Search Results (10,861)

High-precision robotics is frequently compromised by joint compliance, a factor often over-simplified by traditional rigid-body modeling. This research investigates the structural dynamics of a two-link manipulator, addressing critical discrepancies between experimental data and conventional models. Much like biological musculoskeletal systems, joint flexibility fundamentally influences the dynamic response of articulated structures. While traditional rigid-joint models accurately capture mode shapes, they yield excessive natural frequency prediction errors with peaks reaching 72%. To bridge this gap, a refined Flexible-Joint Finite Element Model (FJFEM) is developed to mimic adaptive joint compliance. This model is integrated with a bio-inspired computational framework (a Double-Stage Genetic Algorithm Framework (DSGAF)) to identify configuration-dependent joint stiffness across the operational workspace, where experimental frequencies $f_1$ and $f_2$ shift nonlinearly from 25.5 Hz to 44 Hz and 92.2 Hz to 51 Hz, respectively. Experimental validation demonstrates that this evolutionary strategy reduces frequency tracking errors to less than 3.5% across all positions, achieving an average identification routine runtime of 1.8 s. By capturing nonlinear compliance behavior, this framework provides a robust foundation for the design, online calibration, and vibration control of advanced flexible robotic systems. Full article

The transition to net-zero energy systems involves substantial uncertainty in exogenous conditions such as policy, fuel prices, and technology deployment. Conventional energy system optimization models, formulated as forward problems, excel at identifying a single least-cost solution but provide limited insight into the diverse configurations feasible within an acceptable cost range. This study proposes a hierarchical inverse-analysis framework integrating a genetic algorithm (GA) and linear programming (LP). The upper-level GA explores a broad space of exogenous conditions, including selected fuel-price assumptions, technology-cost conditions, equipment capacities, end-use electrification rates, CO₂-capture installation rates, and CO₂-storage limits, while the lower-level LP rigorously optimizes operations for each candidate. The framework applies explainable AI (SHAP) to identify dominant cost-determining factors and their interactions, and employs k-means clustering to compress the high-dimensional feasible solution space into illustrative archetypes. As an illustrative demonstration, the framework is applied to a hypothetical 2050 net-zero case for the Kanto region. The framework, under the assumed conditions, generates diverse feasible solutions, identifies influential cost-related conditions and their interactions, and organizes the generated solution set into five illustrative archetypes. The proposed framework extends energy system modeling beyond single-optimum solutions toward interpretable decision-support analytics for long-term net-zero planning under deep uncertainty. Full article

This paper presents an optimization-based framework for active vibration control of rotor bearing systems using external surface-bonded piezoelectric patches. The rotor bearing system is modelled using the Finite Element Method (FEM), enabling the coupling between the shaft and the flexible piezoelectric actuators. A Linear Quadratic Regulator (LQR) is adopted to achieve optimal feedback control considering the balance between vibration reduction and control effort. The central contribution of this work is a comprehensive actuator placement optimization of the axial and angular position of the piezoelectric patches along the shaft. Firstly, axial positions are selected by maximizing a multimodal weighted Modal Strain Energy (MSE) criterion over a selected number of bending modes. In the second stage, which constitutes the main novelty of this work, the angular position of each pair of bonded piezoelectric patches is optimized. Each piezoelectric pair generates control moments at each extremity of the patch. The influence of the angular separation between independent piezoelectric pairs bonded at different axial locations is investigated through an iterative optimization framework. The optimized actuator placements are subsequently employed within an LQR-based active vibration control framework. The parameters of the controller are selected using a Genetic Algorithm (GA). Numerical simulations are performed on a bi-disk flexible rotor bearing system. The results of the numerical simulations demonstrate that the combined axial-circumferential optimization significantly enhances the controllability of the rotor system and improves the multimodal vibration suppression capability, achieving an improvement of approximately 93%. The proposed methodology offers a physically meaningful and computationally efficient framework, guaranteeing symmetric and effective vibration control. Full article

