Search Results (676)

To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and extrinsic camera parameters accurately. Based on the pinhole imaging model, disparity maps were obtained via pixel matching to reconstruct high-precision 3D ablation morphology. A mathematical model was established to analyze how key imaging parameters—baseline distance, focal length, and depth of field—affect reconstruction accuracy in micro-imaging environments. Focusing on trace element detection in WC-Co alloy samples, the reconstructed ablation craters enabled the precise calculation of ablation volumes and revealed their correlations with laser parameters (energy, wavelength, pulse duration) and the physical-chemical properties of the samples. Multivariate regression analysis was employed to investigate how ablation morphology and plasma evolution jointly influence LIBS quantification. A nonlinear calibration model was proposed, significantly suppressing matrix effects, achieving R² = 0.987, and reducing RMSE to 0.1. This approach enhances micro-scale LIBS accuracy and provides a methodological reference for high-precision spectral analysis in environmental and materials applications. Full article

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Background: The influence of the initial ablation modality on pulmonary vein (PV) reconnection and substrate characteristics in redo procedures for recurrent atrial fibrillation (AF) remains unclear. We assessed how different thermal strategies—ablation index (AI)-guided radiofrequency (RF) versus cryoballoon (CB) ablation—affect remapping findings during redo pulmonary vein isolation (PVI). Methods: We included patients undergoing redo ablation between 2015 and 2024 with high-density electroanatomic mapping. Initial PVI modalities were retrospectively classified as low-power, long-duration (LPLD) RF; high-power, short-duration (HPSD) RF; or second-/third-generation CB. Reconnection sites were mapped using multielectrode catheters. Redo PVI was performed using AI-guided RF. Segments showing PV reconnection were reisolated; if all PVs remained isolated and AF persisted, posterior wall isolation was performed. Results: Among 195 patients (LPLD: 63; HPSD: 30; CB: 102), complete PVI at redo was observed in 0% (LPLD), 23.3% (HPSD), and 10.1% (CB) (p < 0.01 for LPLD vs. HPSD). Reconnection patterns varied by technique; LPLD primarily affected the right carina, while HPSD and CB showed reconnections at the LSPV ridge. Organized atrial tachycardia was least frequent after CB (12.7%, p < 0.002). Conclusion: Initial ablation strategy significantly influences PV reconnection and post-PVI arrhythmia patterns, with implications for redo procedure planning. Full article

In this study, a novel direct laser beam turning (DLBT) approach is proposed for the precision machining of AISI 308L austenitic stainless steel, which eliminates the need for cutting tools and thereby eradicates tool wear and vibration-induced surface irregularities. A nanosecond-pulsed Nd:YAG fiber laser (λ = 1064 nm, spot size = 0.05 mm) was used, and Ø1.6 mm × 20 mm cylindrical rods were processed under ambient conditions without auxiliary cooling. The experimental framework systematically evaluated the influence of scanning speed, pulse frequency, and the number of laser passes on dimensional accuracy and material removal efficiency. The results indicate that a maximum diameter reduction of 0.271 mm was achieved at a scanning speed of 3200 mm/s and 50 kHz, whereas 0.195 mm was attained at 6400 mm/s and 200 kHz. A robust second-order polynomial correlation (R² = 0.99) was established between diameter reduction and the number of passes, revealing the high predictability of the process. Crucially, when the scanning speed was doubled, the effective fluence was halved, considerably influencing the ablation characteristics. Despite the low fluence, evidence of material evaporation at elevated frequencies due to the incubation effect underscores the complex photothermal dynamics governing the process. This work constitutes the first comprehensive quantification of pass-dependent diameter modulation in DLBT and introduces a transformative, noncontact micromachining strategy for hard-to-machine alloys. The demonstrated precision, repeatability, and thermal control position DLBT as a promising candidate for next-generation manufacturing of high-performance miniaturized components. Full article

Financial market forecasting remains challenging due to complex nonlinear dynamics and regime-dependent behaviors that traditional models struggle to capture effectively. This research introduces the Adaptive Financial Reservoir Network with Hypernetwork Flow (AFRN–HyperFlow) framework, a novel ensemble architecture integrating Echo State Networks, temporal convolutional networks, mixture density networks, adaptive Hypernetworks, and deep state-space models for enhanced financial time-series prediction. Through comprehensive feature engineering incorporating technical indicators, spectral decomposition, reservoir-based representations, and flow dynamics characteristics, the framework achieves superior forecasting performance across diverse market conditions. Experimental validation on 26,817 balanced samples demonstrates exceptional results with an F1-score of 0.8947, representing a 12.3% improvement over State-of-the-Art baseline methods, while maintaining robust performance across asset classes from equities to cryptocurrencies. The adaptive Hypernetwork mechanism enables real-time regime-change detection with 2.3 days average lag and 95% accuracy, while systematic SHAP analysis provides comprehensive interpretability essential for regulatory compliance. Ablation studies reveal Echo State Networks contribute 9.47% performance improvement, validating the architectural design. The AFRN–HyperFlow framework addresses critical limitations in uncertainty quantification, regime adaptability, and interpretability, offering promising directions for next-generation financial forecasting systems incorporating quantum computing and federated learning approaches. Full article

