Search Results (977)

This study presents the development and evaluation of an automated ultraviolet-C irradiation system for maize seed treatment, emphasizing disinfection performance, environmental control, and vision-based monitoring. The system features dual 8-watt ultraviolet-C lamps, sensors for temperature and humidity, and an air extraction unit to regulate the microclimate of the chamber. Without air extraction, radiation stabilized within one minute, with internal temperatures increasing by 5.1 °C and humidity decreasing by 13.26% over 10 min. When activated, the extractor reduced heat build-up by 1.4 °C, minimized humidity fluctuations (4.6%), and removed odors, although it also attenuated the intensity of ultraviolet-C by up to 19.59%. A 10 min ultraviolet-C treatment significantly reduced the fungal infestation in maize seeds by 23.5–26.25% under both extraction conditions. Thermal imaging confirmed localized heating on seed surfaces, which stressed the importance of temperature regulation during exposure. Notable color changes ($\Delta E>2.3$) in treated seeds suggested radiation-induced pigment degradation. Ultraviolet-C intensity mapping revealed spatial non-uniformity, with measurements limited to a central axis, indicating the need for comprehensive spatial analysis. The integrated computer vision system successfully detected seed contours and color changes under high-contrast conditions, but underperformed under low-light or uneven illumination. These limitations highlight the need for improved image processing and consistent lighting to ensure accurate monitoring. Overall, the chamber shows strong potential as a non-chemical seed disinfection tool. Future research will focus on improving radiation uniformity, assessing effects on germination and plant growth, and advancing system calibration, safety mechanisms, and remote control capabilities. Full article

Atmospheric turbulence severely limits the stability and reliability of free-space optical (FSO) uplinks by inducing wavefront distortions and random intensity fluctuations. This study investigates the use of reinforcement learning (RL) with beacon-based feedback for adaptive beam shaping in a spatial light modulator (SLM)-controlled FSO link. The RL agent dynamically adjusts phase patterns to maximize received signal strength, while the beacon channel provides turbulence estimates that guide the optimization process. Experiments under low, moderate, and high turbulence levels demonstrate that incorporating beacon feedback can enhance link stability in severe conditions, reducing signal variability and suppressing extreme fluctuations. In low-turbulence scenarios, the performance is comparable to non-feedback operation, whereas under high turbulence, beacon-assisted control consistently achieves lower coefficients of variation and improved bit error rate (BER) performance. Under high turbulence replay experiments—where the best-performing RL-learned phase patterns are reapplied without learning—further show that configurations trained with feedback retain robustness, even without real-time turbulence measurements under high turbulence. These results highlight the potential of integrating contextual feedback with RL to achieve turbulence-resilient and stable optical uplinks in dynamic atmospheric environments. Full article

Cave systems serve as key interfaces connecting surface and underground carbon cycles, and research on their carbon dynamics provides a unique perspective for revealing the mechanisms of carbon transport and transformation in karst critical zones. In this study, we established a multi-factor monitoring framework spanning the atmosphere–soil–cave continuum and associated meteorological conditions, continuously recorded cave microclimate parameters (temperature, relative humidity, atmospheric pressure, and cave winds) and CO₂ concentrations across atmospheric–soil–cave interfaces, and employed stable carbon isotope (δ¹³C) tracing in Mahuang Cave, a typical karst cave in southwestern China, from 2019 to 2023. The results show that the seasonal amplitude of atmospheric CO₂ and its δ¹³C is small, while soil–cave CO₂ and δ¹³C fluctuate synchronously, exhibiting “high concentration-light isotope” signatures during the rainy season and the opposite pattern during the dry season. Cave CO₂ concentrations drop by about 29.8% every November. Soil CO₂ production rates are jointly controlled by soil temperature and volumetric water content, showing a threshold effect. The δ¹³C response exhibits nonlinear behavior due to the combined effects of land-use type, vegetation cover, and soil texture. Quantitative analysis establishes atmospheric CO₂ as the dominant source in cave systems (66%), significantly exceeding soil-derived contributions (34%). At diurnal, seasonal, and annual scales, carbon-source composition, temperature and precipitation patterns, ventilation effects, and cave structure interact to control the rhythmic dynamics and spatial gradients of cave microclimate, CO₂ levels, and δ¹³C signals. Our findings enhance the understanding of carbon transfer processes across the karst critical zone. Full article

