Search Results (2,990)

Background/Objectives: Complex Regional Pain Syndrome (CRPS) is a debilitating pain condition with complex pathophysiology and limited treatment efficacy. Whole-body cryostimulation (WBC) has shown promising results in other chronic pain syndromes, but no studies to date have examined its use in CRPS. To evaluate the safety, feasibility, and potential benefits of WBC in a female patient with CRPS of the ankle. Methods: A 65-year-old female outpatient with type I CRPS at the right ankle underwent 15 WBC sessions (3 min at −110 °C) over two weeks, without any concurrent pharmacological or rehabilitative interventions. Assessments at baseline and post-intervention included standardized measures of pain (VAS, SF-MPQ), disability (PDI), catastrophizing (PCS), mobility (TUG, Chair Stand Test), strength and ROM (goniometry, MRC), psychosocial status (SF-36, WHO-5, PSQI, BDI, STAI), and MRI of the right knee and ankle. Results: Post-treatment, the patient showed substantial improvements in pain (VAS −66.7%, SF-MPQ −51.7%), function (TUG −31.8%), muscle strength, psychological well-being, and quality of life. MRI and edema measurements indicated stabilization or regression of inflammatory features. No adverse effects were reported. Conclusions: This case suggests that WBC may represent a safe, well-tolerated, non-pharmacological intervention for CRPS, with potential to improve pain, function, and well-being. Full article

This study explores a novel class of polynuclear superhalogen anions featuring heterovalent central atoms from groups 13 (B, Al) and 15 (P, As). The investigated species follow a modified general formula, (X_nY_n’F_{{(3n+5n’)+1}})⁻ where X = B and/or Al, Y = P and/or As, and n + n′ = 2–4. Low-energy isomers were identified using the Coalescence Kick method and subsequently optimized at the MP2/aug-cc-pVDZ level of theory. Electronic stability was assessed via the outer valence Green’s function (OVGF) approach with the same aug-cc-pVDZ basis set. All examined anions exhibit exceptional electronic stability, with vertical electron detachment energies (VDEs) ranging from 10.70 to 12.37 eV, significantly exceeding the superhalogen threshold of 3.65 eV. Thermodynamic analyses indicate that aluminum atoms play a crucial role in stabilizing larger clusters by acting as a structural “glue”, thereby suppressing fragmentation through the loss of neutral XF₃ or YF₅ units. In contrast, larger non-metallic analogs show an increased propensity toward dissociation. The potential of the heterovalent polynuclear superhalogen anions as weakly coordinating anions (WCAs) was further evaluated through molecular electrostatic potential (ESP) analysis. The results demonstrate that combining different central atoms within boron-based frameworks leads to a more homogeneous charge distribution, enhancing weakly coordinating behavior. Full article

Diagnosis of cardiomyopathies often depends on overt phenotypic manifestations, delaying patient management. This underscores the need for population-level opportunistic screening tools using clinically indicated CT scans to detect subclinical myocardial disease. This study developed an Ensemble Machine Learning (ML) model to automatically segment the left ventricular myocardium from CT data and estimate the probability of underlying myocardial disease using radiomic feature analysis. A total of 60 CT scans (~12,000 images) were used to train ML models for left ventricular myocardium segmentation, including scans from both healthy individuals and patients with myocardial disease. A novel Ensemble model was developed and externally validated on 10 independent CT scans. Subsequently, 100 unseen CT scans were segmented manually and automatically for radiomic feature analysis. After removing highly correlated features through intra-class variation and correlation filtering, the refined dataset was used for model training and testing. Key predictive features were identified, and model performance was evaluated. The four best-performing models (Unet++, ED w/ASC, FPN, and TresUNET) were combined to form an Ensemble model, achieving a final DICE score of 0.882 after hyperparameter optimization. External validation yielded a DICE score of 0.907. Radiomic feature analysis identified 15 key predictors of myocardial disease in both manual and automatic segmentation datasets. The model demonstrated strong performance in detecting underlying myocardial disease, with AUCs of 0.85 and 0.8, respectively. This study presents a fully automated CT-based framework for LV myocardial segmentation and radiomic phenotyping that accurately estimates the probability of underlying myocardial disease. The model demonstrates strong generalizability across different CT protocols and highlights the potential role of AI-driven CT analysis for early, non-invasive cardiomyopathy screening at a population level. Full article

