Search Results (573)

The cleaning unit is a key functional component of grain combine harvesters, yet its operating parameters are still predominantly adjusted according to operator experience, resulting in limited adaptability to fluctuating working conditions. To enhance the intelligence and stability of the cleaning process, this study develops a fuzzy control approach supported by data-driven performance modeling. Based on multi-condition bench experiments, feeding rate, fan speed, cleaning sieve vibration frequency, and sieve opening were selected as input variables. Gaussian Process Regression (GPR) models were established to describe the nonlinear relationships between operating parameters and cleaning loss rate and impurity rate, and impurity rate was inferred online to compensate for the absence of a reliable sensor. Taking feeding rate variation as the primary disturbance, a dual-input fuzzy control strategy was designed using loss rate monitoring and model-predicted impurity rate as feedback signals. Simulation and bench test results show that, under small and moderate load disturbances (±20% and ±35%), the proposed method reduces either impurity rate or cleaning loss rate through coordinated parameter adjustment. Under large disturbances (±50%), performance deterioration cannot be fully eliminated, but its extent is alleviated compared with open-loop conditions. Full article

This study addresses the gap between predictive optimization and monitoring-oriented risk concentration by introducing a hierarchical Fuzzy Cognitive Map (FCM) framework for financial risk assessment. Financial distress prediction models are employed to estimate firm-level default probabilities and are required to comply with regulatory standards. IFRS 9 and Basel III/IV frameworks emphasize model explainability, scenario analysis and causal transparency, which are essential for compliance purposes. The methodology aggregates correlated financial ratios into financial concepts through unsupervised clustering. Concepts interact through a learned coupling matrix and a controlled multi-step propagation, which enables the amplification of risk signals. A small residual correction is applied at the final readout, preserving the interpretability of the proposed framework. The framework was applied to two severely imbalanced benchmark bankruptcy datasets. It achieved higher precision–recall performance than Logistic Regression (PR–AUC $\approx 0.32$ vs. $0.27$), improved calibration (Brier score $\approx 0.046$ vs. $0.089$) and maintained competitive Recall@Top–K under tight supervisory monitoring budgets. Hierarchical FCM achieved predictive performance comparable to nonlinear models while maintaining concept-level interpretability. Our findings demonstrate that structured concept aggregation combined with interaction-based propagation provides a transparent alternative to purely predictive black-box models in financial distress assessment and is aligned with regulatory frameworks. 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

This paper proposes to estimate the travel time at each edge of an urban street network using Artificial Neural Networks (ANNs). To improve the ANN performance and minimize errors in manual design, an Algorithm Auto-Design ANN Topology (A-DANNT) is introduced that automatically determines the most suitable architecture for regression problems. The methodology implements an algorithm based on Tabu Search, called the Best R-Value Determination algorithm (BR-vD), which optimizes the topology obtaining a lower Mean Square Error (MSE) and a higher correlation coefficient. The process is developed in three phases: first, the variables that impact the travel time are analyzed; then, the proposed algorithm is used to find the best topology; and finally, the travel times are estimated. The proposal is evaluated in two case studies: in the first, the algorithm automatically designs the architecture, and a 0.99366 correlation coefficient is achieved between the results and the objectives. In the second case, the performance of the algorithm is compared with a fuzzy travel time model, achieving a 0.99898 correlation coefficient. In both cases, the proposed algorithm is capable of designing topologies with coefficients greater than 0.99 and Mean Absolute Errors (MAEs) of 3.2765 and 0.6957 s, respectively. Full article

In order to achieve the carbon peaking and carbon neutrality goals in China’s transportation sector, this paper examines the regional data in transportation carbon emissions across China, investigates the shifting trends of the carbon emission centroid over time, and proposes a novel representation using fuzzy set theory and rough set theory for carbon emission prediction. This paper employs the ESDA model to analyze the regional distribution of carbon emissions in the transportation sector across 30 provinces in China for the years 2005, 2010, 2015, and 2020. Utilizing the economic centroid model and standard deviation ellipse, the trend of carbon emission centroid shifts in China’s transportation sector is examined, revealing that the carbon emission centroid for all four time points is located in Henan Province. Subsequently, focusing on Henan Province, ridge regression analysis is conducted to identify the driving factors influencing carbon emissions in the transportation sector from 2005 to 2020. Lastly, a combined approach integrating scenario analysis and the STIRPAT model is employed to forecast carbon emissions in the transportation sector of Henan Province for the period 2021–2035. The findings suggest that high-carbon-emission regions in China’s transportation sector gradually extend from the eastern coastal areas to the southwestern regions, with an overall trend of the carbon emission centroid shifting northward. The carbon emission centroid for the years 2005, 2010, 2015, and 2020 is consistently located in Henan Province. Ridge regression analysis indicates that population size, transportation energy consumption intensity, energy structure, transportation economic share, and per capita GDP all have promoting effects on carbon emissions in Henan Province’s transportation sector. Based on the combined approach of scenario analysis and the STIRPAT model, it is predicted that the transportation sector in Henan Province may reach its carbon peak between 2027 and 2029. These conclusions facilitate the formulation of region-specific emission reduction policies and measures tailored to the transportation sector. Full article

