Search Results (1,279)

Deep learning algorithms are pivotal in the identification and classification of microseismic signals in mines subjected to impact pressure. However, conventional machine learning techniques often struggle to balance interpretability, computational efficiency, and accuracy. To address these challenges, this paper presents a hybrid feature selection and Transformer-based model for microseismic signal classification. The proposed model employs a hybrid feature selection method for data preprocessing, followed by an enhanced Transformer for signal classification. The study first outlines the underlying principles of the method, then extracts key seismic features—such as zero-crossing rate, maximum amplitude, and dominant frequency—from various microseismic signal types. These features undergo importance and correlation analyses to facilitate dimensionality reduction. Finally, a Transformer-based classification framework is developed and compared against several traditional deep learning models. The results reveal significant differences in the waveforms and spectra of different microseismic signal types. The selected feature parameters exhibit high representativeness and stability. The proposed model achieves an accuracy of 90.86%, outperforming traditional deep learning approaches such as CNN (85.2%) and LSTM (83.7%) by a considerable margin. This approach provides a reliable and efficient solution for the rapid identification of microseismic events in rockburst-prone mines. Full article

This study provides a detailed comparative analysis of a three-hybrid intrusion detection method aimed at strengthening network security through precise and adaptive threat identification. The proposed framework integrates an Autoencoder-Gaussian Mixture Model (AE-GMM) with two supervised learning techniques, XGBoost and Logistic Regression, combining deep feature extraction with interpretability and stable generalization. Although the downstream classifiers are trained in a supervised manner, the hybrid intrusion detection nature of the framework is preserved through unsupervised representation learning and probabilistic modeling in the AE-GMM stage. Two benchmark datasets were used for evaluation: NSL-KDD, representing traditional network behavior, and UNSW-NB15, reflecting modern and diverse traffic patterns. A consistent preprocessing pipeline was applied, including normalization, feature selection, and dimensionality reduction, to ensure fair comparison and efficient training. The experimental findings show that hybridizing deep learning with gradient-boosted and linear classifiers markedly enhances detection performance and resilience. The AE–GMM-XGBoost model achieved superior outcomes, reaching an F1-score above 0.94 ± 0.0021 and an AUC greater than 0.97 on both datasets, demonstrating high accuracy in distinguishing legitimate and malicious traffic. AE-GMM-Logistic Regression also achieved strong and balanced performance, recording an F1-score exceeding 0.91 ± 0.0020 with stable generalization across test conditions. Conversely, the standalone AE-GMM effectively captured deep latent patterns but exhibited lower recall, indicating limited sensitivity to subtle or emerging attacks. These results collectively confirm that integrating autoencoder-based representation learning with advanced supervised models significantly improves intrusion detection in complex network settings. The proposed framework therefore provides a solid and extensible basis for future research in explainable and federated intrusion detection, supporting the development of adaptive and proactive cybersecurity defenses. Full article

Solar-aided power generation offers a pathway to reduce the carbon dioxide emissions from existing coal-fired plants. This study addresses the gap in comparing different solar integration modes by conducting a thermo-economic analysis of a 600 MW coal-fired system retrofitted with a solar tower. Four integration modes were designed and rigorously compared, encompassing series and parallel configurations at either the high-exergy reheater or the lower-exergy economizer. A detailed thermodynamic model was developed to simulate its off-design and annual performance. The results showed that integration at the primary reheater outperformed the economizer integration. Specifically, the parallel configuration at the primary reheater (Mode II) achieved the highest annual solar-to-electricity efficiency of 18.43% at a thermodynamically optimal heliostat field area of 125,025.6 m². Economic analysis revealed a trade-off, with the minimum levelized cost of energy (LCOE) of −0.00929 USD/kWh for Mode II occurring at the economically optimal area of 321,494 m² due to greater coal and emission savings. Sensitivity analysis across two other locations confirmed that the annual solar-to-electricity efficiency and LCOE are directly influenced by solar resource quality, but the thermodynamically optimal and economically optimal heliostat field area remain consistent. This work demonstrates that parallel integration with the primary reheater presents a favorable and practical configuration, balancing high solar-to-electricity conversion efficiency with favorable economics for hybrid solar–coal power plants. Full article

