Search Results (4,915)

Observation of welding features is important for GMAW training and instruction because the welding arc, molten pool, filler wire, and groove can be difficult to distinguish during welding. In this study, a compact, low-cost, and practical visualization system was developed to support gas metal arc welding (GMAW) skill training from both the welder’s and instructor’s perspectives. The system consists of a welder-side unit and an instructor-side unit and uses a commercial camera, optical filters, a wide-angle lens, and a compact computer. Welding images were acquired under actual GMAW conditions, and the effects of optical filter selection, exposure time, tone mapping, and trimming methods were investigated. A 600 nm long-pass filter and an exposure time of 20,000 μs provided a suitable balance between arc-light suppression, brightness stability, and image clarity. Gamma correction improved the visibility of key regions, including the molten pool, arc, torch, groove, and wire. In addition, low-pass-filtered centroid tracking enabled stable trimming of the weld region from wide-angle images. The developed system achieved real-time display and recording of standardized welding images, demonstrating its potential to support GMAW training through improved image visibility, real-time monitoring, and standardized image recording, while also providing visual data for post-weld review and future skill-assessment applications. Full article

Tomato is one of Thailand’s most significant economic crops, generating substantial export value and serving as a primary source of income for local farmers. However, the traditional manual grading process often fails to comply with the Thai Agricultural Standard TACFS 1503–2007, as grading decisions rely heavily on individual experience and subjective perception, resulting in inconsistent quality. Existing automated systems face the challenges of low accuracy, high costs, and complex hardware, while many are incompatible with Thailand’s grading standards. This study presents a multi-view tomato grading system (MVT-Grader), utilizing a dataset acquired from Doi Kham Food Products Co., Ltd. (Third Royal Factory, Tao Ngoi) under controlled lighting conditions. Subsequently, MVT-Grader is built on a custom-designed lightweight CNN architecture with an adjusted spatially aware loss function to enhance the model’s sensitivity in detecting subtle surface defects and color variations. The proposed model was trained using tomato images captured from two and three different viewpoints via a low-cost webcam setup and processed by a GPU-embedded system. Experiments conducted using stratified 5-fold cross-validation on a real-world industrial dataset demonstrate average grading accuracies of 99.43% (two-view) and 99.64% (three-view). Furthermore, the proposed Real-Time Lightweight CNN with Spatially Aware Loss Optimization achieves processing speeds of 87 ms and 114 ms per tomato for two- and three-view cases, respectively. Compared with MVCNN-Siamese, SDF-ConvNets, and Multi-View Spatial Network, the proposed system outperforms the others in both accuracy and speed, improving accuracy by 1.6–6.11% and reducing processing time by 39–49 ms. Full article

Aiming at the problems of glare interference, local overexposure and detail loss caused by artificial light sources such as vehicle lamps and street lamps in nighttime road scenes, as well as the challenges of existing glare suppression models with large parameters, high computational complexity and difficulty in deploying on edge devices, this paper proposes a lightweight glare suppression network (LGSNet) based on ghost depthwise separable convolution and Lightweight Parallel Attention. Based on the U-Net architecture, the network introduces ghost depthwise separable convolution blocks (GhostDSC) in the encoder and decoder, which generates ghost features through cheap linear transformations by exploiting feature map redundancy, significantly reducing model parameters and computational costs while maintaining feature representation ability. Meanwhile, a Lightweight Parallel Attention (LPA) module is designed in the decoder stage, which integrates channel attention and pixel attention in parallel, enhancing the network’s attention to glare regions and edge details with extremely low parameter increment to improve detail recovery accuracy. In addition, a joint loss function consisting of background loss, glare loss and reconstruction loss is constructed to collaboratively optimize glare suppression and detail preservation. Experimental results on the public Flare7K++ dataset and the self-built nighttime road glare dataset NRGD show that the proposed method has only 7.45 M parameters, much lower than standard U-Net and Uformer. It achieves competitive results on full-reference metrics such as PSNR, SSIM, LPIPS and no-reference metrics such as NIQE, BRISQUE, PIQE, and can effectively suppress various types of glare interference and restore obscured scene details. It achieves a superior trade-off between model complexity and enhancement performance, significantly reducing the parameter count and computational overhead compared to heavy baselines, thereby offering a highly efficient solution for resource-aware glare suppression tasks. Full article

