Search Results (15,189)

The calibration of suspension geometry involves highly nonlinear kinematic relationships and leads to challenging optimization landscapes that are difficult to explore efficiently with classical local methods. Quadratic Unconstrained Binary Optimization (QUBO) provides a unified discrete formulation that enables the use of a wide range of metaheuristic solvers, but its practical behavior in realistic engineering-inspired problems remains insufficiently benchmarked. This paper presents a computational benchmarking study of classical, parallel, and hybrid metaheuristic solvers applied to a QUBO-formulated double wishbone suspension geometry problem. A symbolic multi-body kinematic model is constructed and discretized into a large-scale QUBO instance capturing camber and caster tracking objectives across multiple roll conditions. Using a fixed low-resolution binary encoding, we systematically evaluate solver performance in terms of objective value, runtime, and time-to-solution trade-offs. The benchmark includes standard simulated annealing and tabu search, parallel simulated annealing, population-based annealing, bandit-controlled hybrid heuristics, and continuous-relaxation-based ADMM methods with and without annealing refinement. Extensive experiments conducted on a Euro-HPC pre-exascale system demonstrate that parallel and hybrid solvers can achieve substantial reductions in wall-clock time—often exceeding two orders of magnitude—while attaining objective values comparable to classical simulated annealing. The results reveal clear trade-offs between solution quality and computational efficiency, and highlight how solver structure influences performance on large QUBO instances derived from symbolic engineering models. Rather than focusing on final design optimality, this study provides a reproducible reference benchmark and practical insights into solver behavior for QUBO-based engineering optimization problems. Full article

Generative Adversarial Networks (GANs) perform well on natural images but often fail in domains governed by strict geometric or symbolic constraints. This work focuses on convolutional GANs and studies how their inductive biases interact with two contrasting types of synthetic image data: fractal patterns, characterized by self-similarity and scale-invariant local structure, and Euclidean shapes, defined by simple geometric primitives and rigid global constraints. Using multiple convolutional GAN architectures (DCGAN, WGAN-GP, and SNGAN), two resolutions (64 × 64 and 128 × 128), and a suite of evaluation metrics, we compare adversarial training behavior on these datasets under tightly controlled conditions. Fractal datasets yield stable training dynamics and perceptually plausible generations, whereas Euclidean shape datasets consistently exhibit structural failure modes that persist under higher resolution, smoother shape representations, and architectural stabilization. Geometry-aware metrics reveal severe violations of global shape consistency in Euclidean outputs that are not reliably captured by standard perceptual or distributional measures such as FID, SSIM, or LPIPS. We argue that these findings reflect a fundamental inductive bias of convolutional generative models toward a locally rich, scale-repeating structure rather than globally constrained geometry. Rather than indicating that fractals are intrinsically easier to model, our results show that Euclidean geometry exposes limitations of adversarial generative learning that remain hidden under conventional evaluation. From this perspective, fractal datasets serve as informative diagnostic benchmarks for probing how adversarially trained convolutional generators handle scale-invariant structure versus globally constrained geometry, and our results highlight the need for domain-aware metrics and alternative architectural biases when applying generative models to structured or symbolic data. Full article

Accurate prediction of wear behavior in polymer nanocomposites remains challenging due to the coupled influence of operating conditions and evolving surface morphology. In this study, a surface-aware triboinformatics framework is proposed to predict the dry sliding wear behavior of multi-walled carbon nanotube (MWCNT) reinforced epoxy composites by integrating operating parameters with run-wise atomic force microscopy (AFM) surface descriptors. Wear experiments were conducted using a Taguchi L16 design by varying CNT content (0–0.75 wt.%), applied load (10–40 N), sliding speed (183–458 rpm), and sliding distance (500–1250 m). AFM-derived parameters, including Ra, Rq, Z-range, and surface area difference, were extracted from the worn surface corresponding to each experimental run. Multiple regression-based machine learning models were evaluated using leave-one-out cross-validation, with ensemble-based models providing the best predictive performance (R² > 0.85 with low RMSE and MAE). Feature importance and partial dependence analyses identified CNT content as the dominant factor controlling wear reduction, followed by Z-range and Ra, highlighting the critical role of surface damage severity. Neat epoxy exhibited a maximum wear loss of 0.444 mg, whereas the 0.75 wt.% CNT composite showed values as low as 0.003 mg under comparable conditions, corresponding to a reduction of approximately 99%. The proposed framework enables mechanistically interpretable wear prediction and supports the design of durable polymer composites, contributing to SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production). Full article