In the field of intelligent inspection, high-definition video data collected by quadruped robot dogs face severe transmission and storage constraints. Although existing advanced lossy video coding standards can significantly improve compression efficiency, they inevitably introduce severe compression artifacts in low-bit-rate scenarios. To address this issue, this paper proposes a video decoding quality enhancement network named Video Quality Restoration Network (VQRNet), based on a dual-stream architecture. Specifically, the Local Feature Extraction component incorporates a Progressive Feature Fusion Module (PFFM) with a four-stage progressive structure. By integrating reparameterized convolution and attention mechanisms, PFFM focuses on capturing high-frequency texture details to repair small-scale distortions. Simultaneously, the Multi-Scale Lightweight Spatial Attention Module (MLSA) performs spatial feature recalibration, leveraging multi-scale convolution to adaptively identify and enhance key spatial regions, specifically addressing multi-scale distortion. In the Global Feature Extraction component, the State-Space Attention Module (SSAM) combines State-Space Models (SSMs) with attention mechanisms to capture long-range dependencies and contextual information, for large-scale distortions caused by high-intensity compression. To verify the performance of the proposed algorithm, a dedicated dataset comprising 20 real-world video sequences captured by quadruped robot dogs (partitioned into 15 training and 5 testing sequences) was constructed, and the VTM 23.4 reference software was employed to simulate compression degradation using four quantization parameters (QP 30, 35, 40, and 45). Experimental results demonstrate that VQRNet outperforms state-of-the-art quality enhancement methods in terms of core metrics, including PSNR and SSIM, specifically including MIRNet, NAFNet, TRRHA, and CTNet. In the QP = 30 scenario, VQRNet achieves an average PSNR of 40.33 dB, a significant improvement of 3.32 dB over the VTM 23.4 baseline (37.01 dB), while demonstrating significant advantages in computational complexity and parameter efficiency—requiring only 5.27 G FLOPs and 1.40 M parameters, with an average inference latency of only 11.82 ms per 128 × 128 patch. This work provides robust technical support for the efficient video perception of quadruped robot dogs. Full article

For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks (i) temporal continuity (stable detection across consecutive frames to prevent flickering misclassifications), (ii) multi-field-of-view (FoV) sensing, and (iii) integrated defenses against both digital and natural degradation. This paper presents two principal contributions: (1) a three-layer defense framework integrating feature squeezing, inference-time temperature scaling (softmax $\tau$ = 3 without distillation training), and entropy-based anomaly detection with sequence-level temporal voting; (2) a 500 sequence dual-FoV benchmark (30k base frames, 150k with perturbations) from aiMotive, Waymo, Udacity, and Texas sources across four operational design domains. The unified defense stack achieves 79.8% mAP on a 100-sequence test set (6k base frames, 30k with perturbations), reducing attack success rate from 37.4% to 18.2% (51% reduction) and high-risk misclassifications by 32%. Cross-FoV validation and temporal voting enhance stability under lighting changes (+3.5% mAP) and occlusions (+2.7% mAP). Defense improvements (+9.5–9.6% mAP) remain consistent across native 3D (aiMotive, Waymo) and projected 2D (Udacity, Texas) annotations. Preliminary recapture experiments (n = 15 scenarios) show 2.5% synthetic–physical ASR gap (p = 0.18), though larger validation is needed. Code, models, and dataset reconstruction tools are publicly available. Full article

Radio frequency fingerprints (RFFs), arising from inherent hardware imperfections, serve as distinctive features for device identification. The location- and time-dependent nature of the wireless channel directly affects RFF-based device identification, making it challenging under different channel conditions. This is primarily because the training and test datasets containing RFFs may not overlap within the same feature-space domain. In this work, the mentioned issue is addressed as a domain adaptation problem. For this objective, we propose the use of a triplet-learning-based domain-adversarial neural network within a hybrid framework named TripletDANN. We leverage the triplet loss, enabling the network to focus exclusively on device-specific latent representations under different channel conditions, while employing an adversarial loss to prevent the network from exploiting channel-specific characteristics. With this aim, data aggregation is performed together with channel labeling. The generalization capability of TripletDANN is evaluated on previously unseen test data collected across different locations under two distinct scenarios. Raw I/Q signals of 15 Wi-Fi devices are used as a case study. The proposed TripletDANN model achieves up to $88.52\%$ average device classification accuracy across the different data collection locations. On average, TripletDANN attains up to a $5\%$ performance improvement over its counterpart model. Moreover, data augmentation is employed to improve the overall performance, and a highest accuracy of $96.71\%$ is achieved on experimentally collected test data from an unseen location. Full article