The high speed and manoeuvrability of cross-domain unmanned aerial vehicles (CDUAVs) significantly reduce the handover window between mid-terminal guidance stages, challenging reliable target acquisition. To address this, we propose an optimisation method for interceptor selection and field-of-view (FOV) cooperative coverage based on high-probability region (HPR) modelling. First, a predictive error covariance propagation model is constructed based on error propagation theory, and the HPR is established via eigenvalue decomposition. Second, the cooperative detection by heterogeneous interceptor seekers is formulated as a coverage optimisation problem with mixed FOV, and a cost minimisation model under complete coverage constraints is established. Finally, an improved genetic algorithm (IGA) is employed for solution, and a coverage area ratio screening mechanism based on two-dimensional close-packing theory is designed to enhance optimisation efficiency. Simulation results demonstrate that the probabilistic modelling approach for CDUAV HPR can accurately characterise the anisotropic distribution of target position uncertainty; the algorithmic mechanism reduces redundant computational load by 62.5% and shortens the optimisation time by 36.7%; the intelligent coverage optimisation framework provides a more generalisable solution for cooperative detection by heterogeneous interceptors’ seekers under conditions of target localisation uncertainty. Full article

This study investigates the integration of a multi-configuration parametric shading system in buildings with continuous ramp designs to enhance daylight performance and visual comfort. Focusing on the Harbourside Art Museum in Bristol, UK, the research explores how discrete-configuration parametric façade configurations can be optimized to balance daylight access and glare control in complex spatial environments. A parametric simulation workflow was developed using Rhino, Grasshopper, Ladybug, and Honeybee, supported by Radiance and Daysim engines for Climate-Based Daylight Modelling (CBDM). Three performance metrics—Useful Daylight Illuminance (UDI), Annual Sunlight Exposure (ASE), and Daylight Glare Probability (DGP)—were employed to evaluate baseline and optimized models. Optimization was performed using Galapagos (single-objective genetic algorithm, population size = 50 individuals, 100 generations, convergence tolerance = 0.001; the fitness function maximized UDI while penalizing ASE excess above 75 h/year and GFI below 0.75, using a weighted single-objective score: Fitness = UDI − 0.3 × (ASE/250) + 0.3 × GFI) and Colibri 2.0 combined with Design Explorer for exhaustive multi-objective combinatorial analysis. Results from the base model showed high daylight availability but excessive glare, particularly along the ramp. Through systematic optimization, the study identified façade and contextual configurations that achieved a UDI of 0.77, an ASE of 74, and a glare-free index of 0.81. The findings demonstrate that orientation-specific multi-configuration shading, when integrated with contextual design parameters, significantly improves the daylighting performance of architecturally complex spaces. This research offers a replicable methodology for designers aiming to integrate responsive daylighting strategies in public and exhibition buildings. Full article

Accurate crop mapping in fragmented agricultural landscapes is challenged by overlapping crop calendars and redundancy among multi-source time-series variables. Using Sentinel-1/2 imagery from December 2022 to December 2023, we constructed 275 season-specific spectral–phenological feature–month variables (125 for summer crops and 150 for winter crops) for rice, maize, soybean, winter wheat, and winter rapeseed in Jiangsu Province, China. An auxiliary binary Random Forest (RF) was used to estimate out-of-bag (OOB) permutation-based predictive contributions and construct search priors. A prior-guided genetic algorithm (GA) then identified compact subsets, with crop-specific five-class RF models used both to evaluate candidate subsets and to produce the final classifications. A fixed stratified 80/20 development–validation split was maintained throughout the analysis, with the validation subset reserved for final assessment. August and April were the principal discriminative periods for summer and winter crops, respectively, while VH backscatter and SWIR-related indices, particularly STI and NDTI, showed recurrent predictive contributions across crops. On the independent validation subset, the optical/vegetation-index scheme, SAR-only scheme, and the complete feature library achieved mean target-crop $F_1$-scores of 78.42%, 83.74%, and 86.96%, respectively. The GA-selected subsets retained 9–39 variables and achieved a mean five-class overall accuracy of 91.77% and a mean target-crop $F_1$-score of 93.95%. After non-target classes were merged into a single background class, the integrated seasonal maps achieved overall accuracies of 81.20–95.03% on the same validation subset. Supplementary classifier comparisons indicated that subset effects depended on the crop and learning algorithm. The findings support crop-specific, interpretable dimensionality reduction within the RF workflow, while broader transferability requires multi-year and multi-region evaluation. Full article

