Search Results (436)

Passive solar shading must balance energy saving, daylight availability, glare control, and thermal comfort under contrasting climates. This study develops and validates a lightweight, interpretable light-thermal-energy coupling framework for early-stage shading optimization in building retrofits and new construction. It addresses two questions: how shading geometry, envelope performance, and thermal inertia should adapt to hot low-latitude and cold high-latitude regions, and how their coupled performance can be quantified. The framework combines solar geometry, the Perez radiation model, surface irradiance calculation, indoor ray tracing/voxel illuminance simulation, daylight glare probability (DGP) assessment, and a 6R3C (6-Resistance, 3-Capacitance) transient thermal network. A full-factorial matrix of 120 design combinations was evaluated using shading scale, glazing performance, envelope thermal resistance, and thermal mass as variables, with energy use, thermal response, daylight availability, and DGP as objectives. Results show climate-dependent thermal inertia: it stabilizes indoor temperature in cold regions but increases heat accumulation in hot regions. The optimal schemes satisfy visual comfort (DGP < 0.40) and achieve energy savings up to 44.2% for retrofits and 50.0% for new buildings in hot regions, and 14.6% in cold regions. The framework provides transparent decision support for climate-adaptive, low-carbon building design and complements EnergyPlus, TRNSYS, and Radiance. By supporting energy-efficient retrofits and climate-responsive new construction, the proposed approach contributes to sustainability by reducing dependence on mechanical heating and cooling, improving operational resource efficiency, and maintaining indoor thermal and visual comfort. Full article

Robust monitoring of weld pool dynamics is critical for automated arc welding; however, single-modality sensors are frequently constrained by severe optical interference and high-frequency environmental noise. To address these limitations, we propose M2WPR-Net, a novel multimodal framework that synergizes visual and acoustic signals for simultaneous weld width regression and physical quality classification. The architecture employs a dual-stream ResNet50 backbone to process heterogeneous sensory data. Specifically, the visual stream utilizes a Convolutional Block Attention Module (CBAM) to suppress intense arc glare and localize the weld pool. Concurrently, the acoustic stream transforms 1D audio sequences into 2D Gramian Angular Summation Field (GASF) textures, which are subsequently refined by Squeeze-and-Excitation (SE) networks to isolate target frequency channels. A central contribution of this study is a bidirectional cross-modal attention mechanism based on Query–Key–Value (Q-K-V) matrix operations. Overcoming the shortcomings of static feature concatenation, this module dynamically aligns the modalities, enabling acoustic cues to guide visual feature extraction and vice versa, thereby mitigating information bottlenecks. Optimized via a joint multi-task loss function, the proposed M2WPR-Net significantly outperforms existing single-modal and conventional fusion baselines. Experimental results demonstrate that the network achieves a Mean Absolute Error (MAE) of 0.18 mm for width prediction and a 93.5% accuracy in penetration state classification, confirming its resilience and practical applicability in complex industrial welding environments. Full article

This study investigates the integration of a multi-configuration parametric shading system in buildings with continuous ramp designs to enhance daylight performance and visual comfort. Focusing on the Harbourside Art Museum in Bristol, UK, the research explores how discrete-configuration parametric façade configurations can be optimized to balance daylight access and glare control in complex spatial environments. A parametric simulation workflow was developed using Rhino, Grasshopper, Ladybug, and Honeybee, supported by Radiance and Daysim engines for Climate-Based Daylight Modelling (CBDM). Three performance metrics—Useful Daylight Illuminance (UDI), Annual Sunlight Exposure (ASE), and Daylight Glare Probability (DGP)—were employed to evaluate baseline and optimized models. Optimization was performed using Galapagos (single-objective genetic algorithm, population size = 50 individuals, 100 generations, convergence tolerance = 0.001; the fitness function maximized UDI while penalizing ASE excess above 75 h/year and GFI below 0.75, using a weighted single-objective score: Fitness = UDI − 0.3 × (ASE/250) + 0.3 × GFI) and Colibri 2.0 combined with Design Explorer for exhaustive multi-objective combinatorial analysis. Results from the base model showed high daylight availability but excessive glare, particularly along the ramp. Through systematic optimization, the study identified façade and contextual configurations that achieved a UDI of 0.77, an ASE of 74, and a glare-free index of 0.81. The findings demonstrate that orientation-specific multi-configuration shading, when integrated with contextual design parameters, significantly improves the daylighting performance of architecturally complex spaces. This research offers a replicable methodology for designers aiming to integrate responsive daylighting strategies in public and exhibition buildings. Full article

