Search Results (4,763)

It is an accepted paradigm in the already matured industry of Quantum Key Distribution (QKD) implementations that when the quantum channel is attacked or unresponsive, the system reverts to classical security. Thus, in times of crises, when the quantum system is severely damaged, the saving resort is considered to be the classical solution. This paper explores the opposite approach. In the case of disaster, when parts of the classical part of the key distribution system are broken, are there any possible crisis management options to give some limited functionality? The result of this research shows that if the classical channel fails, the quantum channel can still produce and distribute keys. The experimental results of the contingency QKD show that, using positive operator-valued measurements (POVMs), keys can still be produced and shared. The scheme described in this paper uses the quantum channel only to distribute imperfect keys. Any one distributed key has a theoretical overlap of approximately $75\%$ between Alice’s key and Bob’s key, respectively. The experimental POVM circuit is implemented with two different Naimark dilation approximations: one using $R_z$ gates and the other using $R_y$ gates. The practical implementation results are close to the theoretical analysis. As the keys have a partial overlap, the encryption/ decryption algorithm also needs to adjust to this reality. The encryption/decryption algorithm used in the experiments is a repetition algorithm that is simple but shows the resilience of the scheme. Ultimately, the classical channel is not used during the contingency QKD at all, while the quantum channel is assumed trusted under a restricted adversary model in which Eve is limited to individual attacks. Under this model, partial secrecy is retained for all non-zero channel error rates below a pre-agreed threshold. Full article

Wheat is a major staple crop, and storage mold growth poses a severe threat to grain safety and quality stability. Natural mold development in stored wheat exhibits subtle, localized, and highly heterogeneous characteristics. Existing unimodal methods and global fusion approaches generally suffer from insufficient local feature sensitivity, hindering fine-grained mold severity grading. To address this limitation, we propose a Mask-Guided Fine-Grained Fusion Network, a weakly supervised framework based on local RGB–HSI fusion. This framework employs a dynamic parallel A/B experimental design to construct time-matched proxy labels via weakly supervised learning. A standardized preprocessing pipeline including single-kernel extraction, foreground segmentation, and cross-modal registration is established to resolve RGB–HSI spatial misalignment, ensuring physical-level spatial consistency of multimodal features. The model incorporates a Foreground-Aware Spectral Recalibration (FASR) module to suppress background noise, a Mask-Guided Dilated Cross-modal Local Attention (MDCLA) mechanism to establish fine-grained local mappings between RGB visual phenotypes and hyperspectral responses, and a sample-level adaptive fusion strategy to dynamically weight features by modal reliability, enhancing representation of complex samples across all mold stages. Experiments show that the Mask-Guided Fine-Grained Fusion Network achieves 0.9689 classification accuracy, 0.9698 Macro-F1 score, and 0.0593 Mean Absolute Error (MAE), significantly outperforming state-of-the-art unimodal deep models and global attention fusion baselines. This work provides a proof-of-principle framework for fine-grained non-destructive mold risk assessment in stored wheat. Full article

Exploitation of deep (>4000 m) tight geothermal reservoirs is constrained by low native permeability and premature thermal breakthrough, limiting sustainable heat recovery. Here, we investigate THM (thermo–hydro–mechanical) controls on fluid flow and heat transport during cold-water reinjection in deep tight sandstone reservoirs through an integrated framework linking two-dimensional mechanistic analysis with three-dimensional field-scale modeling. A two-dimensional thermo-poroelastic model reveals that strong thermal contrasts induced by cold-fluid injection cause contraction of the rock framework and transient pore-space dilation under confinement, producing short-term permeability enhancement. This process alters local flow capacity and redirects early cold-front migration, with persistent impacts on subsequent heat transport. Field-scale simulations further quantify the coupled effects of well spacing and reinjection temperature on thermal breakthrough, defined as a 1 °C decline in production-well temperature. Increased well spacing delays cold-front arrival and significantly retards breakthrough, whereas lower reinjection temperature enhances early heat extraction but accelerates convective transport, leading to earlier breakthrough. The combination of thermally enhanced permeability and intensified convection promotes preferential flow channels, increasing breakthrough risk. Balancing thermal-breakthrough delay against the heat-extraction driving force, the simulations delineate a favorable engineering window for the investigated conditions and clarify how cooling-sensitive permeability evolution affects preferential flow and reservoir-scale thermal response. Full article

