Search Results (200)

This paper presents a physics-guided machine learning algorithm for classifying femur fracture presence and subtype using non-ionising radiofrequency (RF) spectral data. Multi-sensor S-parameter responses were generated from a femur phantom model across 1.0–3.0 GHz, producing 104 specimens representing intact bone and three fracture geometries. An exploratory, effect-size-driven band-selection algorithm identified a compact discriminative region between 1.74 and 1.90 GHz. Interpretable classifiers, including k-nearest neighbours (KNN), decision trees, linear discriminant analysis, and Naïve Bayes, were evaluated under strict specimen-level hold-out protocols to prevent data leakage. The KNN algorithm achieved 99.3% frame-level accuracy and 100% specimen-level accuracy for binary fracture detection while maintaining strong robustness in multiclass subtype classification, validated through sensor ablation and leave-one-subtype-out testing. The results demonstrate that compact, interpretable algorithms operating on band-limited RF spectra can achieve reliable, radiation-free fracture classification, supporting future development of continuous and edge-deployable monitoring systems. Full article

In this paper, a novel multiband microwave resonator is proposed and investigated for non-invasive glucose sensing applications. The structure is based on a compact, planar spiral-like geometry fed by a Coplanar waveguide (CPW) transmission line, designed to support multiple resonant modes through nested concentric rings. A full electromagnetic model was developed to predict the resonance behavior analytically, achieving excellent agreement with Computer Simulated Technology (CST) simulations across four resonant frequencies (2.7, 6.44, 8.0, and 12.8 GHz). The sensor demonstrated high glucose sensitivity at multiple frequencies, with peak values reaching 0.05 dB/mg/dL and 0.038 dB/mg/dL at 10.1 GHz and 6.22 GHz, respectively. To enhance conformability and skin contact, the antenna was further transformed into a semi-cylindrical flexible form suitable for finger-wrapping. Despite the mechanical deformation, the structure preserved its resonance while offering enhanced near-field interaction with biological tissues. The folded sensor achieved a sensitivity of 0.032 dB/mg/dL at 5.25 GHz and a peak gain of 6.05 dB, validating its robustness for wearable deployment. The clear correlation between reflection magnitude and glucose level (with R > 0.99) confirms the sensor’s potential as a passive, multiband, and non-invasive glucose monitoring platform. The physics-informed residual deep learning framework significantly enhances prediction accuracy, achieving an RMSE of 0.28 mg/dL, MARD of 0.13%, and confining 100% of both training and holdout predictions within the <5% ISO-like risk region, thereby ensuring robust and clinically reliable non-invasive glucose estimation. Full article

To investigate the structural characteristics of Ir_n clusters (n = 9–30) and their interaction with NH₃, the CALYPSO structure-prediction method was employed to identify the lowest-energy configurations. The Lennard–Jones potential was then used to compute the binding energy and average binding energy, thereby evaluating size-dependent stability. The results show that Ir_n clusters evolve from relatively open motifs to compact three-dimensional frameworks as n increases. Meanwhile, the average binding energy increases overall and exhibits several locally stable size regions, indicating a pronounced size effect. Based on slab and cluster models, NH₃ adsorption was further examined on the Ir₁₃ cluster as a representative system due to its high structural stability as a “magic-number” cluster. The calculated adsorption energies demonstrate that the Ir₁₃ cluster exhibits substantially stronger adsorption than the bulk Ir surface, with low-coordinated Ir atoms playing a key role in strengthening the interaction and enhancing adsorption activity. Adsorption-configuration analysis indicates that NH₃ preferentially binds to active surface sites via the N lone pair. These findings clarify the relationship between structural stability and adsorption performance of Ir clusters and provide theoretical support for Ir-based materials in NH₃ catalytic conversion and high-sensitivity gas detection, and offer insights relevant to improving NH₃ monitoring in underground coal mine environments. Full article

