Search Results (331)

Deadwood is essential for the forest ecosystem productivity and stability. A growing body of evidence indicates that deadwood-inhabiting microbes are effective decomposition agents, yet little is known about how changes in microbial communities during the initial deadwood decay. In a small forest area, we performed dense sampling from the top, middle, and bottom portions of two representative Pinus massoniana cultivars logs to track deadwood xylem microbiota shift during the initial deadwood decay. We found xylem mycobiota varied dramatically during the initial deadwood decay. Deadwood microbes might largely originate from the endophytic microbes of living trees during the initial deadwood decay. Notably, bark type is an important driving factor for xylem mycobiota changes during the initial deadwood decay. Ten upregulated metabolites were screened out by a univariate analysis approach. Moreover, our correlation analysis suggests that enriched microbes at class level was significantly correlated with the upregulated metabolites during the initial deadwood decay. Our work provides new insights into the process of mycobiota and metabolite changes during the initial deadwood decay. Full article

This paper studies the adaptive practical tracking control (PTC) problem for a class of uncertain nonlinear systems (UNSs) with nontriangular structured uncertain terms and unknown parameters, where the boundary of nontriangular structured uncertain terms depends on all state variables. Based on the improved adaptive backstepping technique, the state feedback tracking controller and update laws are first constructed. Then, by seeking the linear relationship between the state vector and the error vector, and by utilizing the comparison principle, it is verified that the developed adaptive PTC scheme can ensure that all signals of the closed-loop system are bounded and the tracking error converges to a bounded region. Finally, two examples, including a numerical example and the dual-motor drive servo system, are provided to show the effectiveness of this control method. Full article

Oral squamous cell carcinoma (OSCC), an aggressive and poorly prognosed subtype of head and neck squamous cell carcinoma (HNSCC), has prompted urgent calls for innovative therapeutic approaches. Evodiamine (EVO), a natural alkaloid extracted from the Chinese herb Evodia rutaecarpa, has demonstrated significant potential in curbing tumor cell proliferation and slowing tumor expansion. However, its specific effects on cell senescence within the context of OSCC have remained shrouded in uncertainty. This study delves into the mechanisms of EVO’s impact on OSCC by harnessing databases such as the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and CellAge to pinpoint potential targets and carry out in-depth bioinformatics analysis. The findings reveal that EVO can markedly enhance the expression of the androgen receptor (AR) in OSCC cells, inducing cellular senescence and thereby inhibiting tumor progression. Furthermore, the research indicates that AR expression is considerably lower in OSCC tissues than in normal tissues. This low expression of AR in tumor tissues is closely associated with advanced clinical stages and unfavorable prognoses in HNSCC patients. These discoveries open up new avenues for therapeutic strategies, and suggest that AR holds promise as a potential therapeutic target for OSCC, and EVO may amplify its antitumor effects by enhancing AR-mediated cellular senescence in the treatment of OSCC. Full article

Roof water inrush accidents in coal mine driving roadways occur frequently in China, accounting for a high proportion of major coal mine water hazard accidents and causing serious losses. Aiming at the lack of research on the mechanism of roof water inrush in driving roadways and the difficulty of predicting water inrush accidents, this paper constructs a local damage criterion for coal–rock mass and a seepage–fracture coupling model based on peridynamics (PD) bond theory. It identifies three zones of water-conducting channels in roadway surrounding rock, the water fracture zone, the driving fracture zone, and the water-resisting zone, revealing that the damage degree of the water-resisting zone dominates the transformation mechanism between delayed and instantaneous water inrush. A discriminant function for the effectiveness of water-conducting channels is established, and a single-index prediction and evaluation system based on damage critical values is proposed. A “geometry damage” dual-control water inrush prediction model within the PD framework is constructed, along with a non-local action mechanism model and quantitative prediction method for water inrush. Case studies verify the threshold for delayed water inrush and criteria for instantaneous water inrush. The research results provide theoretical tools for roadway water exploration design and water hazard prevention and control. Full article

Background: F. hirta vahl is a famous Chinese medicinal plant. The root is the main organ accumulating bioactive compounds, and its development is directly related to the yield and quality of the harvested F. hirta. However, the molecular mechanisms underlying the bioactive compound biosynthesis occurring during the root development of F. hirta are unknown. Method: Transcriptome and widely targeted metabolome analyses were performed to investigate gene expression and metabolite variation during the development of F. hirta taproots. Results: A total of 3792 differentially expressed genes (DEGs) were identified between the one- and three-year-old F. hirta taproots; they are related to circadian rhythm–plant, phenylpropanoid biosynthesis, starch and sucrose metabolism, and plant–pathogen interaction pathways. In total, 119 differentially accumulated metabolites (DAMs) were identified between the one- and three-year-old F. hirta taproots, including flavonols, phenolic acids, and coumarins compounds. Integrative transcriptome and metabolome analyses revealed a significant correlation between 172 DEGs and 21 DAMs; they were predominantly enriched for processes associated with phenylpropanoid biosynthesis, flavonoid biosynthesis, plant hormone signal transduction, and stilbenoid, diarylheptanoid, and ginerol biosynthesis. In addition, 26 DEGs were identified to be significantly correlated with the DAMs that accumulated in the phenylpropanoid biosynthesis pathway, and these DEGs may be the key genes for the biosynthesis of F. hirta active compounds. Conclusions: The phenylpropanoid biosynthesis pathway plays a dual role in both development and bioactive compound synthesis in F. hirta taproots. These findings provide a molecular regulatory network in the relationships between F. hirta taproot development and the accumulation of secondary metabolites. The identification of candidate genes and pathways provides a genetic resource for quality control and future molecular breeding in F. hirta. Full article

