Search Results (946)

Existing ferroresonance fault identification methods often suffer from high misclassification rates, strong threshold dependency, and insufficient noise resistance. To bridge this gap, we propose a novel ferroresonance fault recognition method based on the Markov transition field (MTF) and three-branch Gaussian clustering (TBGC). Firstly, a symplectic geometric algorithm is employed to denoise the resonance feature signal, extract effective dominant modes, and reshape the series. Secondly, the reshaped feature series is converted into a Pixel matrix image employing the MTF. Subsequently, the gray-level co-occurrence matrix (GLCM) is utilized to extract the two-dimensional texture features of MTF images corresponding to different resonance types and construct corresponding TBGC models. Finally, the overvoltage sequence to be recognized is input into the TBGC model after feature extraction, and accurate discrimination of ferroresonance types is achieved based on cosine similarity. The analysis of fault recording data indicates that this method achieves 100% discrimination accuracy in eight test cases, surpassing the comparative method (maximum accuracy of 62.5%) by 37.5%, thereby validating its effectiveness and accuracy in ferroresonance identification. Full article

In recent years, radar-based gesture recognition technology has been widely applied in industrial and daily life scenarios. However, increasingly complex application scenarios have imposed higher demands on the accuracy and robustness of gesture recognition algorithms, and challenges such as clutter interference, inter-gesture similarity, and spatial–temporal feature ambiguity limit recognition performance. To address these challenges, a novel framework named CECL, which incorporates the Convolutional Block Attention Module (CBAM) into a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, is proposed for high-accuracy radar-based gesture recognition. The CBAM adaptively highlights discriminative spatial regions and suppresses irrelevant background, and the CNN-LSTM network captures temporal dynamics across gesture sequences. During gesture signal processing, the Blackman window is applied to suppress spectral leakage. Additionally, a combination of wavelet thresholding and dynamic energy nulling is employed to effectively suppress clutter and enhance feature representation. Furthermore, an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm further eliminates isolated sparse noise while preserving dense and valid target signal regions. Experimental results demonstrate that the proposed algorithm achieves 98.33% average accuracy in gesture classification, outperforming other baseline models. It exhibits excellent recognition performance across various distances and angles, demonstrating significantly enhanced robustness. 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

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

Introduction: Psychotic symptoms (PS) in Alzheimer’s disease (AD) are associated with unfavorable prognosis, including accelerated functional decline and reduced survival. Multiple neurotransmitter systems have been implicated in the pathophysiology of PS, with the serotonergic system emerging as particularly relevant. Materials and Methods: Between 2010 and 2020, 120 patients with prodromal AD and 26 cognitively healthy controls underwent comprehensive evaluation, including clinical history, neurological and neuropsychological assessment, neuroimaging, and lumbar puncture. All participants underwent longitudinal clinical monitoring for a minimum of five years or until the emergence of PS. In February 2024, baseline cerebrospinal fluid (CSF) serotonin (5-HT) concentrations were quantified using competitive ELISA (FineTest, Wuhan, China). Results: CSF 5-HT levels were significantly elevated (p < 0.003) in patients who subsequently developed psychosis (n = 49) compared with those who remained free of PS during the 8-year follow-up (n = 19). A threshold of 4.89 ng/mL yielded 80% sensitivity for identifying individuals at risk. Baseline Neuropsychiatric Inventory (NPI; p < 0.001) and Unified Parkinson’s Disease Rating Scale part III (UPDRS III; p < 0.009) scores also demonstrated strong discriminative capacity. Conclusions: Measurement of CSF 5-HT and detailed clinical profiling in prodromal AD may provide predictive value for psychosis onset within 8 years of diagnosis. To our knowledge, this is the first study to report CSF 5-HT findings in AD patients. Full article

Background/Objectives: Accurate preoperative discrimination between women with ovarian pathology and healthy controls, as well as between benign and malignant ovarian tumors, remains challenging. This study aimed to evaluate the usefulness of osteopontin and galectin-7 on the diagnosis of ovarian tumors. Methods: This prospective single-center case–control study was conducted at the Third Department of Obstetrics & Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece, between 2018 and 2024. Preoperative serum levels of osteopontin, galectin-7, and established tumor markers (CA-125, CA19-9, CA15-3, CEA, AFP) were analyzed. Biomarker distributions were compared using non-parametric tests. Associations with clinical variables were explored using correlation analyses. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. Results: The study population included 116 women: 52 healthy controls, 45 patients with benign ovarian tumors, and 19 patients with malignant ovarian tumors. Serum osteopontin and galectin-7 levels did not differ significantly between control and study group (p = 0.562 and p = 0.138, respectively), nor between benign and malignant tumors (p = 0.784 and p = 0.140, respectively). Osteopontin showed no discriminatory ability (AUC = 0.47), while galectin-7 demonstrated weak discrimination (AUC = 0.63). A combined model yielded modest improvement (AUC = 0.69), remaining below clinically meaningful thresholds. CA-125 was the only biomarker significantly associated with malignancy (OR = 1.03, p = 0.038). Galectin-7 levels were higher in premenopausal women and inversely correlated with age, suggesting demographic rather than malignant influence. Conclusions: Despite strong biological relevance, circulating osteopontin and galectin-7 did not provide meaningful diagnostic discrimination between women with ovarian pathology and healthy controls or between benign and malignant ovarian tumors. CA-125 remained the most informative serum marker in this setting. Future efforts should focus on multi-marker strategies integrated with imaging and clinical assessment. Full article

