Search Results (323)

The Qilian Mountains serve as a crucial ecological security barrier in western China, and the soil structural stability of alpine meadows directly affects regional ecological security and the sustainable utilization of grasslands. However, current research on grazing mostly relies on short-term artificially controlled experiments, which differ greatly from the pattern of long-term natural grazing. Herein, this study abandoned the artificially controlled grazing method and selected sampling areas with stable grazing regimes for more than a decade. Taking no grazing (CK) as the control, four treatments were established, including light grazing (LG), moderate grazing (MG), heavy grazing (HG) and extreme grazing (EG). The particle size distribution and stability of mechanically stable and water-stable soil aggregates in different soil layers were determined. Combined with environmental and biological factors, the effects of grazing on the structure and stability of soil aggregates were elucidated. The results showed that no grazing improved the mechanical stability of soil aggregates but reduced their water stability. Light and moderate grazing maintained a balanced and resistant soil structure, with the surface soil being more fragile than the subsurface soil. Heavy and extreme grazing led to severe structural degradation, with the subsurface soil being more fragile than the surface soil. Soil aggregate stability was jointly regulated by elevation, soil properties, root biomass, nitrogen forms, mineralization and microbial biomass. In conclusion, from the perspective of soil structural stability and sustainable utilization, light and moderate grazing represent the optimal utilization mode for the alpine meadows of the Qilian Mountains. This mode not only maintains the structural stability of subsurface soil aggregates but also balances biological cementation and physical disturbance, thus avoiding the insufficient water stability under no grazing and the risk of structural fragmentation under heavy or extreme grazing. Environmental and biological factors mediated the divergent responses of mechanical and water stability to different grazing intensities. The findings of this study provide a scientific basis and new insights for the rational grazing management and soil conservation of alpine meadows in the Qilian Mountains. Full article

Controlled Environment Agriculture (CEA) has become a key driver of vertical farming (VF), offering innovative solutions for the sustainable production of ornamental plants in urban environments with limited arable land. This review examines recent advances in VF technologies and their applications in foliage and flowering ornamental plant production. The literature indicates that precise environmental control, including optimized LED lighting spectra, hydroponic and aeroponic nutrient delivery, and automated climate regulation, can significantly enhance plant growth, morphological characteristics, color intensity, and overall market quality of ornamental species. In addition, VF systems demonstrate substantial reductions in water consumption, pesticide use, and land requirements compared with conventional cultivation methods. However, several challenges remain, including high-energy demand, economic feasibility, and the need for crop-specific environmental optimization for different ornamental species. This review synthesizes current research on VF systems, highlights the integration of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and data-driven management tools, and evaluates their potential to improve production efficiency and sustainability in ornamental horticulture. Overall, vertical farming represents a promising approach for high-quality ornamental plant production, although further research is required to optimize energy efficiency and cultivation protocols for diverse ornamental crops. Full article

Background: In cancer surgery, resection of the primary tumor and regional lymph nodes (LNs) is critical. Adequate LN examination is essential to detect metastasis, which determines the cancer stage. Fluorescence emission allows for visual differentiation and rapid monitoring of LNs. Methods: Cancer tissue is stained with a fluorescent dye (indocyanine green, ICG) to identify LNs. Fluorescence is induced from the stained LNs using LED light, and a photosensor coupled with a speaker detects the fluorescence signal and triggers an audible alarm. Filters are applied to prevent false alarms. Results: Upon LN detection, an alarm is emitted from the speaker, and the results are recorded using the LED and a digital multimeter (DMM). In clinical trials, ICG is injected to induce LN fluorescence staining, followed by LED irradiation to induce the fluorescent wavelength and verify LN imaging. Discussion: In clinical trials, ICG stains both LNs and blood vessels, which may lead to false positives. To address this limitation, artificial intelligence algorithms can be trained to specifically identify LNs. Conclusions: Detection of fluorescence wavelengths via photosensors allows for rapid identification of LNs, confirmed through an audible alarm, thereby reducing surgical time. This method shows potential for broad application in cancer surgery. Full article