Multimodal object detection leverages the complementary characteristics of visible (RGB) and infrared (IR) imagery, making it well-suited for challenging scenarios such as low illumination, occlusion, and complex backgrounds. However, most existing fusion-based methods rely on static or heuristic strategies, limiting their adaptability to dynamic environments. To address this limitation, we propose CLSANet, a cognitive learning-based self-adaptive network that enhances detection performance by dynamically selecting and integrating modality-specific features. CLSANet consists of three key modules: (1) a Dominant Modality Identification Module that selects the most informative modality based on global scene analysis; (2) a Modality Enhancement Module that disentangles and strengthens shared and modality-specific representations; and (3) a Self-Adaptive Fusion Module that adjusts fusion weights spatially according to local scene complexity. Compared to existing methods, CLSANet achieves state-of-the-art detection performance with significantly fewer parameters and lower computational cost. Ablation studies further demonstrate the individual effectiveness of each module under different environmental conditions, particularly in low-light and occluded scenes. CLSANet offers a compact, interpretable, and practical solution for multimodal object detection in resource-constrained settings. Full article

The Mw 7.8 Gorkha Earthquake of 25 April 2015 triggered over 25,000 landslides across central Nepal, with 4775 events concentrated in Gorkha District alone. Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. Using a balanced dataset of 4775 landslide and non-landslide instances, we evaluated the performance of Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models through spatial cross-validation, SHapley Additive exPlanations (SHAP) explainability, and ablation analysis. The RF model outperformed all others, achieving an accuracy of 87.9% and a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) value of 0.94, while XGBoost closely followed (AUC = 0.93). Ensemble models collectively classified over 95% of observed landslides into High and Very High susceptibility zones, demonstrating strong spatial reliability. SHAP analysis identified elevation, proximity to fault, peak ground acceleration (PGA), slope, and rainfall as dominant predictors. Notably, the inclusion of post-seismic rainfall substantially improved recall and F1 scores in ablation experiments. Spatial cross-validation revealed the superior generalizability of ensemble models under heterogeneous terrain conditions. The findings underscore the value of integrating post-seismic hydrometeorological factors and spatial validation into susceptibility assessments. We recommend adopting ensemble models, particularly RF, for operational hazard mapping in earthquake-prone mountainous regions. Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience. Full article

Background: Vaginal intraepithelial neoplasia (VaIN) is a rare but potentially precancerous condition strongly associated with human papillomavirus (HPV) infection. Despite increased detection rates due to HPV screening and colposcopy, diagnosis and management remain challenging. This study aimed to evaluate the epidemiological characteristics, risk factors, and outcomes of VaIN in patients referred to a tertiary academic center. Methods: We conducted a retrospective analysis of 48 patients who underwent colposcopy-directed vaginal biopsies between January 2019 and June 2024 at the Medical University of Warsaw. Data collected included patient demographics, HPV status, cytology, histopathology, and treatment outcomes. Patients were grouped based on the presence and grade of VaIN (VaIN 1 vs. VaIN 2/3). Statistical analyses were performed using SPSS software. Results: VaIN was diagnosed in 24 patients (50%), VaIN was confirmed in half of the cohort, VaIN 2 in 30%, and VaIN 3 in 18% of cases. HPV infection and prior cervical pathology were significantly associated with VaIN diagnosis (P = 0.03 and P = 0.05, respectively), and high-risk HPV infection correlated with higher-grade lesions (P = 0.04). Among VaIN 2+ cases, most patients required laser ablation or surgical excision, while VaIN 1 often regressed spontaneously. Regression occurred in 11 cases, and high-risk HPV infection was inversely associated with spontaneous regression (P = 0.04). Conclusions: This study confirms the central role of HPV, particularly high-risk subtypes, in VaIN pathogenesis. Conservative management may be appropriate for VaIN 1, while VaIN 2+ requires active intervention. HPV genotyping should be integrated into diagnostic workups, and long-term follow-up is essential due to the risks of persistence and recurrence. Full article