Umami is a fundamental taste sensation, often described as a delicious and pleasant flavor perception. To enhance or complement the original flavor and meet the tastes of diverse regions, umami dipeptides have been extensively utilized in global food manufacturing. Currently, the application and purification techniques of dipeptides are relatively mature, while their umami mechanisms and molecular modification are both scarce. In this work, the 3D structure of the umami dipeptide target T1R1/T1R3 was first obtained through sequence alignment and homology modeling, then followed by the successful construction of a database containing 400 samples of dipeptides. Subsequently, the complex models of T1R1/T1R3, respectively, with DG (Asp-Gly) and EK (Glu-Lys) (i.e., T1R1_DG/T1R3, T1R1/T1R3_DG, T1R1_EK/T1R3, and T1R1/T1R3_EK) were obtained via molecular docking and virtual screening. Finally, based on comparative molecular dynamics (MD) simulation trajectories, the binding free energy was calculated to investigate receptor–ligand recognition and conformational changes, providing some implications for potential modifications of umami dipeptides. T1R1 tends to bind relatively small umami dipeptides, whereas T1R3 does the opposite, both of which favor the recognition of acidic and hydrophilic dipeptides. By comparing strategies such as hydroxyl introduction and chain length alteration, electrostatic effects may be more important than non-polar effects in molecular design. This work not only explores the recognition mechanism of umami dipeptides with the receptor T1R1/T1R3 showing certain theoretical significance, but also provides feasible suggestions for dipeptide screening and modification having certain application value. Full article

Tight gas wells in Southwest oil and gas fields have significant production and high sand output intensity. The sand out of the wellhead has a certain erosion effect on the downstream pipeline, the equipment, and affects the normal production. This paper models and simulates the desander used at the wellhead according to the real parameters of the tight gas wellhead, and explores the effects of gas production, pressure, temperature, sand particle size, water content, and other factors on the desander’s sand removal efficiency. This paper combines the principle of fluid dynamics to analyze the internal mechanism of the effect trend and according to the simulation results uses the Pearson correlation coefficient quantification of the effect of each operating parameter to explore the optimal boundary condition parameters applicable to the desander. From the simulation results, it can be seen that the separation efficiency of the desander is the highest when the gas production rate is 4 × 10⁴ m³/d, the pressure is 7 MPa, and the lower the working temperature is, the larger is the gravel particle size. Combined with the sand management problems occurring in the field of tight gas wells, suggestions are made for the optimization of the operating parameters and structure of the desander, which will provide a basis for supporting the rapid production and large-scale beneficial development of tight gas fields. Full article

The mechanisms underlying formation energy depletion after initial fracturing and post-refracturing production decline in shale oil horizontal wells remain poorly understood. This study proposes a novel numerical simulation framework for refracturing processes based on a three-dimensional fully coupled hydromechanical model. By dynamically reconfiguring the in situ stress field through integration of production data from initial fracturing stages, our approach enables precise control over fracture propagation trajectories and intensities, thereby enhancing reservoir stimulation volume (RSV) and residual oil recovery. The implementation of fully coupled hydromechanical simulation reveals two critical findings: (1) the 70 m fracture half-length generated during initial fracturing fails to access residual oil-rich zones due to insufficient fracture network complexity; (2) a 3–5° stress reorientation combined with reservoir repressurization before refracturing significantly improves fracture network interconnectivity. Field validation demonstrates that refracturing extends fracture half-lengths to 97–154 m (38–120% increase) and amplifies RSV by 125% compared to initial operations. The developed seepage–stress coupling methodology establishes a theoretical foundation for optimizing repeated fracturing designs in unconventional reservoirs, providing critical insights into residual oil mobilization through engineered stress field manipulation. Full article