Background: N-Acetylneuraminic acid (Neu5Ac), the predominant form of sialic acid, exhibits potent antioxidant, anti-inflammatory, and anti-glycation properties in preclinical studies, suggesting potential dermatological benefits. However, robust clinical evidence supporting the efficacy of oral Neu5Ac supplementation on human skin conditions remains lacking. Methods: A randomized, double-blind, placebo-controlled trial was conducted with 55 Chinese women (40–65 years) who received 120 mg/day Neu5Ac (n = 27) or a matching placebo (n = 28) for 84 days. Skin pigmentation, hydration, biomechanical properties, and dermis echogenicity were evaluated at baseline, D28, D56, and D84 using standardized clinical and instrumental assessments. Results: Both clinical (Pantone color card) and instrumental (photographic) assessments showed that oral Neu5Ac supplementation significantly improved skin lightness on both pigmentary spots and surrounding normal skin compared with placebo at day 84. In addition, stratum corneum hydration and skin biologic extensibility were significantly increased in the Neu5Ac group. Dermal echogenicity showed numerical improvement but did not reach statistical significance. Self-assessment indicated that 100% of the Neu5Ac participants reported improvements in skin whiteness, radiance, elasticity, firmness, and hydration, with mean satisfaction scores of 9.1/10 versus 7.9/10 for placebo. Conclusions: Daily oral supplementation with 120 mg Neu5Ac for 84 days significantly promoted localized spot brightening, enhanced skin hydration, and improved skin elasticity, providing the first clinical evidence supporting Neu5Ac as a safe and effective oral cosmetic ingredient for skin anti-aging. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

Pulse oximetry is an essential standard monitor in modern anesthetic practice, enabling continuous noninvasive assessment of arterial oxygen saturation and pulse rate throughout the perioperative period. Since its introduction into clinical medicine, pulse oximetry has significantly improved patient safety by facilitating early detection of hypoxemia and physiologic deterioration. Despite its widespread use, clinicians may underrecognize the technical principles, physiologic assumptions, and limitations that influence measurement accuracy. This review provides a perioperative perspective on pulse oximetry, including the physics of photoplethysmography, sensor technologies, and practical considerations for optimal probe placement and signal acquisition. Sources of inaccuracy such as motion artifact, low perfusion states, dyshemoglobinemias, ambient light interference, skin pigmentation, and venous pulsation are discussed in detail. The review further examines perioperative applications across preoperative evaluation, intraoperative monitoring, and postoperative recovery, while also exploring advanced parameters including perfusion index (PI) and pleth variability index (PVI). Emerging innovations such as multi-wavelength systems and artificial intelligence (AI)-enhanced signal analysis are also highlighted. A comprehensive understanding of pulse oximetry allows anesthesiologists to appropriately interpret monitor data, recognize device limitations, and optimize perioperative patient care. Full article

Autonomous driving systems face critical perception failures in dense fog, where conventional RGB cameras suffer from severe degradation due to atmospheric scattering and reduced visibility. This paper presents an adaptive multi-modal fusion framework that synergistically integrates gated imaging with 3D LiDAR point clouds to achieve robust obstacle detection under visibility conditions as low as 50 m. Unlike standard cameras that passively capture scattered ambient light, gated cameras employ time-synchronized active illumination to physically filter backscattered photons, preserving structural features even in low-visibility scenarios. We propose a novel Adaptive Feature-Weighting Network (AFW-Net) that dynamically adjusts sensor modality contributions based on real-time environmental degradation assessment. The framework incorporates three key innovations: (1) a cross-modal feature extraction module that exploits the complementary physical properties of gated imaging and LiDAR, (2) an attention-based adaptive fusion mechanism that quantifies per-modality reliability through uncertainty estimation, and (3) a degradation-aware training strategy using weather-specific augmentation. Extensive experiments on the Princeton Automated Driving Dataset demonstrate that our approach maintains detection average precision (AP) above 82% under dense fog conditions (50 m visibility), representing a 23.7% improvement over state-of-the-art RGB-LiDAR fusion methods that exhibit substantial performance degradation to 58.4% AP. Ablation studies validate the necessity of each component, and cross-dataset evaluation confirms the generalization capability of the proposed framework. The adaptive weighting mechanism proves particularly effective, dynamically rebalancing modality contributions across the gated imaging and LiDAR branches while maintaining LiDAR geometric constraints. This work establishes a robust perception paradigm for safety-critical autonomous systems operating in low-visibility environmental conditions. Full article

