Search Results (350)

The large yellow croaker (Larimichthys crocea) is susceptible to microbial contamination during storage due to its high protein and moisture contents. This study was designed to find a new way to reduce bacteria in large yellow croakers by combining slightly acidic electrolyzed water (SAEW) with nano-bubble (NB) technology. Exploring the effects of available chlorine concentration (ACC), processing time, and water temperature on the bacteria reduction effect of the SAEW-NB treatment for large yellow croakers. Also, the effects of the SAEW-NB combined treatment on sensory evaluation, total viable counts (TVCs), total volatile basic nitrogen (TVB-N), texture, taste profile, and volatile flavor compounds of large yellow croakers were analyzed during the storage period at 4 °C. The results show that the SAEW-NB treatment achieved significantly enhanced microbial reduction compared to individual treatments. Under the conditions of a 4 °C water temperature, 40 mg/L ACC, and 15 min treatment, the SAEW-NB treatment inhibited the increases in physical and chemical indexes such as TVC and TVB-N, maintained the fish texture, and delayed the production of off-flavor volatiles such as aldehydes, alcohols, esters, and ketones, compared with the control group (CG) during storage at 4 °C. In conclusion, the SAEW-NB treatment could better retard fish spoilage, extending the shelf life by approximately 2 days. It might be a promising new industrial approach for large yellow croakers’ storage. Full article

In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem. Full article

Biochar has garnered considerable attention as a soil amendment due to its unique physicochemical properties. Its application not only enhances soil carbon sequestration but also improves nutrient availability. Incorporating biochar into soil is regarded as a promising strategy for mitigating global climate change while delivering substantial environmental and agricultural benefits. In this study, biochar was extracted from Siraitia grosvenorii and subsequently modified through alkali treatment. A laboratory incubation experiment was conducted to assess the effects of unmodified (JMC) and modified (GXC) biochar, applied at different rates (1%, 2%, and 4%), on organic carbon mineralization and soil nutrient dynamics. Results indicated that, at equivalent application rates, JMC-treated soils exhibited lower CO₂ emissions than those treated with GXC, with emissions increasing alongside biochar dosage. After the incubation, the 1% JMC treatment exhibited a mineralization rate of 17.3 mg·kg⁻¹·d⁻¹, which was lower than that of the control (CK, 18.8 mg·kg⁻¹·d⁻¹), suggesting that JMC effectively inhibited organic carbon mineralization and reduced CO₂ emissions, thereby contributing positively to carbon sequestration in Siraitia grosvenorii farmland. In contrast, GXC application significantly enhanced soil nutrient levels, particularly increasing available phosphorus (AP) by 14.33% to 157.99%. Furthermore, partial least squares structural equation modeling (PLS-SEM) identified application rate and pH as the key direct factors influencing soil nutrient availability. Full article

In Taiwan, hydro-meteorological data are fragmented across multiple agencies, limiting the effectiveness of coordinated flood response. To address this challenge and the increasing uncertainty associated with extreme rainfall, a real-time disaster prevention platform has been developed. This system integrates multi-source data and geospatial information through a cluster-based architecture to enhance pluvial flood management. Built on a Service-Oriented Architecture (SOA) and incorporating Internet of Things (IoT) technologies, AI-based convolutional neural networks (CNNs), and 3D drone mapping, the platform enables automated alerts by linking sensor thresholds with real-time environmental data, facilitating synchronized operational responses. Deployed in New Taipei City over the past three years, the system has demonstrably reduced flood risk during severe rainfall events. Region-specific action thresholds and adaptive strategies are continually refined through feedback mechanisms, while integrated spatial and hydrological trend analyses extend the lead time available for emergency response. Full article