The food industry increasingly relies on automated vision systems to ensure product quality, consistency, and safety. However, the visual classification of vegetables remains challenging due to high intra-class variability, illumination differences, and subtle morphological similarities between categories. This study evaluates the effectiveness of combining CNNs with four advanced Vision Transformer (ViT) architectures: DeiT (Data-efficient Image Transformer), CoaT (Co-Scale Conv-Attentional Transformer), CvT (Convolutional Vision Transformer), CrossViT (Cross-Attention Vision Transformer) for the automatic classification of 15 vegetable types. All models were implemented within a unified CNN–ViT hybrid framework to enhance both local feature extraction and global contextual reasoning. We processed all images under identical conditions to ensure a fair comparison and reproducibility. Results demonstrate that the hybrid architectures significantly outperform the standalone CNN baseline, with CvT achieving an approximate global accuracy in the range of 96.6–98.88% and consistently strong performance across visually complex classes such as cabbage, brinjal, and pumpkin. These findings confirm that hybrid CNN–ViT models are highly effective for visual food analysis, offering a robust and scalable solution for quality control, automated inspection, and classification of agricultural products. The methodology presented here may also be extended to other food items, including gels and processed products, highlighting its versatility and industrial relevance. Full article

This study investigates the impact of a Virtual Reality (VR)-based intervention on the enhancement of executive functions—cognitive flexibility, inhibitory control, and working memory—in children diagnosed with Autism Spectrum Disorder (ASD). Employing a single-case experimental design with repeated measures, the research was conducted with two male participants, aged 9 and 10, both formally diagnosed with ASD. The intervention was structured into four phases: Baseline (no training), Intervention (targeted VR training), Generalization (skill transfer testing), and Follow-up (maintenance assessment). Each participant engaged in a total of 18 tasks (six per executive function), delivered through immersive VR environments featuring gamified elements, adaptive feedback, and increasing difficulty. Each task consisted of up to 15 sub-items, scored as correct or incorrect. Results indicate consistent improvements across executive function domains during the intervention phase, with partial maintenance at follow-up and evidence of task generalization. Given the single-case framework and limited sample size, findings should be interpreted as exploratory and hypothesis-generating rather than population-generalizable. The study provides proof-of-concept evidence supporting the feasibility and potential of immersive VR-based executive function training for ASD populations, warranting further validation through larger-scale controlled trials. Full article

Colitis-associated carcinoma (CAC) represents ~1% of colorectal carcinomas and has important differences from sporadic colorectal carcinoma (sCRC). The precursors and carcinomas that arise in the setting of IBD are uniquely challenging to visualize by endoscopy and diagnose via histology, and the rising prevalence of IBD amplifies the challenges of surveillance to informed management. Although in broad strokes, CAC and sCRC share molecular features (~85% chromosomal instability pathway 15% microsatellite instability high (MSI-H)), CAC has a distinct distribution of molecular abnormalities, including lower frequencies of APC and KRAS mutations, greater prevalence of IDH1R132H, and more frequent copy number alterations (e.g., MYC amplifications), and functional data indicate that most CACs show far less dependence on Wnt signaling than sCRC, suggesting a distinct pathogenesis from the earliest stages. Although there are significant gaps in our knowledge of the pathogenesis of CAC, our understanding is growing. This review summarizes how chronic colitis reshapes epithelial homeostasis and somatic evolution, resulting in the distinctive pathogenesis of CAC, and highlights knowledge gaps that could be addressed by applying multimodal technologies to well-annotated clinical material. The review is structured in two sections, the first introducing the IBDs and the homeostatic mechanisms that preserve integrity and prevent colorectal neoplasia. The second section compares failure modes in sporadic and colitic settings and describes the differences in the resulting neoplasms. Full article

