Search Results (1,321)

The rapid development of intelligent systems has introduced increasingly sophisticated optimization problems across diverse domains. While contemporary metaheuristic algorithms, including the recent Artificial Lemming Algorithm (ALA), have shown considerable promise, they frequently encounter difficulties such as premature convergence, inadequate local refinement, and diminished performance in high-dimensional multimodal environments. To overcome these issues, this study presents HALA, a new hybrid dual-subpopulation optimizer that effectively integrates an enhanced ALA with the SHADE algorithm. HALA employs two interacting subpopulations: one leverages an improved ALA with hybrid t-distribution and Levy flight perturbations to promote persistent long-range exploration and diversity preservation; the other applies SHADE’s success-history adaptation and external archive for accurate local exploitation. Periodic bidirectional elite migration facilitates knowledge transfer between the subpopulations, reducing early stagnation in the enhanced ALA and strengthening SHADE’s global search capability. HALA is thoroughly benchmarked against 17 advanced metaheuristics, including ALA, LSHADE, LSHADE-SPACMA, AOOA, BAEO, BPBO, CCO, CEO, CQALA, DFL, DMOA, DHOA, FGO, KLA, PGA, SO, and SOO, using the IEEE CEC2017 suite in 10, 30, 50, and 100 dimensions and the IEEE CEC2022 suite in 10 dimensions. Comprehensive analyses involving qualitative visualization, convergence curves, boxplots, and statistical tests indicate that HALA achieves competitive or superior solution quality, comparable or faster convergence, and robust stability on a substantial proportion of the test instances. In particular, HALA obtains the most favorable Friedman average ranking values among the compared algorithms, which are 2.55, 2.38, 2.34, and 2.55 for the 10-, 30-, 50-, and 100-dimensional CEC2017 functions, respectively, and 2.58 for the 12 10-dimensional CEC2022 functions. Moreover, HALA is successfully applied to five well-known constrained engineering design problems—pressure vessel, rolling element bearing, tension/compression spring, cantilever beam, and gear train—where it reliably achieves optimal or near-optimal results that match or surpass the compared methods. These findings underscore HALA’s competitive strength and broad potential for practical engineering optimization. Full article

Respiratory diseases are a major global health challenge. However, identification of respiratory diseases is often limited by subjectivity, environmental noise and inter-clinician variability. This study presents an explainable multimodal deep learning framework for recording-level multiclass classification of respiratory audio signals. The proposed system integrates two complementary representations—a spectro-temporal encoder based on a CNN–BiLSTM-attention architecture and a handcrafted acoustic-feature encoder capturing acoustic descriptors commonly used in respiratory-audio analysis, including MFCCs, zero-crossing rate, spectral centroid, spectral bandwidth, chroma, RMS energy, and spectral rolloff features. These branches are combined through late-stage fusion to leverage both data-driven representation learning and domain-informed acoustic cues. The proposed model was trained and internally evaluated on the Asthma Detection Dataset Version 2, comprising five respiratory categories: bronchial disease, asthma, COPD, healthy, and pneumonia. Mono conversion, resampling to 16 kHz, 100–2000 Hz band-pass filtering, amplitude normalisation, fixed 4 s trimming or zero-padding, training-only augmentation, handcrafted-feature extraction, mel-spectrogram generation, quality control auditing, and stratified recording-level partitioning have been applied in the pre-processing steps. Across five repeated experiments with different random seeds, the proposed hybrid model achieved a mean held-out recording-level test accuracy of $0.9099\pm 0.0163$, balanced accuracy of $0.8936\pm 0.0152$, macro F1-score of $0.8937\pm 0.0177$, macro ROC–AUC of $0.9867\pm 0.0010$, and macro PR–AUC of $0.9489\pm 0.0044$. Conventional machine learning baseline comparisons showed that the proposed model achieved stronger internal accuracy, balanced accuracy, macro recall, macro F1-score, and macro ROC–AUC than classical machine learning algorithms trained on handcrafted acoustic features, although Random Forest remained competitive in macro PR–AUC. Ablation analysis shows that the deep spectro-temporal branch was the primary contributor to predictive performance, while the handcrafted branch provided complementary interpretable acoustic information rather than consistently improving all classification metrics. Explainability was incorporated using Grad-CAM and Integrated Gradients for spectrogram-based interpretation and SHAP for handcrafted-feature attribution. Domain-shift evaluation on the ICBHI Respiratory Sound Database and a COPD-focused cohort revealed substantial dataset shift effects, including poor healthy-case recognition on ICBHI and seed-dependent COPD recognition in the COPD-focused cohort. Identifier-aware sensitivity analyses showed lower performance than the main recording-level split, suggesting that subject-like or source-level overlap may inflate internal performance estimates. The findings should be interpreted as promising internal held-out recording-level algorithmic performance with limited external transfer, rather than evidence of readiness for clinical use. Full article

