Search Results (600)

Entrepreneurial ecosystems are multi-actor, uncertain, and dynamic environments in which policymakers and investors must balance innovation, resilience, and cost. Despite the growing literature on entrepreneurial ecosystems, much of the existing research has focused on identifying the components and relationships among actors and has provided less prescriptive frameworks for evaluating resource allocation policies before implementation. To address this gap, this study presents a digital twin-based and fuzzy multiobjective optimization framework for resource orchestration in entrepreneurial ecosystems. The proposed framework combines dynamic ecosystem representation with multiobjective decision-making under uncertainty and allows for the testing of different resource allocation and policy scenarios before actual intervention. To solve the model, exact optimization in GAMS was used for small- and medium-sized samples, and NSGA-II and ACO algorithms were used for large-scale problems. The advantage of the proposed method is that, unlike purely descriptive approaches or deterministic models, it simultaneously considers uncertainty, time dynamics, and trade-offs between innovation, resilience, and cost in an integrated decision-making framework. Experimental evaluation was conducted based on simulated data calibrated with reliable public sources, and the performance of the algorithms was compared with reference methods in terms of computational time, solution quality, and stability. The results showed that metaheuristics, especially NSGA-II, significantly reduced the solution time in large-scale problems and at the same time produced solutions closer to the Pareto frontier and with greater stability. Sensitivity analysis also showed that in the designed scenarios, policy budgets have a more prominent effect on innovation, while resource capacity and structural diversification play a more important role in enhancing resilience. Also, improving resource efficiency has had the greatest effect on reducing the total system cost. From a theoretical perspective, the present study operationally models the logic of resource orchestration in entrepreneurial ecosystems through the integration of digital twins and fuzzy multi-objective optimization. From a managerial perspective, this framework acts as a decision-making engine that allows for ex ante testing of policies, clarification of trade-offs, and extraction of resource allocation rules under uncertainty. Full article

Upper limb motor dysfunction resulting from stroke requires effective rehabilitation solutions; however, current exoskeletons are limited by single-input control, inadequate adaptation to various rehabilitation stages, and restriction to one limb. This study presents the development of a three-degree-of-freedom upper limb rehabilitation exoskeleton with three core innovations: (1) a neuro-fuzzy adaptive admittance control architecture that integrates human–robot interaction force and joint angular velocity as dual inputs for real-time damping adjustment, enabling accurate capture of dynamic movement intentions; (2) a Brunnstrom stage-specific fuzzy rule base that directly links clinical rehabilitation needs to adaptive control parameters; (3) a bilateral adaptable mechanical structure, allowing dual-upper limb training to enhance practical application. By combining radial basis function (RBF) neural network-based adaptive proportional–integral–derivative (PID) control with fuzzy variable-parameter admittance control, the system achieves a maximum trajectory tracking error of less than 1.2° and a root mean square (RMS) error of ≤0.13°. Trajectory tracing experiments confirm an RMS error of 2.99 mm for a circular trajectory at Bd = 2. The proposed strategy, validated through position tracking, admittance interaction, and trajectory tracing experiments, effectively balances tracking accuracy and human–machine compliance, providing valuable technical support for robot-assisted upper limb rehabilitation. Full article

Efficient aero-acoustic regression is critical for unmanned aerial vehicle (UAV) design and urban air mobility operations, where noise mitigation is essential for regulatory compliance and public acceptance. While data-driven fuzzy Takagi–Sugeno–Kang (TSK) systems have shown potential for modeling complex aero-acoustic behaviors in UAV applications, their performance is strongly affected by input dimensionality and rule-base complexity. This work extends previous research on dimensionality reduction for genetic algorithm-optimized fuzzy systems by conducting a comparative benchmark on an aero-acoustic database regression task relevant to drone propulsion noise prediction. Several TSK architectures are evaluated, including zero- and first-order models, different membership function granularities, and clustering-based rule-generation strategies. In addition, a physics-based heuristic TSK rule system incorporating aero-acoustic knowledge is introduced and compared against data-driven fuzzy configurations. Model performance is primarily assessed through graphical regression analysis and optimization convergence behavior, with a focus on computational efficiency, structural complexity, and qualitative prediction trends—critical considerations for onboard UAV systems and real-time acoustic monitoring. The results highlight the trade-offs between data-driven learning and physics-informed rule construction, demonstrating that physics-based heuristics can reduce optimization complexity while preserving physically consistent behavior. This study provides practical insights into the design of interpretable and efficient fuzzy regression models for UAV aero-acoustic applications, supporting next-generation drone acoustic signature management. Full article