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich physiological patterns. We hypothesize that by combining information from these signals, we can efficiently estimate sleep dynamics of patients receiving CPAP treatment. Methods: The airflow signals were obtained from CPAP titration devices, denoted as CPAP-airflow, while the PPG signals were collected using the PranaQ TipTraQ (TTQ001), a fingertip-worn wearable device. We separately trained one-dimensional convolutional neural networks for CPAP-airflow and PPG signals and fused their outputs through probabilistic ensembling to predict sleep stages. The ensemble method is a late-fusion soft-voting scheme that computes a linearly weighted combination of synchronized softmax probability vectors from the modality-specific models. Results: For three-stage classification (Wake, REM, NREM), the PPG-based and CPAP-airflow-based models achieved overall Cohen’s kappa scores of 0.511 and 0.452, respectively, while the ensembled model improved the overall kappa to 0.587. The F1-score for the REM stage improved to 0.706 using the ensemble method, compared to 0.685 and 0.532 achieved by the individual models, respectively. In the four-stage classification (Wake, REM, Light, Deep) task, a deep sleep sensitivity of 0.596 was attained through the application of probabilistic ensembling. Conclusions: A fusion scheme of complementary information from the CPAP and PPG enhances the accuracy of sleep stage detection and hence enables more precise sleep monitoring, especially with an improved REM identification. Clinical implications include applying the proposed algorithm to improve in-home auto-CPAP titration by capturing REM-related respiratory instability and avoiding under-titration in REM-dominant OSAHS, better reflecting the patient’s true nocturnal respiratory needs. Full article

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R² = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article

Anthropogenic disturbances and climate warming threaten the rare paleoendemic species Tetracentron sinense. To identify the divers of its latitudinal adaptation, we integrated functional trait differentiation, environmental filtering, and phylogenetic conservatism. We measured 35 functional traits (leaf morphology, nutrient stoichiometry, stomatal traits, whole-plant architecture) across four natural populations spanning the species’ latitudinal range: BMXS (Baima Snow Mountain), DFD (Dafengding), FP (Foping), LGS (Leigong Mountain). Using correlation analysis, principal component analysis, and phylogenetic community metrics, we found that T. sinense dominated all communities. Populations exhibited divergent strategies: DFD expanded leaf area for light capture under high rainfall and shaded conditions; FP increased height and crown width to compete for light; LGS enhanced nutrient-use efficiency under phosphorus limitation; BMXS promoted phosphorus uptake under nitrogen limitation (N/P < 14). Trait variation correlated significantly with elevation, solar radiation, and temperature. PCA explained 90.44% of total variance, and standardized effect size (SES) values for phylogenetic signals range from −2.031 to 1.973; Phylogenetic signals were stronger in co-occurring taxa than in T. sinense. T. sinense populations in BMXS and FP are structured by competitive exclusion, while those in LGS and DFD by habitat filtering. We conclude that T. sinense achieves latitudinal adaptation by overcoming phylogenetic niche conservatism through phenotypic plasticity. While leaf economic traits remain evolutionarily conserved and niches in glacial refugium are relatively stable, populations adjust trait syndromes via metabolic shifts and structural trade-offs in response to heterogeneous environmental filters. Identifying these adaptive strategies can guide seed sourcing for restoration efforts under climate change. Full article