Between 2021 and 2023, the French market surveillance authority anonymously bought a sample of 50 toys containing a total of 103 individual LEDs from French retailers and had them tested by third-party laboratories accredited to perform tests in compliance with the international standard for electric toy safety IEC 62115, editions 2003 and 2017. Compliance with the harmonized EN IEC version of this standard is mandatory in the European Union under directive 2009/48/EC. The results revealed that 15 toys (30% of the tested toys) had at least one LED that did not comply with the eye safety requirements: 10 LEDs were non-compliant with respect to the retinal blue-light hazard, and 5 LEDs were non-compliant with respect to the retinal thermal hazard. A subsample of 10 toys with a total of 19 LEDs was assessed using the respective test methods corresponding to the 2003 and 2017 editions of the standard. The 2017 test method was found to be more permissive than the 2003 test method, a conclusion explained by the use of different risk assessment distances. Full article

Professional UAV thermal imaging systems are widely used for inspection, environmental monitoring, search and rescue, agriculture, and technical diagnostics. However, their cost limits their use in education, preliminary field screening, rapid prototyping, and low-resource applications. This study evaluates a minimum-cost indirect UAV thermal sensing workflow based on a DJI Mini 4K consumer drone, a lightweight Servo King9000 smartphone, and a UTi260M smartphone-connected infrared thermal camera. In the proposed configuration, the smartphone displayed and recorded the thermal stream, while the onboard RGB camera of the UAV recorded the smartphone-displayed infrared video during flight. The aim was not to develop a radiometric UAV thermal imaging platform, but to determine whether such a low-cost configuration can provide qualitative presence/absence indication of clear thermal hotspots and to identify its operational limits. The system was experimentally assessed under no-payload and payload conditions, daylight and nighttime illumination, and several low-altitude operating heights. Additional motor-region thermal observations were performed using a UTi260T handheld thermal camera under loaded and unloaded operating conditions. The complete UAV–payload configuration had a measured mass of approximately 340 g, corresponding to an effective added payload of 91 g and a payload-to-UAV mass ratio of 36.5%. Payload operation reduced near-ground flight endurance from approximately 25 min to 14 min 40 s. The maximum observed motor-region temperature increased from 24.9 °C under unloaded operation to 42.0 °C under loaded operation, while motor thermal asymmetry increased from 4.8 °C to 7.6 °C. Nighttime and low-glare operation improved the readability of the smartphone-displayed thermal stream, with the most practical usability observed at approximately 10–20 m. The results show that the proposed workflow is feasible only for short-range qualitative thermal screening and clear hotspot presence/absence indication. The UAV-recorded video should not be interpreted as direct thermal data, but as an RGB recording of a smartphone display showing thermal information. Therefore, the workflow is not suitable for quantitative temperature measurement, radiometric thermal mapping, or accurate thermal shape delineation. The main operational limits are payload mass, suspended-load oscillation, display readability, reduced endurance, motor-region thermal loading, sensitivity to payload alignment, and the absence of raw radiometric data. Direct UTi260M smartphone-recorded thermal frames were additionally used for pixel-size-assisted qualitative verification of practical reference thermal targets, including a human-sized target and a vehicle-sized target, at selected low-altitude operating heights. Full article

