Search Results (10,285)

In factory-controlled recirculating aquaculture systems, precise regulation of water velocity is crucial for optimizing shrimp feeding behavior and improving aquaculture efficiency. However, quantitative analysis of the impact of water velocity on shrimp behavior remains challenging. This study developed an innovative multi-objective behavioral analysis framework integrating detection, tracking, and behavioral interpretation. Specifically, the YOLOv8 model was employed for precise shrimp detection, ByteTrack with a dual-threshold matching strategy ensured continuous individual trajectory tracking in complex water environments, and Kalman filtering corrected coordinate offsets caused by water refraction. Under typical recirculating aquaculture system conditions, three water circulation rates (2.0, 5.0, and 10.0 cycles/day) were established to simulate varying flow velocities. High-frequency imaging (30 fps) was used to simultaneously record and analyze the movement trajectories of Litopenaeus vannamei during feeding and non-feeding periods, from which two-dimensional behavioral parameters—velocity and turning angle—were extracted. Key experimental results indicated that water circulation rates significantly affected shrimp movement velocity but had no significant effect on turning angle. Importantly, under only the moderate circulation rate (5.0 cycles/day), the average movement velocity during feeding was significantly lower than during non-feeding periods (p < 0.05). This finding reveals that moderate water velocity constitutes a critical hydrodynamic window for eliciting specific feeding behavior in shrimp. These results provide core parameters for an intelligent Litopenaeus vannamei feeding intensity assessment model based on spatiotemporal graph convolutional networks and offer theoretically valuable and practically applicable guidance for optimizing hydrodynamics and formulating precision feeding strategies in recirculating aquaculture systems. Full article

We reported on a generation of 27 nm spectral bandwidth, two-port output ultrashort pulses directly from an all-normal-dispersion passively mode-locked Yb-fiber laser. Based on the nonlinear polarization rotation (NPR) mode-locking technique, high pump power and optical devices with high damage thresholds were introduced to achieve broad spectral bandwidth and strong output power. The dual wavelengths were emitted from the clockwise and counterclockwise ports, respectively, and self-started mode-locking was achieved. The bidirectional output laser generates stable pulses with up to 223.5 mW average power at a 46.04 MHz repetition rate, corresponding to a pulse energy of 5 nJ. The bidirectional ultrashort outputs of the laser provide potential applications in supercontinuum generation and medical and biological applications. Full article

Based on the G85 high-fill subgrade project in east Gansu Province, this study conducts one-dimensional compression tests in the laboratory on both disturbed and in situ-compacted loess. Through the combination of the test results of remolded soil, compaction standards for each layer of the subgrade fill are established, and quality inspections of the compacted subgrade are performed. The experimental results demonstrate that the compression deformation of remolded loess exhibits a positive correlation with compaction degree and a negative correlation with moisture content. Under constant compaction degree conditions, axial pressure and deformation follow a linear relationship, whereas under fixed conditions, the relationship adheres to a quadratic trend. Specimen void ratios show minimal variation within the 25–100 kPa stress range but undergo significant reduction between 100 and 400 kPa. Under an axial compressive load of 100–200 kPa, the compression coefficient at a height of 10 m within the subgrade ranges from 0.163 to 0.171 MPa⁻¹. At a height of 6 m, it ranges from 0.177 to 0.183 MPa⁻¹, and at 1 m, from 0.183 to 0.186 MPa⁻¹. These values indicate that the compaction quality throughout the subgrade corresponds to a low compressibility level. However, the compaction quality near the slopes on both sides is slightly lower than that along the centerline of the subgrade. Overall, the compaction quality meets the required standards. Full article

Sixth-generation (6G) wireless technology has facilitated the rapid development of the Internet of Things (IoT), enabling various end devices to be deployed in applications such as wireless multimedia sensor networks. However, most end devices encounter difficulties when dealing a large amount of IoT video data due to their lack of computational resources for visual object tracking. Discriminative correlation filter (DCF)-based tracking approaches possess favorable properties for resource-constrained end devices, such as low computational costs and robustness to motion blur and illumination variations. Most current DCF trackers employ multiple features and the spatial–temporal scale space to estimate the target state, both of which may be suboptimal due to their fixed feature dimensions and dense scale intervals. In this paper, we present an adaptive mapped-feature and scale-interval method based on DCF to alleviate the problem of suboptimality. Specifically, we propose an adaptive mapped-feature response based on dimensionality reduction and histogram score maps to integrate multiple features and boost tracking effectiveness. Moreover, an adaptive temporal scale estimation method with sparse intervals is proposed to further improve tracking efficiency. Extensive experiments on the DTB70, UAV112, UAV123@10fps and UAVDT datasets demonstrate the superiority of our method, with a running speed of 41.3 FPS on a cheap CPU, compared to state-of-the-art trackers. Full article

