Search Results (1,252)

Free-space optical (FSO) communications have emerged as a promising complement to conventional radio-frequency (RF) systems due to their high bandwidth, low interference, and license-free spectrum. Visible-light FSO communication, using laser diodes or LEDs, offers potential for short-range data links, but performance is highly wavelength-dependent under varying atmospheric conditions. This study presents an experimental evaluation of three visible laser diodes at 650 nm (red), 532 nm (green), and 405 nm (violet), focusing on their optical output power, quantum efficiency, and modulation behavior across a range of driving currents and frequencies. A custom laboratory testbed was developed using an Atmega328p microcontroller and a Visual Basic control interface, allowing precise control of current and modulation frequency. A silicon photovoltaic cell was employed as the optical receiver and energy harvester. The results demonstrate that the 650 nm red laser consistently delivers the highest quantum efficiency and optical output, with stable performance across electrical and modulation parameters. These findings support the selection of 650 nm as the most energy-efficient and versatile wavelength for short-range, cost-effective visible-light FSO communication. This work provides experimentally grounded insights to guide wavelength selection in the development of energy-efficient optical wireless systems. Full article

Rock bolts are crucial components in the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low-light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium- to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments. Full article

This study investigates the spatial heterogeneity of carbon pools in young Betula sp. stands on former arable lands in the southern Moscow region, Russia. The findings could be useful for the current estimates and predictions of the carbon balance in such forest ecosystems. The research focuses on understanding the interactions between plant cover and the environment, i.e., how environmental factors such as stand density, tree diameter and height, light conditions, and soil properties affect ecosystem carbon pools. We also studied how heterogeneity in edaphic conditions affects the formation of plant cover, particularly tree regeneration and the development of ground layer vegetation. Field measurements were conducted on a permanent 50 × 50 m sampling plot divided into 5 × 5 m subplots, in order to capture variability in vegetation and soil characteristics. Key findings reveal significant differences in carbon stocks across subplots with varying stand densities and light conditions. This highlights the role of the spatial heterogeneity of soil properties and vegetation cover in carbon sequestration. The study demonstrates the feasibility of indirect estimation of carbon stocks using stand parameters (density, height, and diameter), with results that closely match direct measurements. The total ecosystem carbon stock was estimated at 80.47 t ha⁻¹, with the soil contribution exceeding that of living biomass and dead organic matter. This research emphasizes the importance of accounting for spatial heterogeneity in carbon assessments of post-agricultural ecosystems, providing a methodological framework for future studies. Full article

The textile industry’s reliance on synthetic dyes contributes significantly to pollution, highlighting the need for sustainable alternatives like biopigments. This study investigates the production and application of the biopigment prodigiosin, which was produced by Pseudomonas putida with a yield of 1.85 g/L. Prodigiosin was prepared under acidic, neutral, and alkaline conditions, resulting in varying protonation states that influenced its affinity for cotton and polyester fibers. Three surfactants (anionic, cationic, non-ionic) were tested, with non-ionic Tween 80 yielding a promising color strength (above 4) and fastness results with neutral prodigiosin at 1.3 g/L. Cotton and polyester demonstrated good washing (color difference up to 14 for cotton, 5 for polyester) and light fastness (up to 15 for cotton, 16 for polyester). Cellulose acetate, used in the conventional printing process as a thickener, produced superior color properties compared to commercial thickeners. Neutral prodigiosin achieved higher color strength, and cotton fabrics displayed halochromic properties, distinguishing them from polyester, which showed excellent fastness. Prodigiosin-printed samples also exhibited strong antimicrobial activity against Pseudomonas aeruginosa and retained halochromic properties over 10 pH cycles. These findings suggest prodigiosin as a sustainable dye alternative and pH sensor, with potential applications in biomedical materials, such as antimicrobial and pH-responsive wound dressings. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Fish coloration plays an important role in reproduction and camouflage, yet capturing color variation under field conditions remains challenging. We present a standardized, semi-automated protocol for measuring body coloration in the popular model fish threespine stickleback (Gasterosteus aculeatus). Individuals are photographed in a controlled light box within minutes of capture, and color is sampled from eight anatomically defined standard sites in human-perception-based CIELAB space. Analyses combine univariate color metrics, multivariate statistics, and the ΔE* perceptual difference index to detect subtle shifts in hue and brightness. Validation on pre-spawning fish shows the method reliably distinguishes males and females well before full breeding colors develop. Although it currently omits ultraviolet signals and fine-scale patterning, the approach scales efficiently to large sample sizes and varying lighting conditions, making it well suited for population-level surveys of camouflage dynamics, sexual dimorphism, and environmental influences on coloration in sticklebacks. Full article