In this study, a hybrid deep learning model that combines the VGG16 and ResNet101V2 architectures is proposed for image-based fire detection. In addition, a balanced drone guidance algorithm is developed to efficiently assign tasks to available UAVs. In the fire detection phase, the hybrid model created by combining the VGG16 and ResNet101V2 architectures has been optimized with Global Average Pooling and layer merging techniques to increase classification success. The DeepFire dataset was used throughout the training process, achieving an extremely high accuracy rate of 99.72% and 100% precision. After fire detection, a task assignment algorithm was developed to assign existing drones to fire points at minimum cost and with balanced load distribution. This algorithm performs task assignments using the Hungarian (Kuhn–Munkres) method and cost optimization, and is adapted to direct approximately equal numbers of drones to each fire when the number of fires is less than the number of drones. The developed system was tested in a Python-based simulation environment and evaluated using performance metrics such as total intervention time, energy consumption, and task balance. The results demonstrate that the proposed hybrid model provides highly accurate fire detection and that the task assignment system creates balanced and efficient intervention scenarios. Full article

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care. Full article

Background/Objectives: Parkinsonian gait is characterized by impaired motor adaptability, altered temporal organization, and reduced movement variability. While data augmentation is commonly used to mitigate class imbalance in gait-based machine learning models, conventional strategies often ignore physiological differences between healthy and pathological movements, potentially distorting meaningful motor dynamics. This study explores whether preserving healthy motor variability while selectively augmenting pathological gait signals can improve the robustness and physiological coherence of gait pattern classification models. Methods: Eight patients with Parkinsonian gait patterns and forty-eight healthy participants performed walking tasks on the Motigravity platform under hypogravity conditions. Full-body kinematic data were acquired using wearable inertial sensors. A selective augmentation strategy based on smooth time-warping was applied exclusively to pathological gait segments (×5, σ = 0.2), while healthy gait signals were left unaltered to preserve natural motor variability. Model performance was evaluated using a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture across multiple augmentation configurations. Results: Selective augmentation of pathological gait signals achieved the highest classification performance (94.1% accuracy, AUC = 0.97), with balanced sensitivity (93.8%) and specificity (94.3%). Performance decreased when augmentation exceeded an optimal range of variability, suggesting that beneficial augmentation is constrained by physiologically plausible temporal dynamics. Conclusions: These findings demonstrate that physiology-informed, selective data augmentation can improve gait pattern classification under constrained data conditions. Rather than supporting disease-specific diagnosis, this proof-of-concept study highlights the importance of respecting intrinsic differences in motor variability when designing augmentation strategies for clinical gait analysis. Future studies incorporating disease-control cohorts and subject-independent validation are required to assess specificity and clinical generalizability. Full article

This study applies spectral analysis and singular spectrum analysis (SSA) to mean annual runoff of the Dniester River for 1950–2024 to identify dominant periodic components governing the hydrological regime of this transboundary basin shared by Ukraine and Moldova. The novelty lies in a basin-specific integration in the first systematic application of a combined spectral–SSA framework to the Dniester River, enabling consistent characterization of runoff variability and assessment of large-scale natural drivers. Time series from three gauging stations are analysed to develop data-driven runoff models and medium-term forecasts. Four stable groups of periodic variability are identified, with characteristic timescales of approximately 30, 11, 3–5.8, and 2 years, corresponding to major atmospheric–oceanic oscillations (AMO, NAO, PDO, ENSO, QBO) and the 11-year solar cycle. Cross-spectral and coherence analyses reveal a statistically significant relationship between solar activity and river discharge, with an estimated lag of about 2 years. SSA reconstructions explain more than 80% of discharge variance, indicating high model reliability. Forecast comparisons show that spectral methods tend to amplify long-term trends, CNN–LSTM models produce conservative trajectories, while a hybrid ensemble approach provides the most balanced and physically interpretable projections. Ensemble forecasts indicate reduced runoff during 2025–2028, followed by recovery in 2029–2034, supporting long-term water-resources planning and climate adaptation. Full article