Polyurethane foam is widely used as a primary filling material in car seats. While it provides good damping and energy absorption, the mechanical properties are complex but play a vital role in vibration attenuation and vehicle ride comfort. This study proposes a comprehensive experimental and analytical method to characterize the visco-hyperelastic properties of seat-grade polyurethane foam. Quasi-static and dynamic compression tests were conducted on foam blocks to obtain load–deflection curves and dynamic stiffness. A visco-hyperelastic material model was developed, where the hyperelastic response was derived via the hereditary integral and difference-stress method, and viscoelastic behavior was captured using a Prony series fitted to dynamic stiffness data. The model was validated using finite element simulations, showing good agreement with experimental results in both static and dynamic conditions. The proposed method enables accurate characterization of the visco-hyperelastic material properties of seat-grade polyurethane foam. Full article

Background: Liver fibrosis, a consequence of various chronic liver diseases, is characterized by excessive accumulation of extracellular matrix (ECM), leading to impaired liver function and potentially progressing to cirrhosis or hepatocellular carcinoma. The molecular mechanisms underlying liver fibrosis are complex and not fully understood. In vivo experiments are essential for studying the molecular mechanisms of the disease. However, the diverse principles behind mouse modeling techniques for liver fibrosis can complicate the elucidation of specific fibrotic mechanisms. Methods: Five distinct liver fibrosis models were utilized: CONTROL, NASH (non-alcoholic steatohepatitis), BDL (bile duct ligation), TAA (thioacetamide), and CCl₄ (carbon tetrachloride). Patents for these drugs were reviewed using Patentscope^® and Worldwide Espacenet^®. ScRNA-seq was performed to analyze and compare the cellular and molecular differences in these models. Results: The analysis revealed that, particularly in the drug-induced fibrosis models, hepatic stellate cells (HSCs), Kupffer cells, and T-cell subsets exhibit distinct regulatory patterns and dynamic remodeling processes across different liver fibrosis models. These findings highlight the heterogeneity of immune responses and extracellular matrix (ECM) remodeling in various models, providing important insights into the complex mechanisms underlying liver fibrosis. Conclusions: The study enhances our understanding of liver fibrosis development and provides valuable insights for selecting the most representative animal models in future research. This comprehensive analysis underscores the importance of model-specific immune responses and ECM remodeling in liver fibrosis. Full article

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus Steiner & Buhrer (pine wood nematodes, PWN), impacts forest carbon sequestration and climate change. However, satellite-based PWD monitoring is challenging due to the limited spatial resolution of Sentinel’s MSI sensor, which reduces its sensitivity to subtle biochemical alterations in foliage. We have, therefore, developed a slope product index (SPI) for effective detection of PWD using single-date satellite imagery based on spectral gradients in the visible and near-infrared (VNIR) range. The SPI was compared against 15 widely used vegetation indices and demonstrated superior robustness across diverse test sites. Results show that the SPI is more sensitive to changes in chlorophyll content in the PWD detection, even under potentially confounding conditions such as drought. When integrated into Random Forest (RF) and Back-Propagation Neural Network (BPNN) models, SPI significantly improved classification accuracy, with the multivariate RF model achieving the highest performance and univariate with SPI in BPNN. The generalizability of SPI was validated across test sites in distinct climate zones, including Zhejiang (accuracyZ_Mean = 88.14%) and Shandong (accuracyS_Mean = 78.45%) provinces in China, as well as Portugal. Notably, SPI derived from Sentinel-2 imagery in October enables more accurate and timely PWD detection while reducing field investigation complexity and cost. Full article

Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) algorithm, an advanced deep reinforcement learning method, was developed to generate faster and more efficient evacuation routes compared to traditional models. The A3C model was tested in various scenarios, including different environmental conditions and numbers of agents, and its performance was compared with the Deep Q-Network (DQN) algorithm. The results showed that A3C achieved evacuations 43.86% faster on average and converged in fewer episodes (100 vs. 250 for DQN). In dynamic environments with moving threats, A3C also outperformed DQN in maintaining agent safety and adapting routes in real time. As the number of agents increased, A3C maintained high levels of efficiency and robustness. These findings demonstrate A3C’s strong potential to enhance evacuation planning through improved speed, adaptability, and scalability. The study concludes by highlighting the practical benefits of applying such models in real-world emergency response systems, including significantly faster evacuation times, real-time adaptability to evolving threats, and enhanced scalability for managing large crowds in high-density environments including airport terminals. The A3C-based model offers a cost-effective alternative to full-scale evacuation drills by enabling virtual scenario testing, supports proactive safety planning through predictive modeling, and contributes to the development of intelligent decision-support tools that improve coordination and reduce response time during emergencies. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