The optimization of agrochemical spraying can be approached by increasing the efficiency of product distribution, which improves application quality and the biological effectiveness of the treatment. This study presents the development and evaluation of four distinct control strategies to adjust the hydraulic system of a new small, low-cost, electric, vertical variable-rate sprayer based on variations in travel speed, aiming to maintain a constant spray volume during operation and, thereby, increase distribution efficiency. The evaluated algorithms were developed from a mathematical model of the prototype’s hydraulic system obtained from experimental data and using the system identification tool in MATLAB software version 2021. Two open-loop algorithms (linear regression and Fuzzy) and two closed-loop algorithms (Integral and Fuzzy-PD with output integration) were developed. The evaluation was conducted through simulations, using a normalized speed data series provided by the United States Environmental Protection Agency. Performance evaluation results determined that the Fuzzy-PD algorithm with output integration showed the best performance (ISE = 0.21 × 10⁻⁵), followed by the linear regression algorithm (ISE = 3.38 × 10⁻⁵). The results demonstrated that, compared to applications based on fixed rates defined by nominal parameters, the developed sprayer showed potential to improve the uniformity of spray distribution in the field. Full article

Research on eco-friendly and energy-efficient machining processes has gained significant importance within the domain of sustainable production. This study is focused on enhancing the energy performance and sustainability of the milling process. Four machine learning (ML) models, namely, multiple linear regression (MLR), support vector regression (SVR), Gaussian process regression (GPR), and adaptive network-based fuzzy inference system (ANFIS), were proposed to estimate specific energy consumption (SEC) in the milling of Ti6-Al4-V under two eco-benign cooling conditions: cryogenic and minimum quantity lubrication (MQL). Several statistical metrics, including normalized mean absolute error (nMAE), mean absolute percentage error (MAPE), normalized root mean square error (nRMSE), maximum absolute percentage error (maxAPE), coefficient of determination (R²), and Willmott’s index of agreement (IA), were employed to validate the performances of the ML models. A high level of agreement between the predicted and experimental SEC data for both the training and test datasets supports the reliability of the proposed ML models. Although the MLR model performed well, the results revealed that the other ML models demonstrated better overall performance. According to the statistical metrics, the models’ predictive performance improved in the following sequence: MLR, SVR, GPR, and finally ANFIS, which demonstrated the highest predictive capability. Full article

Addressing the challenges of agricultural water scarcity requires widespread adoption of water-saving irrigation technologies (WSIT) by farmers, yet actual adoption rates remain persistently low. To investigate farmers’ intention to adopt WSIT, this study employs the social–ecological systems framework and analyzes samples of 3007 farmers using a mixed-methods approach combining binary logistic regression and fuzzy-set qualitative comparative analysis (fsQCA). The results indicate that cognitive levels, social environment, production conditions, grassroots governance, and policy environment exert significant positive effects on farmers’ intention to adopt WSIT. The study identifies several conditional configurations leading to high adoption intention, including endowment-driven, governance-substitution, internalization-driven, contextual configuration, and resilience-compensation pathways. Further analysis reveals that an integrated “soft power” enablement system, which is composed of effective grassroots governance, deep individual cognition, targeted policy support, and a favorable social environment, could effectively overcome constraints posed by limited production conditions or exposure to natural risk. These findings provide critical insights for relevant sectors to develop differentiated policies to promote WSIT adoption. Full article