The size and mass of fish are crucial parameters in aquaculture management. However, existing research primarily focuses on conducting fish size and mass estimation under ideal conditions, which limits its application in actual aquaculture scenarios with complex water quality and fluctuating lighting. A non-contact size and mass measurement framework is proposed for complex underwater environments, which integrates the improved FishKP-YOLOv11 module based on YOLOv11, stereo vision technology, and a Random Forest model. This framework fuses the detected 2D key points with binocular stereo technology to reconstruct the 3D key point coordinates. Fish size is computed based on these 3D key points, and a Random Forest model establishes a mapping relationship between size and mass. For validating the performance of the framework, a self-constructed grass carp dataset for key point detection is established. The experimental results indicate that the mean average precision (mAP) of FishKP-YOLOv11 surpasses that of diverse versions of YOLOv5–YOLOv12. The mean absolute errors (MAEs) for length and width estimations are 0.35 cm and 0.10 cm, respectively. The MAE for mass estimations is 2.7 g. Therefore, the proposed framework is well suited for application in actual breeding environments. Full article

Structured Light LiDAR is susceptible to lens scattering and temperature fluctuations, resulting in some level of distortion in the captured point cloud image. To address this problem, this paper proposes a high-performance 3D point cloud Least Mean Square filter based on Decision Tree, which is called the D−LMS filter for short. The D−LMS filter is an adaptive filtering compensation algorithm based on decision tree, which can effectively distinguish the signal region from the distorted region, thus optimizing the distortion of the point cloud image and improving the accuracy of the point cloud image. The experimental results clearly demonstrate that our proposed D−LMS filtering algorithm significantly improves accuracy by optimizing distorted areas. Compared with the 3D point cloud least mean square filter based on SVM, the accuracy of the proposed D−LMS filtering algorithm is improved from 86.17% to 92.38%, the training time is reduced by 1317 times and the testing time is reduced by 1208 times. Full article

In order to solve the problem of the efficiency reduction and complex manufacturing of traditional grating couplers under environmental temperature fluctuations, a Si₃N₄ high-efficiency grating coupler integrating a distributed Bragg reflector (DBR) and thermo-optical tuning layer is proposed. In this paper, the double-layer DBR is used to make the down-scattered light interfere with other light and reflect it back into the waveguide. The finite difference time domain (FDTD) method is used to simulate and optimize the key parameters such as grating period, duty cycle, incident angle and cladding thickness, achieving a coupling efficiency of −1.59 dB and a 3 dB bandwidth of 106 nm. In order to further enhance the temperature stability, the amorphous silicon (a-Si) thermo-optical material layer and titanium metal serpentine heater are embedded in the DBR. The reduction in coupling efficiency caused by fluctuations in environmental temperature is compensated via local temperature control. The simulation results show that within the wide temperature range from −55 °C to 150 °C, the compensated coupling efficiency fluctuation is less than 0.02 dB, and the center wavelength undergoes a blue shift. This design is compatible with complementary metal-oxide-semiconductor (CMOS) processes, which not only simplifies the fabrication process but also significantly improves device stability over a wide temperature range. This provides a feasible and efficient coupling solution for photonic integrated chips in non-temperature-controlled environments, such as optical communications, data centers, and automotive systems. Full article