Artificial optical radiation, spanning from 100 nm to 1 mm, encompasses ultraviolet (UV) and infrared (IR) light. UV light is well known for its risks on the skin and eyes. Recently, there has been growing interest in light at 405 nm (violet-blue light, VBL) due to its antimicrobial properties and perceived safety for mammalian cells when administered in controlled amounts. This research delved into the impact of 405 nm VBL on corneal and retinal pigment epithelial cell cultures. ARPE-19 and corneal BCE C/D 1b cells were exposed to VBL for varying doses, according at different exposure times, to evaluate cell viability, oxidative stress levels and apoptotic indicators. A 3D printed prototype with 14 LEDs centred at 405 nm wavelength was used to ensure uniform distribution of light during exposure. Cell viability was assessed using the MTT assay, measurement of oxygen species (ROS) production was carried out, and Western blot analysis was employed to study catalase and SOD-1 expression and apoptotic marker activation. Exposure to 405 nm VBL for both term (3 h) and prolonged durations (9 h) led to a weak decrease in cell viability in ARPE-19 cells, whereas the effect on BCE C/D 1b cells was negligible. There was no increase in ROS production, with catalase and SOD-1 expression remaining stable, suggesting no pro-oxidative stress effects in these models. Moreover, no activation of caspase-3 and accumulation of cytochrome C were found. Based on our results, exposure to 405 nm light at regulated levels does not pose a threat to the viability of the tested cell lines and does not lead to oxidative stress and apoptosis under these conditions. These results suggest a favourable cytocompatibility profile for these specific ocular cell models, laying a foundation for further investigations into its ocular safety. Full article

Light quality is a critical regulatory factor for the growth and nutritional quality of hydroponic lettuce (Lactuca sativa L.), and red–blue combined light serves as a key artificial light source for protected horticulture. This study aimed to investigate the effects of different red–blue (R:B) light ratios on the growth, photosynthetic pigment content, nutritional quality, antioxidant capacity, and mineral nutrient content and accumulation of hydroponic lettuce. Lettuce was cultivated under four R:B light treatments (CK: pure red light, 100:0; T1: 90:10; T2: 80:20; and T3: 60:40) with a uniform photosynthetic photon flux density of 350 µmol m⁻²s⁻¹ and a 12 h photoperiod. The results showed that all red–blue combined light treatments significantly improved the above physiological and nutritional indices compared with monochromatic red light (CK), with the fresh weight increased by 0.73 to 0.78 times and different R:B ratios inducing distinct tissue-specific and element-specific responses in lettuce. Specifically, T3 (60:40) exhibited the highest root dry weight (0.57 ± 0.02 g plant⁻¹), inhibited excessive leaf elongation to form a compact plant architecture, and its chlorophyll a and b contents increased significantly by 1.6 and 2.25 times compared with CK, respectively. Furthermore, T3 markedly enhanced the accumulation of soluble sugar (0.36 times higher), soluble protein (1.16 times higher), and vitamin C (4.09 times higher), reduced the nitrate content to 0.58 times that of CK, and showed the highest antioxidant capacity (polyphenol content and DPPH free radical scavenging rate), with antioxidant traits positively correlated with the blue light proportion. In contrast, T2 (80:20) effectively promoted plant biomass accumulation and exhibited the most balanced mineral nutrient profile, with significant increases in nitrogen, calcium, and magnesium accumulation, and it also upregulated chlorophyll synthesis to enhance carbon assimilation. T1 (90:10) had moderate regulatory effects on both lettuce growth and nutritional quality and was favorable for potassium accumulation in lettuce tissues. These findings clarify the differential regulatory mechanisms of red–blue light ratios on hydroponic lettuce and provide a theoretical basis for the precise configuration of LED lighting in greenhouse lettuce production. Lettuce producers can select specific R:B ratios according to actual cultivation demands, and the regulatory effects of such light ratios on red leaf lettuce varieties merit further exploration. Full article