Water pollution caused by increasing industrial and human activity remains a serious environmental challenge, especially due to the persistence of organic contaminants in aquatic systems. Photocatalysis offers a promising and eco-friendly solution, but in the case of bismuth oxide (Bi₂O₃) there is still a limited understanding of how structural and morphological features influence photocatalytic performance. In this work, a straightforward hydrothermal synthesis method followed by controlled calcination was developed to produce phase-pure α- and β-Bi₂O₃ with tunable morphologies. By varying the hydrothermal temperature and reaction time, distinct structures were successfully obtained, including flower-like, broccoli-like, and fused morphologies. XRD analyses showed that the final crystal phase depends solely on the calcination temperature, with β-Bi₂O₃ forming at 350 °C and α-Bi₂O₃ at 500 °C. SEM and BET analyses confirmed that morphology and surface area are strongly influenced by the hydrothermal conditions, with the flower-like β-Bi₂O₃ exhibiting the highest surface area. UV–Vis spectroscopy revealed that β-Bi₂O₃ also has a lower bandgap than its α counterpart, making it more responsive to visible light. Photocatalytic tests using Rhodamine B showed that the flower-like β-Bi₂O₃ achieved the highest degradation efficiency (81% in 4 h). Kinetic analysis followed pseudo-first-order behavior, and radical scavenging experiments identified hydroxyl radicals, superoxide radicals, and holes as key active species. The catalyst also demonstrated excellent stability and reusability. Additionally, Methyl Orange (MO), a more stable and persistent azo dye, was selected as a second model pollutant. The flower-like β-Bi₂O₃ catalyst achieved 73% degradation of MO at pH = 7 and complete removal under acidic conditions (pH = 2) in less than 3 h. These findings underscore the importance of both phase and morphology in designing high-performance Bi₂O₃ photocatalysts for environmental remediation. Full article

Three-dimensional (3D) reconstruction from images has significantly advanced due to recent developments in deep learning, yet methodological variations and diverse application contexts pose ongoing challenges. This systematic review examines the state-of-the-art deep learning techniques employed for image-based 3D reconstruction from 2019 to 2025. Through an extensive analysis of peer-reviewed studies, predominant methodologies, performance metrics, sensor types, and application domains are identified and assessed. Results indicate multi-view stereo and monocular depth estimation as prevailing methods, while hybrid architectures integrating classical and deep learning techniques demonstrate enhanced performance, especially in complex scenarios. Critical challenges remain, particularly in handling occlusions, low-texture areas, and varying lighting conditions, highlighting the importance of developing robust, adaptable models. Principal conclusions highlight the efficacy of integrated quantitative and qualitative evaluations, the advantages of hybrid methods, and the pressing need for computationally efficient and generalizable solutions suitable for real-world applications. Full article

To ensure the year-round efficient production of high-quality cherry tomatoes, this study evaluated how four cherry tomato cultivars can enhance yield and quality through optimized nutrient solution and supplementary lighting. Nutrient solutions (N1 and N2) were adjusted, with EC at 1.6 dS/m (N1: nitrogen 10.7 me/L, phosphorus 2.7 me/L, potassium 5.3 me/L) during flowering stage, and 2.4 dS/m (N1: nitrogen 16 me/L, phosphorus 4 me/L, potassium 8 me/L; N2: nitrogen 10.7 me/L, phosphorus 5.4 me/L, potassium 10.8 me/L) from fruit setting to harvest. N1 used standard adjustments, while N2 was optimized by adding solely with KCl and KH₂PO₄. Lighting treatments included L1 (natural light) and L2 (supplemental red/blue light). The application of N2 effectively decreased nitrate levels while it significantly enhanced the content of soluble sugars, flavor, and overall palatability, especially fruit coloring in cherry tomatoes, irrespective of supplementary lighting conditions. However, such optimization also increased sourness or altered the sugar–acid ratio. Supplementary lighting generally promoted the accumulation of soluble sugars, sweetness, and tomato flavor, although its effects varied markedly among different fruit clusters. The combination of optimized nutrient solutions and supplementary lighting exhibited synergistic effects, improving the content of soluble sugars, vitamin C, proteins, and flavor. N1 combined with L2 achieved the highest plant yield. Among the cultivars, ‘Linglong’ showed the greatest overall quality improvement, followed by ‘Baiyu’, ‘Miying’, and ‘Moka’. In conclusion, supplementary lighting can enhance the effect of nitrogen on yield and amplify the influence of phosphorus and potassium on fruit quality improvement in cherry tomatoes. The findings of this study may serve as a theoretical basis for the development of year-round production techniques for high-quality cherry tomatoes. Full article

