Search Results (1,391)

Accurate prediction of fracturing pressure is critical for operational safety and fracturing efficiency in unconventional reservoirs. Traditional physics-based models and existing deep learning architectures often struggle to capture the intense fluctuations and complex temporal dependencies observed in actual fracturing operations. To address these challenges, this paper proposes a multi-source data-driven fracturing pressure prediction model, a model integrating TCN-BiLSTM-Attention Mechanism (Temporal Convolutional Network, Bidirectional Long Short-Term Memory, Attention Mechanism), and introduces a feature selection mechanism for fracture pressure prediction. This model employs TCN to extract multi-scale local fluctuation features, BiLSTM to capture long-term dependencies, and Attention to adaptively adjust feature weights. A two-stage feature selection strategy combining correlation analysis and ablation experiments effectively eliminates redundant features and enhances model robustness. Field data from the Sichuan Basin were used for model validation. Results demonstrate that our method significantly outperforms baseline models (LSTM, BiLSTM, and TCN-BiLSTM) in mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²), particularly under high-fluctuation conditions. When integrated with slope reversal analysis, it achieves sand blockage warnings up to 41 s in advance, offering substantial potential for real-time decision support in fracturing operations. Full article

The state of charge (SOC) serves as a critical indicator for evaluating the remaining driving range of electric vehicles (EVs), and its prediction is of significance for alleviating range anxiety and promoting the development of the EVs industry. This study addresses two key challenges in current SOC prediction technologies: (1) the scarcity of multi-step prediction research based on real driving conditions and (2) the poor performance in multi-scale temporal feature extraction. We innovatively propose the Wavelet-Guided Temporal Feature Enhanced Informer (WG-TFE-Informer) prediction model with two core innovations: a wavelet-guided convolutional embedding layer that significantly enhances anti-interference capability through joint time-frequency analysis and a temporal edge enhancement (TEE) module that achieves the collaborative modeling of local microscopic features and macroscopic temporal evolution patterns based on sparse attention mechanisms. Building upon this model, we establish a multidimensional SOC energy consumption prediction system incorporating battery characteristics, driving behavior, and environmental terrain factors. Experimental validation with real-world operating data demonstrates outstanding performance: 1-min SOC prediction accuracy achieves a mean relative error (MRE) of 0.21% and 20-min SOC prediction exhibits merely 0.62% error fluctuation. Ablation experiments confirm model effectiveness with a 72.1% performance improvement over baseline (MRE of 3.06%) at 20-min SOC prediction, achieving a final MRE of 0.89%. Full article

In cold regions, solar radiation triggers the spring ablation of river ice layers, thereby changing their physical traits and mechanical behavior. This study uses the Heilongjiang River section near Mohe Arctic Village as the research prototype area. It analyzes the impact of solar radiation on ice density and uniaxial compressive strength through indoor simulation tests and multiple regression analysis, aiming to reveal the influence mechanism on uniaxial compressive strength. The results show that after applying a cumulative amount of simulated solar radiation of 84 MJ/m², the ice density decreases by 3.88%, and the loss rate of uniaxial compressive strength can exceed 50%. Solar radiation promotes the transformation of the uniaxial compressive failure mode from ductile to brittle. The established multiple regression model attains a coefficient of determination of 0.891. In the spring ice-melting period in cold regions, the impact of solar radiation on ice strength should be fully considered in the design of ice condition early warnings and water conservancy projects for ice flood prevention. Full article