Rice has excellent nutritional quality as a dietary food and is easily puffed. The aim of this study was to investigate the effects of puffing technology on the physicochemical parameters, structure properties and volatile components of brown rice (BR) and refined rice (RR). XRD and FT-IR spectroscopic data demonstrated that puffing under high temperature and pressure conditions triggered starch gelatinization, concurrently reducing starch crystallinity and inducing a V-type polymorphic structure. In addition, it substantially weakened hydrogen bonding networks in rice flour. In detail, 136 volatile compounds of raw and puffed rice were analyzed by HS-SPME-GC-MS, and the results showed that aldehydes, ketones, and pyrazines were the main volatile aroma compounds after puffing. By correlation analysis, benzaldehyde, 2-octenal, 2-methoxy-phenol, and furfural were identified as key contributors. The volatile components, especially ketones and alcohols, were higher in the BR as compared to those in the RR, with a significant difference observed between the two (p < 0.05). Combined with sensory evaluation, 1212CH was found to have a high score (17.63). These results could provide a theoretical basis for understanding the effect of puffing on rice flour and the volatile components of puffed products. Full article

The economic efficacy of logistics infrastructure is being reshaped by the dual forces of digitalization and the labor market. However, a new-era “investment return paradox” has emerged. Digitalization and an abundant labor force are theoretically positive forces, so why does their combination, when coupled with capital investment, paradoxically engender negative emergence that suppresses growth? Conceptualizing the regional economy as a Socio-Technical System (STS), this paper unravels this paradox by identifying and theorizing an “adaptive lag trap”. Using provincial panel data from China, we first provide empirical validation for this trap, identifying a significant negative three-way interaction involving labor quantity (coef. = −0.218, p < 0.05). We then demonstrate that high-skilled labor quality is the key to mitigating this trap. While its direct interactive effects are not statistically significant, our analysis uncovers a robust and theoretically potent pattern: a higher-skilled workforce systematically reverses the negative trend of the interaction effect. The split-sample test provides the clearest evidence of this pattern, showing the coefficient pivoting from negative (−0.0572) in the low-skill subsample to positive (+0.109) in its high-skill counterpart. Our findings establish that high-skill human capital is a necessary condition to circumvent the “adaptive lag trap”, underscoring the imperative for a policy shift from investing in the scale of labor to cultivating its skill structure within a co-evolutionary framework. Full article

Bridge support displacement is a crucial indicator for evaluating the deformation states of supports and main girders. In this study, innovative methods were established based on long-term monitoring data from two kilometer-scale railway bridges, aimed at early warning of main girder deformation consistency and assessment of support wear conditions. First, outliers were identified and eliminated using a moving interval generalized Grubbs method. Second, the variation patterns of support displacement induced by temperature and train loads were systematically analyzed. Third, an early-warning method was proposed based on the optimal probability distribution model of support displacement difference to determine the warning threshold for main girder deformation consistency. Additionally, a method for evaluating support activity performance using jamming parameters was introduced to quantitatively assess support wear conditions. This research demonstrates that the proposed methods provide novel and effective approaches for the early warning and assessment of support and girder deformation, contributing to enhanced structural health monitoring and maintenance strategies. Full article

To investigate the impact of microencapsulation on the volatile organic compounds (VOCs) in Zanthoxylum oil, this study compared unencapsulated Zanthoxylum oil (ZO) with microencapsulated Zanthoxylum oil (MZO) using physicochemical analysis, sensory evaluation, and molecular sensory analysis. Sensory evaluation revealed significant differences in aroma attributes between ZO and MZO, whereas no notable differences were observed in numbing intensity or overall acceptability. Colorimetric analysis indicated significant distinctions between the two samples. Electronic nose (E-nose) analysis demonstrated a reduction in overall aroma intensity for MZO compared to ZO. Gas chromatography–mass spectrometry (GC–MS) identified 43 VOCs, including 22 compounds present in both samples, accounting for 46.8% of the total. Terpenes represented the predominant class in both ZO (69.7%) and MZO (68.2%). Comprehensive analysis based on odor activity value (OAV) and variable importance in projection (VIP) identified nine volatile compounds as key aroma contributors. Gas chromatography–ion mobility spectrometry (GC–IMS) detected 90 the volatile organic compounds (VOCs), with esters (30.38%) and heterocyclic compounds (10.42%) predominating in ZO, while esters (29.08%) and alcohols (26.12%) were predominant in MZO. Compared to ZO, MZO exhibited increased levels of alcohols (from 12.04% to 26.12%) and terpenes (from 1.39% to 3.53%), but decreased levels of acids (from 5.77% to 2.72%) and aldehydes (from 10.29% to 4.62%). This approach provides a comprehensive assessment of flavor quality before and after microencapsulation, offers a scientific basis for quality control, and facilitates the development and utilization of Zanthoxylum oil resources. Full article