Land use changes within urban agglomerations exhibit significant spatiotemporal heterogeneity and regional diversity. In urban agglomeration land simulation, traditional models often struggle to systematically capture these variations. We introduce the GCTP, a novel framework that integrates guided Geographical zoning, Convolutional Neural Networks (CNN)-Transformer, and the Patch-generating Land Use Simulation (PLUS) model. Initially, guided K-means clustering was employed for geographic zoning to characterize regional spatial non-stationarity. Then, a CNN-Transformer network leveraged self-attention mechanisms to capture multi-scale spatial correlations, obtaining pixel-level development probabilities. Finally, these probabilities were fused with PLUS- Land Expansion Analysis Strategy (LEAS) outputs to drive PLUS- Cellular Automata with multi-type Random Seeds (CARS) for patch-level simulation. The results demonstrate the following: (1) The embedding of guided zoning enabled the model to achieve an Overall Accuracy (OA) of 0.941, effectively mitigating global simulation bias. (2) The optimal simulation performance occurred at a fusion weight of 0.81, yielding a Kappa of 0.8917 and an Figure of Merit (FoM) of 0.3830, significantly exceeding a single model. (3) The 2030 simulation indicates that the GCTP model effectively reduces isolated pixels at urban fringes. The GCTP generates neighborhood patterns with high spatial compactness and geographic consistency. This study highlights the significant advantages of integrating long-range spatial perception with geographical heterogeneity constraints in the land expansion simulation of urban agglomerations. The findings support more precise territorial spatial planning practices. Full article

Accurate irrigated cropland mapping is critical for agricultural water management and food security. Existing image-based irrigation mapping workflows primarily rely on vegetation indices and synthetic aperture radar (SAR) backscatter features, which have limited capacity to characterize the temporal evolution of irrigation processes and crop growth conditions. The AlphaEarth Foundation (AEF) model developed by Google DeepMind provides compact embeddings with temporal semantic information learned via self-supervision, yet their utility for irrigation mapping has not been systematically assessed. In this study, a comprehensive assessment of AEF embeddings for irrigated cropland mapping was performed in terms of feature separability, classification performance, and spatiotemporal transferability. Experiments were conducted in two representative irrigated regions: the Guanzhong Plain in China and Kansas in the USA. Class separability of the 64 embedding dimensions was quantified using the Jeffries–Matusita (JM) distance. Then, the AEF embeddings were compared with the Sentinel feature set (Sentinel-2 bands, normalized difference vegetation index(NDVI), enhanced vegetation index(EVI), normalized difference water index(NDWI) and Sentinel-1 vertical transmit vertical receive(VV), vertical transmit horizontal receive(VH)) using K-means clustering and supervised classifiers, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Finally, transfer experiments across 2022 and 2024 in the Guanzhong Plain and Kansas were conducted to examine cross-year and cross-region performance. The results showed that AEF embeddings consistently provide stronger class separability in both study areas, with a maximum JM distance of 1.58 (A29). Using AEF embeddings, RF achieved overall accuracies (OA) of 0.95 in the Guanzhong Plain and 0.93 in Kansas, outperforming models based on Sentinel-1/2 bands and indices. Notably, unsupervised K-means clustering on AEF embeddings yielded OA > 0.85, indicating high intrinsic separability between irrigated and rainfed croplands. Transfer experiments further demonstrate stable temporal transfer (cross-year OA > 0.87), whereas cross-region transfer is constrained by differences in irrigation regimes, crop phenology and management practices, resulting in limited spatial generalization (OA~0.3). Overall, this study demonstrates the potential of high-information-density representations from geospatial foundation models for irrigated cropland mapping and provides methodological and technical insights to support transfer learning and operational mapping over large areas. Full article

This study experimentally evaluates the thermal performance of a compact parabolic trough solar collector (PTSC) operating under actual solar conditions in Chachapoyas, a high-Andean city in northern Peru characterized by frequent cloud cover and variable irradiance. Despite the growing interest in solar thermal systems, few studies have assessed PTC behavior under high-altitude, diffuse radiation conditions typical of Andean regions. The PTSC, aligned along the north–south axis and equipped with a manual solar tracking system, was monitored for 30 consecutive days. Solar irradiance, ambient temperature, and water inlet/outlet temperatures were recorded at 30 min intervals using a DAVIS Vantage Pro Plus weather station and infrared thermometers (±0.5 °C accuracy). Thermal efficiency was determined from the ratio of useful heat gain to incident solar energy, based on instantaneous irradiance data. Results showed peak irradiance values of 1000 W m⁻² and maximum outlet water temperatures of 85 °C, achieving an average efficiency of 68 ± 2.5%. The collector maintained stable operation even under fluctuating radiation, confirming its suitability for domestic hot-water and low-temperature industrial applications. These findings provide the first experimental evidence of efficient solar-thermal conversion in cloudy highland environments of Peru, supporting the deployment of decentralized renewable energy systems in the Andean region. Full article