Compact multi-channel airborne radar stations increasingly rely on an LED-based visible light communication (VLC) service link under radio-frequency spectrum restrictions and strict end-to-end delay constraints. Despite the directional nature of optical links, the VLC channel remains vulnerable to active optical interference and signal injection; furthermore, when an AI-enabled integrity monitor is embedded into the receiver, the AI decision layer becomes a direct target of evasion and online poisoning. This paper proposes a lightweight, interpretable AI-based attack detection architecture in which a Poisson photon-counting observation model is used to form physically consistent features over the preamble and control-sequence interval, while the final decision is produced by an AI ensemble combining a monotonic logistic detector and a one-class detector. The considered threat profile includes sustained illumination and synchronized flashes (jamming/blinding), spoofing via false preambles, replay of recorded fragments, and online data poisoning during self-calibration. The adequacy of solutions is assessed using the detection probability P_D (ensemble: P_D ≥ 0.90 for DC-jamming mean-count increment Δλ_DC ≈ 7.56, pulsed-interference mean-count increment Δλ_pulse ≈ 12.89, and spoofing signal-scaling factor α ≈ 1.02), the false-alarm probability P_FA = 0.045, and the per-packet end-to-end latency (bounded by the observation-window duration LΔT = 20 μs, where window length L = 20 and interval duration ΔT = 1 μs), which confirms real-time CPU operation without GPU acceleration. Full article

Accurate indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GPS-denied environments, especially for small-object detection and under low-light conditions. We propose Robust Wavelet-Aware YOLO (RWA-YOLO), a vision-based detection framework that integrates a wavelet-aware attention fusion module with a dual multi-path aggregation mechanism to enhance small-object detection and multi-scale feature representation. UAV-mounted LEDs are utilized to ensure robust visual perception in low-light indoor scenarios. The UAV’s three-dimensional position is estimated through multi-view geometric triangulation without relying on external beacons or artificial markers. Beyond static localization, the system is validated under dynamic flight conditions, demonstrating smooth and temporally coherent trajectory reconstruction suitable for real-time control loops (update rate $\approx 25\mathrm{FPS}$). Extensive experiments in real indoor environments achieve centimeter-level localization accuracy (root mean square error: 9.9 mm, 95th percentile error: 13.5 mm), outperforming state-of-the-art vision-based methods and achieving accuracy comparable to or better than representative hybrid ultra-wideband–vision systems reported in the literature. These results confirm the effectiveness, robustness, and real-time capability of RWA-YOLO for indoor UAV navigation in constrained environments. Full article

The rapid expansion of algorithmic systems has transformed cognition into an increasingly distributed and collective enterprise, giving rise to what can be described as cognitive assemblages, dynamic constellations of humans, institutions, data infrastructures, and artificial agents. This paper traces the historical and conceptual evolution that has led to this shift. First, we show how cognition, once conceived as the property of autonomous individuals, has progressively become embedded in socio-technical networks in which algorithmic processes participate as co-agents. Second, we revisit the progressive awareness of human cognitive limits, from bounded rationality to contemporary theories of extended mind. These frameworks anticipate and help explain today’s hybrid cognitive ecologies. Third, we assess the philosophical implications for Enlightenment ideals of autonomy, rationality, and self-governance, showing how these concepts must be reinterpreted in light of pervasive algorithmic intermediation. Finally, we examine global initiatives that seek to integrate augmented cognitive capacities into large-scale cybernetic forms of societal coordination, ranging from digital platforms and data spaces to AI-driven governance systems. These developments offer new opportunities for steering complex societies under conditions of globalization, environmental disruption, and the rise of autonomous intelligent systems, yet they also raise profound questions regarding control, accountability, and democratic legitimacy. We argue that understanding cognitive assemblages is essential to designing socio-technical systems capable of supporting collective intelligence while preserving human values in an era of accelerating complexity. Full article