Objectives: Transaxillar Impella 5.0/5.5 implantation is a hybrid surgical and fluoroscopy-guided procedure. We evaluated the learning curve using radiation exposure as a marker of procedural efficiency and assessed whether structured mentorship accelerates procedural proficiency. Methods: This retrospective single-center study included consecutive transaxillar Impella 5.0/5.5 implantation attempts by two surgeons. Surgeon A adopted the technique independently, whereas Surgeon B was trained under direct proctorship. The primary endpoint was radiation exposure (dose–area product), and the secondary endpoint was fluoroscopy time. Temporal trends were analyzed by regression, and CUSUM plots were generated. Results: Of 104 procedures, 14 were excluded (12 transaortic, 2 unsuccessful). Ninety procedures were analyzed (74 Surgeon A, 16 Surgeon B). In Surgeon A, radiation exposure decreased significantly with increasing case number. In Surgeon B, no significant association between case number and radiation exposure was observed. Fluoroscopy time was not associated with case number in either group. CUSUM analysis suggested an early increase followed by stabilization in Surgeon A, whereas no clear pattern was observed in Surgeon B. The between-surgeon interaction was not statistically significant. ECMELLA configuration was the only independent predictor of increased radiation exposure, whereas device type, surgery type, and patient age were not significant predictors. Conclusions: Transaxillar Impella implantation appears to have a measurable early learning phase. Structured mentorship may attenuate the early learning phase, although this finding remains exploratory. Full article

In the context of warehouse management systems, navigation constitutes a critical area of research for enhancing operational efficiency. This paper introduces a novel hybrid algorithm designated as jump point search-enhanced ant colony optimization (JPS-EACO). Initially, the jump point search (JPS) algorithm generates a preliminary path rapidly. Subsequently, pheromone values are distributed in the vicinity of this path, establishing a non-uniform initial pheromone distribution across the entire environmental grid. To bolster the algorithm’s global search capacity, the heuristic function of the standard ant colony optimization (ACO) is refined. Furthermore, an adaptive pheromone evaporation strategy is integrated to regulate the pheromone update process throughout the iterative procedure. Additionally, the optimal route generated by the algorithm undergoes refinement. This involves the elimination of superfluous nodes and the smoothing of corners through the application of Bézier curves, which enhances the path’s smoothness and practical feasibility. The performance of the proposed JPS-EACO method was evaluated through simulations conducted on grid environments of dimensions 20 × 20 and 30 × 30. For the 20 × 20 grid, the algorithm demonstrated rapid convergence, requiring an average of 1.9 iterations. It achieved a reduction in route length of 5.21% compared to several reference algorithms. In the 30 × 30 scenario, the mean number of iterations was 8.2, and the resultant path length was shortened by a minimum of 3.64%. Moreover, in 40 × 40 and 50 × 50 grid environments, the algorithm also demonstrates stable and superior performance. These outcomes indicate that JPS-EACO offers faster convergence, shorter paths, and superior path smoothness. Finally, validation through real-world experiments confirmed the method’s effectiveness and its practical applicability. Full article