(1) Background: A decrease in corneal hydration during refractive surgery is observed clinically as well as in laboratory settings, but the associated consequences are not yet fully understood. The purpose of this paper is to analyze the impact of the gain of ablation efficiency due to hydration changes during cornea refractive surgery. (2) Methods: We developed a simulation model to evaluate the influence of hydration changes on the ablation algorithms used in laser refractive surgery. The model simulates different physical effects of an entire surgical process, simulating the shot-by-shot ablation process based on a modeled beam profile. The model considers corneal hydration, as well as environmental humidity, along with the laser beam characteristics and ablative spot properties for evaluating any hydration changes and their effect on laser refractive surgery. (3) Results: Using pulse lists collected from actual treatments, we simulated the gain of efficiency during the ablation process. Ablation efficiency is increased due to dehydration effects during laser treatments. Longer treatments suffer larger dehydration effects and are more prone to overcorrections due to gain of efficiency than shorter treatments. (4) Conclusions: The improper use of a model that overestimates or underestimates the effects derived from the hydration dynamics during treatment may result in suboptimal refractive corrections. This model may contribute to improving emmetropization and the correction of ocular aberrations with improved laser parameters that can compensate for the changes in ablation efficiency due to hydration changes in the cornea. Full article

Bacteria-mediated cancer therapy represents a novel and promising strategy for targeted drug delivery to solid tumors. Multiple studies have demonstrated that various Bifidobacterium species can selectively colonize the hypoxic microenvironments characteristic of solid tumors. Leveraging this property, Bifidobacterium has been explored as a delivery vector for a range of anti-cancer approaches such as immunotherapy, nanoformulated chemotherapeutics, and gene therapy. Notably, anti-angiogenic genes such as endostatin and tumstatin have been successfully delivered to colorectal tumors using Bifidobacterium infantis and Bifidobacterium longum, respectively. Additionally, Bifidobacterium bifidum has been employed to transport doxorubicin and paclitaxel nanoparticles to breast and lung tumor sites. Furthermore, both Bifidobacterium longum and Bifidobacterium bifidum have been utilized to deliver nanoparticles that act as synergistic agents for high-intensity focused ultrasound (HIFU) therapy, significantly enhancing tumor ablation, particularly in triple-negative breast cancer (TNBC) models. While these pre-clinical findings are highly encouraging, further clinical research is essential. Specifically, studies are needed to investigate the colonization dynamics of different Bifidobacterium species across various tumor types and to evaluate their potential in delivering diverse cancer therapies in human patients. Full article

Background: Pulsed field ablation (PFA) is a novel non-thermal modality for pulmonary vein isolation (PVI) in atrial fibrillation (AF), offering myocardial selectivity through irreversible electroporation while sparing surrounding structures. However, concerns have emerged regarding potential subclinical hemolysis, reflected by alterations in biochemical markers such as lactate dehydrogenase (LDH). Methods: We conducted a retrospective, single-center study involving 249 patients undergoing PVI: 121 treated with PFA (PulseSelect or FARAPULSE) and 128 with radiofrequency (RF) ablation (PVAC catheter). Laboratory parameters were assessed at baseline, post-procedure, and at discharge, including hemoglobin, hematocrit, red blood cell (RBC) count, platelet count, creatinine, and LDH. The primary endpoint was the variation in blood cell indices; the secondary endpoint was the evaluation of LDH and hematocrit changes. Statistical analysis included t-tests and chi-square tests. Results: Baseline characteristics and pre-procedural labs did not differ significantly between groups. No significant changes in hemoglobin, hematocrit, RBC count, platelet count, or creatinine were observed post-ablation or at discharge. However, LDH levels significantly increased in the PFA group both post-procedurally and at discharge (p < 0.001), without concurrent changes in other blood cell parameters. Conclusions: PFA and RF ablation yield comparable hematological profiles after PVI, with no significant impact on key blood cell parameters. Nonetheless, the consistent rise in LDH levels in the PFA group suggests mild, subclinical hemolysis or tissue injury due to more extensive lesions. While supporting the hematologic safety of PFA, these findings underscore the need for further studies to assess the clinical significance of these biochemical alterations, particularly in high-risk patients or extensive ablation settings. Full article

The increasing deployment of photovoltaic (PV) systems necessitates robust fault detection mechanisms to ensure operational reliability and safety. Conventional approaches, however, struggle in complex industrial environments characterized by high noise, data incompleteness, and class imbalance. This study proposes an innovative Advanced CNN-BiLSTM architecture integrating multi-scale feature extraction with hierarchical attention to enhance PV fault detection. The proposed framework employs four parallel CNN branches with kernel sizes of 3, 7, 15, and 31 to capture temporal patterns across various time scales. These features are then integrated by an adaptive feature fusion network that utilizes multi-head attention. A two-layer bidirectional LSTM with temporal attention mechanism processes the fused features for final classification. Comprehensive evaluation on the GPVS-Faults dataset using a progressive difficulty validation framework demonstrates exceptional performance improvements. Under extreme industrial conditions, the proposed method achieves 83.25% accuracy, representing a substantial 119.48% relative improvement over baseline CNN-BiLSTM (37.93%). Ablation studies reveal that the multi-scale CNN contributes 28.0% of the total performance improvement, while adaptive feature fusion accounts for 22.0%. Furthermore, the proposed method demonstrates superior robustness under severe noise (σ = 0.20), high levels of missing data (15%), and significant outlier contamination (8%). These characteristics make the architecture highly suitable for real-world industrial deployment and establish a new paradigm for temporal feature fusion in renewable energy fault detection. Full article