Ride comfort is a critical issue in vehicle dynamics, as excessive vibrations adversely affect passenger comfort and human health. This paper presents a comparative performance analysis of a passive suspension system, fuzzy logic control (FLC), and a newly designed adaptive robust control (ARC) strategy applied to a nonlinear quarter-car suspension–seat–driver model. The primary objective is to improve ride comfort while maintaining vibration levels within accepted health criteria. First, the nonlinear dynamic model of the suspension–seat–driver system is established. The FLC structure and rule base are determined based on heuristic knowledge. Passive and FLC-based systems, while effective to some extent, suffer from limited adaptability to external disturbances and modeling uncertainties, slower convergence, and suboptimal vibration attenuation. The main contribution of this study is the design and implementation of a novel adaptive robust controller that effectively handles modeling uncertainties, external disturbances, and parameter variations. Different controller placement approaches within the system are also investigated. Numerical simulations are conducted under identical operating conditions for the uncontrolled system and all control strategies. The results demonstrate that although the FLC improves ride comfort compared to the passive system, the proposed ARC achieves the best overall performance, providing superior vibration attenuation, faster convergence, and enhanced robustness for nonlinear vehicle suspension systems. Quantitatively, the ARC reduces head acceleration RMS from 0.1693 m/s² (passive) and 0.1422 m/s² (FLC) to 0.0705 m/s², and upper torso RMS from 0.1689 m/s² (passive) and 0.1417 m/s² (FLC) to 0.0703 m/s², corresponding to approximately 58% reduction relative to passive and 50% improvement over FLC. Full article

This paper investigates ship–pier collision risk caused by yaw deviation in inland bridge waterways. The proposed framework is conceived for fixed auxiliary thruster installation in bridge areas, rather than retrofitting shipboard propulsion systems. A proactive intervention scheme is developed based on state estimation and short-horizon prediction. A Kalman filter is used for state fusion and short-horizon motion prediction. Yaw events are detected via a threshold rule with consecutive-decision logic. An extended state observer (ESO) is adopted to estimate lumped disturbances and model uncertainties. A fuzzy self-tuning PID law is then applied to generate thruster commands for closed-loop corrective control. Numerical simulations suggest that, relative to rudder-only recovery, thruster-assisted intervention yields improved restoration behavior, reduced lateral deviation accumulation, and increased minimum clearance to bridge piers under the tested conditions. Additional tests with cross-current disturbances indicate that the risk-triggered scheme with ESO-based compensation can maintain stable recovery and a higher safety margin. The proposed approach provides an engineering-oriented pathway to extend bridge-area risk management from warning-level assessment to executable control intervention. Full article

Accurate estimation of vehicle sideslip angle is vital for the stability and safety of four-wheel independent drive electric vehicles (4WIDEVs), but it faces challenges, including model uncertainties caused by tire yaw stiffness variations and system delays. This paper proposes a novel adaptive fusion strategy that combines the dynamic robust observer (DRO) and the improved adaptive square-root unscented Kalman filter (ASUKF). The DRO is designed based on a two-degrees-of-freedom vehicle model and ensures stability through linear matrix inequalities (LMIs), effectively handling parameter uncertainties and time delays; the ASUKF utilizes a three-degrees-of-freedom model and the magic formula tire model, combined with Sage–Husa adaptive filtering, to address the nonlinear tire dynamics. The key innovation of this paper is the introduction of a fuzzy-rule-based adaptive weighting mechanism that dynamically adjusts the fusion weights of the DRO and ASUKF in real time, thereby exploiting their complementary advantages under uncertainty and nonlinear conditions. The simulation and experimental validations demonstrate that this method significantly improves estimation accuracy, reducing the estimation error of vehicle sideslip angle by an average of 9.36%, and maintains robust performance and dynamic adaptability in various conditions, providing a reliable solution for the real-time state estimation of intelligent electric vehicles. Full article