Arrhythmogenic cardiomyopathy (ACM) is a genetic cardiac disease characterized by a progressive loss of cardiomyocytes associated with fibrofatty myocardial replacement, resulting in a heightened risk of ventricular arrhythmias and sudden cardiac death. ACM is a common cause of sudden death in young individuals, and exercise has been proven to be a factor in disease progression. Current therapeutic strategies, including lifestyle modification, antiarrhythmic pharmacological therapy, catheter ablation, and the placement of implantable cardioverter-defibrillators, remain primarily palliative options rather than addressing the underlying molecular substrate. The pathogenesis of ACM includes complex molecular and cellular mechanisms, linking genetic mutations to structural and electrical anomalies of the ventricle. The lack of targeted therapies contributes to a challenging approach to the disease. It highlights the need for a better understanding of the mechanisms that lead to myocardial remodeling and arrhythmic predisposition. With the help of animal models (especially murine) and induced pluripotent stem cells, there have been advances in understanding the molecular pathogenesis of ACM. In this review, we summarized some of the pathogenic molecular pathways involved in the development of ACM and emerging therapies targeted towards disease modification, not just prevention. Full article

In histopathological image segmentation, existing methods often show low sensitivity to small lesions and indistinct boundaries, leading to missed detections. Since, in clinical diagnosis, the consequences of missed detection are more serious than false alarms, this study proposes ClinSegNet, a recall-oriented and human-centred framework for reliable histopathology screening. ClinSegNet employs a composite optimisation strategy, termed HistoLoss, which balances stability and boundary refinement while prioritising recall. An uncertainty-driven refinement mechanism is further introduced to target high-uncertainty cases with limited fine-tuning cost. In addition, a clinical data processing pipeline was developed, where pixel-level annotations were automatically derived from IHC-to-H&E mapping and combined with public datasets, enabling effective training under limited clinical data conditions. Experiments on the NuInsSeg and NuInsSeg-UHS datasets showed that ClinSegNet achieved recall scores of 0.8803 and 0.8917, further improved to 0.8983 and 0.9053 with HITL refinement, while maintaining competitive Dice and IoU. Comparative and ablation studies confirmed the complementary design of the framework and its advantage in capturing small or complex lesions. In conclusion, ClinSegNet provides a clinically oriented, recall-prioritised framework that enhances lesion coverage, reduces the risk of missed diagnosis, and offers both a methodological basis for future human-in-the-loop systems and a feasible pipeline for leveraging limited clinical data. Full article

Aero-engine ablation detection is a critical task in aircraft health management, yet existing rotation-based object detection methods often face challenges of high computational complexity and insufficient local feature extraction. This paper proposes an improved YOLOv11 algorithm incorporating Context-guided Large-kernel attention and Rotated detection head, called CLR-YOLOv11. The model achieves synergistic improvement in both detection efficiency and accuracy through dual structural optimization, with its innovations primarily embodied in the following three tightly coupled strategies: (1) Targeted Data Preprocessing Pipeline Design: To address challenges such as limited sample size, low overall image brightness, and noise interference, we designed an ordered data augmentation and normalization pipeline. This pipeline is not a mere stacking of techniques but strategically enhances sample diversity through geometric transformations (random flipping, rotation), hybrid augmentations (Mixup, Mosaic), and pixel-value transformations (histogram equalization, Gaussian filtering). All processed images subsequently undergo Z-Score normalization. This order-aware pipeline design effectively improves the quality, diversity, and consistency of the input data. (2) Context-Guided Feature Fusion Mechanism: To overcome the limitations of traditional Convolutional Neural Networks in modeling long-range contextual dependencies between ablation areas and surrounding structures, we replaced the original C3k2 layer with the C3K2CG module. This module adaptively fuses local textural details with global semantic information through a context-guided mechanism, enabling the model to more accurately understand the gradual boundaries and spatial context of ablation regions. (3) Efficiency-Oriented Large-Kernel Attention Optimization: To expand the receptive field while strictly controlling the additional computational overhead introduced by rotated detection, we replaced the C2PSA module with the C2PSLA module. By employing large-kernel decomposition and a spatial selective focusing strategy, this module significantly reduces computational load while maintaining multi-scale feature perception capability, ensuring the model meets the demands of high real-time applications. Experiments on a self-built aero-engine ablation dataset demonstrate that the improved model achieves 78.5% mAP@0.5:0.95, representing a 4.2% improvement over the YOLOv11-obb which model without the specialized data augmentation. This study provides an effective solution for high-precision real-time aviation inspection tasks. Full article