Background: Exercise is a key component in managing hypertension, yet adherence remains low. Beyond deliberate decision-making, implicit attitudes also play an important role in exercise behavior as automatic and unconscious evaluative processes. Traditional studies mostly rely on reaction time measures, which are susceptible to practice effects and fail to capture dynamic neural processing. Objectives: This study aimed to examine whether the EEG entropy derived from implicit attitude processing can better predict exercise behavior than traditional reaction time measures in patients with hypertension. Methods: Fifty-seven hypertensive patients completed affective and instrumental implicit association tests (IATs) with EEG recording. Seven entropy features were extracted. Multiple machine learning algorithms were applied to compare the predictive performance of reaction time with EEG entropy features. The random forest model was used to analyze the importance ranking of features from different brain regions. Results: EEG entropy outperformed reaction times in distinguishing exercisers from non-exercisers. Affective implicit attitudes consistently demonstrated stronger accuracy than instrumental attitudes. Envelope entropy showed the most robust and significant group differences. For the random forest (RF) classifier of envelope entropy, classification accuracies were 71.9% for the affective IAT (incompatible task only), and 71.9% for the model combining affective and instrumental IAT features. Frontal and central regions contributed most to classification. Conclusions: EEG entropy, particularly envelope entropy during affective IAT-incompatible tasks, provides superior discrimination of exercise behavior than reaction time measures. This suggests that exercise behavior is closely linked to the neural complexity underlying affective conflict processing. These findings advance our understanding of the neural dynamic patterns linking implicit attitudes and exercise behavior and suggest EEG entropy as a promising tool for assessing and intervening exercise behavior. Full article

The increasing integration of electric vehicles (EVs) and renewable energy sources has accelerated the adoption of DC microgrids, where maintaining voltage stability and effective power sharing remains a critical challenge. Hybrid energy storage systems (HESS), combining batteries and supercapacitors, are commonly employed to address dynamic power variations. However, conventional proportional–integral (PI)-based control strategies for HESS can exhibit performance limitations under nonlinear and varying operating conditions. To overcome this drawback, this paper presents an adaptive neuro-fuzzy inference system (ANFIS)-based control strategy for HESS located in a DC microgrid, with comparative evaluation against both conventional PI and traditional Fuzzy Logic controller (FLC) schemes. The proposed approach is evaluated using a detailed MATLAB/Simulink R2024a model of a DC microgrid including EVs. Simulation results show that, under normal operating conditions, the ANFIS-based control demonstrates improved transient response, reduced voltage fluctuations, and effective coordination between the battery and supercapacitor during renewable power variations, compared to PI and FLC-controlled systems. In addition to nominal performance assessment, this work investigates the vulnerability of the ANFIS controller to cyber-attacks. Two representative attack scenarios, false data injection (FDI) and denial-of-service (DoS), are applied to critical measurement and control signals of HESS. Simulation results reveal that, although the DC-bus voltage regulation is largely maintained during attack intervals, cyber manipulation significantly disrupts the intended HESS power-sharing behavior. Full article

Polo is a high-speed equestrian sport that imposes mechanical demands on horses and turf, yet limited research has examined the functional behavior of polo playing surfaces. This study characterizes the spatial variability of mechanical surface properties across turf polo pitches representing high-, medium-, and low-handicap categories. Three fields were assessed using lightweight field-based instruments, including the Impact Test Device (ITD), Rotational Peak Shear (RPS) tester, Going Stick© for penetration (GSP) and shear (GSS), and a TDR probe for volumetric moisture content (VMC%). A total of 210–223 grid-based sampling points per pitch were analyzed to evaluate mechanical responses under vertical and horizontal loading conditions. Significant differences among pitches were observed, with ITD and VMC emerging as the indicators of surface behaviour. Spatial analysis revealed heterogeneous within-pitch patterns, expressed as directional gradients and localized variability. Linear discriminant analysis demonstrated that the combined measurements could differentiate pitches associated with different handicap levels with high classification accuracy (0.88). Although the applied instruments do not replicate full equine biomechanical loading, they proved effective in detecting spatial variability in surface uniformity, a functional property relevant to performance and equine welfare. These findings support integration of spatially explicit surface assessments into routine turf management practices. Full article