Phalaenopsis is a widely cultivated ornamental plant of considerable economic value worldwide. However, traditional growing medium, sphagnum moss, is limited and non-renewable. It also decomposes slowly and is prone to environmental issues. Therefore, there is an urgent need to identify more environmentally friendly and efficient alternatives. Biochar, a sustainable material with excellent physical and chemical properties, has been recognized as an effective promoter of plant growth. In this study, we investigated the influence of biochar derived from three raw materials (corn straw, bamboo, and walnut) mixed1 with coconut shell at ratios of 1:2, 1:10, and 4:1, on the growth of Phalaenopsis ‘Big Chili’. Over a 150-day controlled experiment, we evaluated multiple growth parameters, including plant height, crown width, total root length, total projected area, total surface area, and root volume. Compared to the traditional growing medium, the optimal biochar-coconut shell mixture (maize straw biochar: coconut shell = 1:2) increased plant height and crown width by 7.55% and 6.68%, respectively. Root metrics improved substantially, with total root length increasing by 10.96%, total projected area by 22.82%, total surface area by 22.14%, and root volume by 38.49%. Root biomass in the optimal treatment group increased by 42.47%, while aboveground and belowground dry weights increased by 6.16% and 77.11%, respectively. These improvements were closely associated with favorable substrate characteristics, including low bulk density, high total and water-holding porosity, moderate aeration, and adequate nutrient availability. These findings demonstrate that substrate characteristics critically influence plant performance and that biochar–coconut shell mixtures, particularly at a 1:2 ratio, represent a viable and sustainable alternative to sphagnum moss for commercial cultivation of Phalaenopsis. Full article

The planting area of the maize–soybean strip intercropping system has been increasing annually in the Hexi Corridor oasis irrigation area of China. However, long-term irrational water resource utilization and the excessive mono-application of fertilizers have led to significantly low water and nitrogen use efficiency in this cropping system. To explore the sustainable production model of high yield and high water–nitrogen productivity in maize–soybean strip intercropping, we established three irrigation levels (low: 60%, medium: 80%, and sufficient: 100% of reference crop evapotranspiration) and three nitrogen application levels (low: maize 230 kg ha⁻¹, soybean 29 kg ha⁻¹; medium: maize 340 kg ha⁻¹, soybean 57 kg ha⁻¹; and high: maize 450 kg ha⁻¹, soybean 85 kg ha⁻¹) for maize and soybean, respectively. Three irrigation levels without nitrogen application served as controls. The effects of different water–nitrogen combinations on multiple indicators of the maize–soybean strip intercropping system, including yield, water–nitrogen productivity, and quality, were analyzed. The results showed that the irrigation amount and nitrogen application rate significantly affected the kernel quality of maize. Specifically, the medium nitrogen and sufficient water (N2W3) combination achieved optimal performance in crude fat, starch, and bulk density. However, excessive irrigation and nitrogen application led to a reduction in the content of lysine and crude protein in maize, as well as crude fat and crude starch in soybean. Appropriate irrigation and nitrogen application significantly increased the yield in the maize–soybean strip intercropping system, in which the N2W3 treatment had the highest yield, with maize and soybean yields reaching 14007.02 and 2025.39 kg ha⁻¹, respectively, which increased by 2.52% to 138.85% and 5.37% to 191.44% compared with the other treatments. Taking into account the growing environment of the oasis agricultural area in the Hexi Corridor and the effects of different water and nitrogen supplies on the yield, water–nitrogen productivity, and kernel quality of maize and soybeans in the strip intercropping system, the highest target yield can be achieved when the irrigation quotas for maize and soybeans are set at 100% ET0 (reference crop evapotranspiration), with nitrogen application rates of 354.78~422.51 kg ha⁻¹ and 60.27~71.81 kg ha⁻¹, respectively. This provides guidance for enhancing yield and quality in maize–soybean strip intercropping in the oasis agricultural area of the Hexi Corridor, achieving the dual objectives of high yield and superior quality. Full article

