Search Results (3,711)

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

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

Autonomous unmanned aerial vehicles (UAVs) must decide when to trust onboard perception, when to request edge support, and when to avoid acting under poor visual or communication conditions. This study develops a risk-aware edge-assisted UAV perception framework that combines calibrated visual confidence with next-window service-level agreement (SLA) feasibility. The local branch uses MobileNetV3-Small for fast onboard color recognition, while the edge branch uses ResNet-18 for stronger remote inference. Low-confidence samples are offloaded only when the SLA predictor estimates that the wireless link is feasible; otherwise, the system enters fallback, meaning that the current prediction is not treated as immediately actionable. The evaluation follows a hard cross-illumination split: indoor and fluorescent light samples are used for training and validation, and indoor night and sunlight samples are reserved for testing. Under this setting, the local model achieves 76.89% accuracy and 73.25% macro-F1, while the edge model achieves 81.26% accuracy and 77.58% macro-F1. The SLA predictor, trained on enhanced telemetry features while preserving the original target label, achieves 85.74% accuracy, 85.57% macro-F1, 0.9420 ROC-AUC, and 0.9585 PR-AUC on temporally held-out records. The joint policy achieves 93.23% coverage and 79.90% success over active decisions, using local inference for 82.76% of the samples, edge offloading for 10.47%, and fallback for 6.77%. These results indicate that the framework is best understood as a tunable risk management layer for UAV perception rather than a pure accuracy maximization classifier. It avoids blind offloading and reduces forced decisions when both visual confidence and communication feasibility are weak. Full article

The increasing adoption of Plant Factories with Artificial Lighting (PFAL) has intensified the reliance on Internet of Things (IoT) technologies for real-time monitoring and control of environmental and operational variables. While IoT-based architectures enable precise resource management and productivity optimization, PFAL systems remain highly vulnerable to component failures, sensor malfunctions, communication faults, and energy disruptions, which may compromise crop integrity and system reliability. These risks are particularly critical in low-cost and small-scale PFAL implementations, where maintenance capacity and redundancy are often limited. Existing IoT-based PFAL monitoring systems typically address either hardware or software redundancy in isolation and rarely incorporate a dedicated maintenance-oriented fault detection layer validated under realistic multi-failure scenarios. This study addresses these challenges by proposing a low-cost redundant system architecture for PFAL applications that simultaneously integrates (1) hardware redundancy through multi-sensor configurations; (2) analytical redundancy based on residual generation and threshold-based fault isolation; and (3) a maintenance-oriented fault detection layer capable of identifying abnormal internal device conditions. Experimental validation was conducted using four hardware configurations—Arduino Nano with Ethernet, ESP32, STM32 with Wi-Fi, and STM32 with Ethernet—evaluated across five fault scenarios: dust accumulation, water exposure, high temperature, fire detection, and physical impact. The STM32 with Ethernet configuration consistently achieved the fastest fault detection response times across all tested scenarios. Future work will focus on the integration of machine learning-based predictive maintenance algorithms, multi-node PFAL network deployments, and long-term field validation. Full article

The increasing incidence of traffic accidents involving elderly pedestrians has highlighted the necessity for effective strategies to improve visibility in low-light environments. Conventional safety garments based on retroreflective materials or optical fibers exhibit limitations, including passive operation and low luminance. In this study, a textile-based organic light-emitting diode (OLED) safety garment with automatic light-sensing functionality is proposed to overcome these limitations. The OLED devices were fabricated on an ultrathin polyethylene terephthalate (PET) substrate and transferred onto a textile substrate to maintain flexibility and wearability. A light-emitting module incorporating a LilyPad Arduino and ambient light sensor was implemented to enable automatic illumination under low-light conditions. The fabricated textile-based OLED exhibited a luminance of 550 cd/m² at 4.5 V and maintained stable performance after transfer, with a T₅₀ lifetime of 485 h. Thermal analysis showed a minimal temperature increase of 2.9 °C after 5 h of operation, remaining below body temperature. Moreover, mechanical testing confirmed over 95% luminance retention after 2,000 bending cycles. The fabricated OLED-based luminous safety garment exhibited lightweight wearability with a total weight of 140 g and improved visibility at observation distances of up to 50 m under low-light conditions. These results indicate that the proposed OLED-based luminous safety garment can offer a viable solution for enhancing the safety of elderly pedestrians. 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