A study on the sources, bioconcentration, and translocation of heavy metals in Haloxylon ammodendron in the Eastern Junggar Coalfield, Xinjiang, China, was conducted and evaluated. The quantities of Pb, Cd, and Cr were 1.2, 22.5, and 1.9 times higher than the baseline values of Xinjiang soils, respectively. The mean concentrations of these heavy metals in the rhizosphere soil of Haloxylon ammodendron were 48.81, 17.74, 93.25, 3.32, 29.05, and 26.95 mg/kg. The exceedance rates for Cd, Cr, and Pb in bare soil were 100%, 99.03%, and 75.73%, respectively, indicating significant accumulation of heavy metals, with Cd demonstrating the highest enrichment degree. Most sampling sites showed moderate pollution according to the Pollution Load Index (PLI). Meanwhile, the Pollution Index (PN) indicated elevated pollution levels at all the sampling sites, with Cr identified as the first contaminant. The absolute principal component score–multiple linear regression (APCS-MLR) model revealed three principal sources of heavy metal pollutants in soil: 44.2% from natural processes and mining activities, 22.7% from industrial coal combustion and sewage, and 33.1% of undetermined origins. The bioconcentration factors (BCFs) and translocation factors (TFs) revealed Haloxylon ammodendron to have clear accumulation and translocation abilities with respect to these heavy metals. The fuzzy membership function showed that the overall assessment score for Haloxylon ammodendron was 9.1325, indicating the substantial remediation potential of Haloxylon ammodendron for heavy metal pollutants, especially for Cd. Furthermore, Haloxylon ammodendron demonstrated substantial Pb and Cr accumulation and remediation ability. Haloxylon ammodendron exhibited remarkable heavy metal accumulation and translocation abilities, making it a suitable tool for phytoremediation in the study area. The findings of this study will prove useful in promoting and implementing sustainable mining practices and safeguarding regional ecological security and may contribute to advancing local ecological conservation and social economic development. Full article

This study evaluates the feasibility of estimating stiffness-related parameters and pulse wave velocity (PWV) in a controlled in vitro circulatory setup using artificial silicone vessels with systematically varied Shore A hardness and wall thickness. From synchronized pressure and capacitive waveforms, fiducial points and engineered features are extracted, together with pump settings (stroke volume and heart rate). A Sugeno-type adaptive neuro-fuzzy inference system (ANFIS) is used for hardness-level prediction and benchmarked against linear regression and contemporary machine-learning/deep-learning baselines using stratified cross-validation. PWV estimates derived via hardness-to-elasticity conversion models and the Moens–Korteweg formulation are evaluated against a reference PWV obtained within the same experimental configuration. Under these controlled conditions, the proposed pipeline shows strong agreement with reference labels and measurements. The results should be interpreted as an in vitro validation step; translation to biological tissues or in vivo data will require external validation, calibration of material-property mapping, and robustness testing under physiological variability and measurement noise. Full article

In the fight against global climate change, the transportation sector is of critical importance because it is one of the major causes of total greenhouse gas emissions worldwide. Although urban rail transit systems offer a lower carbon footprint compared to road transportation, accurately forecasting the energy consumption of these systems is vital for sustainable urban planning, energy supply management, and the development of carbon balancing strategies. In this study, forecasting models are designed using five different machine learning (ML) algorithms, and their performances in predicting the energy consumption and carbon footprint of urban rail transit systems are comprehensively compared. For five distribution-center substations, 10 years of monthly energy consumption data and the total carbon footprint data of these substations are used. Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Neural Network (NAR-NN) models are developed to forecast these data. Model hyperparameters are optimized using a 20-iteration Random Search algorithm, and the stochastic models are run 10 times with the optimized parameters. Results reveal that the SVR model consistently exhibits the highest forecasting performance across all datasets. For carbon footprint forecasting, the SVR model yields the best results, with an $R^2$ of 0.942 and a MAPE of 3.51%. The ensemble method XGBoost also demonstrates the second-best performance ($R^2=0.648$). Accordingly, while deterministic traditional ML models exhibit superior performance, the neural network-based stochastic models, such as LSTM, ANFIS, and NAR-NN, show insufficient generalization capability under limited data conditions. These findings indicate that, in small- and medium-scale time-series forecasting problems, traditional machine learning methods are more effective than neural network-based methods that require large datasets. Full article

Directional swelling pressure is a critical yet often overlooked factor governing the instability of expansive soil slopes. Most existing studies simplify swelling behavior as a uniform or purely vertical stress, thereby underestimating the distinct contribution of horizontal swelling pressure. In this study, an integrated framework combining the Fuzzy Analytic Hierarchy Process (FAHP), multivariate regression analysis based on 35 expansive soil samples, and three-dimensional strength-reduction numerical modeling was developed to systematically evaluate the mechanistic roles of vertical and horizontal swelling pressures in slope deformation. The FAHP and regression analyses indicate that water content is the dominant factor controlling both the free swell ratio and swelling pressure, leading to predictive relationships that link swelling behavior to fundamental physical indices. These empirical correlations were subsequently incorporated into a three-dimensional numerical model of a representative Neogene expansive soil slope. The simulation results demonstrate that neglecting swelling pressure results in substantial discrepancies between predicted and observed displacements. Vertical swelling pressure induces moderate surface uplift but exerts a limited influence on overall failure patterns. In contrast, horizontal swelling pressure markedly amplifies downslope displacement—by more than four times under saturated conditions—reduces the factor of safety by 24.7%, and promotes the progressive development of a continuous slip surface. These findings clearly demonstrate that horizontal swelling pressure is the dominant driver of progressive failure in expansive soil slopes. This study provides new mechanistic insights into swelling-induced deformation and offers a quantitative framework for incorporating directional swelling stresses into slope stability assessment, design optimization, and mitigation strategies for geotechnical structures in expansive soil regions. Full article