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language models (LLMs) have demonstrated strong performance in phishing-related classification tasks, but their operational costs and reliance on external providers limit their practical adoption in many business environments. This paper presents a detection pipeline for malicious websites and investigates the feasibility of Small Language Models (SLMs) using raw HTML code and URLs. A key advantage of these models is that they can be deployed on local infrastructure, providing organisations with greater control over data and operations. We systematically evaluate 15 commonly used SLMs, ranging from 1 billion to 70 billion parameters, benchmarking their classification accuracy, computational requirements, and cost-efficiency. Our results highlight the trade-offs between detection performance and resource consumption. While SLMs underperform compared to state-of-the-art proprietary LLMs, the gap is moderate: the best SLM achieves an F1-score of 0.893 (Llama3.3:70B), compared to 0.929 for GPT-5.2, indicating that open-source models can provide a viable and scalable alternative to external LLM services. Full article

To meet consumer demand for high-quality fruit and replace traditional subjective assessment methods, there is a growing interest in objective, quantitative, and non-destructive testing techniques within the agricultural and food industries. This study explores the integration of near-infrared (NIR) spectroscopy with machine learning for the quality detection of apricot–plum hybrids, aiming to provide a rapid and efficient technical approach. Two cultivars, ‘Fengweimeigui’ and ‘Weidi’, were selected for analysis. The relationships between various quality attributes were analyzed using analysis of variance (ANOVA) and Pearson correlation. Raw spectral data were preprocessed using Savitzky–Golay (SG) smoothing, and principal component analysis (PCA) was employed to reduce the high dimensionality of the spectral data. The scores of the first 15 principal components (PCs) were extracted as input features for the subsequent models. A comparative study was conducted between backpropagation neural network (BPNN) and support vector machine (SVM) models. The results indicated that during the color-break period, significant differences existed across all quality indicators except for dry matter content, with significant correlations observed among these parameters. The results demonstrated that BPNN achieved the best predictive performance for total phenols content, peel L*, peel b*, vitamin C content, flavonoids content, soluble solids content, soluble sugars content, and soluble protein content in ‘Weidi’ and ‘Fengweimeigui’ from the color-turning to the ripening stages. The ${\mathrm{R}}_{\mathrm{P}}^2$ values for these indicators were 0.968, 0.966, 0.950, 0.939, 0.939, 0.923, 0.921, and 0.905, respectively, with residual predictive deviation (RPD) values exceeding 3.0. These findings indicate that near-infrared (NIR) spectroscopy is a feasible tool for the rapid detection of plum–apricot quality. However, the model performance for Flesh a* requires further optimization. In conclusion, the combination of NIR spectroscopy and machine learning enables the rapid, efficient, and non-destructive quality assessment of plum–apricot hybrids, providing robust technical support for maturity prediction and quality control in commercial production. Full article

This paper reports a magnetically tunable U-band metallic waveguide isolator based on the ferromagnetic resonance (FMR) absorption effect. The device features a BaFe₁₂O₁₉ (BaM) single-crystal array integrated into a rectangular waveguide. By leveraging the high intrinsic magnetocrystalline anisotropy and narrow FMR linewidth of the single-crystal material, the isolator achieves high-frequency operation with a significantly reduced external bias field. Experimental results demonstrate a broad continuous tuning range from 50 GHz to 66 GHz. The device exhibits exceptional efficiency, with a typical insertion loss of less than 0.5 dB (minimum 0.24 dB) and an isolation exceeding 15 dB across the operating band. The cascaded array configuration ensures uniform magnetization and stable performance. This combination of ultra-low insertion loss and frequency agility makes the proposed isolator an ideal candidate for next-generation adaptive millimeter-wave communication and radar systems. Full article