Sentiment analysis plays a crucial role in domains such as financial news, user reviews, and public opinion monitoring, yet existing approaches face challenges when dealing with long and domain-specific texts due to semantic dilution, insufficient context modeling, and dispersed emotional signals. To address these issues, a multi-granularity attention-based sentiment analysis model built on a transformer backbone is proposed. The framework integrates sentence-level and document-level hierarchical modeling, a different-dimensional embedding strategy, and a cross-granularity contrastive fusion mechanism, thereby achieving unified representation and dynamic alignment of local and global emotional features. Static word embeddings combined with dynamic contextual embeddings enhance both semantic stability and context sensitivity, while the cross-granularity fusion module alleviates sparsity and dispersion of emotional cues in long texts, improving robustness and discriminability. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed model. On the Financial Forum Reviews dataset, it achieves an accuracy of 0.932, precision of 0.928, recall of 0.925, F1-score of 0.926, and AUC of 0.951, surpassing state-of-the-art baselines such as BERT and RoBERTa. On the Financial Product User Reviews dataset, the model obtains an accuracy of 0.902, precision of 0.898, recall of 0.894, and AUC of 0.921, showing significant improvements for short-text sentiment tasks. On the Financial News dataset, it achieves an accuracy of 0.874, precision of 0.869, recall of 0.864, and AUC of 0.895, highlighting its strong adaptability to professional and domain-specific texts. Ablation studies further confirm that the multi-granularity transformer structure, the different-dimensional embedding strategy, and the cross-granularity fusion module each contribute critically to overall performance improvements. Full article

Virtual Power Plant (VPP) is capable of aggregating and intelligently coordinating diverse distributed energy resources, among which the accuracy of load forecasting is a key factor in ensuring their regulation capability. To address the periodicity and complex nonlinear fluctuations of electricity load data, this study introduces a Cyclic Order Mapping (COM) encoding method, which maps weekly and intraday sequences into continuous ordered variables on the unit circle, thereby effectively preserving load periodic features. On the basis of the COM encoding, a novel forecasting model is proposed by integrating Bidirectional Long Short-Term Memory (BiLSTM) networks, an efficient self-attention mechanism, and the Kolmogorov–Arnold Network (KAN). This model is termed BiLSTM-Att-KAN. Comparative and ablation experiments were conducted to assess the scientific validity and predictive accuracy of the proposed approach. The results confirm its superiority, achieving a Root Mean Square Error (RMSE) of 141.403, a Mean Absolute Error (MAE) of 106.687, and a coefficient of determination (R²) of 0.962. These findings demonstrate the effectiveness of the proposed model in enhancing load forecasting performance for VPP applications. Full article

The newly discovered multi-colored corundum (gem quality) alluvial deposit in Malipo, Yunnan Province, is one of the most famous sapphire deposits in China. However, the coloration mechanism and genesis of red-blue colored corundum (RBCC) remain enigmatic. In this study, conventional gemological techniques such as ultraviolet–visible (UV-vis) spectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were employed on an RBCC suite, with a view to unravel its coloration mechanism and compositional characteristics. The results show that the element pairs of Cr³⁺, Fe²⁺-Ti⁴⁺, and Fe³⁺-Fe³⁺ in principle contribute to the red coloration, while the blue color in corundum is predominantly caused by the Fe²⁺-Ti⁴⁺ pair, and subordinately by Cr³⁺ and Fe³⁺. Cr is likely the cause of the purple color. The Cr content in the red zone is significantly higher than that in the blue zone, while the Ti and V contents in the red zone are notably lower than in the blue zone. High Cr/Ga and (V + Cr)/Ga values of the Malipo RBCC suggest a metamorphic origin. All color zones of RBCC demonstrate stability in Ga content and an extremely low content of Mg, with minor fluctuations in Fe content, indicating that the formation process of the Malipo RBCC was influenced by magma mixing. Full article

