Search Results (22,404)

Accurately simulating the asymmetric spectral profiles of blue LEDs is crucial for photobiological research, yet it remains a challenge for traditional symmetric models. This study proposes a novel spectral simulation model that effectively captures these asymmetries. The proposed model structure is partly motivated by known broadening and dispersion mechanisms observed in real LED spectra; it employs a ‘base model + correction’ framework, where an Exponentially Modified Gaussian (EMG) function captures the primary spectral shape and falling edge and an Asymmetric Pseudo-Voigt (APV) function corrects the deviations on the rising edge. Requiring only the central wavelength and bandwidth as user inputs, the simulation results exhibit a high degree of agreement with the experimental data spectra. The model provides a rapid and robust tool for pre-evaluating light sources against regulatory criteria (e.g., >99% of the spectral intensity is in the 400–500 nm band), thereby enhancing the efficiency of experimental design in blue light protection studies. Full article

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article

This study investigates the influence of façade-design parameters on daylighting performance in hot arid climates, with a particular focus on Egypt. A total of nine façade configurations of a residential building were modeled and simulated using Autodesk Revit and Insight 360, varying three critical variables: glazing type (clear, blue, and dark), Window-to-Wall Ratio (WWR) of 15%, 50%, 75%, and indoor wall finish (light, moderate, dark) colors. These were compared to the Leadership in Energy and Environmental Design (LEED) daylighting quality thresholds. The results revealed that clear glazing paired with high WWR (75%) achieved the highest Spatial Daylight Autonomy (sDA), reaching up to 92% in living spaces. However, this also led to elevated Annual Sunlight Exposure (ASE), with peak values of 53%, exceeding the LEED discomfort threshold of 10%. Blue and dark glazing types successfully reduced ASE to as low as 0–13%, yet often resulted in underlit spaces, especially in private rooms such as bedrooms and bathrooms, with sDA values falling below 20%. A 50% WWR emerged as the optimal balance, providing consistent daylight distribution while maintaining ASE within acceptable limits (≤33%). Similarly, moderate color wall finishes delivered the most balanced lighting performance, enhancing sDA by up to 30% while controlling reflective glare. Statistical analysis using Pearson correlation revealed a strong positive relationship between sDA and ASE (r = 0.84) in highly glazed, clear glass scenarios. Sensitivity analysis further indicated that low WWR configurations of 15% were highly influenced by glazing and finishing types, leading to variability in daylight metrics reaching ±40%. The study concludes that moderate glazing (blue), medium WWR (50%), and moderate color indoor finishes provide the most robust daylighting performance across diverse room types. These findings support an evidence-based approach to façade design, promoting visual comfort, daylight quality, and sustainable building practices. Full article

Anthraquinone acid dyes are widely used in dyeing polyamide due to their good exhaustion and brightness. While ionic interactions primarily govern dye–fiber bonding, the molecular weight (Mw) of these dyes can significantly influence migration, apparent color strength, and fastness behavior. This study offers comparative insight into how the Mw of structurally similar anthraquinone acid dyes impacts their diffusion, fixation, and functional outcomes (e.g., UV protection) on polyamide 6 fabric, using Acid Blue 260 (Mw~564) and Acid Blue 127:1 (Mw~845) as representative low- and high-Mw dyes. The effects of dye concentration, pH, and temperature on color strength (K/S) were evaluated, migration index and zeta potential were measured, and UV protection factor (UPF) and FTIR analyses were used to assess fabric functionality. Results showed that the lower-Mw dye exhibited higher migration tendency, particularly at increased dye concentrations, while the higher-Mw dye demonstrated greater color strength and superior wash fastness. Additionally, improved UPF ratings were associated with higher-Mw dye due to enhanced light absorption. These findings offer practical insights for optimizing acid dye selection in polyamide coloration to balance color performance and functional attributes. Full article