Background: Risk stratification in non-ischemic dilated cardiomyopathy (DCM) remains challenging because left ventricular ejection fraction (LVEF) and late gadolinium enhancement (LGE) do not fully capture the underlying myocardial substrate. Septal native T1 mapping provides a quantitative assessment of diffuse myocardial abnormalities and may contribute to myocardial tissue characterization within a multiparametric CMR framework. Methods: This retrospective single-center study included 45 consecutive patients with non-ischemic DCM referred for clinically indicated CMR at Perrino Hospital, Brindisi, Italy, between November 2023 and November 2025. All examinations were performed using a standardized CMR protocol including cine imaging, LGE, and native T1 mapping on a 1.5-T Siemens Healthineers scanner. Septal native T1 was used as the primary mapping parameter because of its established reproducibility and robustness for myocardial tissue characterization. Patients were followed for a composite endpoint including all-cause mortality, major ventricular arrhythmic events, appropriate ICD therapy, and hospitalization for heart failure. Endpoint coding was verified, and all analyses were performed using the final validated dataset. Results: During a median follow-up of 15 months, 14 patients (31.1%) experienced the composite endpoint. Patients with events had lower LVEF (27.1 ± 7.8% vs. 48.3 ± 10.5%; p < 0.001), higher LVEDVi (142.6 ± 28.5 vs. 110.6 ± 23.4 mL/m²; p = 0.001), and higher septal native T1 values among patients with available T1 measurements (1047.5 ± 25.0 vs. 1031.5 ± 24.3 ms; p = 0.065). ROC analysis identified a septal native T1 threshold of 1042 ms for prediction of the composite endpoint, with an exploratory AUC of 0.70. Event-free survival was lower in patients with septal native T1 ≥ 1042 ms. Given the limited number of events, all regression and hierarchical analyses should be interpreted as exploratory and hypothesis-generating. Conclusions: Higher septal native T1 values were observed in patients experiencing adverse clinical outcomes; however, native T1 was not independently associated with the composite endpoint in exploratory Cox regression analyses. Full article

Background: Left ventricular (LV) dysfunction remains the leading cause of mortality in transfusion-dependent beta-thalassemia (TDßT), yet conventional echocardiography often fails to detect early myocardial impairment. This study aimed to comprehensively evaluate LV function in children with TDßT using three-dimensional echocardiography (3DE) and speckle-tracking strain analysis, comparing diagnostic performance with conventional two-dimensional (2D) parameters. Results: 50 TDßT patients were compared to 50 matched controls and exhibited preserved conventional LV ejection fraction (EF) on 2D (65.31 ± 7.12% vs. 69.21 ± 3.87%, p = 0.001), but 3DE revealed significant ventricular dilation with higher end-diastolic volume index (75.50 ± 17.99 vs. 65.63 ± 11.86 mL/m², p = 0.002) and end-systolic volume index (22.28 ± 7.85 vs. 18.21 ± 5.14 mL/m², p = 0.003). Despite preserved 3D EF (70.79 ± 5.98% vs. 72.07 ± 5.76%, p = 0.276), global longitudinal strain (GLS) was significantly impaired (−18.56 ± 2.37% vs. −21.47 ± 1.86%, p < 0.001). 3D volumetric parameters demonstrated superior diagnostic performance (AUC for LVEDVI Z-score = 0.874) compared to conventional indices. Transfusion duration correlated strongly with ventricular volumes (r = 0.569 for EDV, p < 0.001), while serum ferritin showed no significant correlation with cardiac parameters. Conclusions: Children with TDßT develop early subclinical LV dysfunction detectable by 3DE and strain analysis despite preserved conventional systolic indices. 3D volumetry and GLS should be integrated into routine cardiac surveillance protocols to enable timely therapeutic intervention. Full article