This study evaluates the mechanical behavior and durability of a silty soil stabilized with Portland cement and recycled ground glass powder (GGP). The porosity–cement index (η/C_iv) was applied to predict unconfined compressive strength ($q_u$), splitting tensile strength ($q_t$), and accumulated mass loss (ALM) under wetting–drying cycles. Mixtures were prepared with cement contents of 3%, 6%, and 9%, GGP contents of 5%, 15%, and 30%, and dry unit weights of 13.5, 14.5, and 15.5 kN/m³, and were cured for 7, 28, and 90 days. The experimental program consisted of a large dataset, comprising 486 mechanical tests (unconfined compressive and splitting tensile strength) and 81 durability tests, providing a robust basis for both empirical modeling and machine learning analysis. The results confirmed a strong power-law relationship between η/C_iv and both $q_u$ and $q_t$, achieving high coefficients of determination (R² > 0.98). The strength coefficient (A) increased consistently with curing time and GGP addition, indicating enhanced pozzolanic reactivity and matrix densification. After 90 days, $q_u$ increased by over 250% and $q_t$ by nearly 700%. Durability tests revealed exponential reductions in ALM with higher density and binder content, achieving values below 0.5% for the densest mixtures, which contained 30% GGP. These findings validate the η/C_iv index as an effective predictor of strength and durability in soil–cement–GGP geomaterials, establishing a solid basis for future integration with machine learning models. The implementation of twenty-eight machine learning presets for predicting $q_u$, $q_t$, and ALM demonstrated that the Matern 5/2 Gaussian Process Regression and the trilayered neural network are the most suitable algorithms, achieving R² values higher than 0.987 in both the validation and testing stages. Full article

This study investigates the effects of surface thermochemical treatments using boriding, nitriding, and boronitriding on the microstructure and mechanical properties of martensitic stainless steel AISI 420 ESR. Powder-pack boriding, gas nitriding, and sequential boronitriding processes were applied to enhance surface hardness, wear resistance, and adhesion. The microstructural and mechanical properties of the surface samples were analyzed using scanning electron microscopy, energy-dispersive spectroscopy, X-ray diffraction, microhardness, and nanoindentation testing. Tribological behavior was analyzed using a pin-on-disk tribometer under dry-sliding wear conditions, with applied normal loads of 5 N and 10 N and a sliding distance of 1000 m. The results showed that the borided samples exhibited the highest surface hardness, up to 1182 HV_0.05, as well as brittle fracture and spallation with poor adhesion, while the boronitrided layer offered excellent adhesion. The boronitriding condition demonstrated a synergistic balance, combining high wear resistance (5.92 × 10⁻⁷ mm³N⁻¹m⁻¹ and 4.96 × 10⁻⁷ mm³N⁻¹m⁻¹) and reduced friction (~0.78 and ~0.67) for loads of 5 N and 10 N, respectively, without brittle fractures on the coating layer. These results confirm that duplex coating treatment is an effective strategy for improving the surface performance of AISI 420 ESR components subjected to severe operating conditions. Full article

Fatigue behaviors in asphalt mixtures are influenced by multiple factors, including strain level, strain waveform, and temperature, as well as rest periods. This complexity makes the analysis and interpretation of fatigue data particularly challenging. Dissipated energy (DE) is effective for developing unified fatigue models that characterize asphalt mixture behavior across varying temperatures and strain levels. However, its applicability requires further validation across a broader range of loading scenarios, especially those involving diverse strain waveforms and rest periods. This research aimed to apply the dissipated energy method to analyze fatigue behaviors of asphalt mixture subjected to extended combinations of strain waveforms and temperatures, as well as rest periods. It was found that strain waveform significantly impacts DE values and the rate of DE variation in asphalt mixtures, which contributes to differences in fatigue life at varying strain waveforms. The initial DE (IDE) indicator establishes a distinct correlation with the fatigue life of the asphalt mixture, unaffected by strain waveforms or strain levels. However, this IDE-fatigue life relationship is influenced by rest periods and temperatures. Longer rest periods shift the IDE-fatigue life curve toward a higher fatigue life, indicating improved performance. Through IDE analysis, a generalized model was formulated to represent IDE-fatigue life relationships across broad strain waveforms and strain levels, as well as rest periods, facilitating fatigue life prediction under changing conditions. This research provides valuable insights into the fatigue characteristics and underlying mechanisms of asphalt mixtures from an energy perspective. Full article