Low-voltage ride-through (LVRT) capability is essential for grid-connected photovoltaic (PV) systems, especially as rising renewable integration challenges grid stability during voltage disturbances. Existing LVRT methods often target isolated control functions, leading to limited system resilience. This paper presents a unified control strategy integrating DC-link voltage regulation, reactive power injection, and overvoltage mitigation using a coordinated fuzzy logic framework. The proposed architecture employs a cascaded control structure comprising an outer voltage loop and an inner current loop with feed-forward decoupling, synchronized via a Synchronous Reference Frame Phase-Locked Loop (SRF-PLL). At its core is a dual-input, single-output Fuzzy Logic Controller (FLC), featuring optimized membership functions and dynamic rule-based logic to manage multiple control objectives during grid faults. The proposed FLC-based unified LVRT controller for grid-tied PV system was implemented and validated for both symmetrical and asymmetrical fault conditions in MATLAB/Simulink 2023b platform. The proposed FLC-based LVRT controller achieves voltage sag compensation of 97.02% and 98.4% for symmetrical and asymmetrical faults, respectively, outperforming conventional PI control, which achieves 94.02% and 96.5%. The system maintains a stable DC-link voltage of 800 V and delivers up to 78% reactive power support during faults. Fault detection and recovery are completed within 200 ms, complying with Bangladesh grid code requirements. This integrated fuzzy logic approach offers a significant advancement for enhancing grid stability in high-renewable environments and supports reliable renewable utilization, and more sustainable grid operation in developing regions. Full article

Breast cancer poses a global health risk and requires precision and accessibility in diagnostic measures. Ultrasound imaging is vital for breast lesion identification due to its safety, cost-effectiveness, and real-time capabilities. This paper presents a new fuzzy system architecture that utilizes ultrasound-based radiomics features to classify breast cancers. In order to ensure uniformity and consistency in shape-based characteristics limited to tumors, we calculate parameters such as elongation, compactness, spherical disproportion, and volumetrics following IBSI recommendations. We employ a hierarchical fuzzy system tree to handle high-dimensional data space and to identify the most discriminative characteristics. The selected features are incorporated into a modular fuzzy logic design that promotes transparency and maintains an auditable decision history according to clinical interpretability. Our framework enables the more accurate classification of breast cancer while addressing the beliefs and values prevalent in clinical applications. Tested on an independent set of data, the model achieved high accuracy of 99.60%, with low overfitting and strong generalization. To enhance its generalizability, we validated it on an internal dataset, attaining a sensitivity of 93.65%, a specificity of 99.24%, an AUC of 0.996, and an 18% reduction in unnecessary biopsies, as demonstrated through decision curve analysis, demonstrating substantial clinical utility across various settings. The findings confirm the system’s ability to identify intricate radiomic patterns linked to cancer. Due to its computing efficiency, it may be executed in real time during routine screening. The proposed radiomics-based fuzzy classification framework may offer a clinically beneficial approach for differentiating benign from malignant breast lesions. Explainability is enhanced with user-friendly artifacts for clinicians, including ranking IF-THEN rules and counterfactuals, all of which were validated in usability trials that demonstrated increased trust among radiologists compared to other technologies. Enhanced differentiation in the classification of various lesion types will decrease unnecessary biopsies. This approach integrates radiomics features with transparent and interpretable fuzzy logic to deliver enhanced predictors and a comprehensible framework for users, including physicians, to facilitate decision-making. This approach advances precision medicine standards through the early detection of lesions using more specific and systematic diagnostic instruments. Full article

Background and Objectives: Psychosocial symptoms and oral behaviors can complicate routine dental care, yet available screeners yield multiple separate scores. Explainable artificial intelligence offers a pragmatic way to integrate such multidomain measures into a single, auditable output that can support screening-oriented stratification and standardized documentation (non-diagnostic). Therefore, we aimed to develop an interpretable, deterministic Mamdani fuzzy inference system (FIS) integrating GAD-7, PHQ-9, and OBC-21 into a 0–10 psychobehavioral composite score (PCS) to support screening-oriented stratification and standardized documentation (non-diagnostic). Materials and Methods: Cross-sectional multicenter study in 18 private dental clinics in Romania (October 2024–March 2025; n = 460). A rule-based Mamdani Type-1 FIS was specified a priori (48 rules; triangular membership functions; centroid defuzzification) without supervised training. Internal evaluation assessed coherence across severity strata, robustness to predefined input perturbations (±1 point; ±5%) and membership-function variation (±10%), and benchmarking against linear composites (Z-mean; PCA PC1). Results: Median PCS was 2.30 (IQR 2.03–3.56). PCS correlated with GAD-7 (Spearman ρ = 0.886), PHQ-9 (ρ = 0.792), and OBC-21 (ρ = 0.687) (all p < 0.001), increased monotonically across anxiety and depression severity strata, and was higher in high OBC-21 risk. Robustness was excellent under input perturbations (ICC(3,1) = 0.983 for ±1 point; 0.992 for ±5%) and high under ±10% membership-function variation (ICC(3,1) = 0.959). Concordance with linear baselines was high (Spearman ρ = 0.956 for Z-mean; 0.955 for PCA PC1), with a small systematic nonlinearity at higher scores. Conclusions: PCS provides a fully auditable, rule-based integration of three patient-reported measures with coherent internal behavior and robustness to plausible measurement noise and specification changes. This study reports internal evaluation of a deterministic, rule-based aggregation; external clinical validation against independent outcomes is required before any clinical utility claims. Full article