Accurate prediction of the load–settlement response of axially loaded piles remains challenging because the pile–soil interface undergoes progressive elastoplastic shear deformation accompanied by stress-dependent volumetric changes. Conventional one-dimensional load-transfer models are computationally efficient but usually rely on empirical or hyperbolic fitting functions, making it difficult to explicitly describe the coupled evolution of interface shear hardening, volumetric hardening, and radial effective stress. Although three-dimensional elastoplastic models provide a more rigorous mechanical representation, their high computational cost limits routine engineering application. To address this gap, this study develops a double-hardening elastoplastic load-transfer model for axially loaded piles based on a physically interpretable pile–soil interface constitutive formulation. In the proposed model, the Hardening Soil model is used to characterize interface shear hardening, while the Modified Cam-clay model is introduced to describe volumetric hardening. These two mechanisms are coupled through a stress–dilatancy relationship. According to the loading direction and the position of the current stress point relative to the shear and volumetric yield surfaces, the p′–q stress plane is divided into elastic, shear-hardening, volumetric-hardening, and coupled double-hardening regions. The corresponding incremental constitutive equations are derived and embedded into a conventional load-transfer framework. The model is validated using interface direct shear tests and field-scale static pile load tests. The predicted shear stress–displacement curves and pile-head load–settlement responses agree well with the measured data. Quantitative evaluation shows that the MAPE values are lower than 5%, the maximum relative errors are below 7.6%, and the R² values exceed 0.96 for all validation cases. Full article

Improving detection performance across diverse synthetic aperture radar (SAR) scenes remains challenging because different datasets exhibit different levels of target–background separability. To address this issue, we propose a constant false alarm rate (CFAR)-guided mode-selective network (CGMSN), which selects an appropriate feature-fusion mode according to the CFAR target–background separation margin. Specifically, CFAR is used as an interpretable statistical tool to construct an anomaly response map. The separation margin is then calculated by comparing the average CFAR anomaly responses of annotated target regions and their surrounding contextual backgrounds. Based on this indicator, a You Only Look Once version 8 (YOLOv8)-based mode-selective detector is constructed with three key components. First, a lightweight representation-enhanced backbone that integrates ResNet18 and a dilated convolutional spatial pyramid (DCSP) module is adopted to improve contextual representation while maintaining moderate model complexity. Second, a mode-selective neck (MSN) is designed with three predefined fusion modes, where the appropriate fusion depth is selected according to the CFAR-guided target–background separation margin of each dataset. Third, a complete intersection over the union modulated head (CMH) is developed to enhance classification-regression alignment and suppress clutter-induced responses. Experiments on SAR-Aircraft-1.0, High-Resolution SAR Images Dataset (HRSID), and SAR Ship Detection Dataset (SSDD) indicate that datasets with smaller CFAR target–background separation margins benefit from deeper fusion, while datasets with larger separation margins can adopt shallower fusion. Moreover, the proposed CGMSN achieves superior performance over representative detectors, demonstrating its effectiveness on the evaluated SAR datasets with diverse scene characteristics. Full article

Background: Prolonged cardiac monitoring increases atrial fibrillation (AF) detection after ischemic stroke or transient ischemic attack (TIA). The AS5F score was developed to identify patients at higher risk of post-stroke AF, but its performance in real-world populations remains incompletely characterized. We aimed to externally validate the AS5F score and to evaluate whether left atrial dilatation (LAD) improves risk prediction. Methods: We conducted a retrospective single-center study including 410 patients with ischemic stroke or TIA who underwent prolonged Holter monitoring between 2021 and 2025. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to assess discrimination. Model calibration was evaluated using the Hosmer–Lemeshow test. Results: AF was detected in 33 patients (8.0%). The AS5F score was significantly associated with AF detection (OR 1.07 per point; 95% CI 1.03–1.11; p < 0.001), showing modest discrimination (AUC 0.69). Age alone demonstrated similar performance (AUC 0.69). LAD was strongly associated with AF (OR 4.00; 95% CI 1.79–8.93; p = 0.001) but had lower discriminatory ability (AUC 0.61). In patients with available echocardiographic data (n = 369), a combined age + LAD model achieved an AUC of 0.73 with adequate calibration. The improvement compared with AS5F was not statistically significant. Conclusions: In this external real-world cohort, AS5F demonstrated moderate discrimination for post-stroke AF detection. A simplified model combining age and left atrial dilatation showed numerically higher performance and may represent a pragmatic strategy for risk stratification in clinical practice. Full article