Interpreting complex clinical time series is vital for patient safety and care, as it is both essential for supporting accurate clinical assessment and fundamental to building clinician trust and promoting effective clinical action. In complex time series analysis, decomposing a signal into meaningful underlying components is often a crucial means for achieving interpretability. This process is known as time series disentanglement. While deep learning models excel in predictive performance in this domain, their inherent complexity poses a major challenge to interpretability. Furthermore, existing time series disentanglement methods, including traditional trend or seasonality decomposition techniques, struggle to adequately separate clinically crucial specific components: static patient characteristics, condition trend, and acute events. Thus, a key technical challenge remains: developing an interpretable method capable of effectively disentangling these specific components in complex clinical time series. To address this challenge, we propose CoTD-VAE, a novel variational autoencoder framework for interpretable component disentanglement. CoTD-VAE incorporates temporal constraints tailored to the properties of static, trend, and event components, such as leveraging a Trend Smoothness Loss to capture gradual changes and an Event Sparsity Loss to identify potential acute events. These designs help the model effectively decompose time series into dedicated latent representations. We evaluate CoTD-VAE on critical care (MIMIC-IV) and human activity recognition (UCI HAR) datasets. Results demonstrate successful component disentanglement and promising performance enhancement in downstream tasks. Ablation studies further confirm the crucial role of our proposed temporal constraints. CoTD-VAE offers a promising interpretable framework for analyzing complex time series in critical applications like healthcare. Full article

Precise extraction of rural settlements in alpine regions is critical for geographic data production, rural development, and spatial optimization. However, existing deep learning models are hindered by insufficient datasets and suboptimal algorithm structures, resulting in blurred boundaries and inadequate extraction accuracy. Therefore, this study uses high-resolution unmanned aerial vehicle (UAV) remote sensing images to construct a specialized dataset for the extraction of rural settlements in alpine mountainous areas, while introducing an innovative shadow mitigation technique that integrates multiple spectral characteristics. This methodology effectively addresses the challenges posed by intense shadows in settlements and environmental occlusions common in mountainous terrain analysis. Based on the comparative experiments with existing deep learning models, the Swin Transformer was selected as the baseline model. Building upon this, the Feature Fusion Swin Transformer (FF-SwinT) model was constructed by optimizing the data processing, loss function, and multi-view feature fusion. Finally, we rigorously evaluated it through ablation studies, generalization tests and large-scale image application experiments. The results show that the FF-SwinT has improved in many indicators compared with the traditional Swin Transformer, and the recognition results have clear edges and strong integrity. These results suggest that the FF-SwinT establishes a novel framework for rural settlement extraction in alpine mountain regions, which is of great significance for regional spatial optimization and development policy formulation. Full article

Compared to other multi-source image fusion tasks, visible and SAR image fusion faces a lack of training data in deep learning-based methods. Introducing structural priors to design fusion networks is a viable solution. We incorporated the feature hierarchy concept from computer vision, dividing deep features into low-, mid-, and high-level tiers. Based on the complementary modal characteristics of SAR and visible, we designed a fusion architecture that fully analyze and utilize the difference of hierarchical features. Specifically, our framework has two stages. In the cross-modal enhancement stage, a CycleGAN generator-based method for cross-modal interaction and input data enhancement is employed to generate pseudo-modal images. In the fusion stage, we have three innovations: (1) We designed feature extraction branches and fusion strategies differently for each level based on the features of different levels and the complementary modal features of SAR and visible to fully utilize cross-modal complementary features. (2) We proposed the Layered Strictly Nested Framework (LSNF), which emphasizes hierarchical differences and uses hierarchical characteristics, to reduce feature redundancy. (3) Based on visual saliency theory, we proposed a Gradient-weighted Pixel Loss (GWPL), which dynamically assigns higher weights to regions with significant gradient magnitudes, emphasizing high-frequency detail preservation during fusion. Experiments on the YYX-OPT-SAR and WHU-OPT-SAR datasets show that our method outperforms 11 state-of-the-art methods. Ablation studies confirm each component’s contribution. This framework effectively meets remote sensing applications’ high-precision image fusion needs. Full article