Solar panels are an important device converting light energy into electricity not only from the sun but also from artificial light sources such as light emitting diodes (LEDs) or lasers. Recent advances in solar cell technologies enable them to be flexible, allowing them to be attached to things with different sizes and shapes. Therefore, it is challenging for AI-equipped systems to automatically recognize and distinguish flexible solar panels from other surrounding objects in realistic, complicated environments. Traditional recognition methods usually suffer from low recognition accuracy and high computational cost. Hence, this paper proposes a deep learning method for solar panel recognition using a complete work flow that includes data acquisition and dataset construction, YOLOv8-based model training, real-time solar panel recognition, and extended functionality. The proposed method demonstrates the accurate identification of realistic flat and flexible solar panels, including bent and partially shaded panels, with a mean average precision (mAP)@0.5 of 99.4% and an mAP@0.5:0.95 of 90.4%. The Pareto front for the multi-objective loss function minimization problem is also investigated to determine the optimal set of weighting parameters for the loss components. Furthermore, another functionality is added to detect the sizes of different solar panels if multiple ones co-exist. These features provide a promising foundation for further usage of the proposed deep learning approach to recognize flexible solar panels in realistic contexts. Full article

Red light suppresses melatonin and helps in improving reproductive efficiency in donkeys during the non-breeding season (November–February). In this study, the effects of red, blue, and white LED light were assessed. For this purpose, 40 adult Dezhou donkeys were divided into 4 groups, each receiving equal treatment for 40 days. All groups received 8 h of natural light. Additionally, the red group received 6 h of 50 lux of red LED light (468 nm) directed at a single eye. The blue group received 6 h of 50 lux of blue LED light (468 nm). The white group received 6 h of 50 lux of white LED light (468 nm), and the control group received only 8 h of natural sunlight. Blood samples were collected on the 21st, 28th, 34th, and 40th day of the experiment to analyze plasma hormone concentrations of progesterone (P4), Inhibin B (INH-B), Testosterone (T), Activin-A, Luteinizing Hormone (LH), Follicle Stimulating Hormone (FSH), Antimullerian Hormone (AMH), and Melatonin. In conclusion, red LED light directed at one eye showed the most promising results, elevating plasma hormone concentrations of testosterone (T), Activin A, LH, FSH, AMH, and melatonin. Full article

Artificial light provides many benefits to humankind, allowing fundamental activities to continue during night; however, it also poses multiple risks to humans and wildlife and is recognized as a significant driver of global environmental change. Changes in natural light/darkness cycles caused by artificial light at night (ALAN) can affect amphibians, the most threatened vertebrate group globally. The aim of this study was to determine the effects of long-term exposure to constant nighttime light on the morphology of the thyroid glands of Triturus ivanbureschi metamorphosed juveniles using histological analysis. A cool LED light with a color temperature of 6000 K was selected, as this spectrum is commonly used in outdoor lighting. Larvae were raised at a natural day–night light regime. After metamorphosis, juveniles were randomly divided into a control group maintained under natural dark nighttime conditions (<0.1 lux) and a treatment group exposed to LED light (30 lux) at night for 60 days. Standard histological techniques (H&E) and immunohistochemical (IHC) staining were used to examine the thyroid glands. There were no significant differences in the absolute volume densities between the light-treatment and control groups; however, subtle morphological variations were observed. Immunohistochemical analysis of Tg immunostaining revealed a significant difference between the light-treatment and control groups, indicating that the thyroid gland of newts exposed to light has a stronger signal, suggesting the accumulation of thyroglobulin at the apical surface of the follicular cells. As LED lighting continues to expand globally, understanding how different light spectra, intensities, and exposure durations influence thyroid function, particularly during early life stages, remains an important direction for future research. Full article