This study aims to develop an instance segmentation model for the joint segmentation of pomelo fruits and stems in complex natural orchard environments, with particular emphasis on slender, small-scale, and easily occluded stem targets. To this end, YOLO-MOD, an improved instance segmentation algorithm based on YOLOv11-seg, is proposed. Specifically, Omni-Dimensional Dynamic Convolution (ODConv) is introduced into the C3k2 module to enhance complex feature representation; a Multi-Scale Dilated Attention (MSDA) module is embedded to improve the multi-scale semantic perception of slender stem regions; and the original upsampling operator is replaced with DySample to strengthen fine-grained boundary recovery. Experimental results show that, compared with the original YOLOv11-seg, YOLO-MOD improves the Box mAP@50 and Mask mAP@50 by 2.9% and 3.9%, respectively. For the Stem class, the Box mAP@50 and Mask mAP@50 increase from 71.9% to 77.8% and from 68.4% to 76.2%, respectively. These results indicate that YOLO-MOD can achieve fine-grained segmentation of pomelo fruits and stems on the dataset used in this study. However, its generalization capability across different orchards, seasons, pomelo varieties, and fruit types still requires further evaluation, and its practical effectiveness in an integrated robotic harvesting system remains to be further validated. Full article

This paper introduces and investigates a novel two-parameter sequence, termed the biparametric Appell extension (B-App-Ex) and denoted by ${\mathcal{B}}_n(x;\lambda ,\alpha )$. Standard classical Appell sequences often lack sufficient structural parameters, which can limit their operational flexibility in certain advanced spectral schemes. To address this limitation, we construct an enhanced operational framework by integrating a binomial structural kernel ${(1+w)}^{\lambda }$ with a linear exponential scaling $e^{\alpha xw}$ entirely within the Appell class. We provide a rigorous logical deduction of the fundamental properties of this sequence, including its explicit power series representation, a characteristic three-term recurrence relation, and a governing second-order differential equation (DEq.). A significant contribution of this work is the establishment of analytically exact connection and inverse connection formulas between the B-App-Ex basis and various classical orthogonal polynomial (COP) families. Numerical verification via a collocation-based projection framework demonstrates that these algebraic kernels achieve near-machine epsilon precision (≈${10}^{-15}$), remaining stable even for high-order approximations. Furthermore, by isolating the dilation factor $\alpha$, we establish an $O\left(N\right)$ computational complexity that offers a reduction in latency by approximately two orders of magnitude compared to classical matrix-based transformations. The results demonstrate that the proposed biparametric (Bip.) extension offers a versatile and highly optimized analytical template for modeling complex dynamic systems where structural shifting and spatial scaling must be tuned simultaneously. Full article