In modern organizations, IT Service Management (ITSM) relies on the efficient handling of large volumes of unstructured textual data, such as support tickets and incident reports. This study investigates the automated classification of IT support requests as a data-driven decision-support task within a real-world enterprise ITSM context, addressing challenges posed by multilingual content and severe class imbalance. We propose an applied machine-learning and natural language processing (NLP) pipeline combining text cleaning, stratified data splitting, and supervised model training under realistic evaluation conditions. Multiple classification models were evaluated on historical enterprise ticket data, including a Logistic Regression baseline and transformer-based architectures (multilingual BERT and XLM-RoBERTa). Model validation distinguishes between deployment-oriented evaluation on naturally imbalanced data and diagnostic analysis using training-time class balancing to examine minority-class behavior. Results indicate that Logistic Regression performs reliably for high-frequency, well-defined request categories, while transformer-based models achieve consistently higher macro-averaged F1-scores and improved recognition of semantically complex and underrepresented classes. Training-time oversampling increases sensitivity to minority request types without improving overall accuracy on unbalanced test data, highlighting the importance of metric selection in ITSM evaluation. The findings provide an applied empirical comparison of established text-classification models in ITSM, incorporating both predictive performance and computational efficiency considerations, and offer practical guidance for supporting IT support agents during ticket triage and automated request classification. Full article

Within the framework of evidence theory, maximum entropy is regarded as a measure of total uncertainty that satisfies a comprehensive set of mathematical properties and behavioral requirements. However, its practical applicability is severely questioned due to the high computational complexity of its calculation, which involves the manipulation of the power set of the frame of discernment. In the literature, attempts have been made to reduce this complexity by restricting the computation to singleton elements, leading to a formulation based on reachable probability intervals. Although this approach relies on a less specific representation of evidential information, it has been shown to provide an equivalent maximum entropy value under certain conditions. In this paper, we present an experimental comparative study of two algorithms for calculating maximum entropy in evidence theory: the classical algorithm, which operates directly on belief functions, and an alternative algorithm based on reachable probability intervals. Through numerical experiments, we demonstrate that the differences between these approaches are less pronounced than previously suggested in the literature. Depending on the type of information representations to which it is applied, the original algorithm based on belief functions can be more efficient than the one using the reachable probability interval approach. This is an interesting result, and a reason for choosing one algorithm over the other depending on the situation. Full article

Large Language Models are increasingly used for high-level robotic reasoning, yet their latency and stochasticity complicate their direct use in low-level control. Moreover, extracting actionable navigation cues from multimodal context incurs inference costs that are challenging for embedded platforms. We present a plug-and-play framework that augments a finite-state machine with asynchronous velocity suggestions generated by a Large Language Model, using an off-the-shelf DistilGPT-2 model running on-device on a Jetson AGX Orin. The system extracts task-relevant cues from the current context and integrates them only if they satisfy deadline, schema, and kinematic validation, thereby preserving a deterministic 50 Hz control loop with a <5 ms fallback path. We compare multiple Large Language Models for embedded robot control and quantify trade-offs among model size, inference time, and output validity. To assess whether the Large Language Models add value beyond signal processing, we include an ablation against a standard smoothing baseline; the results indicate that the Large Language Models contribute anticipatory, context-dependent adjustments that are not captured by filtering alone. Experiments in Gazebo and on a real TurtleBot3 reduce the final position error from 0.246 m to 0.159 m and improve trajectory efficiency from 0.821 to 0.901 without increasing control-loop latency. Approximately 80% of the Large Language Models’ outputs pass validation and are applied. Overall, the framework reduces developer effort by enabling behavioral changes at the prompt level while maintaining interpretable, robust edge-based navigation. Full article

