Search Results (3,848)

The study focuses on the infrared nondestructive detection of inclusion-type internal defects in glass-fiber-reinforced plastic (GFRP) wind turbine blade specimens, which were designed to simulate the laminated material structure and typical hidden defects of in-service blades. To address the difficulty of detecting internal defects in in-service wind turbine blades, this paper establishes an active thermal imaging defect detection and recognition system using a halogen lamp as the infrared thermal excitation source and a high-resolution thermal imaging camera as the detection component. To improve the recognition of defect contour information in infrared images, a method combining bidimensional filtering empirical mode decomposition (BFEMD), Gaussian filtering, and Otsu threshold segmentation is proposed. The BFEMD procedure decomposes the infrared image into bidimensional intrinsic mode function components and residual components, Gaussian filtering suppresses noise in the selected components, and Otsu threshold segmentation extracts the defect contours. Experimental results show that the combined algorithm can enhance defect targets in infrared images, improve visibility and contour integrity, and provide a higher detection rate for wind turbine blade defects under different defect depths and materials. Full article

This study presents a headset-type biofluorometric gas sensor incorporating a CMOS camera for continuous, non-invasive monitoring of transcutaneous ethanol from the ear canal. The sensor employs alcohol dehydrogenase (ADH) to catalyze the NAD⁺-to-NADH conversion during ethanol oxidation, enabling quantitative measurement through NADH fluorescence detection (λ_ex = 340 nm, λ_em = 490 nm). The integrated system comprises a wireless CMOS camera, an ADH-immobilized cotton mesh enzyme membrane, UV-LED excitation source, optical bandpass filters, and a dual convex lens assembly housed in a 3D-printed headset powered by a lithium battery. Key improvements include a 3.5-fold enhancement in fluorescence collection efficiency achieved through optimized dual convex lens configuration. Systematic screening of seven cotton mesh materials identified Iwatsuki cotton mesh as the optimal enzyme immobilization substrate, exhibiting minimal autofluorescence and 14.2-fold higher water retention capacity compared to H-PTFE membranes. The glutaraldehyde-crosslinked ADH-immobilized cotton mesh maintained enzymatic activity for over 45 min with a 10-fold improvement in signal-to-noise ratio. The system demonstrated a dynamic detection range spanning 10 ppb to 10 ppm for gaseous ethanol and exhibited high selectivity against interfering volatile organic compounds in skin gas, including methanol, acetaldehyde, formaldehyde, and acetone. Human experiments validated the system’s practical performance. Following alcohol consumption, subjects wore the device for 50 min while real-time fluorescence monitoring captured dynamic ethanol concentration changes in the ear canal. The dose-dependent fluorescence response—approximately 2-fold higher at 0.4 g/kg versus 0.04 g/kg alcohol intake—correlated well with calibration data. This headset-type biofluorometric sensor enables unrestrained continuous monitoring of ear canal ethanol, providing a novel wearable platform for alcohol metabolism assessment with potential applications in health monitoring and clinical research. Full article

Background: Identifying potential targets for bioactive compounds is crucial for elucidating the mechanisms of action and drug development. Methods: This study presents ComTarget, a computational tool that integrates 3D molecular shape similarity analysis (based on combined 3D descriptors, C3DD) with reverse docking to predict protein targets for small molecules. ComTarget screens against a library of 4429 unique protein targets derived from 26,272 PDB complexes. Results: Validation on benchmark datasets (DEKOIS 2.0 and DUDE-Z) demonstrated that the C3DD molecular similarity calculation method effectively enriches active ligands by capturing critical 3D shape information not evident from chemical topology alone. It outperformed conventional 2D fingerprint methods and offered a favorable balance between shape sensitivity and computational efficiency, serving as a rapid pre-screening filter within the integrated workflow. For FDA-approved drugs (e.g., Imatinib, Aspirin) and natural products (e.g., Berberine). ComTarget identified targets consistent with reported therapeutic targets or putative off-targets in the literature, while also revealing potential targets aligned with the compounds’ pharmacological mechanisms. Conclusions: As a local program, ComTarget offers flexibility in computational resources customization and is freely available for polypharmacology studies, drug repurposing, and adverse reaction prediction. Full article