Accurate vessel traffic flow prediction provides an important data basis for intelligent shipping management, including maritime traffic monitoring, navigational risk awareness, waterway organization, and emission-related assessment. Although recent studies have advanced spatiotemporal, graph-based, and hybrid forecasting methods, improving the predictive ability of a conventional ConvLSTM backbone without introducing substantially more complex model structures remains underexplored in grid-based waterway scenarios. This study proposes a lightweight CA-ConvLSTM framework for grid-level vessel inflow and outflow prediction. AIS-derived flow data and MERRA-2 meteorological variables are rasterized onto a common spatial grid and fused at an early stage. A residual dilated convolution module with dilation rates of 1, 2, and 4 is used to extract multi-scale spatial dependencies, and a channel attention mechanism is applied before ConvLSTM-based temporal prediction to adaptively reweight the fused flow-meteorological feature channels. Experiments using AIS and MERRA-2 data from the northern Bohai Strait waterway show that the proposed framework improves baseline ConvLSTM performance. Compared with ConvLSTM, CA-ConvLSTM reduces MSE and MAE by 24.93% and 12.55% for outflow prediction, and by 24.80% and 12.82% for inflow prediction. These results suggest that spatially aligned meteorological fusion, multi-scale spatial feature extraction, and channel-wise feature weighting can effectively enhance ConvLSTM-based grid-level vessel traffic flow prediction without relying on complex model fusion or heavy graph-based architectures. Full article

Background: Chronic kidney disease presents a significant health challenge among the elderly, with recent data indicating a 13.9% prevalence for early stages (1–3) and a lower 0.6% prevalence for advanced stages. Notably, many geriatric patients die from cardiovascular complications before reaching end-stage kidney disease, highlighting the critical interplay between renal and cardiovascular health. Central to this connection is endothelial dysfunction, considered the initial trigger for cardiovascular mortality. We aimed to investigate the correlation between different measurement methods demonstrating endothelial dysfunction and sVE-cadherin levels. Another objective was to examine the relationship between decreased glomerular filtration rate (GFR) and sVE-cadherin levels. We hypothesized an inverse relationship between impaired renal function, endothelial dysfunction, and sVE-cadherin. Methods: The study included geriatric patients with CKD who were not receiving RRT. Non-geriatric patients, those with cardiovascular disease, atrial fibrillation, heart failure, active immunosuppressive use, active infection, history of active malignancy, Raynaud’s phenomenon, and renal transplantation patients were excluded. Demographic data of the patients, nailfold capillary measurements, carotid intima-media thickness, flow-mediated dilatation, sVE-cadherin, and serum fibroblast growth factor 23 (FGF23) levels were measured. Results: We analyzed 96 patients. Key findings revealed a significant inverse correlation between serum sVE-cadherin levels and glomerular filtration rate (GFR), suggesting that, as kidney function declines, endothelial integrity is compromised. Interestingly, patients treated with sodium–glucose co-transporter-2 inhibitors had notably lower sVE-cadherin levels, indicating the possible modulatory effect of these drugs on endothelial function. Additional correlations were observed: fibroblast growth factor 23 levels were positively related to capillary diameter, and carotid intima-media thickness was associated with mean platelet volume. Declining GFR corresponded to reductions in capillary count, while use of dipeptidyl peptidase-4 inhibitors was linked to higher capillary density. Over a 2.3-year follow-up, survivors had higher lymphocyte counts (p = 0.088, not statistically significant) and baseline sVE-cadherin levels tended to be higher in those who died, although this was not statistically significant. Conclusions: These findings suggest that uremic toxins may worsen endothelial injury by disrupting intercellular connections, highlighting the complex pathogenic environment in CKD. Given these insights, the need for standardized diagnostic thresholds for endothelial dysfunction in geriatric CKD patients is clear. Serum sVE-cadherin emerges as a promising novel biomarker for assessing endothelial health, offering potential for earlier intervention and improved cardiovascular outcomes. It may be a potent indicator of endothelial dysfunction and should be featured in future studies of elderly CKD patients. Full article