The sustainable stabilization of clayey soils has become a critical strategy for improving their mechanical performance while reducing environmental impact. This study compares two distinct stabilization systems applied to the same low-plasticity clay (CL) from Cartagena de Indias, Colombia: (i) lime catalyzed with sodium chloride (NaCl) and (ii) xanthan gum (XG) reinforced with polypropylene fibers (PPF). A series of laboratory tests was performed to evaluate the unconfined compressive strength (q_u) and small-strain stiffness (G_o) of both systems under controlled compaction and curing conditions. The lime–NaCl system demonstrated accelerated early-age strength and stiffness development, reaching q_u values above 2.5 MPa and G_o exceeding 10 GPa after 28 days of curing, mainly attributed to enhanced pozzolanic reactions catalyzed by NaCl. Conversely, the XG–PPF blends exhibited progressive improvements in mechanical performance, achieving notable gains after 90 days due to the polymeric bonding of XG and the fiber–matrix reinforcement that enhanced ductility and post-peak behavior. When normalized through the porosity–binder index, both systems exhibited power-law trends, with the lime–NaCl mixtures displaying higher exponents indicative of cementation-controlled behavior, while the XG–PPF mixtures showed lower exponents consistent with interparticle bonding and network formation. These results highlight the complementary mechanisms of chemical and biopolymeric stabilization, providing insights into the selection of sustainable binders tailored to specific design requirements in tropical clays. This research demonstrated that the implementation of machine learning models enhanced the fitting accuracy of the two soil stabilization methods when compared with traditional mathematical regression models commonly used in geotechnical engineering. Among the tested approaches, the neural network and Gaussian process regression models exhibited the best performance, achieving R² values ranging from 0.917 to 0.980 during the validation stage. Full article

The increasing size and complexity of wind turbines have intensified the need for reliable real-time condition monitoring and health assessment. However, conventional numerical models often involve high computational demand, limiting their applicability for real-time digital twin implementation. This paper proposes a physics-based digital twin framework for the real-time health monitoring of a 3 MW class wind turbine. A physics-based numerical model was developed using Modelica 4.0.0 to simulate the electrical and mechanical behaviors of the wind turbine based on supervisory control and data acquisition (SCADA) inputs. Data preprocessing and wind speed calibration strategies were applied to reconcile nacelle-measured SCADA data with the turbine design specifications. Furthermore, reduced-order models (ROMs) were integrated with the physics-based numerical model to predict the thermal states of the generator and gearbox. Key operational parameters were selected through correlation analysis to enable accurate temperature prediction. Validation results demonstrate that the proposed digital twin accurately reproduces the dynamic behavior of the wind turbine, with the ROM-based temperature predictions showing agreement with SCADA measurements. The overall framework achieves a computation time within one second, indicating its suitability for real-time diagnostic and predictive maintenance applications. Full article

Impure rock salt is increasingly used as a host medium for underground hydrogen and compressed air energy storage in China; however, its permeability evolution under stress remains insufficiently constrained. This study presents a systematic experimental and modeling investigation of the permeability behavior of impure rock salt from the Pingdingshan (Henan) and Yunying (Hubei) salt mines. Nineteen cylindrical specimens were subjected to full-process triaxial permeability testing, including initial measurements, hydrostatic damage recovery, and staged deviatoric loading. A hydrostatic recovery stage (15 h at 40 MPa) was applied to reduce coring- and machining-induced micro-damage, resulting in a permeability reduction in one to three orders of magnitude. After recovery, the initial permeability decreases nonlinearly with increasing effective stress and converges to approximately 10⁻²¹ m² at stress levels corresponding to in situ burial depths. During deviatoric loading, permeability exhibits a two-stage response: a rapid increase associated with early damage and microcrack initiation, followed by saturation once the dilatant volumetric strain exceeds approximately 1–2%. Impurity content influences both the magnitude and evolution of permeability by modifying the initial pore structure and damage development; however, the response is non-monotonic and region-dependent due to differences in dominant impurity mineralogy. Based on the experimental results, a semi-theoretical permeability model incorporating effective stress, dilatant strain, and impurity content was developed. The model reproduces the observed permeability evolution under different confining pressures with good agreement, providing a practical framework for evaluating the hydraulic integrity of impure rock salt in underground energy storage applications. Full article