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research. Full article

Magnetic resonance imaging (MRI) super-resolution (SR) enables high-resolution reconstruction from low-resolution acquisitions, reducing scan time and easing hardware demands. However, most deep learning-based SR models are large and computationally heavy, limiting deployment in clinical workstations, real-time pipelines, and resource-restricted platforms such as low-field and portable MRI. We introduce CHARMS, a lightweight convolutional–Transformer hybrid with attention regularization optimized for MRI SR. CHARMS employs a Reverse Residual Attention Fusion backbone for hierarchical local feature extraction, Pixel–Channel and Enhanced Spatial Attention for fine-grained feature calibration, and a Multi-Depthwise Dilated Transformer Attention block for efficient long-range dependency modeling. Novel attention regularization suppresses redundant activations, stabilizes training, and enhances generalization across contrasts and field strengths. Across IXI, Human Connectome Project Young Adult, and paired 3T/7T datasets, CHARMS (~1.9M parameters; ~30 GFLOPs for 256 × 256) surpasses leading lightweight and hybrid baselines (EDSR, PAN, W2AMSN-S, and FMEN) by 0.1–0.6 dB PSNR and up to 1% SSIM at ×2/×4 upscaling, while reducing inference time ~40%. Cross-field fine-tuning yields 7T-like reconstructions from 3T inputs with ~6 dB PSNR and 0.12 SSIM gains over native 3T. With near-real-time performance (~11 ms/slice, ~1.6–1.9 s per 3D volume on RTX 4090), CHARMS offers a compelling fidelity–efficiency balance for clinical workflows, accelerated protocols, and portable MRI. Full article

The construction cost of offshore wind farms (OWFs) is heavily influenced by vessel scheduling and meteorological uncertainties. To address these challenges, this paper proposes a constraint-driven hierarchical optimization framework for the coordinated scheduling of installation vessels (IVs) and transport vessels (TVs). First, a Mixed-Integer Linear Programming (MILP) model is established to describe the operational constraints, which is then decomposed into two interrelated sub-problems: vessel path planning and scheduling optimization. For path planning, the problem is modeled as a Multiple Traveling Salesman Problem (MTSP) to ensure balanced fleet workloads. This stage is solved via a tailored three-stage heuristic combining balanced sweep clustering and penalized local search. For scheduling optimization, a hybrid Earliest Deadline First (EDF)-Simulated Annealing (SA) strategy is employed, where EDF generates a strictly feasible baseline to warm-start the SA optimization. Furthermore, a stochastic optimization approach integrates historical meteorological data to ensure schedule robustness against weather uncertainty. The validity of the framework is supported by two real-world OWF cases, which demonstrate total cost reductions of 15.44% and 13.20%, respectively, under stochastic weather conditions. These results demonstrate its effectiveness in solving high-constraint offshore engineering problems. Full article

Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods provide computational efficiency, enabling large-domain simulations and excelling in texture prediction and multi-layer builds. PF approaches deliver superior thermodynamic fidelity for interface dynamics, solute partitioning, and nonequilibrium rapid solidification through CALPHAD coupling. Hybrid CA–PF frameworks strategically balance efficiency and accuracy by allocating PF to solidification fronts and CA to bulk grain competition. Recent algorithmic innovations—discrete event-inspired CA, GPU acceleration, and machine learning—extend scalability while maintaining predictive capability. Validated applications across Ni-based superalloys, Ti-6Al-4V, tool steels, and Al alloys demonstrate robust process–microstructure–property predictions through EBSD and mechanical testing. Persistent challenges include computational scalability for full-scale components, standardized calibration protocols, limited in situ validation, and incomplete multi-physics coupling. Emerging solutions leverage physics-informed machine learning, digital twin architectures, and open-source platforms to enable predictive microstructure control for first-time-right manufacturing in aerospace, biomedical, and energy applications. Full article

With the rapid development of online aquatic product trading, traditional centralized platforms are facing increasing pressure in terms of data security, privacy protection, and trust. Problems such as tampering with transaction records, weak identity authentication, privacy leakage, and the difficulty of balancing matching efficiency with security limit the further development of these platforms. To address these issues, this paper proposes a blockchain-based identity authentication and access control scheme for online aquatic product trading. The scheme first introduces a dual authentication mechanism that combines a verifiable random function with a Schnorr-based zero-knowledge proof, providing strong decentralized identity verification and resistance to replay attacks. It then designs a dynamic access control strategy based on a multi-dimensional reputation model, which converts user behavior, attributes, and historical transaction performance into a comprehensive trust score used to determine fine-grained access rights. In addition, an AES-PEKS hybrid encryption method is employed to support encrypted keyword search and order matching while protecting the confidentiality of order data. This paper implements a multi-channel architecture for aquatic product trading prototype system on Hyperledger Fabric. This system separates registration, order processing, and reputation management into different channels to improve concurrency and enhance privacy protection. Security analysis shows that the proposed solution effectively defends against replay attacks, key leaks, data tampering, and privacy theft. Performance evaluation further demonstrates that, compared to a single-chain architecture, the multi-channel design, while increasing security mechanisms, maintains a stable throughput of approximately 223 tx/s even when concurrency reaches 600–800 tx/s, ensuring normal operation of the trading system. These results indicate that this solution provides a practical technical approach and system-level reference for building secure, reliable, and efficient online aquatic product trading platforms. Full article