Lipoprotein(a) [Lp(a)] has emerged as a major genetically determined cardiovascular risk factor that extends beyond the traditional low-density lipoprotein cholesterol (LDL-C)-centred model of atherosclerotic disease. Despite optimal LDL-C lowering, a substantial proportion of patients continue to experience cardiovascular events, highlighting the clinical relevance of residual cardiovascular risk. This review summarizes current evidence regarding the epidemiology, genetics, pathophysiology and therapeutic implications of Lp(a) in cardiovascular disease. Epidemiological, genetic, and Mendelian randomization studies consistently demonstrate an independent and likely causal association between elevated Lp(a) and atherosclerotic cardiovascular disease, ischemic stroke, calcific aortic valve stenosis, heart failure, and recurrent cardiovascular events, even in patients with well-controlled LDL-C levels. Lp(a) promotes atherosclerosis through proatherogenic, proinflammatory, and prothrombotic mechanisms, largely mediated by oxidized phospholipids and the structural homology of apolipoprotein(a) with plasminogen. Current guidelines increasingly recognize Lp(a) as a risk-enhancing factor capable of refining cardiovascular risk stratification beyond traditional algorithms, thus recommending measuring Lp(a) at least once in a lifetime in all adults. Accurate measurement and standardization of Lp(a) remain essential in clinical practice due to apo(a) isoform size variability; reporting in molar concentrations (nmol/L) is preferred as it better reflects particle number being less affected by isoform size variations and improves the reliability of cardiovascular risk stratification. Collectively, these findings support the integration of Lp(a) into precision-based cardiovascular prevention strategies and suggest a paradigm shift from an exclusively LDL-centric approach toward genetically informed risk assessment and treatment. Full article

We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method for estimating causal arc weights, and genetic algorithms for model tuning. The proposed influence comparison method simplifies expert elicitation by reducing the cognitive load of direct weight estimation, while the genetic algorithm ensures alignment of forecasts with observed or expert-derived data. A forecasting algorithm based on incremental changes in concept levels enhances the sensitivity of the output variable to factor variations. To illustrate the applicability of the framework, we construct a decision-support model for predicting weight-loss success under diverse psychological, behavioral, and environmental conditions. Simulation results demonstrate how factor ranking, scenario modeling, and paired influence analysis provide actionable insights for decision-making. Beyond the weight-loss domain, the approach is generalizable to a wide range of knowledge-based systems requiring robust integration of expert judgment, fuzzy reasoning, and evolutionary optimization. Full article

The article presents a methodology for multi-objective compromise optimization of the process of turning on CNC machines with robotic loading by the methods of determining the optimum compromise area and carrying out optimization, using the generalized arithmetic mean utility function with weight coefficients. The methodology has been applied to determine the optimal cutting conditions, ensuring the best combination of technological parameters for the CNC turning process when machining parts made of 42CrMoS4 steel with cutting tools from different manufacturers. A complex study and modeling of the main technological parameters (production rate, cutting tool lifetime, and roughness of machined surfaces) in the CNC turning process have been performed depending on the conditions of cutting. By applying a genetic algorithm, the optimal conditions for implementing the process using both optimization methods have been determined, and a comparative analysis of the technological parameters has been made. Models have been created for predicting the number of machined parts, which exclude unplanned stops for tool changes, under the specified optimal conditions of cutting, taking into account the capacity of the tool magazine, the loading mechanism, and the volume of the production batch. Full article