Single-phase ground faults account for more than 80% of total faults in distribution networks. However, the introduction of distributed generation complicates power grid topology, leading to strong nonlinearity and non-stationarity in the zero-sequence current. This limits the accuracy of traditional faulted line selection methods. To address this problem, a CNN–ResNet-based method for faulted line selection for single-phase ground faults in distribution networks is proposed. Firstly, a 10 kV arc ground fault simulation test platform is built to analyze the nonlinear distortion characteristics of fault current. The WOA–VMD algorithm, optimized by permutation entropy, is used to denoise the zero-sequence current signal. The Gram Angular Difference Field (GADF) is then adopted to convert the one-dimensional signal into a two-dimensional image that retains its temporal characteristics. A hybrid deep learning model is constructed by fusing the one-dimensional time-domain features extracted by CNN and the two-dimensional time-frequency image features extracted by ResNet34. Matlab/Simulink simulations and physical experimental verification demonstrate that the proposed method achieves a training accuracy of over 97%, with zero misjudgments recorded in 15 arc grounding fault tests, representing a significant improvement in accuracy compared with existing diagnostic algorithms. It can adapt to complex scenarios such as high-resistance grounding and changes in neutral point grounding mode, effectively improving the accuracy and robustness of faulted line selection and providing technical support for the safe operation of distribution networks. Full article

Isopentenyltransferase (IPT) is the rate-limiting enzyme in cytokinin biosynthesis and plays a critical role in plant acclimation to abiotic stress. To explore soybean IPT genes, we performed genome-wide identification, bioinformatics analysis, and molecular experimental validation to systematically characterize the features and functions of the soybean IPT (GmIPT) gene family. We identified 15 GmIPT genes in the soybean genome, which are unevenly distributed across 12 chromosomes; their evolutionary expansion is primarily driven by whole-genome duplication events. Phylogenetic analysis of soybean IPT proteins with those from Arabidopsis, rice and maize clustered them into four groups, exhibiting lineage-specific functional specialization. GmIPT genes exhibit significant variations in conserved motifs, gene structure, and cis-acting elements; their promoter regions are enriched in light-responsive, abiotic stress-responsive, and hormone-responsive elements, indicating their involvement in complex transcriptional regulatory networks. Tissue expression profiling revealed that GmIPT7 and GmIPT10 are highly expressed in various tissues, whereas GmIPT14 shows specific expression in flowers and the shoot apical meristem. Transcriptomic analysis and qRT-PCR validation demonstrated that GmIPT7, GmIPT10 and GmIPT15 respond differentially to drought, salt and low-temperature stress, with GmIPT15 exhibiting a transient upregulation at 3 h (p < 0.01) followed by a gradual decline to levels close to the pre-treatment control at 6–12 h under low-temperature stress. We further performed haplotype analysis of GmIPT15 and identified a putative elite haplotype (hap1) associated with cold tolerance based on low-temperature germination index assessment. This study provides useful insights for the future functional characterization of plant IPT genes and offers potential genetic resources and molecular markers that may support molecular-assisted breeding for soybean abiotic stress tolerance. Full article

Promoting older adults’ active travel (AT) is important for healthy ageing, yet the optimal spatial units and scales for built environment (BE) interventions remain unclear. Existing studies often ignore the Modifiable Areal Unit Problem and fail to distinguish macro-scale land-use patterns from micro-scale street design under potentially nonlinear behavior–environment relationships. This study aims to clarify how multi-scale BE influences older adults’ AT and to identify the most effective intervention scale. Using survey data from 2494 older adults in Wuhan, China, we construct six behaviorally meaningful sliding units (5, 10, and 15 min walking network buffers and distance-equivalent Euclidean buffers), derive macro- and micro-scale indicators from GIS, census data, and street view images, and build separate Extreme Gradient Boosting (XGBoost) models with Accumulated Local Effects plots for interpretation. A model comparison reveals pronounced scale effects: network-based buffers systematically outperform circular buffers, and the 15 min walking network buffer emerges as the optimal intervention unit. Across all scales, BE variables contribute more to model performance than socio-demographic factors, and macro-scale attributes (e.g., land-use mix, facility density, and transit access) consistently outweigh micro-scale street features. Nonlinear effects and thresholds are identified for key density, accessibility, and streetscape indicators. These findings underscore the necessity of multi-scale analysis and support planning “15 min life circles” for older adults that prioritize macro-scale land-use and facility optimization, complemented by targeted, context-specific street-level improvements to create safe, age-friendly walking environments. Full article