Living wall systems are increasingly used in indoor environments as elements of biophilic design; however, plant growth in these systems relies almost entirely on artificial lighting. While light-emitting diode (LED) technology offers flexible spectral properties, limited information is available on how commercially available white LEDs with different correlated color temperatures (CCTs) affect plant performance in indoor vertical greenery systems. The present study evaluated the effects of three white LED lamps differing in CCT, specifically warm-white (2700 K), neutral-white (4000 K), and cool-white (6500 K), on the growth, flowering, physiological performance, and visual perception of Spathiphyllum wallisii Regel cultivated in an indoor living wall system under exclusively artificial lighting. Plants were grown for eight months under a 12 h photoperiod, and growth parameters, flowering, SPAD index, photosystem II efficiency (F_v/F_m), and biomass accumulation were assessed. In addition, a questionnaire survey evaluated visual preferences under the different lighting conditions. Plant growth parameters, flowering, and physiological performance were largely unaffected by CCT, indicating that S. wallisii can adapt to a wide range of white LED spectra under low-irradiance conditions (~16–18 μmol m⁻² s⁻¹). However, cool-white lighting was associated with slightly higher canopy coverage and aboveground biomass compared to other treatments, although overall differences among CCTs were relatively limited. In contrast, survey results indicated a clear preference for neutral-white lighting, which was most frequently perceived as providing the most natural plant appearance and the most suitable illumination for indoor living walls. These findings suggest that neutral-white LEDs may provide a suitable balance between maintaining satisfactory plant growth and achieving favorable visual perception in indoor greenery installations. Full article

To address insufficient detail representation, inadequate cross-modal fusion, and limited localization accuracy in object detection under low-light and complex background conditions, this study proposes an improved CenterNet-based multimodal object detection method. The model uses fused images and infrared images as dual-source inputs, where infrared wavelet priors are introduced to enhance texture and structural representation. A Feature Fusion Attention (FFA) module is designed to improve cross-modal feature interaction, while a Heatmap-Guided Detection Head (HGDH) is introduced to explicitly enhance target-related regions during detection. In addition, a two-stack Hourglass backbone is adopted for multi-scale modeling of global semantics and local details. Based on the public LLVIP dataset, a precisely paired fused-image/infrared-image dataset, termed RH-25, was constructed for experiments. On the RH-25 dataset, the proposed method achieves a 3.51% improvement in mAP@0.5 relative to the baseline, as demonstrated by comparative and ablation experiments. Moreover, supplementary experiments on the MFAD dataset indicate potential cross-dataset adaptability under different scenes and multi-class conditions. These results indicate that the proposed method can improve detection performance in low-light and complex environments. 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

FDO’s wide boiling range and complex composition hinder controlled synthesis of high-performance mesophase pitch. Here, FDO was separated into light, middle, and heavy narrow fractions by vacuum distillation. Multi-scale characterization traced molecular evolution and mesophase development. The light fraction consists of three-ring aromatics with short alkyl side chains and shows the lowest reactivity, yielding limited condensation and poor stacking with isotropic regions and dispersed spheres. The middle fraction contains four-ring aromatics with moderately extended chains, exhibiting enhanced reactivity and undergoing nucleation, growth, coalescence, and disintegration of mesophase spheres. However, insufficient volatiles restrict shear orientation, forming a mosaic texture. The heavy fraction has four-ring aromatics with the longest alkyl chains and the lowest substitution degree, giving the highest reactivity. During thermal cracking, long chains release abundant radicals and volatiles; directional escape generates shear, promoting rapid growth and ordered alignment of aromatic lamellae. At 440 °C for 12 h, this fraction yields high-quality mesophase pitch with small-domain texture, a low softening point (295 °C), and high anisotropic content (98.8%). The pitch shows excellent spinnability, and derived carbon fibers (tensile strength ~1.45 GPa, modulus ~151 GPa) outperform a commercial reference processed under identical conditions. This study reveals molecular-level regulation of mesophase evolution by narrow fraction structures. Full article