Aerobic composting shows state-dependent dynamics in key parameters such as temperature, moisture content, oxygen concentration, and pH, and these variables are strongly coupled over time. This coupling makes accurate state identification and process regulation challenging when relying on single-parameter thresholds or experience-based control. To enable robust recognition of composting states throughout the process, we propose an IRRTO-optimized CNN–LSTM–attention model (IRRTO–CNN–LSTM–attention). The model uses a convolutional neural network (CNN) to extract discriminative multivariate features, a long short-term memory (LSTM) network to model temporal dependencies, and an attention module to adaptively emphasize informative features. To address the hyperparameter selection challenge, the Rapidly-exploring Random Tree Optimizer (RRTO) was introduced and further enhanced via four strategies (fluctuating attenuation adaptive regulation, dual-mode guided update, dynamic dimension adaptive perturbation, and dual-mechanism adaptive perturbation regulation), forming the improved IRRTO. The proposed approach was validated using sensor data from windrow composting of pig manure and corn straw. The IRRTO–CNN–LSTM–attention model achieved an overall accuracy of 98.31% in classifying the four states (mesophilic/heating, thermophilic, cooling, and abnormal) on the independent test set, which was 3.39 percentage points higher than the RRTO-based model. These results suggest that the proposed method can accurately identify composting states and support early warning and state-specific regulation in practical aerobic composting systems. Full article

This study presents a structured multi-sensor predictive maintenance framework for CO₂ laser cutting machines based on real-time data acquisition and supervised machine learning. The proposed architecture integrates heterogeneous sensor signals—including vibration, temperature, humidity, and acoustic measurements—through synchronized feature-level fusion to characterize machine operational states. A statistically grounded thresholding strategy, validated using two years of operational observations and controlled experimental perturbations, is employed to distinguish normal and abnormal behavior. Sensor data are processed using a Decision Tree classifier implemented in Python with Scikit-learn, enabling short-horizon probabilistic fault prediction during operational cycles. The system is deployed in a real industrial environment and validated using cross-validation and structured dataset partitioning to assess generalization performance. Results demonstrate reliable fault discrimination capability under controlled operational conditions, highlighting the effectiveness of feature-level sensor integration for early anomaly detection. The modular hardware–software architecture supports adaptability to other CNC platforms with appropriate recalibration and retraining. The proposed framework provides a low-cost, interpretable, and computationally efficient solution for real-time industrial predictive maintenance applications. Full article

Background/Objectives: Pressure pain thresholds (PPTs) are commonly used to quantify mechanical hyperalgesia in migraine and tension-type headache (TTH), but the discriminatory performance of PPTs across neural and muscular sites remains unclear. This study compared nerve- and muscle-related PPTs between migraine and frequent episodic TTH and explored site-specific ROC-derived cut-off values as complementary classification markers. Methods: In this cross-sectional case-group discrimination study, participants with migraine (n = 33) and frequent episodic TTH (n = 31) underwent bilateral PPT assessment (electronic algometry) over the temporalis and tibialis anterior muscles, C5/C6 zygapophyseal joints, peripheral nerves (greater occipital, median, ulnar, radial, posterior tibial, common peroneal), and the second metacarpal region. Results: PPTs were generally lower in the migraine group than in the TTH group. After adjustment for sex and age, the most consistent between-group differences remained at the temporalis muscles bilaterally (left: adjusted mean difference 0.49 kg/cm², 95% CI 0.10 to 0.89, p = 0.015; right: 0.53 kg/cm², 95% CI 0.13 to 0.93, p = 0.011) and at the left tibialis anterior muscle (0.90 kg/cm², 95% CI 0.03 to 1.78, p = 0.044). In the main ROC analysis, the temporalis muscles showed the strongest discriminatory performance (left AUC = 0.733; right AUC = 0.707), whereas tibialis anterior and left posterior tibial nerve sites showed modest, below-threshold discrimination (AUCs < 0.70 despite statistical significance in some cases). Women-only ROC analyses showed a broadly similar pattern, with slightly improved metrics at some sites, particularly the temporalis muscles. Across most sites, likelihood ratios indicated only small-to-moderate shifts in post-test probability. Conclusions: Participants with migraine showed lower PPTs than those with frequent episodic TTH across most assessed sites, with the clearest differences at the temporalis muscles. ROC and PR analyses suggest that PPTs (especially at temporalis sites) may provide complementary, hypothesis-generating discriminatory information, but their overall stand-alone discriminative utility is modest. PPT assessment should therefore be interpreted as an adjunct to clinical evaluation rather than a replacement diagnostic test. Full article