Short-term wind power forecasting is a key enabling technology for wind farm operation optimization, power grid dispatch, and electricity market decision-making. However, existing studies often lack unified standards in data partitioning, input feature construction, and hyperparameter tuning, making fair and reproducible comparisons across models difficult to achieve. To address this issue, this study focuses on day-ahead wind power forecasting for a single wind farm and establishes a benchmarking framework with strict chronological splitting, a shared feature information set, and a consistent hyperparameter tuning budget. Within this framework, six representative models, namely Ridge, XGBoost, LightGBM, DLinear, Transformer, and PatchTST, are systematically evaluated. A two-level evaluation protocol combining a fixed hold-out split and expanding-window rolling validation is adopted to compare model performance from multiple perspectives, including overall accuracy, sensitivity to hyperparameter tuning, robustness across rolling windows, and performance under typical operating scenarios. The results show that model rankings are not fully consistent between the hold-out evaluation and the rolling-validation setting. Under the fixed hold-out split, LightGBM achieved the lowest NRMSE of 10.2326%, while Transformer obtained the lowest NMAE of 6.9944%. In contrast, under the 8-fold expanding-window rolling validation, Transformer achieved the lowest average NRMSE of 8.1684%, followed by LightGBM with 8.7344%. These results indicate that the best performance on a single test split does not necessarily imply the strongest robustness across multiple time windows. In addition, strong tree-based models remain highly competitive in this single-wind-farm forecasting task, whereas more complex deep temporal models do not always deliver stable advantages. Meanwhile, the performance gains brought by hyperparameter optimization vary substantially across models, suggesting that conclusions drawn from default-parameter comparisons are of limited reliability. These findings demonstrate that systematic benchmarking under strict temporal constraints and fair tuning conditions is essential for clarifying the comparative performance, robustness, and engineering applicability of different model families. The study can therefore provide practical guidance for model selection and deployment in short-term wind power forecasting for single wind farms. Full article

Electroretinography (ERG) is a non-invasive technique used to assess retinal function via electrical responses to light stimuli. We established baseline ERG parameters and a standardized recording protocol for collared scops owls (Otus lettia). Twelve eyes of six owls were evaluated. In addition to the pre-release assessment, ocular reflex tests and basic ophthalmic examinations were performed before the induction of anesthesia. Routine radiographic and hematological examinations were performed under general anesthesia, followed by ERG recordings. Under scotopic –20 dB conditions, the a-wave amplitude was 1.78 ± 0.53 μV (implicit time: 37.83 ± 5.52 ms), and the b-wave was 41.59 ± 10.71 μV (100.88 ± 10.9 ms). For scotopic 0 dB mixed responses, the a-wave amplitude was 27.98 ± 5.9 μV (27.64 ± 2.71 ms), and that of the b-wave was 175.51 ± 13.82 μV (97.02 ± 7.01 ms). Under photopic conditions, the a-wave and b-wave amplitudes were 2.88 ± 2.06 μV (28.67 ± 2.77 ms) and 25.53 ± 10.61 μV (77.78 ± 16.18 ms). To the best of our knowledge, this is the first study to establish species-specific baseline ERG parameters for collared scops owls. These findings provide a valuable tool for assessing retinal function in raptors and may serve as a baseline framework for ERG evaluation in other avian species. Full article

At present, although there are many SCARA manipulator solutions with vertical lifting functionality, they generally suffer from high maintenance costs and complex structures. Moreover, systematic performance evaluations based on international standards are lacking, leading to unclear critical performance boundaries such as accuracy and payload in practical applications. To address these issues, this paper designs and manufactures a low-cost SCARA manipulator for educational and research demonstrations as well as light-duty electronic parts assembly scenarios. A “leadscrew + stepper motor” scheme is adopted for vertical lifting, and an Arduino Mega 2560 development board serves as the core controller, significantly reducing system cost. A three-dimensional model is established using SolidWorks 2022, and kinematic simulations are carried out with MATLAB 2024a to preliminarily verify the feasibility of the mechanism. Subsequently, a physical prototype is built and experimental tests are conducted in accordance with the ISO 9283 standard. The experimental results show that the repeatability of the manipulator is controlled within the range of 0.05–0.3 mm, the path deviation caused by vibration lies between −0.52 mm and 0.3 mm, and the maximum payload capacity is 3.91 N. These experimental data can serve as a benchmark for the design and performance comparison of similar low-cost manipulators. Full article