High-cerium NdFeB magnets represent an effective approach for balanced utilization of light rare-earth resources. However, morphology of the grains is highly susceptible to processing parameters, and improper settings can result in extremely low performance during hot-pressing and hot-deformation processes. In this paper, the influence of process parameters on the magnetic properties and microstructure of Nd₁₅Ce₁₅(CoFeGa)_balB_0.92 magnets was clarified by adjusting the conditions of hot-pressing and hot-deformation processes, combined with performance testing and microstructural observation. It was observed that the number of coarse-grained regions within the magnets was significantly reduced, with a substantial decrease in coarse-grain size, and uniform primary phase grains were obtained by adjusting parameters to control the morphology of the grains. Full article

This study investigates the relationship between cardiorespiratory fitness and driving behavior in a highway environment using a driving simulator. A total of 46 young drivers aged 19 to 27 years participated in the experiment. Cardiorespiratory fitness was assessed through the Queen’s College Step Test and heart rate monitoring, allowing participants to be classified into high-fitness and low-fitness groups based on estimated maximum oxygen consumption. Each participant completed three simulated highway driving scenarios under varying traffic and lighting conditions. Driving performance data were continuously recorded, while additional individual and behavioral characteristics were collected through a structured questionnaire. The analysis focused on key performance indicators, including headway distance variability, average speed, and time to collision. Statistical analysis was conducted using regression models. The results indicate that higher physical fitness is associated with greater adaptability in driving behavior, reflected in increased headway variability and slightly higher driving speeds. At the same time, high-fitness drivers exhibited longer time to collision, suggesting improved anticipation and more effective management of traffic conditions. Environmental factors, particularly traffic volume and lighting conditions, remained dominant in shaping driving behavior. Overall, the findings suggest that physical fitness contributes to a more adaptive driving style on highways. By integrating physiological condition into the analysis of driver behavior, this study highlights the importance of considering health-related factors in road safety research and provides insights for developing preventive strategies targeting young drivers. Full article

Background/Objectives: Intracellular delivery of RNA molecules is challenging. To solve this problem, many carrier systems are available, which are based on liposomes or polymers. Cyclodextrins are widely used excipients to increase the solubility of small molecules, but their polymer derivatives are able to deliver macromolecules. In the present study, we aimed to investigate and compare the siRNA and mRNA carrying capacity of a cationic quaternary ammonium β-cyclodextrin polymer (QABCDPS) and polyethylenimine (PEI). Methods: Cytotoxicity of the polymers was tested by the MTT method. Polyplexes were formulated with different nitrogen/phosphate ratios (NP), and their physicochemical properties were examined using dynamic light scattering and zeta potential measurements. Cellular internalization and intracellular effects of the polyplexes were investigated by confocal microscopy and flow cytometry. Results: QABCDPS exhibited lower toxicity compared to PEI, effectively binding both siRNA and mRNA and delivering them into vesicles in the cytoplasm, but showing different internalization patterns. Polyplexes formed with PEI showed stronger biological effect than those with QABCDPS, which can be attributed to the strength of interactions facilitated by the polymers. Conclusions: In summary, QABCDPS is a low-toxicity carrier that shows some promise for mRNA delivery but is ineffective for siRNA silencing under the tested conditions and requires further structural optimization. Full article

Phytoplankton primary production (PP) underpins marine ecosystems. In winter marginal seas, the magnitude and size structure of PP not only sustain overwintering zooplankton but also shape larval fish survival and fishery resources in the following year. We conducted two cruises in the fish overwintering grounds of the East China Sea shelf to investigate the spatial distribution, size structure, and environmental controls of net primary production (NPP). Winter NPP was generally low relative to the annual range. Nutrient concentrations at most stations exceeded potential limitation thresholds, whereas the mixed-layer mean light exposure (LE) fell below the light-saturation threshold at most stations, indicating that insufficient light availability was primarily associated with sub-saturating light conditions of low winter productivity. Among size classes, the nano-sized fraction dominated NPP, followed by the pico-sized fraction, while the micro-sized fraction contributed least; however, the relative contribution of the micro-sized fraction increased in February. Measured values of two key parameters widely used in satellite-based NPP models—PBopt (optimal chlorophyll-specific carbon fixation rate) and F (a dimensionless light-related factor for the vertical distribution of primary production)—were both lower than model predictions, and the magnitude of deviation varied with water depth and mixing conditions. These findings refine our understanding of biogeochemical processes in overwintering grounds of winter marginal seas. Full article