We report an investigation on the cobalt(II) chelation mechanism by a modified α-maltoside ligand 9 decorated with two iminodiacetate (IDA) residues on C6,C6′ positions. Herein we uncovered the capacity of this biodegradable ligand to chelate cobalt(II), an ionic metal contaminant in the environment that is used, in particular, in lithium-ion batteries. The interactions between cobalt(II) and synthesized ligand 9 were systematically studied using different analytical methods such as ¹H and ¹³C NMR, potentiometry, spectrophotometry, ITC, and ICP-AES. We observed a high affinity for the 1:1 complex, one cobalt(II) associated with two iminodiacetate groups, which is 10-fold higher than the 2:1 complex, where each of the two IDA groups interacts alone with a cobalt(II). Taking into account the log β_CoLvalue obtained (≈12.3) with the stoichiometry 1:1, the strength of this complexation with cobalt(II) can be ranked as follows for the most common ligands: IDA < MIDA < NTA < 9 < EDTA < TTHA < DTPA. We further completed a preliminary remediation test with water contaminated with cobalt(II) and recovered cobalt(II) metal using Chelex^® resin, which allowed a recycling of the synthetic ligand for future recovering experiments. The results shed light on the great potential of using this synthetic ligand as an effective and green remediation tool. Full article

Background/Objectives: Rest–activity rhythm (RAR) disturbances can contribute to aging and dementia via metabolic dysregulation. Hydroxycholesterol (OH) is thought to mediate the link between hypercholesterolemia and neurodegeneration. This study compared sleep and RAR parameters between amnestic mild cognitive impairment (aMCI) patients and normal controls (NCs), and examined their associations with plasma 27-OH levels, reflecting peripheral cholesterol metabolism. Methods In total, 18 aMCI patients (76.6 ± 6.1 years) and 21 NCs (70.4 ± 6.7 years) underwent five-day actigraphy and dim light melatonin onset assessment. Plasma 27-OH levels were measured via high-performance liquid chromatography-mass spectrometry. Generalized linear models (GLMs) were used to analyze the relationships between sleep, RAR, and 27-OH levels. Results: The aMCI group had significantly lower 27-OH levels and 27-OH/total cholesterol ratios (p < 0.05). GLM revealed that longer sleep onset latency (SOL) was associated with higher 27-OH levels in aMCI, distinguishing them from NCs. Additionally, in aMCI, longer SOL, lower sleep efficiency (SE), and higher fragmentation index (FI) were associated with an increased 27-OH/total cholesterol ratio (p < 0.05). Higher relative amplitude of RAR was linked to lower 27-OH levels across groups (p < 0.01), but RAR parameters showed no significant association with the 27-OH/total cholesterol ratio. Sleep disturbances, including prolonged SOL, reduced SE, and increased FI, were associated with altered peripheral cholesterol oxygenation in aMCI. Conclusions: Greater RAR amplitude correlated with lower 27-OH levels, regardless of cognitive status. These findings suggest that peripheral cholesterol oxygenation in aMCI is related to both sleep disturbances and circadian rhythm dysregulation, highlighting their role in cholesterol metabolism and neurodegeneration. Full article

To address the issues of loss of important detailed features, insufficient contrast enhancement, and high computational complexity in existing low-light image enhancing methodologies, this paper presents a low-light image enhancement network (MSF-ACA), which uses multi-scale feature fusion and adaptive contrast adjustment. Focus is placed on designing the local–global image feature fusion module (LG-IFFB) and the adaptive image contrast enhancement module (AICEB), in which the LG-IFFB adopts the local–global dual-branching structure to extract multi-scale image features, and utilizes the element-by-element multiplication method to fuse the local details with the global illumination distribution to alleviate the problem of serious loss of image details, while the AICEB incorporates linear contrast enhancement and confidence adaptive stopping mechanism, which dynamically adjusts the computational depth according to the confidence of the feature map, balancing the contrast enhancement and computational efficiency. According to the results of the experiment, the parameter count of MSF-ACA is 0.02 M, and compared with today’s mainstream algorithms, the suggested model attains 21.53 dB in PSNR when evaluated on the LOL-v2-real evaluation dataset, and the BRI is as low as 16.04 on the unpaired dataset DICM, which provides a better detail clarity and color fidelity in visual enhancement, and it is a highly efficient and robust low-light image model. Full article

Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction. Full article

In this work, we demonstrate high-efficiency 385 nm AlGaN-based near-ultraviolet micro light emitting diode (NUV-Micro LED) arrays. The epi structure is prepared using a novel AlN-inserted superlattice electrical blocking layer which enhances hole spreading in the p-type region significantly. The NUV-Micro LED arrays in this work comprise 228 chips in parallel with wavelengths at 385 nm, and each single chip size is 15 × 30 μm². Compared with conventional bulk AlGaN-based EBL structures, the NUV-Micro LED arrays that implemented the new hole spreading enhanced superlattice electrical blocking layer (HSESL-EBL) structure proposed in this work had a remarkable increase in light output power (LOP) at current density, increasing the range down from 0.02 A/cm² to as high as 97 A/cm². The array’s light output power is increased up to 1540% at the lowest current density 0.02 A/cm², and up to 58% at the highest current density 97 A/cm², measured under room temperature (RT); consequently, the WPE is increased from 13.4% to a maximum of 30.18%. This AlN-inserted HESEL-EBL design significantly enhances both the lateral expansion efficiency and the hole injection efficiency into the multi quantum well (MQW) in the arrays, improving the concentration distribution of the holes in MQW while maintaining good suppression of electron leakage. The array’s efficiency droop has also been greatly reduced. Full article

Skin autofluorescence (SAF) detection technology represents a noninvasive, convenient, and cost-effective optical detection approach. It can be employed for the differentiation of various diseases, including metabolic diseases and dermatitis, as well as for monitoring the treatment efficacy. Distinct from diffuse reflection signals, the autofluorescence signals of biological tissues are relatively weak, making them challenging to be captured by photoelectric sensors. Moreover, the absorption and scattering properties of biological tissues lead to a substantial attenuation of the autofluorescence of biological tissues, thereby worsening the signal-to-noise ratio. This has also imposed limitations on the development and application of compact-sized autofluorescence detection systems. In this study, a compact LED light source and a CMOS sensor were utilized as the excitation and detection devices for skin tissue autofluorescence, respectively, to construct a mobile and wireless skin tissue autofluorescence detection system. This system can achieve the detection of skin tissue autofluorescence with a high signal-to-noise ratio under the drive of a simple power supply and a single-chip microcontroller. The detection time is less than 0.1 s. To enhance the stability of the system, a pressure sensor was incorporated. This pressure sensor can monitor the pressure exerted by the skin on the detection system during the testing process, thereby improving the accuracy of the detection signal. The developed system features a compact structure, user-friendliness, and a favorable signal-to-noise ratio of the detection signal, holding significant application potential in future assessments of skin aging and the risk of diabetic complications. Full article

Cyanobacteria are a prolific source of bioactive metabolites with expanding applications in sustainable agriculture and biotechnology. This work explores, for the first time in thermotolerant Colombian isolates, the impact of light spectrum, photoperiod, and irradiance on the co-production of exopolysaccharides (EPS) and indole-3-acetic acid (IAA). Six strains from hot-spring environments were screened under varying blue:red (B:R) LED ratios and full-spectrum illumination. Hapalosiphon sp. UFPS_002 outperformed all others, reaching ~290 mg L⁻¹ EPS and 28 µg mL⁻¹ IAA in the initial screen. Response-surface methodology was then used to optimize light intensity and photoperiod. EPS peaked at 281.4 mg L⁻¹ under a B:R ratio of 1:5 LED, 85 µmol m⁻² s⁻¹, and a 14.5 h light cycle, whereas IAA was maximized at 34.4 µg mL⁻¹ under cool-white LEDs at a similar irradiance. The quadratic models exhibited excellent predictive power (R² > 0.98) and a non-significant lack of fit, confirming the light regime as the dominant driver of metabolite yield. These results demonstrate that precise photonic tuning can selectively steer carbon flux toward either EPS or IAA, providing an energy-efficient strategy to upscale thermotolerant cyanobacteria for climate-resilient biofertilizers, bioplastics precursors, and other high-value bioproducts. Full article