The Tesla turbine operates on viscous shear between parallel discs and, despite its mechanical simplicity, is typically characterized by low efficiency. In the present study, three-dimensional computational fluid dynamics (CFD) simulations performed using ANSYS Fluent are used to examine a hybrid Tesla turbine design in which 0.25 mm thick partial height blades are fitted on the disc faces, with 1 mm distance between them, thereby creating a 0.5 mm flow passage. Simulations employing the k-ω Shear Stress Transport (SST) turbulence model were performed for three blade counts (3, 6, and 9) and three blade geometries (curved, straight, and inverted curve) at rotational speeds from 1000 to 19,000 rpm and inlet pressures of 2 to 4 bar. Comparative analyses with standard 1 mm plane-disc rotors and reduced-gap 0.5 mm plane-disc rotors show that the hybrid arrangement consistently provides better torque and efficiency; this enhancement is not only due to the reduced gap but also to increased pressure-induced momentum and improved flow guidance provided by the blades. The curved blade was found to be the most favourable configuration, and the efficiency was positively related to the number of blades, with a maximum efficiency of 57.5% at 13,000 rpm using nine blades. The analyses sustain the conclusion that adding blades to the rotor discs positions the Tesla turbine model as a hybrid apparatus, combining viscous and pressure mechanisms to significantly enhance turbine performance. Full article

Pear ring rot, caused by Botryosphaeria kuwatsukai, is a major threat to pear production. The resistance of four pear F₁ populations to three B. kuwatsukai isolates was evaluated using detached leaf inoculations, assessed by the Area Under the Disease Progress Curve (AUDPC) and average lesion diameter (ADL). Cluster analysis based on these metrics established a five-level resistance rating scale. All hybrid combinations exhibited clear segregation for resistance, with continuous phenotypic variation and coefficients of variation exceeding 50%, suggesting polygenic inheritance. Broad-sense heritability (H²) of lesion diameter, estimated from replicated inoculations using a linear mixed-model approach, ranged from 0.32 to 0.71 across populations and isolates, indicating that the phenotypic variation was largely under genetic control. Genetic model analysis using the SEA v2.0 package identified a two-major-gene additive-dominant (2MG-AD) model as the best fit for the data across all combinations and isolates, with additive effects predominating. Isolate-specific responses were detected in the ‘Doyenne du Comice’ × ‘Huangguan’ population, and reciprocal differences between ‘Zhongai 1’ × ‘Zaosu’ and its reciprocal cross suggested potential cytoplasmic or maternal effects on resistance expression. Collectively, these findings suggest that pear leaf resistance to B. kuwatsukai is consistent with a two-gene additive-dominant model, supported by moderate-to-high heritability estimates. However, independent validation with additional populations and molecular markers is needed. These results highlight the value of multi-isolate screening, appropriate selection of resistant and susceptible parents, and the use of reciprocal crossing in breeding for broad-spectrum and durable resistance. Full article