Background: Chronic exposure to artificial light at night (light pollution) causes circadian desynchronosis and melatonin deficiency, which is considered an independent driver of metabolic disorders and accelerated aging. However, the long-term effects of chronic desynchronosis on systemic metabolism and liver structure throughout the life cycle, as well as the potential of preventive melatonin administration, remain poorly understood. Objective: To evaluate the effects of chronic dark deprivation and prevention of metabolic disorders by exogenous melatonin on plasma melatonin levels, metabolic profile, liver function, and morphological changes in rats over a 24-month experiment. Methods: A 24-month experiment was conducted on 360 male Wistar rats divided into three groups: control (standard 10:14 h light/dark photoperiod), dark deprivation (DD, constant illumination), and correction (DD+Mel, constant illumination + melatonin 10 mg/kg five times per week). Animals were sacrificed at 6, 12, 18, and 24 months. Plasma melatonin was assessed by ELISA. Biochemical parameters (ALT, AST, LDH, total protein, albumin, bilirubin, glucose, triglycerides, and cholesterol), body weight, liver weight, relative liver weight, and histological parameters (steatosis, fibrosis, nuclear area, nuclear/cytoplasmic ratio, and binucleated hepatocytes) were analyzed. Results: In the DD group, a persistent progressive melatonin deficiency was detected (5.1-fold decrease by 6 months, p < 0.0005), accompanied by hypertriglyceridemia (Cohen’s d = 6.40), hypercholesterolemia (d = 4.59), biphasic dysglycemia (hypoglycemia followed by hyperglycemia), elevated ALT and AST activity (d = 2.60 and 2.46, respectively), hypoproteinemia (d = 5.33), hypoalbuminemia (d = 3.34), and hyperbilirubinemia (d = 3.22–4.37), as well as progressive steatosis (2.8 ± 0.3 points, d = 7.20) and pericellular fibrosis (1.8 ± 0.4 points, d = 4.50). In the DD group, a decrease in relative liver weight during the first 12 months was observed (metabolic disproportion, d = 2.31), reflecting disproportionate body weight gain. In the DD+Mel group, exogenous melatonin restored the biochemical parameters to values that did not differ statistically from the control values (Cohen’s d < 0.2 for most parameters), prevented steatosis (0.8 ± 0.3 points, d = 0.80) and fibrosis (0 points), increased relative liver weight by 24 months (3.83 ± 0.49 vs. 3.27 ± 0.029 in the control, d = 1.60), and increased the hepatocyte nuclear area (58.4 ± 4.1 vs. 48.6 ± 3.8 μm², d = 2.32). Conclusions: Chronic desynchronosis induced by constant illumination leads to persistent melatonin deficiency and complex metabolic and structural liver disturbances modeling accelerated aging. Exogenous melatonin (10 mg/kg five times per week) exhibits pronounced geroprotective, hepatoprotective, and antifibrotic effects, normalizing all biochemical parameters and preventing age-related liver involution. Full article

Adequate polymerization of resin cements in fiber post restorations depends on effective light transmission, which is influenced by post type, design, and length; however, variations in transmitted light radiant energy (TLRE) among systems may compromise bonding and long-term outcomes, and comparative evidence remains limited. This study evaluated radiant exposure and TLRE transmitted by different fiber post systems across varying post lengths. Four fiber post systems (Easy Post, RelyX Fiber Post, iLumi Super Fiber Post, and EZ-Fit Translucent Post) and a glass rod as a control group were tested (n = 40). TLRE was measured through standardized side openings at 1 mm increments after 40 s of light curing and recorded with a radiometer, while the post-microstructure was analyzed using scanning electron microscopy. Hierarchical multiple linear regression was used to assess the effects of post system and length on TLRE. TLRE differed significantly among systems (p < 0.001), with post type and length explaining a substantial proportion of variance; length was a significant negative predictor across all systems. Only 1.20–7.19% of coronal TLRE reached post surfaces. The iLumi post maintained TLRE > 200 mJ/cm² along its length, and microstructural differences were observed between smooth and serrated designs. Fiber post type, configuration, and length significantly influence TLRE, which decreases with increasing length, highlighting the importance of appropriate post selection to optimize resin cement polymerization and clinical performance. Full article