The bZIP gene family play a crucial role in plant growth, development, and stress responses, functioning as transcription factors. While this gene family has been studied in several plant species, its roles in the endangered woody plant Phoebe bournei remain largely unclear. This study comprehensively analyzed the PbbZIP gene family in P. bournei, identifying 71 PbbZIP genes distributed across all 12 chromosomes. The amino acid count in these genes ranged from 74 to 839, with molecular weights varying from 8813.28 Da to 88,864.94 Da. Phylogenetic analysis categorized the PbbZIP genes into 12 subfamilies (A-K, S). Interspecific collinearity analysis revealed homologous PbbZIP genes between P. bournei and Arabidopsis thaliana. A promoter cis-acting element analysis indicated that PbbZIP genes contain various elements responsive to plant hormones, stress signals, and light. Additionally, expression analysis of public RNA-seq data showed that PbbZIP genes are distributed across multiple tissues, exhibiting distinct expression patterns specific to root bark, root xylem, stem bark, stem xylem, and leaves. We also performed qRT-PCR analysis on five representative PbbZIP genes (PbbZIP14, PbbZIP26, PbbZIP32, PbbZIP67, and PbbZIP69). The results demonstrated significant differences in the expression of PbbZIP genes under various abiotic stress conditions, including salt stress, heat, and drought. Notably, PbbZIP67 and PbbZIP69 exhibited robust responses under salt or heat stress conditions. This study confirmed the roles of the PbbZIP gene family in responding to various abiotic stresses, thereby providing insights into its functions in plant growth, development, and stress adaptation. The findings lay a foundation for future research on breeding and enhancing stress resistance in P. bournei. Full article

In recent years, the classification of underwater images has become one of the most remarkable areas of research in computer vision due to its useful applications in marine sciences, aquatic robotics, and sea exploration. Underwater imaging is pivotal for the evaluation of marine eco-systems, analysis of biological habitats, and monitoring underwater infrastructure. Extracting useful information from underwater images is highly challenging due to factors such as light distortion, scattering, poor contrast, and complex foreground patterns. These difficulties make traditional image processing and machine learning techniques struggle to analyze images accurately. As a result, these challenges and complexities make the classification difficult or poor to perform. Recently, deep learning techniques, especially convolutional neural network (CNN), have emerged as influential tools for underwater image classification, contributing noteworthy improvements in accuracy and performance in the presence of all these challenges. In this paper, we have proposed a modified ResNet-18 model for the binary classification of underwater images into raw and enhanced images. In the proposed modified ResNet-18 model, we have added new layers such as Linear, rectified linear unit (ReLU) and dropout layers, arranged in a block that was repeated three times to enhance feature extraction and improve learning. This enables our model to learn the complex patterns present in the image in more detail, which helps the model to perform the classification very well. Due to these newly added layers, our proposed model addresses various complexities such as noise, distortion, varying illumination conditions, and complex patterns by learning vigorous features from underwater image datasets. To handle the issue of class imbalance present in the dataset, we applied a data augmentation technique. The proposed model achieved outstanding performance, with 96% accuracy, 99% precision, 92% sensitivity, 99% specificity, 95% F1-score, and a 96% Area under the Receiver Operating Characteristic Curve (AUC-ROC) score. These results demonstrate the strength and reliability of our proposed model in handling the challenges posed by the underwater imagery and making it a favorable solution for advancing underwater image classification tasks. Full article

The integration of renewable energy sources (RESs) into power systems, particularly in microgrids, is becoming a prominent trend aimed at reducing dependence on traditional energy sources. Replacing conventional synchronous generators with grid-connected RESs through power electronic converters has significantly reduced the inertia of microgrids. This reduction negatively impacts the dynamics and operational performance of microgrids when confronted with uncertainties, posing challenges to frequency and voltage stability, especially in a standalone operating mode. To address this issue, this research proposes enhancing microgrid stability through frequency control based on virtual inertia (VI). Additionally, the Iterative Learning Control (ILC) method is employed, leveraging iterative learning strategies to improve the quality of output response control. Accordingly, the ILC-VI control method is introduced, integrating the iterative learning mechanism into the virtual inertia controller to simultaneously enhance the system’s inertia and damping coefficient, thereby improving frequency stability under varying operating conditions. The effectiveness of the ILC-VI method is evaluated in comparison with the conventional VI (C-VI) control method through simulations conducted on the MATLAB/Simulink platform. Simulation results demonstrate that the ILC-VI method significantly reduces the frequency nadir, the rate of change of frequency (RoCoF), and steady-state error across iterations, while also enhancing the system’s robustness against substantial variations from renewable energy sources. Furthermore, this study analyzes the effects of varying virtual inertia values, shedding light on their role in influencing response quality and convergence speed. This research underscores the potential of the ILC-VI control method in providing effective support for low-inertia microgrids. Full article