This study proposes a hierarchical drive control system to ensure speed stability in dual-motor tracked vehicles operating under complex terrain and heavy-load conditions. The system adopts a two-layer structure. At the upper level, the sliding mode controller is designed for both longitudinal speed regulation and yaw rate control, thereby stabilizing straight line motion and the steering maneuvers. At the lower level, a synchronization mechanism aligns the velocities of the two motors, enhancing the vehicle’s robustness against speed fluctuations. Simulation results demonstrate that, across both heavy load and light load bulldozing scenarios, the deviation between the controller output and the reference command remains within 5$\%$. These findings confirm the accuracy of the control implementation and validate the effectiveness of the proposed framework. Full article

Indirect detection phase control algorithms, such as the dithering algorithm and the stochastic parallel gradient descent algorithm (SPGD), have simple system structures and are applicable to filled-aperture coherent combinations with high efficiency. The performances of these algorithms are limited when applied to a coherent combination of pulsed fiber lasers with a low repetition rate (≤5 kHz). Firstly, due to the overlap of the phase noise frequency and repetition rate, conventional algorithms cannot effectively distinguish the phase noise from the pulse fluctuation, and directly applying filtering will result in the phase information being filtered out. Secondly, if the pulse fluctuation is ignored and only the continuous part of the phase information is utilized, it relies on the presetting of conditions to separate the pulse from the continuous part and loses the phase information of the pulse part. In this article, we propose a waveform self-referencing algorithm (WSRA) based on a two-channel near-infrared laser coherent combination system to overcome the above challenges. Firstly, by modelling a self-referencing waveform, a nonlinear scaling operation is performed on the combined signal to generate a pseudo-continuous signal, which removes the intrinsic pulse fluctuation while preserving the phase noise information. Secondly, the phase control signal is calculated based on the pseudo-continuous signals after parallel perturbation. Finally, the phase noise is corrected by optimization. The results show that our method effectively suppresses the waveform fluctuation at a 5 kHz repetition rate, the light intensity reaches an ideal value (0.9939 $I_{\max }$), and the root-mean-square (RMS) phase error is only 0.0130 $\lambda$. This method does not require the presetting of pulse detection thresholds or conditions, and solves the challenge of coherent combination at a low repetition rate, with adaptability to different pulse waveforms. Full article

Kyoho grapes are rich in nutrients, yet their susceptibility to spoilage poses a significant challenge for postharvest preservation. While light treatment can improve fruit quality and carbohydrate metabolism in postharvest grapes, the potential benefits of combining light treatment with modified atmosphere packaging (MAP) remain unexplored. A preservation method that combined red and blue light treatments with MAP has been developed to enhance postharvest fruit quality and carbohydrate metabolism in Kyoho grapes. Our study showed that this combined treatment significantly increased postharvest fruit hardness, as well as total soluble solids (TSS) and fruiting pedicel water content. It also improved the activities of superoxide dismutase (SOD) and phenylalanine ammonialyase (PAL) and increased the antioxidant, anti-browning capacity. This composite treatment slowed down sucrose decomposition by regulating the activities of key enzymes of carbohydrate metabolism (sucrose synthase (SS), sucrose phosphate synthase (SPS), neutral invertase (NI) and acid invertase (AI)). After 60 days of storage, the glucose, fructose, and sucrose contents of the RP group increased by 13.4%, 30.2%, and 18.1%, respectively, compared to the CK group (p < 0.05). In summary, light combined with modified atmosphere packaging significantly improved the physicochemical properties and sugar metabolism of postharvest grapes. The results indicated that the optimal treatment condition was continuous red-light irradiation combined with MAP. The hardness, TSS content, VC content and glucose content of Kyoho grapes in this treatment group were the best in all treatment groups. Full article