Cadmium (Cd) contamination in soil poses a serious threat to crop quality. Biochar is widely regarded as an effective amendment that can reduce Cd bioavailability and limit Cd uptake by crops. However, studies on the rapid and nondestructive evaluation of crop physiological responses under biochar-mediated alleviation of Cd stress remain insufficient. Spectral modeling methods can enable rapid and nondestructive monitoring of crop physiological status. In this preliminary experiment, Zhengdan 958 maize seedlings grown in Cd-contaminated soil were subjected to five biochar application rates: 0, 10, 30, 50, and 70 g/pot, designated as CK, A1, A3, A5, and A7, respectively. The study established a non-destructive spectral detection model for relative chlorophyll content expressed as SPAD values of maize leaves to achieve spectral inversion of leaf physiological information. The alleviating effect of biochar on Cd stress was evaluated by analyzing SPAD values and Cd accumulation in roots, stems, and leaves. The original spectral data underwent preprocessing steps including multivariate scattering correction, standard normal variable transformation, normalization, trend removal, first-order derivative transformation, and second-order derivative transformation. The effectiveness of different preprocessing methods was compared using partial least squares regression. Feature bands were identified via Pearson correlation analysis, and support vector regression models were established based on genetic algorithm (GA), particle swarm optimization (PSO), and grid search optimization. The results demonstrated that biochar application significantly increased the SPAD values of corn leaves (r = 0.879) and reduced the proportion of bioavailable Cd in soil, with the A7 treatment showing the most substantial decrease (30%). This indicates that biochar effectively mitigates Cd’s inhibitory effect on chlorophyll synthesis, with the alleviation effect enhancing as biochar application rates increased. Validation of the partial least squares regression model revealed that detrended spectra achieved optimal predictive performance (R²c = 0.94, RMSEC = 0.82, R²p = 0.88, RMSEP = 1.15), leading to the development of three optimized support vector regression models: GA-SVR, PSO-SVR, and GS-SVR. The GA-SVR model with a sigmoid kernel demonstrated the best internal validation performance for predicting SPAD values in maize leaves (R²c = 0.95, RMSEC = 0.24; R²p = 0.75, RMSEP = 1.63). This study provides preliminary theoretical support and technical reference for rapid spectral detection of the physiological status of maize under biochar-mediated mitigation of cadmium stress. Full article

Unmanned aerial vehicle (UAV) multispectral imagery provides a promising approach for field-scale retrieval of soil organic matter (SOM) during the bare-soil period. However, tillage-induced surface heterogeneity is often overlooked. This heterogeneity may alter soil spectral responses and model performance. This study examined the effects of surface tillage status on UAV-based SOM retrieval in farmland. UAV multispectral imagery and 108 topsoil samples were collected during the bare-soil period. The SOM values ranged from 1.37 to 30.95 g/kg. Analyses were conducted under three tillage-status settings: undifferentiated tillage status, plowed-leveled status, and plowed-unleveled status. Spectral and textural features were extracted and selected using a genetic algorithm. These features were then used to develop SOM retrieval models with random forest regression, extreme gradient boosting, and support vector regression. For the six original multispectral bands, the correlations between SOM and band reflectance differed among tillage-status settings. They were weak under the undifferentiated tillage status. They were significantly negative under the plowed-leveled status and significantly positive under the plowed-unleveled status. Texture-derived indicators and standard normal variate analysis suggested that the positive correlations under the plowed-unleveled status may be partly associated with surface-structure-related spectral amplitude effects. Integrating textural features improved the overall test-set accuracy metrics. However, statistically detectable reductions in absolute prediction error were mainly observed under the plowed-unleveled status. On the random-split held-out test set, the highest $R^2$ values reached 0.84 and 0.85 under the plowed-leveled and plowed-unleveled statuses, respectively. These results indicate that surface tillage status is an important source of surface heterogeneity. It should therefore be explicitly considered in UAV-based SOM retrieval under the present study conditions. Full article

Accurate crop mapping is important for agricultural monitoring and land management; yet, identifying robust and compact feature subsets from high-dimensional multi-sensor remote sensing data remains challenging, particularly in heterogeneous agricultural landscapes affected by spatial autocorrelation. Although combining multi-sensor data provides complementary spectral and structural information, traditional workflows often neglect spatial dependence during feature evaluation, leading to over-optimistic validation metrics and spatially unstable feature subsets. To address this issue, this study proposes a hierarchical feature selection and subset optimization framework for crop mapping by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. A total of 135 multi-sensor features were constructed, including spectral bands, vegetation indices, SAR metrics, texture descriptors, and phenological statistics. To improve feature compactness and spatial robustness, a multi-stage selection strategy combining correlation-based redundancy removal, spatial cross-validation (SCV) control, Boruta, recursive feature elimination (RFE), L1 regularization, SHapley Additive exPlanations (SHAP), and Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed. Results showed that temporal and phenological features contributed more strongly to crop discrimination than static spectral or SAR features, while multi-sensor integration further improved classification stability. Notably, the proposed framework reduced the feature space from 135 to 12 variables while slightly improving classification performance. The final optimized model achieved an overall accuracy (OA) of 96.98% under SCV and generated spatially consistent crop maps at 10 m resolution. The framework provides an efficient and scalable solution for fine-scale crop mapping in complex agricultural regions and demonstrates the practical potential of incorporating spatial dependence control into feature selection for large-scale agricultural monitoring applications. Full article