This paper investigates an artificial intelligence (AI)-enhanced visible light communication (VLC) system for body area networks (BANs) based on a hybrid modulation framework that jointly employs quadrature amplitude modulation (QAM) and pulse-position modulation (PPM). The dual-modulation strategy leverages the high spectral efficiency of QAM together with the robustness of PPM to light-emitting diode (LED) nonlinearity and timing distortions, enabling simultaneous high-rate and reliable communication, two essential requirements in BAN applications. To address the nonlinear response of light-emitting diodes and the variability in indoor optical channels, the system integrates classical predistortion techniques with a deep learning equalizer combining convolutional neural network (CNN)–transformer layers. This hybrid model captures both local and long-range distortion patterns, improving symbol reconstruction for both modulation branches. The study further examines pilot-assisted equalization and adaptive bit loading, showing that these strategies strengthen link robustness under diverse channel conditions while enhancing spectral efficiency. The proposed architecture demonstrates that combining dual modulation with AI-driven equalization and adaptive transmission strategies leads to a more resilient and efficient VLC system, well-suited for the dynamic constraints of wearable and body-centric communication environments. Full article

Light exposure profoundly influences human emotions and physiology. Yet, adolescents spend considerable time under artificial indoor lighting. Reduced daytime light exposure delays the circadian clock, negatively affecting sleep, cognition, and mood. This pilot study examined whether 470–490 nm enhanced LED lighting modulates mood, sleep quality, and attention among 65 male Korean high school students (mean age = 15.4 years) who participated in a two-week intervention. Both groups were exposed to natural daylight, but the experimental group additionally used LED lighting enriched in the 470–490 nm wavelength range, whereas the control group used LED lighting without modified spectral characteristics. Students were exposed to the assigned lighting from 08:00 to 17:00 during regular school hours for two consecutive weeks. To evaluate the effects of the two-week intervention, pre- and post-assessments included the Beck Depression Inventory (BDI-II), the Richards–Campbell Sleep Questionnaire (RCSQ), the Epworth Sleepiness Scale (ESS), the Perceived Stress Scale (PSS), and the Frankfurter Attention Inventory (FAIR), administered twice at each assessment point. The linear mixed-effect model showed a significant time × group interaction for line errors in the first FAIR trial (F (1, 52) = 5.21, p = 0.027, η² partial = 0.09), suggesting a greater relative reduction in attentional errors in the experimental group compared with the control group. No significant effects were observed for sleep- or mood-related outcomes. These results indicate the potential relevance of wavelength-optimized lighting in educational settings where sustained attention is critical. Future studies with larger samples and longer interventions are required to confirm and extend these findings. Full article

Visible-Near-Infrared (Vis-NIR) spectroscopy, spanning approximately 400 to 2500 nm, is an innovative technology with growing relevance for diagnostics performed at the point of care (POC). This review explores the potential of Vis-NIR in veterinary medicine, highlighting its advantages over complex techniques like Raman and Fourier transform infrared spectroscopy (FTIR) by being rapid, non-invasive, reagent-free, and compatible with miniaturized, portable devices. The methodology involves directing a broadband light source, often using LEDs, toward the sample (e.g., blood, urine, faeces), collecting spectral information related to molecular vibrations, which is then analyzed using chemometric methods. Successful veterinary applications include hemogram analysis in dogs, cats, and Atlantic salmon, and quantifying blood in ovine faeces for parasite detection. Key limitations include spectral interference from strong absorbers like water and hemoglobin, and the limited penetration depth of light. However, combining Vis-NIR with Self-Learning Artificial Intelligence (SLAI) is shown to isolate and mitigate these multi-scale interferences. Vis-NIR spectroscopy serves as an important complement to centralized laboratory testing, holding significant potential to accelerate clinical decisions, minimize stress on animals during assessment, and improve diagnostic capabilities for both human and animal health, aligning with the One Health concept. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article