Real-time detection of fatigue states in air traffic controllers (ATCOs) is crucial for ensuring air traffic safety. Existing methods exhibit limitations such as poor real-time performance, intrusiveness, and susceptibility to lighting and occlusion. This paper proposes FF-YOLO, an improved YOLO11-based deep learning algorithm, to detect ATCO fatigue states through facial feature analysis. A custom dataset comprising 25,154 facial images collected from 10 ATCOs was constructed for model training and validation. The FF-YOLO model introduces the CA-C3K2 module for fine-grained feature extraction under complex lighting, incorporates a spatial–channel attention mechanism for improved detection accuracy during occlusion, and MPDIoU loss for enhanced accuracy on multi-scale facial images with accelerated convergence. Experimental results show FF-YOLO achieves 94.2% mAP@50, 74.7% mAP@50–95, 83.8% precision, and 73.8% recall, with gains of +13.7%, +11.6%, +0.6%, and +5.9% over YOLO11n, respectively, thereby enabling real-time and accurate detection of ATCO fatigue states. Future work will expand datasets with larger and more diverse ATCO populations to enhance generalizability. Full article

To address the challenges of inadequate adaptability, insufficient power, high ground clearance, and limited functionality in existing pineapple field machinery, this study proposes a height-adjustable pineapple field management platform based on previously established cultivation patterns and agronomic requirements. The structural configuration and operational principles of the platform’s power chassis are elucidated, with specific emphasis on the development of the traction power system and modular operational systems. Theoretical and experimental analyses of steering parameters, stability, and field performance were conducted. Finite element simulation analysis of the frame revealed that under full-load conditions, the equivalent elastic strains during descent and ascent phases were 0.000317 and 0.00125, respectively. Maximum equivalent stresses (48.27 MPa and 231.6 MPa for descent and ascent, respectively) were localized at the beam–plate junctions, while peak deformations of 1.14 mm (descent) and 4.31 mm (ascent) occurred at mid-frame and posterior–mid regions, respectively. Field validation demonstrated operational velocities of 0.16–1.77 m/s (forward) and 0.11–0.28 m/s (reverse), with a maximum gradability of 20°. The platform exhibited multifunctional capabilities including weeding, spraying, fertilization, flower induction, harvesting, and transportation, demonstrating its potential to fulfill the operational requirements for pineapple field management. Simultaneously, the overall work efficiency is increased by more than 50%, compared to manual labor. Full article

In this work, the degradation in luminous characteristics of red, green, and blue (RGB) micro-LEDs (10 µm × 10 µm) under electrical stress at 360 K has been investigated. After 280 h of aging, the AlGaInP-based red micro-LEDs exhibit a 31.7% reduction in maximum external quantum efficiency, which is significantly greater than the reductions observed in InGaN-based green and blue micro-LEDs. Specifically, the peak wavelength redshift by 0.6 nm, and blueshift 1.0 nm, and 0.5 nm for RGB micro-LEDs, respectively. The color purity of green and blue micro-LEDs decreases by 3.6% and 0.7%, respectively, resulting in a 7% reduction in color gamut. Full article

An inrush current will be generated inside a converter transformer during no-load switching on or off. The inrush current will cause severe vibration of the winding and other components of the converter transformer. However, the current research on the vibration characteristics caused by the inrush current is insufficient, and the influence of the converter transformer components cannot be effectively evaluated. Therefore, this paper discusses the building of an electromagnetic transient model of no-load closing of a conventional DC converter station, and analyzes the time–frequency characteristics of the inrush current under different working conditions. The finite element model based on the actual converter transformer was built and verified. The vibration characteristics of some converter transformer components under excitation of the inrush current were studied. The research results can monitor the vibration of a converter transformer under different working conditions, and can avoid the damage of converter transformer components caused by an excessive inrush current. Full article