Background/Objectives: Electrocardiogram (ECG)-based myocardial infarction (MI) classifiers may achieve high internal validation performance but show reduced performance when applied to data from another source. The task is a controlled binary healthy-versus-MI benchmark and is not intended to represent real-world chest-pain triage or autonomous clinical deployment. This study evaluated the source-only transportability of engineered 12-lead ECG feature families for binary healthy-versus-MI classification across a cardiologist-annotated hospital dataset and PTB-XL. Methods: The hospital dataset contained 1749 usable recordings from 1434 patients after excluding 206 broken-data records, with 1550 Healthy and 199 MI recordings. The matched PTB-XL binary subset contained 14,982 recordings from 13,436 patients, with 9513 Healthy and 5469 MI recordings. Eleven engineered feature families and five classifier families were compared under preprocessing, patient-aware splitting, source-validation hyperparameter and threshold selection, and bootstrap uncertainty estimation. The reported leading rows are the highest observed configurations in a prespecified benchmark grid, not locked clinical models. Results: Internal performance was higher than strict source-only transfer performance. In the hospital dataset, fiducial interval descriptors with Extra Trees reached balanced accuracy 0.775 and receiver operating characteristic area under the curve (ROC-AUC) 0.855. In PTB-XL, a broad hybrid feature bank with ST-segment information and XGBoost reached a balanced accuracy of 0.898 and ROC-AUC of 0.965. Strict source-only transfer was weaker and asymmetric: the highest observed balanced accuracy was 0.580 for hospital-to-PTB-XL transfer and 0.632 for PTB-XL-to-hospital transfer. Ranking transportability and operating-threshold transportability diverged, most notably for hospital-to-PTB-XL transfer, where ROC-AUC was 0.774 but sensitivity at the source-selected threshold was only 0.164. A secondary target-threshold analysis improved balanced accuracy to 0.682 and 0.640, respectively, but this used target labels only to re-select the operating threshold and was not a strict source-only result. Conclusions: The findings indicate a transportability gap: PTB-XL-to-hospital transfer was more balanced than hospital-to-PTB-XL transfer, but neither direction achieved performance comparable to internal validation. The source-only operating-point results are not acceptable for clinical MI screening or decision support without additional calibration, target-setting validation, and prospective assessment. Full article

Background/Objectives: Mammographic breast density assessment represents a crucial step in the detection of breast cancer, risk stratification, and lesion visibility. However, it is generally quite difficult to distinguish between intermediate density categories, especially BI-RADS B and C, because of the high inter-observer variability between radiologists. This hindrance encourages researchers to develop novel robust and interpretable automated techniques. Thus, a hybrid multi-view deep learning framework based on EfficientNet-B4 and U-Net for BI-RADS B vs. C classification is presented in this work. Methods: The proposed model utilizes the fusion of craniocaudal (CC) and mediolateral oblique (MLO) views at the feature level to capture complementary fibroglandular anatomical and tissue characteristics. Furthermore, the interpretability of the model is ensured with Grad-CAM, which highlights the regions relevant to decision-making. The proposed approach is evaluated on the RSNA mammography dataset, which consists of 23,513 images of 4796 patients, after a patient-wise split, for the purpose of preventing data leakage and guaranteeing a clinically realistic assessment. This protocol offers a more reliable evaluation than the image-by-image assessment strategies usually employed in previous studies. Results: The experimental outcomes obtained indicate an accuracy of 87% and an area under the curve (AUC) of 94.40%. These performance levels are consistent with those reported in recent research, suggesting competitive performance in this complex and difficult classification task. These enhancements are statistically significant compared to the assessed reference values, as confirmed using statistical analysis based on McNemar’s and DeLong’s tests. Furthermore, the qualitative evaluation carried out by experienced and certified radiologists corroborates the clinical pertinence of the highlighted regions. Conclusions: Furthermore, it is found that combining multi-view deep learning and explainable AI within the proposed framework is consistent with observed inter-observer variability and may support more consistent breast density assessment and clinical decision-making. However, further prospective multicenter validation is necessary before any clinical application. Full article