University classrooms, core to higher education, have indoor light environments that directly affect students’ learning efficiency, visual health, and psychological states. This study integrates parametric optical simulation and virtual reality (VR) to explore light environment perception in ordinary university classrooms. Forty college students (18–25 years, ~1:1 gender ratio) participated in real virtual comparative experiments. VR scenarios were optimized via real-time rendering and physical calibration. The results showed no significant differences in subjects’ perception evaluations between environments (p > 0.05), verifying virtual environments as effective experimental carriers. The analysis of eight virtual conditions (varying window-to-wall ratios and lighting methods) revealed that mixed lighting performed best in light perception, spatial perception, and overall evaluation. Light perception had the greatest influence on overall evaluation (0.905), with glare as the core factor (0.68); closure sense contributed most to spatial perception (0.45). Structural equation modeling showed that window-to-wall ratio and lighting power density positively correlated with subjective evaluations. Window-to-wall ratio had a 0.412 direct effect on spatial perception and a 0.84 total mediating effect (67.1% of total effect), exceeding the lighting power density’s 0.57 mediating effect sum. This study confirms mixed lighting and window-to-wall ratio optimization as keys to improving classroom light quality, providing an experimental paradigm and parameter basis for user-perception-oriented design. Full article

Despite recent advances in deep learning for fire detection, much of the current research prioritizes model-centric metrics over dataset fidelity, particularly from a fire safety engineering perspective. Commonly used datasets are often dominated by fully developed flames, mislabel smoke-only frames as non-fire, or lack intra-video diversity due to redundant frames from limited sources. Some works treat smoke detection alone as early-stage detection, even though many fires (e.g., electrical or chemical) begin with visible flames and no smoke. Additionally, attempts to improve model applicability through mixed-context datasets—combining indoor, outdoor, and wildland scenes—often overlook the unique false alarm sources and detection challenges specific to each environment. To address these limitations, we curated a new video dataset comprising 1108 annotated fire and non-fire clips captured via indoor surveillance cameras. Unlike existing datasets, ours emphasizes early-stage fire dynamics (pre-flashover) and includes varied fire sources (e.g., sofa, cupboard, and attic fires), realistic false alarm triggers (e.g., flame-colored objects, artificial lighting), and a wide range of spatial layouts and illumination conditions. This collection enables robust training and benchmarking for early indoor fire detection. Using this dataset, we developed a spatiotemporal fire detection model based on the mixed convolutions ResNets (MC3_18) architecture, augmented with Convolutional Block Attention Modules (CBAM). The proposed model achieved 86.11% accuracy, 88.76% precision, and 84.04% recall, along with low false positive (11.63%) and false negative (15.96%) rates. Compared to its CBAM-free baseline, the model exhibits notable improvements in F1-score and interpretability, as confirmed by Grad-CAM++ visualizations highlighting attention to semantically meaningful fire features. These results demonstrate that effective early fire detection is inseparable from high-quality, context-specific datasets. Our work introduces a scalable, safety-driven approach that advances the development of reliable, interpretable, and deployment-ready fire detection systems for residential environments. Full article

Ultrasonic homogenization is an emerging technique with significant potential to modify the structure and functionality of food ingredients. This study evaluated the effect of ultrasonic homogenization on the rheological behavior and physical stability of aqueous dispersions of flaxseed fiber. Flax mucilage, with health-promoting and techno-functional properties, is of growing interest in several industries. The samples were subjected to different ultrasonic treatments, varying in amplitude (from 40 to 100%) and duration (from 2 to 20 min), with and without preliminary rotor–stator homogenization. The rheological properties were analyzed using small-amplitude oscillatory shear (SAOS) tests and steady shear flow curves. Physical stability was assessed by multiple light scattering. The results revealed that short treatment under ultrasonic homogenization had minimal impact on the viscoelastic parameters and viscosity, regardless of the amplitude used. However, longer treatments significantly reduced both values by at least one order of magnitude or more, indicating the occurrence of microstructural degradation. The relevance of this research lies in its direct applicability to the development of functional foods, since it is concluded that control of the ultrasonic homogenization process conditions must be carefully selected to retain the desirable rheological properties and physical stability. Full article