In response to the issues of premature convergence and insufficient parameter control in Particle Swarm Optimization (PSO) for high-dimensional complex optimization problems, this paper proposes a Multi-Strategy Topological Particle Swarm Optimization algorithm (MSTPSO). The method builds upon a reinforcement learning-driven topological switching framework, where Q-learning dynamically selects among fully informed topology, small-world topology, and exemplar-set topology to achieve an adaptive balance between global exploration and local exploitation. Furthermore, the algorithm integrates differential evolution perturbations and a global optimal restart strategy based on stagnation detection, together with a dual-layer experience replay mechanism to enhance population diversity at multiple levels and strengthen the ability to escape local optima. Experimental results on 29 CEC2017 benchmark functions, compared against various PSO variants and other advanced evolutionary algorithms, show that MSTPSO achieves superior fitness performance and exhibits stronger stability on high-dimensional and complex functions. Ablation studies further validate the critical contribution of the Q-learning-based multi-topology control and stagnation detection mechanisms to performance improvement. Overall, MSTPSO demonstrates significant advantages in convergence accuracy and global search capability. Full article

Metastatic breast cancer (MBC) remains one of the most aggressive and fatal malignancies in women, primarily due to tumor heterogeneity, multidrug resistance, and the limitations of conventional therapeutic approaches. Aim: This review aims to evaluate recent advances in nanomaterial-based photothermal therapy (PTT) platforms and their potential in the treatment of metastatic breast cancer. Method: A comprehensive analysis of current literature was conducted to examine how various nanomaterials are engineered for targeted PTT, with particular emphasis on their mechanisms of action, synergistic applications with chemotherapy, immunotherapy, and photodynamic therapy, as well as their capacity to overcome challenges associated with targeting metastatic niches. Results: The findings indicate that nanotechnology-enabled PTT provides spatiotemporal precision, efficient tumor ablation, and reduced systemic toxicity, while significantly enhancing therapeutic outcomes when integrated into multimodal treatment strategies. Recent preclinical studies and early clinical trials further underscore advancements in imaging guidance, thermal efficiency, and site-specific drug delivery; however, issues related to biocompatibility, safety, and large-scale clinical translation remain unresolved. Conclusions: Nanomaterial-assisted PTT holds substantial promise for improving therapeutic efficacy against metastatic breast cancer. Future research should prioritize optimizing imaging resolution, minimizing adverse effects, and addressing translational challenges to accelerate clinical integration and ultimately enhance health outcomes for women. Full article

Background/Objectives: Since its emergence, COVID-19—caused by the novel coronavirus SARS-CoV-2—has affected millions globally and led to over 1.2 million deaths in the United States alone. This global impact, coupled with the emergence of five new human coronaviruses over the past two decades, underscores the urgency of understanding its pathogenic mechanisms at the molecular level—not only for managing the current pandemic but also preparing for future outbreaks. Small non-coding RNAs (sncRNAs) critically regulate host and viral gene expression, including antiviral responses. Among the molecular regulators implicated in antiviral defense, the microRNA-processing enzyme Drosha has emerged as a particularly intriguing factor. In addition to its canonical role, Drosha also exerts a non-canonical, interferon-independent antiviral function against several RNA viruses. Methods: To investigate this, we employed q/RT-PCR, Western blot, and immunocytochemistry/immunofluorescence in an immortalized normal human lung/bronchial epithelial cell line (NuLi-1), as well as a human colorectal carcinoma Drosha CRISPR knockout cell line. Results: In this study, we observed a striking shift in Drosha isoform expression following infection with multiple SARS-CoV-2 variants. This shift was absent following treatment with the viral mimetic poly (I:C) or infection with other RNA viruses, including the non-severe coronaviruses HCoV-OC43 and HCoV-229E. We also identified a distinct alteration in Drosha’s cellular localization post SARS-CoV-2 infection. Moreover, Drosha ablation led to reduced expression of SARS-CoV-2 genomic and sub-genomic targets. Conclusions: Together, these observations not only elucidate a novel aspect of Drosha’s antiviral role but also advance our understanding of SARS-CoV-2 host–pathogen interactions, highlighting potential therapeutic avenues for future human coronavirus infections. Full article