The frequency and intensity of heatwaves are increasing annually worldwide due to climate change. Combined with the urban heat island effect, elevated heat stress episodes threaten the survival and performance of urban trees, in turn reducing their ecosystem benefits. For this reason, the foliar heat tolerance of 35 Malus genotypes (two species, 32 cultivars, one variety, one hybrid) was evaluated under controlled laboratory assays. Heat injury to foliar tissue was quantified using chlorophyll fluorescence (Fv/Fm) to assess photosystem II (PSII) damage and an electrolyte leakage index (ELI) to evaluate cellular membrane integrity. A preliminary dose–response experiment using six genotypes exposed to a temperature gradient (40–50 °C) was conducted to establish thermal response curves and derive LT₅₀ values (temperature at 50% decline in Fv/Fm). These analyses confirmed substantial genotypic variation in thermal tolerance and identified 45 °C as an optimal discriminatory temperature for large-scale screening. This temperature was subsequently applied to assess heat injury across all 35 genotypes. Measurements were conducted in May (spring foliage) and August (summer foliage) to evaluate ontogenetic influences. In some instances, only one genotype was available for experimental purposes. Consequently, conclusions regarding genotypic differences in heat tolerance are based on replicated datasets, whereas genotypes represented by single-tree sampling are presented for descriptive purposes only. Heat stress significantly affected Fv/Fm and ELI, with strong genotype and seasonal effects recorded. In most genotypes, foliar damage was greater in spring than in summer. Good correlations between Fv/Fm and ELI confirmed their value as complementary physiological measures of heat tolerance in plants. Of the 35 genotypes evaluated, Malus sargentii, M. ‘Prairifire’, M. baccata ‘Jackii’, M. ‘Royal Fountain Huber’ and M. Donald Wyman were the most heat tolerant. The substantial variation in foliar heat tolerance detected across the 35 genotypes tested demonstrates potential for selecting Malus genotypes with superior foliar heat tolerance and highlights opportunities for identifying heat resilient candidates among other under-utilized urban tree taxa. Full article

Accurate extraction of typical load curves (TLCs) is essential for electricity market trading, demand-side management, and optimal design of energy storage systems. However, conventional methods are highly sensitive to anomalous consumption days caused by equipment failures or maintenance, which can distort normal electricity consumption patterns. To address this issue, this paper proposes a two-stage unsupervised framework that integrates a deep sequence model with an anomaly detection algorithm for robust TLC extraction. First, a Transformer-based autoencoder is employed to learn complex temporal dependencies and intrinsic patterns from historical daily load data, extracting robust periodic features by reconstructing the input load sequences. Subsequently, the reconstruction error of each daily load curve is computed as an anomaly assessment metric. These reconstruction error features are then fed into an Isolation Forest algorithm to identify anomaly loads that significantly deviate from the learned normal patterns, without requiring predefined thresholds or labeled data. Validation using real-world commercial and industrial electricity consumption data demonstrates that the proposed method effectively filters out various anomalies (e.g., spikes, troughs, and shape distortions) that conventional methods fail to exclude. The extracted TLCs exhibit improved robustness and representativeness. Further case studies indicate that adopting purified TLCs to guide electricity procurement in market trading facilitates more scientific trading strategies and avoids increased electricity costs caused by distorted load patterns. In summary, the proposed Transformer-Isolation Forest hybrid framework provides an effective data-driven solution for robust TLC extraction. The resulting TLCs can be directly used to guide day-ahead market bidding, optimize power purchase contract decomposition, and assess user demand response potential. Full article

Background/Objectives: Epilepsy is a prevalent chronic neurological disorder, and electroencephalography (EEG) remains essential for its diagnosis and long-term monitoring. Although deep learning-based automatic seizure detection has advanced considerably, existing models typically require extensive parameters and computational resources, limiting their deployment on resource-constrained platforms. Methods: In this study, we propose CosFNet, a hybrid lightweight architecture integrating cosine convolution with an FNet encoder, a Fourier-transform-based token-mixing encoder. The cosine convolution frontend parameterizes convolutional kernels with the cosine function to efficiently capture local spatiotemporal features. The FNet backend replaces traditional self-attention with a parameter-free two-dimensional discrete Fourier transform, enabling global mixing across temporal tokens and hidden feature dimensions with fast Fourier transform-based efficiency. With these advances, the model contains only 19,458 learnable parameters. Results: On the publicly available CHB-MIT dataset, CosFNet achieves a mean segment-level sensitivity of 97.60%, a specificity of 97.12%, an event-level sensitivity of 98.59%, a false detection rate (FDR) of 0.82/h, and an area under the receiver operating characteristic curve (AUC) of 97.87%. On our collected SH-SDU dataset, it attains a mean sensitivity of 92.87%, specificity of 94.74%, an event-level sensitivity of 99.41%, and an AUC of 96.29%. Conclusions: CosFNet achieves competitive detection performance with significantly low complexity, offering a viable pathway toward clinical deployment in resource-limited environments. Full article