Purpose: Obesity, characterized by abnormal fat accumulation with comorbidities, continues to increase dramatically, particularly in the pediatric population. Identifying the environmental and genetic causes underlying the development of obesity during early childhood is crucial for establishing preventive and protective treatments for this complex disease. We aimed to investigate genetic variants related to non-syndromic early-onset childhood obesity. Methods: Whole-exome sequencing was performed in three independent consanguineous families with obesity, including three index cases and two additional affected siblings. Non-synonymous variants with minor allele frequency < 0.01 in all normal populations were filtered using the Genomize-SEQ Platform. Variant confirmations and familial segregations were analyzed by Sanger sequencing. Results: WES revealed a shared ATXN3 gene variant and two known variants of the SH2B1 and ADIPOQ genes, which were reported to be associated with obesity. Additionally, five heterozygous novel gene variants of the ANKK1, NEGR1, OGDH, ABCB1, and GSK3B genes were identified, which are predicted to cause excessive fat accumulation and disruption of energy balance in individuals. Conclusions: We suggest that the cumulative effects of all obesity-associated detected variants lead to the early-onset obesity phenotype observed in individuals. Hence, periodic follow-up and treatment opportunities are recommended for index cases, alongside the adoption of a more active lifestyle and healthy nutrition practices. Full article

Saxitoxin (STX) is one of the most potent natural neurotoxins known and is the only marine toxin to be declared a chemical weapon. In both marine and freshwater systems filter feeding organisms can accumulate saxitoxin and human consumption of toxin-contaminated food can result in paralytic shellfish poisoning. Here we highlight for the first time a functional cell-based assay for the detection of STX on an automated patch clamp (APC) system. We demonstrate that a human embryonic kidney (HEK) cell line expressing human Nav1.6 can rapidly and sensitively detect the presence of a range of sodium ion channel blockers including STX. The use of neutralising monoclonal antibody GT13-A and/or saxiphilin was found to confer specificity to the assay by being able to dissociate between STX (along with closely related analogues) and tetrodotoxin. Finally, the application of the functional assay for the detection of STX in complex samples was evaluated during an international exercise led by the Organisation for the Prohibition of Chemical Weapons (OPCW). The neutralisation of STX activity in blinded samples enabled the indirect detection of the toxin in the relevant samples and provided an alternative orthogonal technique to corroborate the findings of liquid chromatography–mass spectrometry (LC-MS). Collectively this work demonstrates the significant potential for functional assays in the analysis of samples suspected of being contaminated with STX and related sodium ion channel targeting toxins; complementing traditional direct identification methods such as high-performance liquid chromatography with fluorescence detection (HPLC-FLD), LC-MS or enzyme-linked immunosorbent assay (ELISA). Full article

Background: Atrial electrical activity in patients with persistent atrial fibrillation (PeAF) is extremely complex, and identifying optimal ablation targets during ablation procedures remains a significant challenge. This manuscript aims to identify spatiotemporally stable highest dominant frequency (HDF) in the left atrium to provide a reliable basis for Electrophysiologists to locate ablation targets beyond pulmonary veins. Methods: Filtering and spectral estimation were performed on the left atrial intracardiac electrogram (LA-EGM) of PeAF patients to recognize the dominant frequency (DF). Spatiotemporally stable DF features within the left atrium were extracted using spatial aggregation and others to construct a 3D DF distribution model. HDF areas were automatically identified based on personalized thresholds derived from the patient’s DF distribution. Results: Data analysis of 43 PeAF patients demonstrated that spatial aggregation with 2 mm voxel size accurately constructs spatiotemporally stable DF distribution models. The proposed DF model enables the automatic identification of stable HDF areas in PeAF patients. In retrospective clinical cases, 72.1% of patients underwent ablation at these identified sites with effective therapeutic outcomes. Conclusion: Recurring HDF areas during PeAF serve as potential ablation targets. The results of this study provide a reliable basis for determining personalized ablation targets for PeAF patients. Full article

Mediterranean low-pressure systems or cyclones are responsible for many extreme events affecting the region. This study presents a comprehensive analysis of Mediterranean cyclones from 1940 to 2024 using high-resolution ERA5 reanalysis data. This study implements a detection algorithm based on geopotential height minima on three different pressure levels (1000 hPa, 850 hPa and 700 hPa). Cyclone tracks in this study are constructed by linking identified low-pressure centers at successive time steps using a nearest neighbor tracking algorithm. The number of cyclones at 1000 hPa is filtered by matching them with upper levels and restricting them within 150 km from the coast, covering the entire Mediterranean region, which we divided into three subregions: the western Mediterranean, the eastern Mediterranean, and the Black Sea. Seasonal analysis was performed for winter (December–February), spring (March–May), summer (June–August), and autumn (September–November). Our results have recorded 39,933 individual cyclone tracks, where the majority (25,265 cyclones; 63.3%) are short-lived (24–72 h). Regionally, the western Mediterranean has the highest cyclone density, followed by the Black Sea and the eastern Mediterranean. While there is only a small increase in total numbers, a notable increase in cyclone activity is observed during the summer months, particularly in August, with a statistically significant rise of 18.4% since 1980 across the whole Mediterranean region. In the western Mediterranean, this August intensification was even 23.8%. As a result of this, the annual peak of cyclone activity has shifted from May/June to August. Full article