Multi-sensor fire detection technology has been widely adopted in practical applications; however, existing methods still suffer from high false alarm rates and inadequate adaptability in complex environments due to their limited capacity to capture deep time-series dependencies in sensor data. To enhance robustness and accuracy, this paper proposes a novel model named BiLSTM-LN-SA, which integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with Layer Normalization (LN) and a Self-Attention (SA) mechanism. The BiLSTM module extracts intricate time-series features and long-term dependencies. The incorporation of Layer Normalization mitigates feature distribution shifts across different environments, thereby improving the model’s adaptability to cross-scenario data and its generalization capability. Simultaneously, the Self-Attention mechanism dynamically recalibrates the importance of features at different time steps, adaptively enhancing fire-critical information and enabling deeper, process-aware feature fusion. Extensive evaluation on a real-world dataset demonstrates the superiority of the BiLSTM-LN-SA model, which achieves a test accuracy of 98.38%, an F1-score of 0.98, and an AUC of 0.99, significantly outperforming existing methods including EIF-LSTM, rTPNN, and MLP. Notably, the model also maintains low false positive and false negative rates of 1.50% and 1.85%, respectively. Ablation studies further elucidate the complementary roles of each component: the self-attention mechanism is pivotal for dynamic feature weighting, while layer normalization is key to stabilizing the learning process. This validated design confirms the model’s strong generalization capability and practical reliability across varied environmental scenarios. Full article

In cold and highly humid regions, wind turbine blades (WTB) are susceptible to icing, which can have a significant impact on the security and economic operation of turbines. Therefore, precise and prompt icing status detection is pivotal for maintaining wind turbine operational normalcy. In this research, an improved PP-YOLOE network is developed for classifying and detecting the icing state of WTB. First, a dataset of WTB icing is constructed based on a wind tunnel laboratory and expanded to improve the generalization of the model. To enhance feature representation, the network architecture was improved by embedding a coordinate attention (CA) mechanism and integrating atrous spatial pyramid pooling (ASPP) to better capture multi-scale contextual information. Moreover, a key innovation of this work is the novel application of a particle swarm optimization (PSO) algorithm to systematically automate hyperparameter tuning. Through ablation experiments and comparative tests, the improved PP-YOLOE network demonstrates superior overall performance on this dataset, achieving a multiple average precision of 0.94. It surpasses the original model across multiple evaluation metrics, indicating a robust and meaningful enhancement. The improved PP-YOLOE network proposed in this study provides a promising and effective method for WTB icing detection. This work provides a paradigm for applying advanced deep learning techniques to enhance intelligent industrial inspection tasks. Full article

A glow discharge polymer (GDP) has unique physical properties—transparency, brittleness, and hardness—that pose challenges for traditional mechanical machining techniques. We have investigated the microhole fabrication of GDP films using shaped femtosecond laser pulses to study the influence of pulse shape, delay between subpulses, and focusing position on processing precision and efficiency. By precisely controlling pulse characteristics, such as duration, energy, and subpulse intervals, the efficiency, hole morphology, and processing quality were significantly improved. The experimental results demonstrated that femtosecond lasers with subpulses produce smaller and more uniform microholes compared to transform-limited pulses. Furthermore, both the pulse shape and focusing position of the laser were found to further influence ablation efficiency. This study establishes, for the first time, the critical role of temporal pulse shaping in optimizing the femtosecond laser drilling of GDP films, which provides valuable information on optimizing femtosecond laser parameters for precision processing of polymer films and advances the potential for microhole fabrication in industrial applications. Full article