institutional requirements. Recent developments show a trend to combine MCDA with fuzzy logic, machine learning, and uncertainty modeling, although methodological consistency across these approaches remains uneven. The findings suggest that multi-criteria risk assessment for cultural heritage may depend less on introducing new analytical techniques and more on improving the integration of existing methods. Incorporating repeatable environmental observations, sensitivity analyses, multi-threat assessment, and stakeholder participation may support a more coherent and reproducible approach to heritage-risk assessment. Full article

Agriculture is increasingly challenged by water scarcity, climate change, and rising energy demands, requiring more efficient and sustainable irrigation solutions. Conventional irrigation systems often lack the capability to adapt their operation to changing soil conditions and renewable energy availability, resulting in inefficient water and energy use. This study proposes a digital twin-based framework for energy management and irrigation optimization in photovoltaic (PV)-powered smart agriculture systems using Internet of Things (IoT) soil monitoring. The proposed system integrates a physical irrigation infrastructure, an IoT monitoring network, a fuzzy logic control layer, and a digital twin environment that periodically synchronizes the virtual model with IoT measurements to support the system representation and decision-making. The digital twin models soil moisture, temperature, nutrient levels, PV energy generation, battery state of charge, and irrigation water consumption. The virtual representation was periodically aligned with the physical system using measurements transmitted through the long-range (LoRa)-based network. An energy-aware irrigation scheduling strategy was developed to optimize irrigation timing based on soil conditions, battery status, and solar energy availability. The framework was evaluated using field data collected in a real apple orchard through an ESP32-based IoT platform and a standalone PV-powered irrigation system; quantitative experimental validation was performed for the soil twin. The results demonstrate high soil twin synchronization accuracy, with an overall RMSE of 1.47 percentage points and R² of 0.981, based on experimental field measurements. The energy twin and irrigation twin were evaluated using experimentally acquired sensor data together with model-based performance assessment, demonstrating the potential of the proposed digital twin framework for integrated water–energy management in smart agriculture. Full article

The rapid growth of cloud and distributed computing systems has increased the complexity of real-time request distribution under dynamic and multi-factor conditions. In such environments, load-balancing decisions must simultaneously consider uncertain and interdependent parameters, including server load, response time, and resource capacity. Fuzzy logic is an effective tool for modeling such uncertainty; however, the expansion of linguistic variables often leads to a rule-explosion problem, which increases computational complexity and reduces the real-time applicability of fuzzy load-balancing systems. This study proposes an explainable multi-objective quantum-inspired fuzzy optimization approach for scalable load balancing in complex computing environments. The proposed model integrates fuzzy inference with a Grover-inspired classical search strategy to optimize the selection of fuzzy rule subsets. The Grover-inspired component is implemented as a classical simulation rather than a gate-based quantum circuit. A multi-objective evaluation function is formulated to jointly assess rule accuracy, coverage, interpretability, and compactness. This formulation enables the model to reduce redundant fuzzy rules while preserving decision transparency and maintaining reliable load distribution performance. The proposed approach is evaluated in a simulated cloud computing environment with heterogeneous servers and dynamic request arrival patterns. Comparative experiments are conducted against classical load-balancing strategies, conventional fuzzy load balancing, and evolutionary fuzzy optimization methods, including GA-FLB and PSO-FLB. The experimental results show that the proposed model reduces the size of the fuzzy rule base while maintaining competitive response time, load distribution quality, SLA compliance, and decision interpretability. These findings indicate that the integration of Grover-inspired classical search mechanisms with fuzzy reasoning provides a promising direction for developing scalable, compact, and explainable load-balancing models for next-generation intelligent computing systems. Full article

Navigational obstacles pose compound collision and pollution risks, yet conventional quantitative assessment models relying on data-driven “best-estimate” approaches suffer from “alarm masking”, whereby critical risk signals are diluted through averaging. This study develops a conservative hybrid risk assessment framework that preserves critical risk signals while systematically incorporating qualitative factors beyond the reach of quantitative data. The fuzzy inference rules of an existing integrated model were redesigned into a priority-stratified hybrid hierarchical–parallel fuzzy inference system (HHP-FIS); a qualitative evaluation matrix of four categories and 32 items was constructed through a two-stage expert procedure (a Delphi panel of eight officials and an analytic hierarchy process (AHP) survey of 59 experts with 34 valid responses); and a Red Flag Protocol was introduced as a fail-safe veto mechanism. The framework was verified through eighteen paired random-input simulations across two grid systems and a case study of a 68.9-ton drifting fishing vessel near Seongsan Port, Jeju Island. The model upwardly reclassified underestimated low-frequency, high-consequence scenarios, raised the case-study risk from Low (44.6 and 47.7) to Moderate (59.1 and 74.8), with an action level consistent with expert judgment, and was robust to rule-weight perturbations, providing a decision-support tool for obstacle-removal prioritization and marine pollution prevention. Full article

Flooding remains a major hydro-meteorological hazard in KwaZulu-Natal, yet province-wide susceptibility assessments incorporating modelling uncertainty are limited. This study develops an ensemble multi-criteria flood susceptibility framework integrating Analytical Hierarchy Process (AHP), fuzzy logic transformation, and frequency ratio (FR) modelling within a cloud-based geospatial environment. Twelve hydro-geomorphological and environmental conditioning factors, including topography, rainfall, land cover, hydrology, and soil proxies, were normalized using percentile scaling. Three independent flood susceptibility models were generated and combined using ensemble mean aggregation, while pixel-wise standard deviation quantified spatial uncertainty. Model validation employed a 10-year historical flood inventory (2015–2025) comprising 65 documented flood locations. The ensemble flood susceptibility index (FSI) ranged from 0.05 to 1.00, with moderate susceptibility zones covering 51.08% of the province. High and very high susceptibility classes occupied 8.36%, indicating spatially concentrated but hydrologically significant risk hotspots. Uncertainty analysis showed low inter-model variability (0.00–0.11), demonstrating strong methodological stability. Validation results confirmed that 73.85% of historical flood points were located within high susceptibility zones, with over 90% captured within overall susceptible classes. The study introduces a hybrid deterministic–fuzzy–probabilistic ensemble modelling approach combined with pixel-level uncertainty mapping and scalable cloud computation. Findings support disaster risk reduction, urban and catchment planning, and early warning system optimization in flood-prone regions. The framework provides a transferable methodology for data-limited environments requiring reliable and uncertainty-aware flood hazard assessment. Full article