Abnormal shutdown detection in oilfield pumping units presents significant challenges, including degraded image quality under low-light conditions, difficulty in detecting small or obscured targets, and limited capabilities for dynamic state perception. Previous approaches, such as traditional visual inspection and conventional image processing, often struggle with these limitations. To address these challenges, this study proposes an intelligent method integrating multi-scale feature enhancement and low-light image optimization. Specifically, a lightweight low-light enhancement framework is developed based on the Zero-DCE algorithm, improving the deep curve estimation network (DCE-Net) and non-reference loss functions through training on oilfield multi-exposure datasets. This significantly enhances brightness and detail retention in complex lighting conditions. The DAFE-Net detection model incorporates a four-level feature pyramid (P3–P6), channel-spatial attention mechanisms (CBAM), and Focal-EIoU loss to improve localization of small/occluded targets. Inter-frame difference algorithms further analyze motion states for robust “pump-off” determination. Experimental results on 5000 annotated images show the DAFE-Net achieves 93.9% mAP@50%, 96.5% recall, and 35 ms inference time, outperforming YOLOv11 and Faster R-CNN. Field tests confirm 93.9% accuracy under extreme conditions (e.g., strong illumination fluctuations and dust occlusion), demonstrating the method’s effectiveness in enabling intelligent monitoring across seven operational areas in the Changqing Oilfield while offering a scalable solution for real-time dynamic anomaly detection in industrial equipment monitoring. Full article

The widespread use of chemical fertilizers has raised concerns because of their environmental impacts, including soil degradation, water contamination, and biodiversity loss. The integration of organic amendments into agricultural systems provides a sustainable alternative. This study investigates the molecular and physiological traits underlying rapeseed responses to organic amendments based on poultry and plant material mixed with the soil. Plant growth, CO₂ assimilation, metabolic, proteomic, and soil microbial analyses were performed. Results show a significant stimulation of plant growth (100%) and leaf biomass (200%) following amendment application. This response is attributed to enhanced efficiency in light energy use for CO₂ fixation, increased carbohydrate and amino acid production, and improved biomass and yield. Increased upregulation of proteins and antioxidant metabolites such as abscisic acid (ABA) indicates an enhanced capacity to cope with oxidative stress. The amendments activated metabolic mechanisms that improved redox balance and homeostasis, including more efficient light energy use and enhanced antioxidant synthesis. Furthermore, the organic amendments promoted Actinobacteria in the soil, contributing to improved soil quality. These metabolic responses may enhance plant resilience against oxidative stress and environmental fluctuations. These findings highlight promising strategies to enhance crop productivity and resilience, advancing sustainable agriculture and strengthening future food security. Full article

SiCp/Al composites (Silicon Carbide Particle-Reinforced Aluminum Matrix Composites), due to their light weight, high strength, and superior wear resistance, are extensively utilized in aerospace and other sectors; nonetheless, they are susceptible to tool wear and surface imperfections during machining, which negatively impact overall machining performance. Supercritical carbon dioxide minimal quantity lubrication (SCCO₂-MQL) is an environmentally friendly and efficient lubrication method that significantly improves interfacial lubricity and thermal stability. Nonetheless, current lubrication models are predominantly constrained to gas–liquid two-phase scenarios, hindering the characterization of the three-phase lubrication mechanism influenced by the combined impacts of SCCO₂ phase transition and ultrasonic vibration. This study formulates a lubricant film thickness model that incorporates droplet atomization, capillary permeation, shear spreading, and three-phase modulation while introducing a pseudophase enhancement factor β_ps(p,T) to characterize the phase fluctuation effect of CO₂ in the critical region. Simulation analysis indicates that, with an ultrasonic vibration factor Af = 1200 μm·kHz, a lubricant flow rate Q_f = 16 mL/h, and a pressure gradient Δp_tot = 6.0 × 10⁵ Pa/m, the lubricant film thickness attains its optimal value, with Δp_tot having the most pronounced effect on the film thickness (normalized sensitivity S = 0.488). The model results align with the experimental trends, validating its accuracy and further elucidating the nonlinear regulation of the film-forming process by various parameters within the three-phase synergistic lubrication mechanism. This research offers theoretical backing for the enhancement of performance and the expansion of modeling in SCCO₂-MQL lubrication systems. Full article