Achieving sustainable delivery is a critical goal in international engineering projects, which involve interdependent actors—such as contractors, suppliers, and designers—engaged in simultaneous cooperation and competition. This study investigates how coopetition, conceptualized as intensity and balance, affects sustainable delivery performance through dynamic capabilities. Specifically, we introduce exploitation and exploration as mediating capabilities and examine their effects under coopetition structures (horizontal vs. vertical). We use hierarchical regression analyses, relationship critical tests, and the fuzzy set qualitative comparative analysis (fsQCA) approach. Using survey data from 172 global projects, the results show that exploitation and exploration partially mediate the relationship between coopetition intensity and sustainable delivery performance, and fully mediate the effect of coopetition balance. The analysis uncovers a structural differentiation in capability efficacy, showing that exploitation yields stronger effects within horizontal structures, whereas exploration exerts greater influence under vertical structures. fsQCA reveals three complex configurational pathways to sustainable delivery performance, demonstrating the compensatory configurational pathways in which structural characteristics can, under certain conditions, substitute for dynamic capabilities. This study extends the application of coopetition and dynamic capability theories to the context of international engineering projects and underscores the crucial role of governance structures in shaping capability development and sustainable delivery outcomes. Full article

Reliable assessment of bridge technical condition is a key challenge in infrastructure management due to uncertainty, subjectivity, and heterogeneity inherent in inspection-based data. Traditional deterministic evaluation methods often fail to capture the gradual nature of structural deterioration and the complex interactions between bridge components. This study proposes a hybrid methodology that integrates fuzzy logic and artificial neural networks (ANNs) to quantify the overall technical condition of bridge structures using long-term inspection data. A comprehensive dataset, derived from real bridge inspection reports collected over more than 15 years across various regions of Ukraine, served as the basis for model development. Five key input parameters—substructure condition, superstructure condition, deck condition, overall structural condition, and channel and channel protection condition—were employed to compute an integrated Bridge Condition Assessment indicator using a Mamdani-type fuzzy inference system. The resulting fuzzy-based indicator was subsequently used as the target variable for training ANN models. To ensure optimal predictive performance and training stability, Bayesian Optimization was applied for systematic hyperparameter tuning. Model performance was evaluated using standard regression metrics, including MSE, MAE, MAPE, and the coefficient of determination (R²). The results demonstrate that the proposed approach enables accurate approximation of the fuzzy-based Bridge Condition Assessment indicator, with MAPE values as low as 0.2% and R² exceeding 0.982 for the best-performing model. The hybrid framework effectively combines interpretability and scalability, providing a decision-support framework based on fuzzy logic and surrogate modeling for automated fuzzy-based bridge condition assessment, maintenance prioritization, and integration into digital asset management systems. Full article

Amid the expansive evolution of the digital economy and the emergence of enhanced productivity paradigms, exploring the ways in which digital technology affordance propels corporate digital innovation via multifaceted cooperative routes is essential for reconfiguring industrial ecosystems, securing digital market advantages, and promoting superior advancement. This investigation employs the TOE model, merging fuzzy-set qualitative comparative analysis (fsQCA) with regression analysis. Using data from 2206 listed manufacturing companies from the A-share exchanges (2010–2023), it identifies multiple antecedent configuration pathways of digital technology affordance and examines their differential impacts on enterprise digital innovation. Key findings include the following: (1) no solitary factor serves as an obligatory prerequisite for high-quality digital technology affordance. (2) Four configuration pathways were identified: technology-organization-environment tripartite-propelled, technology-organization collaborative-propelled, technology-environment collaborative-propelled, and organization-environment collaborative-propelled variants. (3) The influence of digital technology affordance on digital innovation shows conditional dependence. Under the ternary-driven “technology-organization-environment” or synergy-driven “technology-organization” configurations, and absent conflicting enterprise goals, digital technology affordance promotes digital product innovation. Supported by collaborative configurations of technological investment, digital infrastructure, highly educated talent, institutional measures, and public service efficiency, it fosters digital process innovation. However, isolated technological investment, employees’ educational attainment, and institutional measures inhibit business model innovation. Other configurations lack significant impacts on digital business model innovation. This study elucidates the generation mechanism of digital technology affordance using configuration theory, offering empirical insights for managers to enhance digital innovation and drive high-quality economic development. The study enhances the theoretical depth by exploring technological foundations of digital technologies and addressing generalizability through framework adaptations for global contexts. Full article