In modern industrial systems, the fault diagnosis of rotating machinery is crucial for ensuring safe equipment operation. However, practical fault data are often contaminated by noise, and the scarcity of samples across fault conditions makes effective feature extraction challenging. Moreover, single-sensor measurements provide limited and incomplete information, further degrading the accuracy and reliability of diagnostic models. To address these challenges, this paper proposes an explainable intelligent fault diagnosis for rotating machinery via multi-source information fusion under noisy environments and small sample conditions. Firstly, a multi-sensor data intelligent fusion module (MSDIFM) is developed. It converts multi-sensor vibration signals into time–frequency maps via continuous wavelet transform (CWT). Pixel-level cross-channel fusion is then performed using a variance-driven dynamic weighting strategy to generate a unified fusion map, adaptively highlighting high information channels. Secondly, a multi-dimensional adaptive asymmetric soft-threshold residual shrinkage block (MASRSB) is proposed to implement differentiated and dynamic threshold control for positive and negative features, enhancing representation and discrimination capabilities. Thirdly, the multi-scale Swin Transformer (MSSwin-T) is designed. This module significantly enhances the model’s feature extraction capability by expanding multi-level receptive fields, strengthening key channel representations, and reinforcing cross-window feature interactions. Finally, to validate the effectiveness of the proposed method, experiments are conducted on both the Case Western Reserve University (CWRU) dataset and the self-created PT890 dataset. Results demonstrate that the proposed method exhibits outstanding diagnostic performance and robustness under noisy conditions and with small sample sizes. Full article

Background: Aging is characterized by a decrease in olfactory, attentional, memory, language, and visuospatial/executive abilities. In this context, our study aimed to evaluate the potential effects of Rosmarinus officinalis L. (rosemary) and Carum carvi L. (caraway) essential oils (EOs) on aging. First, we assessed, in 402 participants, the age-related changes in olfactory functions (odor threshold, discrimination, and identification), gustatory perceptions (sweet, sour, salty, and bitter taste), cognitive functions (focusing on attention, memory, language, and visuospatial/executive functions), and their possible correlations with aging. To achieve this, olfactory function, gustatory perception, and cognitive abilities were evaluated in healthy participants across different age groups. Then, to evaluate the age-related decrease in trigeminal function (59 participants), we used rosemary and caraway EOs that contain carvone, limonene, and 1,8-cineole, all of which are considered typical trigeminal stimuli. Methods: Olfactory function was assessed with the Sniffin’ Sticks test, gustatory function by the Taste Strips test, and rosemary and caraway EOs by the ratings of odor pleasantness, intensity, and familiarity using a labeled hedonic Likert-type scale. Results: Olfactory function could be a potential early indicator of attentional, memory, language, and visuospatial/executive dysfunctions. Our data indicated that rosemary and caraway EOs were perceived without any significant decrease in odor pleasantness, intensity, and familiarity ratings in relation to aging. Conclusion: Our results suggest the potential bioactive effects of rosemary and caraway natural EOs as a new strategy to promote healthy aging. Full article

This study aimed to translate, culturally adapt, and validate the Arabic version of the 6-Item Lubben Social Network Scale (LSNS-6). The LSNS-6 was translated, culturally adapted, and administered, alongside the Medical Outcomes Study Social Support Survey (MOS-SSS), to 327 Arabic-speaking adults aged 60 years and older. Internal consistency was examined using Cronbach’s alpha and McDonald’s omega. Confirmatory factor analysis (CFA) tested the hypothesized two-factor structure (Family and Friends), and measurement invariance was evaluated across key sociodemographic and lifestyle variables. Convergent validity was assessed through correlations with MOS-SSS domains. Item response theory (IRT) analyses examined item discrimination and threshold parameters. Latent class analysis (LCA) explored whether the LSNS-6 could identify subgroups with distinct patterns of social connectedness and perceived support. The Arabic LSNS-6 demonstrated good internal consistency (α = 0.83; ω = 0.84) and supported the expected two-factor structure with satisfactory model fit (CFI = 0.963; TLI = 0.931; SRMR = 0.03). Convergent validity was evidenced by moderate correlations with overall perceived social support (r = 0.51). IRT analyses indicated strong discrimination for most items, and LCA identified four distinct latent classes. Overall, the Arabic LSNS-6 is a reliable and valid tool for assessing social isolation among older Arabic-speaking adults. Full article