During a vehicle’s approach to a stop, significant longitudinal impact and pitch oscillations occur due to the decrease in vehicle speed and the substantial nonlinearity of the electro-hydraulic braking (EHB) system. To balance comfort and control accuracy at the end of braking, this paper proposes a comfort braking control strategy based on deceleration evolution characteristics. This method utilizes the adjustable pressure characteristics of the EHB system to construct an adaptive PI (proportional-integral) controller based on fuzzy rules, achieving a smooth transition between normal braking and comfort braking without mode switching. Simultaneously, target deceleration planning is introduced to gradually reduce the vehicle’s deceleration during the approach to a stop. Simulation and real-vehicle test results show that at initial speeds of 36 km/h, 40 km/h, and 44 km/h, the longitudinal deceleration impact amplitude is reduced by approximately 3.8%, 16.7%, and 11.7%, respectively. At 4 s, the vehicle pitch angle is reduced by 3.4%, 3.4%, and 3.8%, respectively. Meanwhile, the average braking distance change is less than 0.05%, and the maximum braking distance change is less than 0.1%. The results demonstrate that this strategy effectively improves braking comfort during the vehicle’s start-stop phase without compromising braking performance. Full article

Health Internet of Things (Health IoT) systems continuously stream sensitive physiological data, making data access governance safety-critical under conflicting objectives such as privacy risk, latency, energy/resource cost, and robustness, especially when conditions change during emergencies. This paper proposes FiB-MOBA-EAFG, a hybrid blockchain–AI framework that separates on-chain accountability from off-chain decision intelligence. Off-chain, fuzzy context inference parameterizes scenario priorities, Pareto-based multi-objective search generates candidate governance policies, an emergency-aware feasibility guard filters unsafe trade-offs, and a bargaining-based selector chooses a single deployable policy. On chain, the blockchain layer records consent commitments, access events, and hashes of the selected policy and decision trace, serving as an immutable audit and accountability substrate rather than an online decision or optimization engine, while raw health data remain off-chain. Using simulation studies of home remote monitoring, clinic telehealth, and emergency triage under stochastic network variation and adversarial device behavior, FiB-MOBA-EAFG improves robustness and yields more repeatable policy selection than rule-based control and scalarized baselines within the evaluated simulation scenarios, while maintaining latency within ranges compatible with modeled edge deployment constraints through explicit emergency-aware feasibility constraints. A budget-matched random-search ablation further indicates that structured Pareto exploration is needed to reliably obtain robust, low-risk governance policies. Full article

In this study, we present an extension of the Takagi–Sugeno fuzzy inference system (TS-FIS) framework based on prototypical fuzzy similarity (PFS) for defect detection in concrete. The key novelty lies in integrating the PFS mechanism into the TS-FIS+ANFIS architecture, thus enabling a hybrid rule–activation mechanism, bringing together fuzzy interpretability with data-driven similarity learning. To describe the ultrasonic concrete defect scenario, a high-fidelity finite element method (FEM) model that combines solid mechanics with fluid acoustics has been developed. From this numerical model, a synthetic dataset of about 36.8 million samples has been generated. The performance of the proposed TS-FIS+ANFIS+PFS classification system has been compared with that of a conventional FIS+ANFIS model, its particle-swarm-optimized (PSO) version and a Decision Tree (DT) classifier. The proposed model achieved the best performance, with a classification accuracy of 85.4% and an inference time of approximately 0.2 ms per sample. In contrast, the conventional, the PSO and the DT classifiers yielded accuracies of 60.5%, 62.0%, and 76.0%, respectively. These results confirm that PFS improves sensitivity and alleviates the computational effort, representing a potential candidate toward the realization of a defect abacus for concrete, an atlas conceived as a systematic collection of defect configurations associated with specific ultrasonic responses. Full article