Furniture surfaces are prone to the accumulation of bacteria, fungi and other micro-organisms, especially in humid environments such as kitchens and bathrooms. The antimicrobial treatment of coatings has been demonstrated to enhance the performance of wood, prolong its service life, and improve hygiene and safety. Consequently, by investigating the most effective preparation process for antimicrobial microcapsules and incorporating them into the coating, the coating can be endowed with antimicrobial properties, thereby expanding its application range. Microcapsules were prepared using a composite wall material consisting of chitosan (CS) and Arabic gum (AG), with tea tree essential oil (TTO) serving as the core material. The best CS-AG coated TTO microcapsules were prepared when the core–wall ratio was 1.2:1, the emulsifier concentration was 2%, the pH was 3, and the mass ratio of AG to CS (m_AG:m_CS) was 3:1. The m_AG:m_CS was identified as the most significant factor affecting the microcapsule yield and encapsulation rate. With the increase in m_AG:m_CS, the antimicrobial rate of the coating against Escherichia coli (E. coli) exhibited a trend of first rising and then falling, while the antimicrobial rate against Staphylococcus aureus (S. aureus) demonstrated a trend of first rising, then falling, and then rising again. The colour difference (ΔE) and gloss exhibited an overall downward trend, the light loss rate demonstrated a fluctuating upward trend, and the roughness exhibited a trend of first falling and then rising. The visible light band transmittance exhibited minimal variation, ranging from 86.43% to 92.76%. Microcapsule 14# (m_AG:m_CS = 3:1) demonstrated remarkable antimicrobial properties (E. coli 65.55%, S. aureus 73.29%), exceptional optical characteristics (light transmittance 92.12%, 60° gloss 24.0 GU), and notable flexibility (elongation at break 18.10%, modulus 0.10 GPa). The waterborne coating was modified by microcapsule technology, thus endowing the coating with antimicrobial properties and concomitantly broadening the scope of application of antimicrobial microcapsules. Full article

Smartphone-based strain monitoring of structural components is an emerging approach to structural health monitoring. However, the existing techniques suffer from limited accuracy and poor cross-device adaptability. This study aims to optimize the smartphone-based Micro Image Strain Sensing (MISS) method by replacing the traditional Connected Component Labeling (CCL) algorithm with the arc-support line segments (ASLS) algorithm, thereby significantly enhancing the stability and adaptability of circle detection in micro-images captured by diverse smartphones. Additionally, this study evaluates the impact of lighting conditions and lens distortion on the optimized MISS method. The experimental results demonstrate that the ASLS algorithm outperforms CCL in terms of recognition accuracy (maximum error of 0.94%) and cross-device adaptability, exhibiting greater robustness against color temperature and focal length variations. Under fluctuating lighting conditions, the strain measurement noise remains within ±0.5 με and with a maximum error of 7.0 με compared to LVDT measurements, indicating the strong adaptability of the optimized MISS method to external light changes. Barrel distortion in microscopic images induces a maximum pixel error of 5.66%, yet the final optimized MISS method achieves highly accurate strain measurements. The optimized MISS method significantly improves measurement stability and engineering applicability, enabling effective large-scale implementation for strain monitoring of civil infrastructure. Full article

Liquid–liquid phase separation (LLPS) is a fundamental biophysical process in which proteins and nucleic acids dynamically demix from the cellular milieu to form membraneless organelles (MLO) with liquid-like properties. Environmental cues, such as light, temperature fluctuations, and pathogen interactions, induce LLPS of fungal proteins with intrinsically disordered regions (IDRs) or multimerization domains, thereby regulating fungal hyphal growth, sexual reproduction, pathogenesis, and adaptation. Recently, LLPS has emerged as a powerful tool for biomolecular research, innovative biotechnological application, biosynthesis and metabolic engineering. This review focuses on the current advances in environmental cue-triggered fungal condensates assembled by LLPS, with a focus on their roles in regulating the fungal physical biology and cellular processes including transcription, RNA modification, translation, posttranslational modification process (PTM), transport, and stress response. It further discusses the strategies of engineering synthetic biomolecular condensates in microbial cell factories to enhance production and metabolic efficiency. Full article