Sustainable supply chains are increasingly vulnerable to systemic risks, such as geopolitical conflicts at critical trade routes like the Strait of Hormuz or climate disasters, which reveal deep Environmental, Social, and Governance (ESG) weaknesses. Conventional optimization often fails in these “deep uncertainty” contexts, where reliable historical data are often scarce and qualitative factors are paramount. This study introduces a simulation-optimization framework that reframes risk management as a decision process rather than a purely computational one. Portfolios are parameterized across five key characteristics—prevention, vulnerability, resilience, recovery, and detection—to enable a genetic algorithm (GA) to generate a diverse ensemble of high-performing strategies. Instead of providing one “best” answer, the GA allows managers to evaluate multiple options against quantitative tail-risk measures and qualitative institutional factors. The framework produces a “trade-off map,” or Pareto frontier, visualizing the cost of protecting against downside risks. By adjusting the GA’s settings, decision makers can toggle between improving current plans and exploring new, structurally different strategies. The numerical results demonstrate that the GA consistently identifies high-performing portfolios, achieving at least 99.55% of the true optimal performance across all metrics while requiring only 25% of the computational evaluation budget of an exhaustive search space. Furthermore, the framework successfully generates a structurally diverse menu of near-optimal alternatives across all performance metrics, consistently outperforming Monte Carlo sampling in the quality of near-optimal solutions identified, particularly for tail-risk measures such as conditional value-at-risk. Ultimately, this approach integrates the manager’s professional judgment regarding non-quantifiable factors, such as political stability and social responsibility, with simulation data to support the selection of a robust, sustainable portfolio. Full article

Plate–fin heat exchangers (PFHEs) are widely used in petrochemical, energy and electric power, aerospace, and other industries with large heat transfer requirements. The development of performance prediction and optimization methods for PFHEs has become increasingly important in the design and operation of such heat exchangers (HEs). This paper establishes a database of flow and heat transfer characteristics for four types of PFHEs with different structural parameters. Based on this database, the back-propagation artificial neural network (BP-ANN) model was optimized using the particle swarm optimization (PSO) algorithm to form the PSO-BP-ANN model for the performance prediction of these four types of PFHEs. This combination has been found to improve the prediction accuracy and generalization ability of the BP-ANN model. Additionally, the non-dominated sorting genetic algorithm II (NSGA-II) method was used to characterize the relationship between four structural parameters to be optimized (the length, height, spacing, and thickness of the HE fin) and the two objective functions (j and f) of the serrated PFHE in laminar flow. This enables the Pareto optimal solution to be obtained. The results show that, under laminar flow conditions (Re = 800), the serrated fin HE achieves the best heat transfer performance when the fin height, spacing, thickness, and length are 9.29, 1.22, 0.16, and 3.06, respectively. Full article

Credit card fraud detection is a typical rare-event classification problem because fraudulent transactions usually account for only a very small proportion of all transactions. Conventional evaluation on balanced or resampled test data may lead to overly optimistic performance estimates. To address this issue, this study proposes a leakage-safe credit card fraud detection framework integrating Random Forest Gini impurity-based feature selection, resampling strategy evaluation, and Genetic Algorithm (GA)-based hyperparameter optimization. The framework was evaluated on the public European credit card fraud dataset containing 284,807 transactions, of which only 492 were fraudulent. The original dataset was first divided into a stratified training set and an untouched original-distribution test set. Feature selection, standardization, resampling, GA optimization, and threshold tuning were performed only on the training data or training folds. The final test set contained 85,443 transactions, including 148 fraudulent transactions, and was used only once for final evaluation. Experimental results show that GA-XGBoost achieved the best overall balance among the optimized models, with a PR-AUC of 0.798, ROC-AUC of 0.967, MCC of 0.814, balanced accuracy of 0.865, fraud-class precision of 0.908, fraud-class recall of 0.730, and fraud-class F1-score of 0.809. Compared with baseline XGBoost, GA-XGBoost improved PR-AUC from 0.741 to 0.798, MCC from 0.766 to 0.814, and fraud-class F1-score from 0.764 to 0.809, while reducing false positives from 22 to 11 and false negatives from 43 to 40. The ablation results further indicate that resampling strategies are not universally beneficial and should be evaluated under the original test distribution. These findings suggest that leakage-safe evaluation and fraud-class-oriented metrics provide a more reliable basis for practical credit card fraud detection. Full article