Background and Objectives: The purpose of this study is to present the clinical results over a 12-month follow-up period in patients after simultaneous bilateral crystalline lens removal with implantation of a Tecnis Eyhance DIB00 monofocal plus lens. Materials and Methods: This is a single-center, 12-month, retrospective study involving 214 eyes of 107 patients operated on between 2021 and 2023. Measurements included uncorrected and best-corrected visual acuity at 30 cm, 70 cm, and distance vision under fixed lighting conditions, instead of focusing on patient-dependent factors, such as pupil size, which varies individually based on sympathetic and parasympathetic nervous systems balance, simulating visual performance in real-world conditions. Contrast sensitivity was also assessed at 6 and 12 months postoperatively. Patients filled out a questionnaire on quality of life after surgery. Results: Uncorrected and best-corrected visual acuity were, respectively, for a 30 cm distance UCVA 0.76/BCVA 0.18 LogMAR; for a 70 cm distance UCVA 0.03/BCVA −0.036 LogMAR; and for distance vision UCVA 0.05/BCVA 0.02. None of the patients reported adverse phenomena such as halo, glare, contrast decrease, or other phenomena after surgery. The average rating of patient satisfaction assessed in a special questionnaire was 8.54 out of 10 points. Conclusions: The Tecnis Eyhance DIB00 lens implanted during bilateral cataract surgery produced good results in terms of uncorrected visual acuity under various lighting conditions, which in our population was better than predicted by the lens manufacturer. Through the 12-month observation period, postoperative refractive values remained stable, with no deterioration of vision. Full article

The intensive care unit (ICU) plays a crucial role in modern hospitals. ICU patients endure severe physical and mental conditions, making it essential to create a healing environment that reduces stress and promotes recovery. Among common environmental parameters, lighting conditions are particularly critical, as patients often face challenges with mobility and body positioning. Kinetic facades with adjustable external shading elements have gained attention for their ability to regulate sunlight effectively. However, their complexity poses challenges for design and implementation. This study proposes a reinforcement learning-based method, using Q-learning to handle discrete facade configurations and adaptive control under varying solar conditions for optimizing facade configurations in ICU rooms. The method aims to: (1) reduce direct sunlight glare and heat; and (2) maximize landscape views. A case study at Providence Alaska Medical Center demonstrates the method’s effectiveness, showing reduced glare and heat gain and improved landscape view availability through simulation. The results highlight the potential of reinforcement learning to address ICU-specific environmental challenges. Full article

Dynamic shading systems offer a promising approach to improving daylight and visual comfort in hot climates. The present work aims to enhance hourly daylighting performance by optimizing useful daylight illuminance (UDI_{500~1000 lx}), illuminance uniformity (Uo), and daylight glare probability (DGP). Radiance and SPEA-2 enable the identification of optimal solutions for corrugated, perforated, and separated trapezoid shading systems. The first multi-objective optimization selected a 15° slat angle, yielding an average illuminance of approximately 782.22 lx, a Uo of 0.7229, 100% coverage in the UDI_{500~1000 lx} range, and a DGP of 0.3324. Fully automatic control of the upper, central, and lower facade sections addressed issues of UDI_{300~500 lx} in June and UDI_{1000~2000 lx} in December. Corrugated shadings achieved over 90% for UDI_{500~1000 lx} during most investigated equinox and solstice conditions, with a minimum Uo of 0.79 in March and approximately 0.6 in December. Perforated inclined panels improved UDI_{500~1000 lx} for morning and afternoon. The fully automated trapezoid-separated shading system increased UDI_{500~1000 lx} coverage to at least 99.47% at 15:00 in March, 92.95% at 15:00 in June, and 92.06% at 12:00 in December, with DGP within imperceptible glare. The methodological scope was limited to a selected office space and point-in-time simulations from 10:00 to 15:00 on representative equinox and solstice days; therefore, the results should be interpreted as simulation-based design guidance rather than full annual or experimentally validated performance. Full article