Background: Breast cancer remains a leading cause of cancer-related mortality, and reliable computational decision support is increasingly viewed as a complement to expert pathological assessment rather than a replacement for it. Variational quantum classifiers (VQCs) and Quantum Support Vector Machines (QSVMs) have recently been promoted as candidate models for medical classification, yet most published comparisons rely on internal hold-out validation alone and report only a single point estimate of discrimination, omitting calibration, decision-analytic value, and explainability—three ingredients that any clinically credible model must furnish. Methods: We assembled a complete quantum–classical machine learning pipeline and evaluated it under a deliberately stringent protocol designed to expose, rather than conceal, the limitations of current Noisy Intermediate-Scale Quantum (NISQ)-era models. The analytical hypothesis was conservative and stated in advance; in light of saturated classical baselines on this benchmark, we did not anticipate a quantum advantage in raw discrimination, and we framed the study as a methodological probe rather than as a competition. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (n = 569) for development and an independent Wisconsin Original (WBC) cohort (n = 683) for external validation, we benchmarked five classical learners (XGBoost, LightGBM, CatBoost, RandomForest, RBF-SVM), two quantum models (an eight-qubit VQC implemented in PennyLane and a ZZ-feature-map QSVM implemented in Qiskit), and a stacked hybrid ensemble. The evaluation framework combined Optuna-driven hyperparameter optimisation, internal–external cross-validation, and external validation on the independent WBC cohort. Robustness and interpretability were then probed through circuit depth and embedding rotation ablation, depolarising noise stress tests, learning curve and feature stability analysis, decision curve analysis, and dual SHAP-based explanations covering both a direct tree-based explanation and a quantum surrogate. Reporting followed the TRIPOD + AI guideline. Results: On the internal test partition, RBF-SVM achieved the highest discrimination (AUC = 0.998), with XGBoost, LightGBM, CatBoost, the hybrid ensemble, and the VQC clustering between 0.992 and 0.996; the QSVM with a ZZ-fidelity kernel underperformed substantially (AUC = 0.727). Pairwise tests for correlated ROC curves indicated that most differences among top models were not statistically significant. On the external WBC cohort, model rankings reorganised, as RBF-SVM (AUC = 0.986, 95% CI 0.946–0.997), RandomForest (0.985, 95% CI 0.945–0.996), VQC (0.983, 95% CI 0.942–0.995), and the hybrid ensemble (0.982, 95% CI 0.941–0.995) all retained near-ceiling discrimination with extensively overlapping confidence intervals. Ablation analysis demonstrated that the choice of embedding rotation is decisive—Z-rotation embeddings collapsed VQC performance to chance levels (AUC ≈ 0.50), whereas X- and Y-rotations preserved it. Depolarising noise up to p = 0.10 had a negligible effect on the VQC, and SHAP analyses converged on worst concave points, mean concave points, and worst area as the dominant predictors across both classical and quantum models. Decision curve analysis showed positive net benefit for both classical and hybrid models across the clinically meaningful threshold range, exceeding both the treat-all and treat-none reference strategies throughout. Conclusions: In the present regime, the principal contribution of QML is not raw discrimination—modern classical learners are already at the data ceiling—but the construction of a rigorous, reproducible, externally validated, and interpretable benchmarking framework in which quantum models can be fairly compared with their classical counterparts. Because evaluation was confined to curated benchmark datasets rather than real-world clinical populations, the interpretability and net benefit findings reported here should be read as benchmark-level evidence and not as a demonstration of readiness for clinical deployment. Full article