Wetland ecosystems have been a central focus of ecological research for an quite some time. Nevertheless, the degradation of wetland riparian zones has markedly accelerated due to anthropogenic activities, climate change, and habitat heterogeneity. The objective of this paper is to investigate the differences in functional traits of riparian plants under changing wetland environments on a karst plateau, as well as to elucidate the adaptive strategies of wetland plants across different habitats. This study examines the Caohai Wetland located on the Guizhou karst plateau, selecting the leaves of four dominant plant species (Phragmites australis, Onopordum acanthium, Galium odoratum, Paspalum distichum) in the Caohai Wetland lakeshore zone and analyzes the influence of soil factors on the variation of plant functional traits within the wetland riparian zone. The results reveal that: (1) significant differences exist in the functional traits of dominant plants in the riparian zones of karst plateau wetlands, with complex interrelationships among these traits; (2) the coefficients of variation for magnesium (Mg) and calcium (Ca) in the soil are notably high (79.53% and 67.21%, respectively), whereas soil oxidation-reduction potential (ORP) exhibits the lowest coefficient of variation (4.36%)—furthermore, the convergent variation in specific leaf area (SLA) and leaf dry matter content (LDMC) directly reflects the strong environmental filtering imposed by this habitat—and (3) redundancy analysis (RDA) indicates that leaf length (LL), specific leaf area (SLA), leaf area (LA), and plant carbon content (PCC) are particularly sensitive to environmental changes, while soil calcium (Ca), total nitrogen (TN), water-dispersible clay (WDR), soil organic matter (SOM), soil moisture content (SPMC), and total potassium (TK) constitute the principal soil factors influencing plant adaptive strategies in karst plateau wetlands. In conclusion, this study demonstrates that adaptation to karst wetland habitats is mediated through trade-offs in the allocation of photosynthetic products and the regulation of carbon (C), nitrogen (N), and phosphorus (P) nutrient balances under calcium-enriched and phosphorus-limited conditions, thereby reflecting the response characteristics of functional traits in karst plateau wetland plants to environmental changes. Full article

Active distribution networks require precise real-time monitoring and control despite measurement outliers and rapid load dynamics. Conventional robust estimators frequently fail to distinguish between transient measurement corruption and genuine physical state mutations, leading to estimation lag or erroneous control actions. To address this, we propose a resilient cyber–physical framework that jointly optimizes robust dynamic state estimation and collaborative voltage control. At the estimation layer, a novel Persistence-Based Robust Extended Kalman Filter (PB-REKF) is developed, which employs a temporal persistence counter to adaptively switch between Huber M-estimation for sporadic outlier suppression and covariance inflation for rapid tracking of persistent state mutations. At the control layer, a chance-constrained Second-Order Cone Programming (SOCP) strategy directly embeds the real-time posterior covariance from the PB-REKF into the voltage safety constraints, creating a data-quality-adaptive security buffer that provides a $95\%$ probabilistic voltage guarantee. Simulations on 5-bus and IEEE 33-bus systems demonstrate that the proposed framework achieves a $29.5\%$ reduction in global RMSE and a $72.8\%$ reduction in peak outlier-window estimation error relative to the standard EKF, while reducing the voltage violation rate from $8.8\%$ to $3.8\%$. The complete estimation and control pipeline requires $1.341$ ms per update step, confirming real-time feasibility. Full article

In conventional question answering systems, general-purpose large language models (LLMs), despite their strong capabilities in language understanding and generation, exhibit notable limitations in scenarios with stringent factuality requirements. Their outputs often lack explicit evidential grounding, making them prone to hallucinations and inconsistent responses. Moreover, LLMs do not inherently guarantee determinism when performing operations over structured data—such as aggregation, conditional filtering, and cross-field constraints—thereby undermining result consistency and reliability. To address these issues, we propose an internal-data-first framework for controllable question answering in high-risk scenarios. The framework categorizes knowledge sources into unstructured documents and structured data, enabling evidence-constrained generation via retrieval-augmented generation (RAG) and database-backed, verifiable query execution via restricted, read-only structured queries. In addition, an UNK branch is introduced as a safe degradation mechanism that triggers refusal when inputs lack sufficient evidence, exceed system capability boundaries, or fail to meet confidence requirements, thereby suppressing hallucinations and unauthorized generation. To enable controlled selection among the two execution pathways (RAG/SQL) and the safety degradation branch (UNK) at the system level, we design a learned router based on XGBoost with confidence-thresholded selective prediction, which preferentially activates UNK refusals for low-confidence or out-of-distribution inputs. We validate the proposed framework using a graduate admissions consultation system as an exemplar application, constructing both a document knowledge base and structured score tables, and conducting controlled comparisons across multiple system variants with multi-metric evaluations. Experimental results indicate that, under the current controlled evaluation setting, the proposed framework exhibits relatively stable behavior under complex query formulations and demonstrates practical engineering potential in high-risk vertical-domain question answering scenarios. Full article

Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disease affecting the axial skeleton, characterized by progressive structural damage and functional impairment. Although biologic therapies targeting tumor necrosis factor and interleukin-17 have improved clinical outcomes, a substantial proportion of patients fail to achieve sustained disease control. Emerging evidence suggests that metabolic alterations may contribute to AS pathogenesis; however, systematic characterization of metabolism-related biomarkers and their regulatory networks remains limited, and the interplay between metabolic dysfunction and immune dysregulation in AS is poorly understood. Two whole-blood GEO datasets (GSE25101, GSE73754; n = 104) were integrated as the primary analytical cohort. A third dataset (GSE11886, n = 18; monocyte-derived macrophages) was included for exploratory cross-tissue analysis. Differential expression analysis identified 847 DEGs, which were refined to 16 metabolism-related genes through weighted gene co-expression network analysis (WGCNA) and GeneCards database filtering. Eleven machine learning algorithms with 5-fold cross-validation were applied to construct diagnostic models and identify hub genes. Validation analyses included immune cell infiltration estimation using CIBERSORT, metabolic pathway activity assessment via ssGSEA, single-cell transcriptomics from GSE268839, functional enrichment through GSEA/GSVA, and chromosomal localization analysis. A competing endogenous RNA (ceRNA) regulatory network was constructed to map post-transcriptional regulation. Natural compounds from 66 AS-treating traditional Chinese medicines were screened against hub genes using deep learning-based binding prediction. Multiple machine learning algorithms achieved comparable cross-validated performance (CV AUC range 0.741–0.836; top five models: 0.805–0.836) using the six hub genes (MFN2, SLC27A3, RHOB, SMG7, AKR1B1, LCOR) identified through SHAP-based feature importance analysis of the PLS model. Leave-one-dataset-out validation between the two whole-blood cohorts showed that all algorithms exceeded an AUC of 0.77 in Round 1 (validate: GSE73754, n = 72; best AUC 0.861), while Round 2 (validate: GSE25101, n = 32) yielded more modest performance (best AUC, 0.715) reflecting the smaller validation sample. Exploratory application to GSE11886 (macrophage-derived samples) showed near-chance performance, consistent with the tissue-source discrepancy. AS patients exhibited significant downregulation of oxidative phosphorylation, TCA cycle, and glycolysis pathways (p < 0.01), accompanied by elevated glutathione metabolism (p < 0.001). Immune cell deconvolution revealed reduced CD8+ T cell proportions correlating with MFN2 downregulation, and increased neutrophil frequencies correlating with SLC27A3 upregulation. Exploratory single-cell analysis indicated that RHOB expression was relatively enriched in border-associated macrophages and fibroblasts, while AKR1B1 was more prominently expressed in vascular endothelial cells and plasmacytoid dendritic cells. The ceRNA network identified 21 miRNAs and 65 lncRNAs forming 86 regulatory interactions, with four key regulatory axes (SATB1-AS1/miR-539-5p/LCOR, FAM95B1/miR-223-3p/RHOB, LINC01106/miR-106a-5p/MFN2, AATBC/miR-185-5p/SMG7) predicted to regulate hub gene expression. Compound screening identified betaine, pyruvic acid, citric acid, etc., as top-ranking candidates, with MFN2 showing the highest binding capacity among hub genes. This study provides an integrative framework linking metabolic reprogramming with immune dysfunction in AS. The six-gene diagnostic signature showed preliminary discriminatory ability in the available datasets, while the ceRNA regulatory network and natural compound screening results prioritize candidate regulatory pathways and compounds for future validation. These findings advance our understanding of AS pathogenesis and may guide future biomarker development and targeted intervention strategies. Full article