Adverse imaging conditions such as fog, rain, and low light degrade the reliability of vision-based Personal Protective Equipment (PPE) detection systems on construction sites, yet most existing models are trained under clear-weather assumptions. This paper introduces a physics-based weather augmentation framework integrated with the YOLOv8n architecture to improve PPE detection robustness under adverse environmental conditions. The original Color Helmet and Vest (CHV) dataset was expanded from 1330 clear-weather images to 6650 images across five conditions using four physically grounded augmentation models: the Koschmieder atmospheric scattering model for fog, the Garg–Nayar streak model for rain, gamma-corrected attenuation with Poisson–Gaussian noise for low light, and a PSF-based glare model for bright sunlight. The weather-resistant model, a clear-weather baseline, and an augmented baseline were evaluated on the same 665-image weather-augmented test set. The weather-resistant model achieves 89.2% mAP50, a 5.7 percentage-point improvement over the clear-weather baseline (83.5%), with a nearly four-fold improvement in cross-condition stability (standard deviation 1.5% vs. 5.7%). Under matched training-data volume, the weather-resistant model still outperforms a conventionally augmented baseline across all five simulated conditions, indicating that these gains stem from physics-based modeling rather than larger training-data volume. The largest gain occurs under low light, where mAP50 improves from 73.4% to 87.9%. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis confirms that the weather-resistant model directs more attention toward PPE regions across all conditions, with the largest improvement under low light (+10.0 percentage points). The lightweight design (3.0 M parameters) and quantitative and qualitative validation on 205 annotated real-world construction site images under normal and low-light conditions provide preliminary evidence of practical applicability. 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

Purpose: To report intraoperative safety and postoperative complications after small-incision lenticule extraction using the 2 MHz femtosecond platform (SMILE Pro; VisuMax 800) in routine practice. Methods: Retrospective consecutive case series at a single center. All planned SMILE Pro procedures were analyzed (916 eyes from 482 patients). Outcomes included completion rate, intraoperative events, postoperative complications stratified as <3 and >3 months, and retreatment rate. Results: Baseline age was 32.9 ± 6.9 years; average preoperative refraction was −3.60 ± 1.90/−0.87 ± 0.76 D (sphere/cylinder) with best corrected visual acuity of −0.08 ± 0.07 logMAR. Procedures were completed in 911 of 916 eyes (99.45%). Suction loss occurred in six eyes (0.66%); one was completed after redocking, four were converted (two ICL, two femtosecond LASIK) and one did not receive a second procedure. No failed lenticule separations occurred. Retreatment was performed in 14 eyes (1.54%): 11 re-LASIK, 2 ICL, and 1 cataract extraction. Early postoperative events (<3 months) were mainly superficial punctate keratitis (3.51%) and dry eye (1.32%); beyond 3 months, events remained uncommon (dry eye 1.65%, photopsia/halo/glare 0.88%). No severe or sight-threatening complications were observed. Conclusions: SMILE Pro on the VisuMax 800 showed a high completion rate, rare intraoperative disruption, low retreatment, and rare, mostly mild postoperative events. These findings support a favorable early safety profile in routine practice; longer-term follow-up is warranted. Full article

Multimodal sensor fusion in intelligent transportation systems faces severe challenges in maintaining reliable visual information acquisition under complex illumination conditions. Extreme low-light and intense glare significantly degrade visible-light sensor imaging quality, making it difficult for single-modal vision systems to maintain reliable target perception. Meanwhile, although infrared sensors provide a relatively stable saliency complement for target regions, modal discrepancies and spatial misalignment between heterogeneous visible and infrared sensors often degrade fusion performance, limiting the practical benefits of multimodal sensing for machine perception. To address these issues, this study proposes Aligned, Dual-Gated, and Saliency-Guided MIRNet (ADS-MIR), a machine perception-oriented visible-infrared sensor fusion framework that enhances the discriminability and structural representation of target regions for roadside perception sensors operating under complex conditions. Specifically, the framework employs a domain alignment layer to mitigate feature distribution discrepancies and spatial misalignment between heterogeneous sensor modalities. An illumination-guided adaptive gating mechanism dynamically modulates bimodal sensor feature contributions, while a saliency-guided frequency decoupling reinforcement strategy reinforces target-related high-frequency edge details. Experimental results on the LLVIP and M3FD datasets demonstrate that ADS-MIR improves the edge information transfer factor ($Q^{AB/F}$) by 49.6% to 111.6% compared with existing methods, highlighting its distinct advantage in preserving target contours and restoring edge information. Furthermore, the enhanced results provide more discriminative input features for downstream object detection, exhibiting more stable perception capabilities under complex illumination and challenging sensing scenarios. Full article