This study presents a modular and scalable wearable functional near-infrared spectroscopy (fNIRS) system for high-resolution cerebral hemodynamic signal acquisition. The system is based on compact optoelectronic modules and supports mixed measurements using short-separation and long-separation channels, offering good scalability and spatial adaptability. The integrated quartz light guide structure improves optical coupling efficiency between the probe and scalp. A series of in vivo experiments validated system performance. In a forearm arterial occlusion experiment, the system accurately captured concentration changes in oxygenated and deoxygenated hemoglobin during blood flow blockade and reperfusion, with large effect sizes (Cohen’s d > 0.9). In a prefrontal cortex Valsalva experiment, the biphasic response characteristic of neurovascular coupling was successfully resolved. In a 2-back working memory task, the system identified a task-related frequency component (0.0227 Hz) and right-lateralized prefrontal cortex activation (p = 0.023). These results demonstrate that the system exhibits a good signal-to-noise ratio and temporal dynamic response, enabling high-resolution mapping of regional hemodynamic changes. This work provides an effective solution for the development of wearable, modular, and high-precision multi-channel fNIRS systems. Full article

Background and Objectives: Brainstem infarctions remain challenging to identify due to their small size, complex anatomy, and known limitations of conventional axial diffusion-weighted imaging (DWI), particularly in the posterior fossa. Thin-section coronal DWI may improve lesion conspicuity by providing higher spatial resolution and an orthogonal imaging perspective. To evaluate whether 3 mm thin-section coronal DWI improves lesion visualization and delineation compared with standard 4 mm axial DWI in patients with MRI-confirmed acute brainstem infarction. Materials and Methods: In this retrospective single-center study, 125 consecutive patients with isolated brainstem infarction confirmed by MRI (January 2021–January 2024) were included. All patients underwent both axial and coronal DWI acquisitions. Lesions were classified by anatomical location and by the imaging plane providing better visualization (“coronal better” vs. “equal”). Lesion volumes were calculated using manual segmentation. Image interpretation was performed independently by two neuroradiologists. Interobserver agreement was assessed using Cohen’s kappa and intraclass correlation coefficient (ICC). Statistical analysis included both parametric and nonparametric tests, with confidence intervals reported. Results: Coronal DWI provided improved or equivalent lesion visualization in all cases. Improved visualization was most frequent in midbrain infarctions (100%) and in a subset of medullary lesions (26.7%). Lesions better visualized on coronal DWI were significantly smaller than those equally visualized (mean volume ~0.23 mL vs. ~0.55 mL, p < 0.0001). Twelve midbrain and eight medullary lesions were identified only on coronal DWI within the imaging protocol, all showing confirmation on ADC and/or FLAIR correlation. Interobserver agreement was substantial to excellent. Conclusions: Thin-section coronal DWI improves visualization and delineation of small brainstem infarctions, particularly in anatomically compact regions. These findings support its role as a complementary sequence rather than a replacement for standard axial imaging. Full article

Background/Objectives: Data on antimicrobial resistance (AMR) in goat-derived E. coli within the Gulf Cooperation Council (GCC) region remain limited, and are largely restricted to studies conducted in Saudi Arabia and the UAE, with no published reports from Qatar. This study provides the first baseline characterization of AMR and extended-spectrum β-lactamase (ESBL) profiles of E. coli isolated from goats in Qatar. Methods: A total of 280 fecal samples were collected from goats across nine locations in Qatar (140 healthy and 140 diseased goats; 12 samples did not yield E. coli cultures). A selective agar medium was used to isolate E. coli, and the isolates were subsequently confirmed using the VITEK^® 2 Compact system. Antimicrobial susceptibility testing was performed to determine resistance profiles, and PCR assays were used to detect ESBL-associated genes. Results: 268 E. coli isolates were recovered from 280 samples. AMR analysis revealed a high prevalence of tetracycline resistance among E. coli isolates (53%), consistently observed across all nine sampling locations. Ampicillin resistance was also widespread. AMR was detected in isolates from both healthy and diseased goats; however, gentamicin resistance was found exclusively in the isolates from diseased animals. Overall, 44 isolates (16%) were classified as multidrug resistant (MDR), while nine isolates (3%) demonstrated ESBL production based on cefotaxime resistance. MDR and ESBL-producing E. coli were detected across all nine locations and in both healthy and diseased animals, with MDR strains occurring more frequently than ESBL producers. PCR analysis identified ESBL-associated genes, namely, bla_CTX-M in nine isolates and bla_TEM in three isolates. Conclusions: Goats in Qatar harbor multidrug-resistant and ESBL-producing E. coli, highlighting their role as AMR reservoirs within a One Health framework. The high resistance rates to commonly used antibiotics, particularly tetracycline and ampicillin, across health statuses and geographic locations suggest potential influences of local management practices and environmental factors. The detection of ESBL genes, notably bla_CTX-M and bla_TEM, underscores the need for prudent antimicrobial use and the implementation of integrated One Health surveillance programs to mitigate potential public health risks and to support national AMR surveillance and antimicrobial stewardship efforts across the region. Full article