Single-lead electrocardiogram (ECG) is widely used in wearable devices for atrial fibrillation (AF) screening. Nevertheless, subtle pathological characteristics like P-waves and f-waves in practical signals are vulnerable to noise contamination. Meanwhile, the scarcity of high-quality annotated abnormal data instances leads to severe class imbalance. To mitigate these issues, we present an end-to-end framework designed for arrhythmia diagnosis using single-lead ECG signals, which integrates quality-aware data augmentation with a Peak-Enhanced attention mechanism. First, to mitigate the problem of data imbalance, a Quality-Aware Generative Adversarial Network (QA-GAN) is designed. This network integrates a signal quality evaluation module based on signal kurtosis, together with a dynamic soft-label training scheme, guiding the generator to prioritize learning high-quality morphological features, thereby synthesizing high-fidelity minority class samples. Second, to accurately capture subtle pathological features in electrocardiograms, a Peak-Enhanced Attention Convolutional Network (PEAC-Net) classification model is proposed. This model incorporates a Peak-Enhanced Attention (PE-Att) module, which employs learnable derivative convolutional kernels to precisely identify the transition points in the ECG signal. Furthermore, by integrating one-dimensional multi-scale dilated convolution (DSGC1D) with bidirectional LSTM, the model achieves effective capturing of both fine-grained local morphological features and long-range global rhythm patterns. Experimental results on the PhysioNet 2017 dataset indicate that the presented model attains an accuracy of 0.902 and a macro-F1 score of 0.880, respectively, outperforming other state-of-the-art models and also exhibiting robust data adaptability on the MIT-BIH dataset. Full article

Global agriculture is seriously threatened by plant diseases, which result in large losses in both productivity and quality. Timely and accurate disease detection is essential for effective crop management and food security. This work presents an improved MobileNetV2 architecture with Modified Lite Attention (MLA) Model for detecting plant leaf disease. Our methodology incorporates pre-processing, feature extraction through attention model, convolution layers, and classifying into diseased or healthy categories. Further, multiclassification of diseases is performed on a dataset comprising 4432 samples including whitefly, leaf spot, leaf curl, yellowish and healthy leaves. The proposed attention model is compared with existing attention models like CBAM (Convolutional Block Attention Model), SE (Squeeze and Excitation), ECA (Efficient Channel Attention) and SDMnet (Spatially Dilated Multi-Scale Network) to validate our hybrid MLA feature extraction technique. Customizing the categorization with fully connected layers and utilisation of a pre-trained MobileNetV2 model allow the system to achieve excellent results. Findings show encouraging accuracy, surpassing 97% compared to existing techniques for multiclass dataset classification. The integration of MobileNetV2 with custom dense layers enables robust detection even with limited datasets, making it ideal for use in mobile or low-resource agricultural environments. Further, the proposed method is tested on the PlantVillage dataset consisting of 10,836 samples using K-Fold cross-validation for K = 5 and K = 4 to obtain an average accuracy of 98.4% and 98.69%, respectively. Full article

Rainfall-induced delayed instability of fractured rock slopes is strongly affected by fracture preferential flow, hydro-mechanical coupling, and spatial matrix heterogeneity. However, the coupled influence of stress-dependent fracture aperture evolution and heterogeneous matrix properties on delayed slope deformation remains insufficiently quantified. In this study, a two-dimensional discrete fracture network (DFN)–equivalent continuum coupled model was established using spectral random field theory and a representative Monte Carlo-generated fracture geometry. The spectral exponent β = 1.0–2.5 was adopted to characterize different degrees of matrix heterogeneity, and rainfall infiltration–stress coupling simulations were conducted under an extreme rainfall scenario followed by drainage. The results indicate that the wetting front advances irregularly in the heterogeneous matrix, while fracture preferential flow accelerates rainwater infiltration and promotes local pore-pressure accumulation near the phreatic surface. After rainfall cessation, water stored in fractures continues to recharge the deep matrix, leading to delayed pore-pressure increase and post-rainfall deformation. The simulated fracture aperture shows an initial closure followed by gradual dilation, which is controlled by the competition between saturation-induced stress redistribution and pore-pressure-driven effective stress reduction. Under a common strength reduction factor of FOS = 1.4, stronger matrix heterogeneity results in more pronounced plastic strain concentration and larger displacement amplitude along the potential slip zone. These findings suggest that fracture aperture evolution and matrix heterogeneity jointly influence delayed deformation and potential failure-zone development in rainfall-affected fractured rock slopes. The conclusions should be interpreted within the scope of a two-dimensional DFN–equivalent continuum numerical framework with prescribed rainfall conditions and representative fracture/random-field realizations. Full article