Continuous customer activity generated by modern digital platforms drives the evolution of behavioral segments over time. Traditional customer segmentation methods typically rely on periodic batch analysis of historical data, producing static snapshots that may quickly become outdated and fail to capture emerging behavioral patterns. This paper presents a monitoring-oriented framework for detecting customer segment evolution and generating timely notifications about meaningful structural changes in the customer population. The proposed system continuously ingests user activity events, incrementally updates customer profiles, and periodically recomputes behavioral segments using fixed-k KMeans clustering over standardized recency, frequency, and monetary (RFM) features. To improve robustness and interpretability, the framework incorporates adaptive event scoring, stability-aware segment validation, drift-aware centroid matching, and persistence-based filtering of transient changes. These mechanisms reduce noisy alerts caused by repeated clustering updates while preserving meaningful signals about evolving customer behavior. The framework is evaluated on the Online Retail II and Instacart datasets under streaming simulation conditions. Experimental results show that the proposed approach maintains stable clustering structures, identifies persistent segment changes, and uncovers economically meaningful customer groups. Compared with static segmentation and periodic clustering baselines, the framework improves clustering quality while enabling continuous monitoring of segment evolution. Overall, the results suggest that adaptive monitoring can extend traditional customer segmentation into a practical continuous analytics process for moderate-scale dynamic environments. Full article

Active sonar tracking of multiple underwater targets is frequently challenged by intense reverberation, which leads to sonar returns that are both non-stationary and non-Gaussian. In such scenarios, the generalized labeled multi-Bernoulli (GLMB) filter, which relies on a Gaussian assumption, often experiences a rise in an Optimal Subpattern Assignment (OSPA) distance, along with recurrent label switching. To mitigate this problem, a robust delta-generalized labeled multi-Bernoulli technique (ST-δ-GLMB) is introduced; it characterizes noise using a Student’s t-distribution and employs variational Bayes to estimate the corresponding parameters. More precisely, the Student’s t-distribution is utilized to represent measurement non-stationarity, and an online variational Bayesian estimation of the noise parameters is conducted within a multi-target framework based on the Student’s t-model. Moreover, without altering the GLMB data-association and label-management machinery, we derive closed-form updates and propagation for the Student’s t-parameters, thereby keeping the recursive computational burden and practical implementability under control. Finally, Monte Carlo simulations and lake-trial data demonstrate that, under non-stationary and heavy-clutter conditions, ST-δ-GLMB maintains stable track continuity and accurate target-number (cardinality) estimates in the presence of non-stationary measurements. Full article

Despite its rich bioactive composition, orange peel dust (OPD), a fine industrial by-product generated during citrus processing in the filter tea industry, has not received much attention as a valuable matrix. Using antioxidant activity (2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and reducing power (RP)), α-amylase inhibitory activity, antimicrobial potential, and sugar composition as function-oriented indicators, this study aimed to compare four green extraction technologies: subcritical water extraction (SWE), pressurized ethanol extraction (PEE), ultrasound-assisted extraction (UAE), and sequential supercritical CO₂–UAE (Sc-CO₂–UAE) applied to OPD derived from Citrus sinensis L. Among thermally driven techniques, PEE at 220 °C had the highest radical-scavenging activity, while UAE showed the broadest antifungal activity against Fusarium spp. and Alternaria alternata, along with selective antibacterial activity against Bacillus cereus. Sequential Sc-CO₂ pretreatment at 300 bar followed by UAE resulted in the highest α-amylase inhibitory activity. Sugar analysis indicated that thermal conditions enhanced carbohydrate hydrolysis, while UAE and Sc-CO₂-UAE maintained structural sugars under mild conditions. All green extraction approaches outperformed conventional maceration. These findings validate OPD as a valuable industrial by-product suitable for sustainable valorization, supporting circular economy concepts in the citrus processing sector. Full article

Amplified Spontaneous Emission (ASE) sources are widely employed as highly stable broadband sources in fields such as high-precision navigation and optical detection. Erbium-doped fiber (EDF), as the core active component in ASE sources, has long been a key subject of thermal stability research. We fabricated a low-doped EDF with an 80 μm-cladding using the vapor phase doping (VPD) technique. This EDF was compared with a commercial 125 μm-cladding EDF using a double-pass forward (DPF) optical path configuration with a narrowband filter. We investigated the temperature-dependent characteristics of the ASE spectra generated by the two EDFs with different parameters. The temperature drift performance of the two EDFs was analyzed based on three critical indicators of the spectrum: mean wavelength, spectral bandwidth, and output power. In comparison with the commonly used EDF, the results show that a properly designed small-cladding EDF with an appropriate length can deliver higher ASE output power and exhibit a lower mean-wavelength temperature drift. This study provides an important guideline for promoting the miniaturization of high-precision fiber-optic sensing devices. Full article