Gearbox housing stiffness strongly influences radiated noise in electric drivetrains, particularly in the absence of engine masking. While high-fidelity vibro-acoustic simulations provide detailed insight, they are computationally demanding for early-stage design screening. This study investigates whether extremely compact spectral descriptors can encode stiffness-related information. The descriptors consist of five 1 kHz band-averaged sound pressure levels between 1 and 6 kHz. These band-averaged quantities are treated as compact spectral descriptors representing the acoustic response of each gearbox housing configuration. The analysis is based on a simulation-derived dataset of twelve spectra representing three ribbing configurations of a single gearbox housing geometry. A Random Forest classifier evaluated using leave-one-out cross-validation (LOOCV) achieved 0.75 accuracy. Confusion matrix analysis indicates clear separation of the flexible concept. Intermediate and rigid configurations show partial spectral overlap. Permutation testing suggests that the observed classification performance exceeds random chance, although uncertainty remains substantial due to the small dataset size. Feature-importance analysis identifies the 2–4 kHz region as the most stiffness-sensitive frequency range, supporting physical interpretations of mid-frequency structural–acoustic coupling. This exploratory study highlights both the potential and the statistical limits of minimal frequency-band descriptors for rapid NVH stiffness screening under small-sample conditions. Full article

The gut–skin axis is increasingly implicated in psoriasis pathogenesis, yet the cross-compartment convergence of molecular programs remains incompletely defined. We constructed a conceptual “Triple-Hit” multi-omics framework by integrating five independent public datasets spanning gut microbial functional remodeling (shotgun metagenomics), systemic immune cell methylomes (PBMC and CD8+ T-cell EPIC 850K), and lesional skin regulatory layers (miRNA and bulk RNA-seq). In the gut compartment, functional profiles exhibited a selective reduction in microbial lipid catabolic potential, including decreased fatty acid degradation and a lowered composite lipid degradation score, alongside heterogeneous shifts across SCFA-associated metabolic pathways. Systemically, PBMC methylomes revealed widespread regional remodeling (45,396 DMRs) enriched for membrane-proximal signaling and cytoskeletal programs, while CD8+ T cells showed specific epigenetic alterations in lipid- and glycosphingolipid-associated loci, suggesting a systemic metabolic–epigenetic alignment. In the skin, we identified a compact miRNA signature (168 DE-miRNAs) and a mechanistically interpretable, directionality-constrained miRNA–mRNA bridge that aligns with an AMP-dominant inflammatory transcriptome, consistent with reduced post-transcriptional restraint. Collectively, these findings support a convergent multi-omics framework linking putative microbial metabolic remodeling, systemic immune priming, and cutaneous effector programs. This study provides a systems-level perspective on psoriasis pathogenesis, highlighting the metabolic–epigenetic–transcriptional convergence as a potential avenue for therapeutic intervention. Full article

The industrial dismantling and recycling of end-of-life photovoltaic (PV) modules require robust visual inspection under dust contamination, inter-class similarity, and constrained edge-computing conditions. This study proposes an end-to-end framework that detects key module components (junction box, backsheet label, aluminum frame, and shadow region) and estimates the aluminum frame gap height for dismantling control. The primary novelty is a dust erosion-aware detection and metrology framework that couples frequency-enhanced visual perception with shadow-guided geometric measurement, while lightweight deployment modules serve as supporting engineering components. Specifically, DWT/FFT-based enhancement with CLAHE is used to improve degraded features, and YOLOv8 is strengthened by GSConv and Coordinate Attention in the backbone and neck; transfer learning, INT8 quantization-aware training, and CMFH-based compact rechecking are further introduced for practical deployment. Experiments show that the proposed method improves mAP@0.5 by 5.08 percentage points over baseline YOLOv8 while increasing speed from 45 to 52 FPS. For geometric metrology, the method achieves 93.0% accuracy with a mean error of 0.45 mm. The results demonstrate an accurate, robust, and edge-deployable solution for the automated inspection and recycling of end-of-life PV modules under dusty conditions. Full article