Experimental validation delivers five quantified outcomes. First, optical pheromone detection achieves 88.7% ± 0.6% accuracy (n = 150, 95% CI), and the dual-modality combined channel achieves 86.1% ± 0.9% (n = 200), with robustness confirmed under 50/60 Hz flicker interference, rapid 200–1200 lux light transitions (485 ms settling), and reflective glare spots. Second, the MQ-135 chemical channel calibration holds R² ≥ 0.999 across temperatures of 15–35 °C and humidity of 30–90%, with maximum voltage drift of 0.093 V at the highest temperature. Third, 3.2× CNN inference speedup through 8-bit quantisation runs at 15 FPS within 1.8 W. Fourth, peripheral subsystems draw a measured mean of 1.19 W ± 0.02 W (n = 60, 95% CI); the complete per-robot system, including the Jetson Orin Nano compute rail, draws 6.15 W ± 0.09 W, enabling six-hour missions from the 55.08 Wh battery. Fifth, localisation across ten trials yields the mean position error 0.074 m and RMSE 0.081 m with 97.5% map coverage; physical multi-robot tests with 5–8 robots confirm map convergence times of 120–210 steps with collision rates below 0.042 per robot per step. To the best of our knowledge, no prior physical swarm platform has simultaneously demonstrated this combination of capabilities under comparable constraints. Full article

This study presents the design and performance evaluation of a hybrid solar-driven system (SOLIVE) that integrates tubular daylighting and buoyancy-driven natural ventilation within a single architectural component for industrial and large-scale buildings. While solar tubes and solar chimneys have been widely studied as independent passive technologies, their combined use in a unified system capable of delivering both daylight and ventilation remains largely unexplored. The proposed system utilizes solar tubes not only for transmitting natural daylight but also as thermal drivers that induce airflow through the stack effect generated by solar heating along the tube surface. A mathematical framework combining photometric daylight modeling and buoyancy-driven airflow analysis was developed to evaluate the system performance. Numerical simulations were conducted for three representative solar reference days (Equinox, Summer Solstice, and Winter Solstice). The influence of the key design parameters, including illuminated surface area (5–15 m²), solar tube diameter (0.1–0.3 m), and ventilated space volume (20–60 m³), was systematically analyzed. The results show that, under the adopted modelling assumptions, the system provides peak illuminance between 376 and 502 lux and ventilation rates up to 20.5 air changes per hour (ACH). These values are discussed as indicative benchmarks with respect to ISO 8995-1 and ASHRAE 62.1, rather than as proof of full real-building compliance, since glare, illuminance uniformity, thermal comfort, occupancy, wind effects and HVAC integration were not fully modelled. These findings demonstrate the potential of the proposed system as an effective passive solution for improving indoor environmental quality and reducing building energy demand in sunny climates. Full article

This study develops and applies a climate-based, user-centred and data-informed framework to assess lighting performance in educational buildings through the integrated use of daylight, high-efficiency LED systems and smart lighting controls. The research was conducted as a case study in university classrooms in Madrid, Spain, using a mixed-methods approach that combined in situ illuminance measurements, climate-based simulations with DIALux Evo 12.1, lighting energy assessment and structured user-perception surveys. The main objective was to quantify the dynamic interaction between daylight availability, electric lighting demand and perceived visual comfort, while assessing the energy-saving potential of daylight-responsive control strategies. Results show that the existing LED systems meet current illuminance requirements, with calculated lighting power density values ranging from 4.38 to 12.47 W/m². However, the analysis also reveals that high daylight availability does not necessarily guarantee better lighting performance, since excessive or uneven daylight can generate spatial imbalance, glare risk, and reduced visual stability. Survey results confirmed a strong student preference for daylight and exterior views but also showed that visual task clarity and glare control remain essential for user-centred lighting design. Overall, the findings demonstrate that effective classroom lighting retrofits should move beyond LED replacement alone towards adaptive, daylight-driven and user-centred control strategies capable of reducing energy use while maintaining visual comfort in educational buildings under Mediterranean climatic conditions. Full article