Environmental protection has become one of the key challenges of our time. This has led to an increase in pro-environmental activities in the field of cosmetics and household chemicals, where manufacturers are increasingly trying to meet the expectations of consumers who are aware of the potential risks associated with the production of cosmetics and household chemistry products. This is one of the most important challenges of today’s industry, given that some of the raw materials still commonly used, such as surfactants, may be toxic to aquatic organisms. Many companies are choosing to use natural raw materials that have satisfactory performance properties but are also environmentally friendly. In addition, modern products are also characterized by reduced consumption of water, resources, and energy in production processes. These measures reduce the carbon footprint and reduce the amount of plastic packaging required. In the present study, seven formulations of environmentally friendly car shampoo concentrates were developed, based entirely on mixtures of bio-based surfactants. The developed formulations were tested for application on the car body surface, allowing the selection of the two best products. For these selected formulations, an in-depth physicochemical analysis was carried out, including pH, density, and viscosity measurements. Comparison of the results with commercial products available on the market was also performed. Additionally, using the multiple light scattering method, the foamability and foam stability were determined for the car shampoos developed. The results obtained indicate the very high application potential of the products under study, which combine high performance and environmental concerns. Full article

Optical Coherence Tomography (OCT) has evolved from a breakthrough ophthalmologic imaging tool into a cornerstone technology in interventional cardiology. After its initial applications in retinal imaging in the early 1990s, OCT was subsequently envisioned for cardiovascular use. In 1995, its ability to visualize atherosclerotic plaques was demonstrated in an in vitro study, and the following year marked the acquisition of the first in vivo OCT image of a human coronary artery. A major milestone followed in 2000, with the first intracoronary imaging in a living patient using time-domain OCT. However, the real inflection point came in 2006 with the advent of frequency-domain OCT, which dramatically improved acquisition speed and image quality, enabling safe and routine imaging in the catheterization lab. With the advent of high-resolution, second-generation frequency-domain systems, OCT has become clinically practical and widely adopted in catheterization laboratories. OCT progressively entered interventional cardiology, first proving its safety and feasibility, then demonstrating superiority over angiography alone in guiding percutaneous coronary interventions and improving outcomes. Today, it plays a central role not only in clinical practice but also in cardiovascular research, enabling precise assessment of plaque biology and response to therapy. With the advent of artificial intelligence and hybrid imaging systems, OCT is now evolving into a true precision-medicine tool—one that not only guides today’s therapies but also opens new frontiers for discovery, with vast potential still waiting to be explored. Tracing its historical evolution from ophthalmology to cardiology, this narrative review highlights the key technological milestones, clinical insights, and future perspectives that position OCT as an indispensable modality in contemporary interventional cardiology. As a guiding thread, the myth of Prometheus is used to symbolize the evolution of OCT—from its illuminating beginnings in ophthalmology to its transformative role in cardiology—as a metaphor for how light, innovation, and knowledge can reveal what was once hidden and redefine clinical practice. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Microbiome analysis has revolutionized our understanding of various biological processes, spanning human health and epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the quantification of microbial taxa and their dynamics. In the study of bacterial abundances, it is becoming more relevant to consider their relationship, to embed these data in the framework of network theory, allowing characterization of features like node relevance, pathways, and community structure. In this study, we address the primary biases encountered in reconstructing networks through correlation measures, particularly in light of the compositional nature of the data, within-sample diversity, and the presence of a high number of unobserved species. These factors can lead to inaccurate correlation estimates. To tackle these challenges, we employ simulated data to demonstrate how many of these issues can be mitigated by applying typical transformations designed for compositional data. These transformations enable the use of straightforward measures like Pearson’s correlation to correctly identify positive and negative relationships among relative abundances, especially in high-dimensional data, without having any need for further corrections. However, some challenges persist, such as addressing data sparsity, as neglecting this aspect can result in an underestimation of negative correlations. Full article