This study evaluated the geotechnical, mineralogical, chemical, and physico-mechanical properties of natural clays from two Portuguese regions, Aveiro and Taveiro, for their potential use as compacted landfill liners. A comprehensive set of tests was conducted, including particle size distribution, Atterberg limits, specific surface area (SSA), cation exchange capacity (CEC), swelling potential, and hydraulic conductivity (K), complemented by X-ray diffraction (XRD) and chemical composition (XRF) analyses. Results showed that Aveiro clays were predominantly fine-grained, with clay fractions exceeding 65% and high Σphyllosilicates content, particularly illite and smectite. These samples exhibited low hydraulic conductivity (K < 1 × 10⁻⁹ m/s), moderate to high plasticity, and good sealing behavior. In contrast, Taveiro clays showed greater textural variability, with higher sand content and a wider range of mineral composition, from kaolinitic to smectitic units. Selected Taveiro samples also achieved acceptable permeability values, particularly those with higher smectite content, but may require strict compaction control or blending with finer materials. The CEC and SSA measurements further distinguished the sealing potential between clay types, correlating with mineralogy and swelling capacity. The use of local clays offers potential cost savings and environmental benefits, including reduced transportation emissions and support for circular economy principles. These findings highlighted the technical viability of Portuguese clays for landfill barrier systems and underscore the importance of localized characterization for optimized liner design. Full article

Scrub typhus, caused by the obligate intracellular bacterium Orientia tsutsugamushi (O. tsutsugamushi), remains a major public-health concern in the Asia–Pacific region. Genome-wide inference is complicated by extensive repetitive DNA and frequent genome rearrangement. We isolated O. tsutsugamushi HMU_001 from a scrub typhus patient on Hainan Island, China. Intracellular morphology was examined and replication was quantified in endothelial cells. Using long-read sequencing with short-read polishing, we generated a closed circular genome and performed standardized comparative analyses across all available complete O. tsutsugamushi genomes. HMU_001 assembled as a 1,895,724 bp genome and, among the 17 complete genomes analyzed in this study, represented the most compact genome. Repeats comprised 873,550 bp (46.08%) and included 72 RAGE loci (4 relatively complete) and 283 insertion sequences (54 intact). Repeat content varied widely and largely explained genome size differences. A core-gene phylogeny resolved four clades with partial geographic structure, while tsa56 genotypes were only partly congruent with it. Genome synteny was generally limited across strains but markedly higher among the closest relatives, consistent with ongoing rearrangement. HMU_001 expands representation of complete O. tsutsugamushi genomes by adding a TA763_A lineage strain from a high-incidence island setting. Comparative analyses support a model in which repeat proliferation and decay drive genome evolution and structural remodeling. Full article

Background: Early childhood caries (ECC) affects a substantial proportion of young children worldwide, and timely screening is essential for early intervention and referral. While deep learning has shown promise for automated dental diagnostics, many existing approaches rely on computationally heavy models that limit deployment in community and mobile settings. This study investigates whether compact convolutional neural networks can achieve clinically meaningful performance for screening dental caries from oral photographs. Methods: We curated a dataset of 435 intraoral images from children aged 3–14 years, annotated by licensed dentists, and performed patient-level stratified splitting to prevent data leakage. Three convolutional neural networks (ResNet-18, MobileNetV3-Small, and EfficientNet-B0) were fine-tuned using ImageNet-pretrained weights and comparatively evaluated for the detection of dental caries from oral photographs. Models were trained with class-weighted cross-entropy loss and evaluated on a held-out test set using sensitivity, specificity, balanced accuracy, ROC-AUC, and PR-AUC with bootstrap 95% confidence intervals. Results: ResNet-18 achieved the highest balanced accuracy (0.929), weighted F1-score (0.954), and perfect sensitivity (1.00), while EfficientNet-B0 achieved the strongest threshold-independent discrimination with the highest ROC-AUC (0.978) and PR-AUC (0.990). MobileNetV3-Small maintained competitive performance (ROC-AUC 0.952; PR-AUC 0.976) with substantially lower computational complexity. Conclusions: In addition to performance evaluation, we incorporated an interpretability analysis using Grad-CAM to examine model decision behavior. The resulting attribution maps predominantly highlighted clinically relevant tooth regions associated with caries, providing evidence that the models rely on meaningful dental features rather than background artifacts. These results demonstrate that compact, deployment-friendly architectures can achieve clinically meaningful performance for ECC detection, supporting their suitability for scalable, real-world screening applications. Full article