Distinguishing between human-made interferences and natural background disturbances is of great significance for the safe operation of terrestrial optical cables because human-caused damage can be halted through timely intervention. To address the problem of small-sample disturbance recognition in distributed acoustic sensing (DAS) systems, this paper proposes a fused CNN–SVM classification model based on hybrid features. A convolutional neural network is employed to extract the high-level spatiotemporal features of disturbance signals, which are subsequently fused with statistical features and fed into a support vector machine for classification. Evaluated on open-source data, the proposed model achieves accuracy improvements of 9.1%, 8.7%, and 2.7% over the conventional CNN, the statistical-feature-based SVM, and the conventional CNN-SVM model, respectively. Furthermore, based on field-measured data, a dataset comprising 5664 samples was constructed, covering four typical disturbance-event types: background noise, drilling, knocking, and digging. The field classification results demonstrate that the three-layer convolutional structure of the model achieves a recognition accuracy of 98.5%. Both the ROC curves and multiple evaluation metrics indicate that the proposed three-layer fused CNN–SVM model delivers better classification performance and more balanced category recognition, offering a feasible reference for similar fiber disturbance engineering tasks. Full article

To investigate the impact resistance of hybrid fiber-reinforced recycled aggregate concrete (RAC) under dry–wet cycles and sulfate attack, hybrid fiber-reinforced recycled aggregate concrete (RAC) was prepared. Dynamic impact compression experiments were conducted using an SHPB test device with a 50 mm diameter. The microstructure of recycled aggregate concrete (RAC) within dry–wet cycles and sulfate attack was examined using SEM. The results indicate that the dynamic compressive strength first rises and then declines with the rise in dry–wet cycles, and increases with the increase in the average strain rate. When the number of dry–wet cycles reaches 16, the dynamic compressive strength reaches its peak, with the B4S6 group achieving a maximum dynamic compressive strength of 59.02 MPa. The dynamic elastic modulus follows a good quadratic parabolic function distribution with respect to the number of dry–wet cycles. Both the incident energy and dissipated energy density initially rise and then reduce with increasing dry–wet cycles. The energy values of RAC with different fiber types follow the order: B4S6 > S6 > B4 > RAC. Under impact loading, the strain rate–strain time history curve of recycled aggregate concrete (RAC) exhibits the change of “increase–decrease–stable–decrease”. With increasing dry–wet cycles, the degree of fragmentation of recycled aggregate concrete (RAC) first increases and then decreases, the fractal dimension first decreases and then increases, and the average particle size first increases and then decreases. SEM results and microscopic reaction mechanisms reveal that in the early stage of dry–wet cycles, sulfate ions generate ettringite and gypsum within the recycled aggregate concrete (RAC), which fill internal cracks and pores, making the concrete denser and enhancing its mechanical properties. Towards the end of the dry–wet cycle, the amount of expansive ettringite and gypsum inside the recycled aggregate concrete (RAC) increases, leading to a sharp increase in pore wall stress, which induces new microcracks in the specimens, manifesting as a decline in mechanical properties at the macroscopic level. Full article

To meet the conflicting demands of real-time simulation and high fidelity for thermal modeling of IGBT modules in digital twin applications, this paper presents a dynamic thermal network parameter updating strategy. A hybrid thermal model is constructed by combining a high-fidelity finite-element-method reference model with a 3-D compact network. Initial thermal resistance and capacitance parameters are obtained via offline calibration and validated against the transient thermal impedance curve. A dynamic identification method based on recursive least squares with precomputed sensitivity matrices is then proposed. It dynamically updates each independent thermal branch using only real-time chip junction temperature measurements. The Vincotech full-bridge IGBT module is used for simulation validation. The proposed method achieves steady-state identification errors of 3.2% for the IGBT chip thermal resistance and 4.5% for the freewheeling diode chip thermal resistance, outperforming particle swarm optimization and dual Kalman filter in both convergence speed and steady-state accuracy. Thus, it satisfies the requirements of real-time tracking and dynamic evolution for thermal models in digital twin systems. Full article