This study proposes a hybrid machine learning framework that integrates structured financial indicators and unstructured textual strategy disclosures to improve firm-level management performance prediction. Using corporate business reports from South Korean listed firms, strategic text was extracted and categorized under the Balanced Scorecard (BSC) framework into financial, customer, internal process, and learning and growth dimensions. Various machine learning and deep learning models—including k-nearest neighbors (KNNs), support vector machine (SVM), light gradient boosting machine (LightGBM), convolutional neural network (CNN), long short-term memory (LSTM), autoencoder, and transformer—were evaluated, with results showing that the inclusion of strategic textual data significantly enhanced prediction accuracy, precision, recall, area under the curve (AUC), and F1-score. Among individual models, the transformer architecture demonstrated superior performance in extracting context-rich semantic features. A soft-voting ensemble model combining autoencoder, LSTM, and transformer achieved the best overall performance, leading in accuracy and AUC, while the best single deep learning model (transformer) obtained a marginally higher F1 score, confirming the value of hybrid learning. Furthermore, analysis revealed that customer-oriented strategy disclosures were the most predictive among BSC dimensions. These findings highlight the value of integrating financial and narrative data using advanced NLP and artificial intelligence (AI) techniques to develop interpretable and robust corporate performance forecasting models. In addition, we operationalize information security narratives using a reproducible cybersecurity lexicon and derive security disclosure intensity and weight share features that are jointly evaluated with BSC-based strategic vectors. Full article

Resistance to imatinib remains a therapeutic challenge, largely driven by point mutations within the kinase domain of the BCR-ABL, among which the T315I substitution constitutes the most clinically significant barrier. Ponatinib effectively inhibits this mutant form but is limited by dose-dependent cardiovascular toxicity, prompting efforts to develop safer and more selective agents. Recent advances highlight aminopyrimidine-derived scaffolds and their evolution into thienopyrimidines, oxadiazoles, and pyrazines with improved activity against BCR-ABL^T315I. Further progress has been achieved with benzothiazole–picolinamide hybrids incorporating a urea-based pharmacophore, which benefit from strategic hinge-region substitutions and phenyl linkers that enhance potency. Parallel research into dual-mechanism inhibitors, including Aurora and p38 kinase modulators, demonstrates additional opportunities for overcoming resistance. Combination strategies, such as vorinostat with ponatinib, provide complementary therapeutic avenues. Natural-product-inspired approaches utilizing fungal metabolites provided structurally diverse scaffolds that could engage sterically constrained mutant kinases. Hybrid molecules derived from approved TKIs, including GNF-7, olverembatinib, and HG-7-85-01, exemplify rational design trends that balance efficacy with improved safety. Molecular modeling continues to deepen understanding of ligand engagement within the T315I-mutated active site, supporting the development of next-generation inhibitors. In this review, we summarized recent progress in the design, optimization, and biological evaluation of small molecules targeting the BCR-ABL^T315I mutation. Full article

Optimizing the furniture layout of middle school laboratories is crucial for improving the emergency safety, operational efficiency, and resilience of teaching buildings. This study used AnyLogic software to model and simulate pedestrian evacuation behavior in a typical middle school laboratory layout. In a standardized laboratory (90.75 m²), we constructed a behavior-oriented multi-agent evacuation model. The model incorporated key student parameters, including shoulder width (312–416 mm), walking speed (1.5–2.5 m/s), and reaction time (10–15 s). To ensure comparability between different layouts, the number of evacuees was fixed at 48. Evacuation performance was evaluated based on total evacuation time, spatial density, and detour distance. The results showed that the hybrid layout achieved the shortest evacuation time (28.0 s), which was 10.3% shorter than the island layout (31.2 s) and 34.7% shorter than the parallel layout (42.9 s). The hybrid layout also had a shorter average detour distance (9.78 m) and the lowest path variability (coefficient of variation CV = 0.33), indicating a more balanced evacuation load and a smaller bottleneck effect. Overall, these findings provide evidence-based recommendations for improving laboratory safety, space utilization, and behavioral adaptability, and provide a quantitative reference for updating educational building codes, school laboratory construction standards, and guidelines for laboratory furniture and safety facility configuration. Full article