Investigating the physiological responses and resistance mechanisms in plants under drought stress provides critical insights for optimizing irrigation water utilization efficiency and promoting the development of irrigation science. In this study, cotton seedlings were cultivated in a light incubator. Three drought stress levels were applied: mild (M1, 50–55% field moisture), moderate (M2, 45–50%), and severe (M3, 40–45%). Transcriptome analysis was performed under mild and severe stress. The results revealed that differentially expressed genes (DEGs) related to proline degradation were down-regulated and proline content increased in cotton. Under different stress treatments, cotton exhibited a stress-intensity-dependent regulation of carbohydrate metabolism and soluble sugar content decreased and then increased. And the malondialdehyde content analysis revealed a dose-dependent relationship between stress intensity and membrane lipid peroxidation. Stress activated the antioxidant system, leading to the down-regulation of DEGs for reactive oxygen species production in the mitogen-activated protein kinase (MAPK) signaling pathway. Concurrently, superoxide dismutase activity and peroxidase content increased to mitigate oxidative damage. Meanwhile, the photosynthetic performance of cotton seedlings was inhibited. Chlorophyll content, stomatal conductance, the net photosynthetic rate, the transpiration rate and water use efficiency were significantly reduced; intercellular carbon dioxide concentration and leaf stomatal limitation value increased. But photosynthesis genes (e.g., PSBO (oxygen-evolving enhancer protein 1), RBCS (ribulose bisphosphate carboxylase small chain), and FBA2 (fructose-bisphosphate aldolase 1)) in cotton were up-regulated to coordinate the photosynthetic process. Furthermore, cotton seedlings differentially regulated key biosynthesis and signaling components of phytohormonal pathways including abscisic acid, indoleacetic acid and gibberellin. This study elucidates the significant gene expression of drought-responsive transcriptional networks and relevant physiological response in cotton seedlings and offers a theoretical basis for developing water-saving irrigation strategies. Full article

Photocatalysis offers a cost-effective and eco-friendly approach for environmental remediation, yet traditional powdered photocatalysts suffer from poor recyclability and separation challenges. To address these limitations, we developed a recyclable carbon fiber-supported composite photocatalyst (CC/TiO₂ NRs@MoS₂ NPs) featuring a three-dimensional hierarchical core–shell architecture. This structure comprises a TiO₂ seed layer, vertically aligned TiO₂ nanorod arrays as the core, and a MoS₂ nanoparticle shell, fabricated via sequential deposition. Under simulated solar irradiation, the TiO₂@MoS₂ heterojunction exhibited significantly enhanced uranium adsorption capacity, achieving a remarkable 97.3% photocatalytic removal efficiency within 2 h. At an initial uranium concentration of 200 ppm, the material demonstrated an exceptional extraction capacity of 976.7 mg g⁻¹, outperforming most reported photocatalysts. These findings highlight the potential of this 3D core–shell design for efficient uranium recovery and environmental purification applications. Full article

Exploring the effects of different water and fertilizer irrigation systems on N₂O and CO₂ emissions is of great significance for promoting sustainable agricultural development. In this study, summer maize in Henan Province was selected as the research object, and field experiments were carried out from 2023 to 2024. A total of 12 water and fertilizer treatments were set up. In situ field measurements of N₂O and CO₂ in farmland were carried out using static chamber gas chromatography to study the effects of different water and fertilizer irrigation systems on N₂O and CO₂ emissions from farmland and the simulation performance of the DNDC model. The results were as follows: (1) Irrigation and fertilization significantly interacted to affect N₂O and CO₂ emissions. (2) The summer maize yield under the B2 treatment was the highest, and the total N₂O and CO₂ emissions under the C3 treatment were the highest. (3) Under the DNDC simulation scenario, the summer maize yields under the real-time irrigation system in 2023 and 2024 increased by 4.43% and 4.38% compared with those under full irrigation. The total N₂O emissions from farmland were reduced by 6.56% and 6.22%, while CO₂ emissions decreased by 14.49% and 14.79%, respectively. The results show that real-time water and fertilizer irrigation systems can promote the yield of summer maize and reduce greenhouse gas emissions. The research results provide a theoretical basis for reducing greenhouse gas emissions from farmland and are significant for promoting sustainable agricultural development. Full article