Road safety depends heavily on the timely identification and repair of potholes; however, detecting potholes is challenging due to various lighting and weather conditions. This work presents an attention-enhanced object detection framework for aerial pothole detection design that relies on a pre-trained backbone, YOLOv8, and a custom feature-extraction network, the Feature Pyramid Network (FPN). An enhanced detection head is used to make the model aware of discriminative areas in space to get accurate localization of a pothole to overcome the major limitations of the standard YOLOv8 used in aerial road inspection, irrespective of the road surface. The underlying architecture incorporates a purpose-built data layer and a preprocessing engine that can accommodate scenarios such as seasonal changes and bad weather. To further enhance learning dynamics, a customized loss function and a new optimizer framework are incorporated to improve convergence towards overall detection reliability. Specifically, a custom differential optimizer that uses layer-wise adaptive learning rates and momentum-based gradient updates to help suppress false positives and accelerate convergence. Conversely, the IoU-based personal loss function, combined with real-time validation, stabilizes training across a range of road conditions. A major feature of the proposed system is its ability to process aerial imagery from unmanned drone platforms. Empirical analysis proves a good result: an average precision of 0.980 with the IoU of 0.5 and an F1-score of 0.97 with a confidence threshold of 0.30. Precision is high (0.97 at the 90-percent confidence level). These metrics show how well the model will be able to balance false positives and false negatives—a critical need in a safety-critical deployment. The results make the framework a potential, scalable, and reliable candidate for integrating smart transportation systems and autonomous vehicle navigation. Full article

In the complex flow fields of channels affected by sluice gates and bridge piers, winter ice transport, accumulation characteristics upstream of the gate, and the determination of entrainment thresholds are crucial for the safe operation of hydraulic projects. In this study, ice transport experiments were conducted with and without bridge piers upstream of the gate to analyze the key factors governing the transport process and accumulation morphology of floating ice. Four machine learning models were evaluated and compared to identify the optimal model for predicting the motion state of floating ice. Based on this optimal model, the discriminant conditions for ice entrainment under both pier configurations were proposed. The results indicate that, driven by incoming hydraulic parameters, gate boundary conditions, and ice discharge, the upstream floating ice undergoes a progressive evolution: “flat accumulation $\to$ “$\nabla$”-shaped accumulation $\to$ wedge-shaped accumulation $\to$ passing through the gate (entrainment)”. Compared to the GBDT, RF, and SVM models, the LR model achieves higher and more stable accuracy, precision, recall, and F1 scores under configurations without and with bridge piers. With AUC values reaching 0.993 and 0.997, respectively, this model demonstrates optimal comprehensive performance in classifying whether floating ice passes through the gate. Furthermore, based on the LR model, explicit algebraic formulas for the critical entrainment thresholds were constructed. Under the experimental conditions, the critical threshold intervals for the relative gate opening (e/H) are [0.170, 0.182] without piers and [0.142, 0.155] with piers. This study provides a solid theoretical foundation and technical support for ice-prevention operations and gate dispatching in cold-region hydraulic engineering under submerged outflow conditions. Full article

Background/Objectives: Spread through air spaces (STAS) represents an aggressive invasion pattern in lung cancer and is associated with unfavorable oncologic outcomes. As STAS is currently identifiable only on postoperative pathology, reliable preoperative, noninvasive prediction remains a clinical challenge. This study aimed to evaluate the feasibility of predicting STAS using ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-derived radiomic and clinicoradiomic models. Methods: In this retrospective study, patients who underwent surgical resection for lung cancer with available preoperative ¹⁸F-FDG PET/CT imaging were analyzed. Radiomic features were extracted from intratumoral and peritumoral regions. Clinical, radiomic-only, and combined clinicoradiomic models were developed using LASSO-based feature selection and multivariable logistic regression. Model performance was evaluated using nested cross-validation, receiver operating characteristic analysis, calibration assessment, and decision curve analysis. Results: Radiomic features reflecting intratumoral metabolic characteristics and peritumoral tissue heterogeneity were significantly associated with STAS. The combined clinicoradiomic model demonstrated superior discriminative performance compared with the clinical and radiomic-only models (mean AUC ≈ 0.75), along with favorable calibration (Brier score = 0.20) and improved clinical net benefit across relevant threshold probabilities. Lower eosinophil count, lower SUVmin_tumor, and lower intratumoral SUV skewness emerged as independent predictors of STAS. Conclusions: Preoperative prediction of STAS in lung cancer is feasible using PET/CT-based radiomic analysis integrating intratumoral, peritumoral, and clinical features. This noninvasive approach provides biologically relevant information beyond conventional anatomical assessment and warrants further validation in prospective, multicenter cohorts. Full article