Multi-stack Polymer electrolyte Membrane Fuel Cell (PEMFC) systems are increasingly adopted in heavy-duty mobility to overcome the power limitations and thermal instability of single-stack configurations. However, the overall energy efficiency, hydrogen utilization, and thermal behavior of multi-stack fuel cell trucks are highly dependent on the applied Power Management System (PMS). In this study, high-fidelity, system-level dynamic model of multi-stack fuel cell truck was developed using Matlab/Simscape^TM, and three PMS approaches (rule-based control, state-machine control, and fuzzy logic control) were comparatively evaluated. The analysis includes coolant temperature regulation, hydrogen consumption, battery State of Charge (SoC) dynamics, and the parasitic power demand of Balance of Plant (BoP) components. Results show that the fuzzy logic PMS provides the most balanced operating profile by smoothing transient fuel cell loading and actively leveraging the battery during high-demand periods. In the thermal domain, the fuzzy logic PMS reduced temperature overshoot by up to 61.20%, demonstrating the most stable thermal control among the three strategies. Hydrogen consumption decreased by 3.08% and 0.89% compared with the rule-based and state-machine PMS, respectively, while parasitic power consumption decreased by 7.12% and 3.32%, confirming improvements in overall energy efficiency. TOPSIS-based multi-criteria decision analysis further showed that the fuzzy logic PMS achieved the highest closeness coefficient (0.9112), indicating superior system-level performance. These findings highlight the importance of PMS design for achieving energy-optimal and thermally stable operation of multi-stack PEMFC trucks and provide practical guidance for future control strategies, heavy-duty mobility applications, and next-generation hydrogen powertrain optimization. Full article

The drying process in indirect solar dryers is strongly influenced by rapidly changing ambient conditions, resulting in highly nonlinear and dynamic system behavior. Accurate modeling is therefore essential for performance evaluation, process optimization, and reliable prediction of the drying chamber temperature, which plays a key role in ensuring efficient moisture removal while preserving the nutritional and sensory quality of dried products. In this study, a fuzzy logic–based modeling approach using the Mamdani inference system is developed to predict the drying chamber temperature over a wide range of operating conditions. Experimental measurements were carried out with solar radiation varying from 400 to 950 W/m² and ambient temperature ranging from 20 to 50 °C, covering both static and dynamic system responses. The fuzzy model employs solar radiation and ambient temperature as input variables, represented by five and three triangular membership functions, respectively, while the drying chamber temperature is defined as the output variable using five triangular membership functions (T1–T5). The Mamdani inference system consists of 15 “if–then” rules, and centroid defuzzification is applied to obtain crisp output values. Model validation across the investigated operating range demonstrates a strong agreement between predicted and experimental temperatures. For example, at a solar radiation of 700 W/m² and an ambient temperature of 46 °C, the predicted chamber temperature is 50.9 °C compared to a measured value of 51.0 °C, while at 750 W/m² and 50 °C, the predicted temperature of 52.0 °C closely matches the experimental value of 51.8 °C. Statistical evaluation yields RMSE = 0.38 °C, MAE = 0.29 °C, and R² = 0.997, demonstrating effective temperature tracking capability within the tested operating range. These results show that the Mamdani fuzzy logic approach can effectively represent the thermal behavior of an indirect solar dryer within the tested operating range. The proposed model also provides a promising basis for the future development of real-time intelligent control strategies aimed at improving energy efficiency and product quality. Full article

The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with quantum-inspired computational optimization is proposed to enhance the control of temperature and flow rates in industrial extraction columns. The hybrid quantum-inspired fuzzy controller is applied to a petroleum extraction column. The controller adopts fuzzy rule weights using a quantum-inspired optimization algorithm. Compared with classical PID and fuzzy controllers, it reduces settling time and solvent consumption. A MATLAB/Simulink-based simulation model of the extraction column was developed to validate the approach. Experimental tests were conducted using synthetic data and varying operational parameters to evaluate control performance. The hybrid controller achieved a 0.7% reduction in phenol consumption and reduced temperature deviations by 2.2% compared to a baseline fuzzy controller. Energy savings ranged from 1% to 2% depending on the operating scenarios. These results were confirmed through repeated simulations and statistical analysis. The proposed system demonstrates the potential of quantum-inspired fuzzy control to enhance process efficiency, reduce energy use, and improve product quality in complex chemical extraction applications. The statistical evaluation was based on repeated simulation runs and comparative performance metrics rather than physical experiments. Full article