Lignins and flavonoids, which are derived from the phenylpropanoid pathway and share common precursors, play an important role in Boehmeria nivea (ramie). Uridine diphosphate-glycosyltransferases (UGTs) are essential for the glycosylation of secondary metabolites and are involved in plant growth and stress responses. Hence, this study aimed to screen candidate UGTs related to lignin/flavonoid glycosylation and stress responses. A total of 84 BnUGTs were identified, and all BnUGTs contain a conserved PGPS domain. Phylogenetic analysis suggested that 10, 5, 1, and 1 putative BnUGTs might be associated with lignin glycosylation, flavonoid glycosylation, and adverse stress, respectively. Further analysis showed that Bnt05T007753.1 expression was upregulated and showed a significant positive correlation with lignin content in the phloem and leaf, reaching up to 710 in the xylem after 75 days of germination. Bnt14T019888.1 expression (in the leaf and xylem) and Bnt06T010117.1 expression (in the xylem) were upregulated and showed a significant positive correlation with lignin and flavonoid content. In the phloem, Bnt14T019888.1 expression was downregulated and showed a significant negative correlation with lignin content. Bnt04T006105.1 expression was upregulated in the stem and leaf under Cd treatment. Overall, we successfully identified four candidate BnUGTs (Bnt05T007753.1, Bnt14T019888.1, Bnt06T010117.1, and Bnt04T006105.1); these findings provide insight into the glycosylation mechanisms of lignins and flavonoids and stress responses in ramie. Full article

In landslide susceptibility evaluation, scientific sampling minimizes potential societal losses and enhances the efficiency of disaster prevention and mitigation. However, traditional sampling methods, such as selecting landslide and non-landslide samples based on equal proportions or area proportions, overlook the different societal losses resulting from landslide omission and misreporting, and the potential societal losses faced by their evaluation results are often not minimized. Therefore, this study proposes a sampling method that takes potential societal losses into account and uses the Landslide Misjudgment Potential Societal Loss Evaluation Index (LMPSLEI) to quantify the total potential social losses in the area due to landslide omission and misreporting. The LMPSLEI is minimized by optimizing the sample ratio, thus minimizing the potential societal losses faced by the evaluation results and enhancing the scientific basis of disaster prevention and mitigation efforts. This study takes the Wenchuan earthquake area as the research region, selects 13 conditional factors and employs two models—Random Forest (RF) and Convolutional Neural Network (CNN)—to conduct case studies. We derive the recommended sample ratio based on the formula, hypothesizing that the LMPSLEI will be minimized under this ratio. The results show that the sample ratio for LMPSLEI minimization in the RF model is similar to the recommended sample ratio, while the sample ratio for LMPSLEI minimization in the CNN model is slightly higher than the recommended sample ratio. The recommended sample ratio can achieve the minimum of LMPSLEI or reach a lower value under different societal losses weights of landslide omission/misreporting, and thus it can be used as a preliminary choice of sampling for landslide susceptibility evaluation considering the potential societal losses. Full article

Multivariate time series forecasting requires modeling complex and evolving spatio-temporal dependencies as well as frequency-domain patterns; however, the existing Transformer-based approaches often struggle to effectively capture dynamic inter-series correlations and disentangle relevant spectral components, leading to limited forecasting accuracy and robustness under non-stationary conditions. To address these challenges, we propose TSGformer, a Transformer-based architecture that integrates multi-scale adaptive graph learning, adaptive spectral decomposition, and cross-scale interactive fusion modules to jointly model temporal, spatial, and spectral dynamics in multivariate time series data. Specifically, TSGformer constructs dynamic graphs at multiple temporal scales to adaptively learn evolving inter-variable relationships, applies an adaptive spectral enhancement module to emphasize critical frequency components while suppressing noise, and employs interactive convolution blocks to fuse multi-domain features effectively. Extensive experiments across eight benchmark datasets show that TSGformer achieves the best results on five datasets, with an MSE of 0.354 on Exchange, improving upon the best baselisnes by 2.4%. Ablation studies further verify the effectiveness of each proposed component, and visualization analyses reveal that TSGformer captures meaningful dynamic correlations aligned with real-world patterns. Full article

Dermatan sulfate (DS) is a remarkably versatile glycosaminoglycan that plays critical roles across a wide array of biological processes. Its unique structure, characterized by repeating disaccharide units of N-acetyl-D-galactosamine (GalNAc) and Iduronic acid (IdoA) with variable sulfation patterns, enables it to interact with numerous biomolecules. These interactions mediate diverse functions, including the organization of the extracellular matrix, promotion of wound healing, and modulation of cancer progression. Despite its broad biological relevance, deciphering DS function remains challenging due to its pronounced structural complexity and heterogeneity. Variations in chain length, disaccharide composition, and sulfation patterns make it difficult to fully characterize DS’s intricate structure–function relationships. In this review, recent developments in biosynthesis, preparation, and applications of DS are summarized. Full article