Lameness significantly compromises dairy cattle welfare and productivity. Early detection enables prompt intervention, enhancing both animal health and farm efficiency. Current computer vision approaches often rely on isolated lameness feature quantification, disregarding critical interdependencies among gait parameters. This limitation is exacerbated by the distinct kinematic patterns exhibited across lameness severity grades, ultimately reducing detection accuracy. This study presents an integrated computer vision and deep-learning framework for dairy cattle lameness detection and severity classification. The proposed system comprises (1) a Cow Lameness Feature Map (CLFM) model extracting holistic gait kinematics (hoof trajectories and dorsal contour) from walking sequences, and (2) a DenseNet-Integrated Convolutional Attention Module (DCAM) that mitigates inter-individual variability through multi-feature fusion. Experimental validation utilized 3150 annotated lameness feature maps derived from 175 Holsteins under natural walking conditions, demonstrating robust classification performance. The classification accuracy of the method for varying degrees of lameness was 92.80%, the sensitivity was 89.21%, and the specificity was 94.60%. The detection of healthy and lameness dairy cows’ accuracy was 99.05%, the sensitivity was 100%, and the specificity was 98.57%. The experimental results demonstrate the advantage of implementing lameness severity-adaptive feature weighting through hierarchical network architecture. Full article

Underwater sensor deployment in military applications requires high precision, yet existing robotic solutions often lack the maneuverability and adaptability required for complex aquatic environments. To address this gap, this study proposes a bio-inspired underwater robot modeled after the marine iguana, which exhibits effective crawling and swimming capabilities. The research aims to develop a compact, multi-functional robot capable of precise sensor deployment and environmental detection. The methodology integrates a biomimetic mechanical design—featuring leg-based crawling, tail-driven swimming, a deployable head mechanism, and buoyancy control—with a multi-sensor control system for navigation and data acquisition. Gait and trajectory planning are optimized using kinematic modeling for both terrestrial and aquatic locomotion. Experimental results demonstrate the robot’s ability to perform accurate underwater sensor deployment, validating its potential for military applications. This work provides a novel approach to underwater deployment robotics, bridging the gap between biological inspiration and functional engineering. Full article

Solar flare prediction models typically use classification, predicting only the probability of categorized events within a time window. This misses critical information, such as how many flares occur, their precise timings, and their intensities. To address this, we propose a paradigm shift to set prediction, directly forecasting a variable-sized set of flare events with detailed characteristics. We demonstrate this approach with FSPT (Flare Set-Prediction Transformer), a transformer-based model adapted from object detection principles. FSPT predicts sets containing individual flare start, peak, and end time offsets, as well as peak X-ray intensity. This work presents the set-prediction framework and the FSPT model, showing its potential for more informative flare forecasting. Full article

When testing the output of piezoelectric devices under different pressures, the friction between the pressure platform and the device causes a large amount of frictional electrical signals to be mixed in the output piezoelectric signal, seriously affecting the measurement accuracy of the piezoelectric signal. The current solution is to encapsulate the contact interface with identical materials to suppress triboelectric interference. However, this work has shown that even when contact separation is implemented at the interface of same media, triboelectric signals can still be generated. The heterogeneous potential distribution of the same material in contact separation has been discovered for the first time through the contact interface potential distribution, proving that charge transfer still exists between the same materials. Atomic force microscopy (AFM) was used to analyze the microstructure of the interface, and it was found that the existence of the surface tip structure would enhance the electron loss. Based on this, a new electron transfer model for surface–tip electron cloud interaction is proposed in this work. In addition, by comparing the output voltage characteristics of the triboelectric nanogenerators (TENGs) of seven polymer materials (e.g., polypropylene (PP), polyethylene (PE), polyvinyl chloride (PVC), polytetrafluoroethylene (PTFE), polyoxymethylene (POM), polyimide (PI), and polyethylene terephthalate (PET)), it was found that the open circuit voltage of PP material was only 0.06 V when they friction with each other, which is 2–3 orders of magnitude lower than other materials. When PP materials are applied to the package of piezoelectric devices, the precision of piezoelectric output characterization can be improved significantly, and a new experimental basis for a triboelectric theory system can be provided. Full article

Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data—2D RGB images from different views, depth images, and 3D point clouds—for the non-invasive estimation of duck body dimensions and weight. A dataset of 1023 Linwu ducks, comprising over 5000 samples with diverse postures and conditions, was collected to support model training. The proposed method innovatively employs PointNet++ to extract key feature points from point clouds, extracts and computes corresponding 3D geometric features, and fuses them with multi-view convolutional 2D features. A Transformer encoder is then utilized to capture long-range dependencies and refine feature interactions, thereby enhancing prediction robustness. The model achieved a mean absolute percentage error (MAPE) of 5.73% and an R² of 0.953 across seven morphometric parameters describing body dimensions, and an MAPE of 10.49% with an R² of 0.952 for body weight, indicating robust and consistent predictive performance across both structural and mass-related phenotypes. Unlike conventional manual measurements, the proposed model enables high-precision estimation while eliminating the necessity for physical handling, thereby reducing animal stress and broadening its application scope. This study marks the first application of deep learning techniques to poultry body dimension and weight estimation, providing a valuable reference for the intelligent and precise management of the livestock industry with far-reaching practical significance. Full article

This study investigates the synergistic effects of integrating ozone nanobubbles (generated via a pure oxygen-fed reactor with nanobubble-diffusing air stones) and biofloc technology (BFT) on water quality optimization, pathogenic load reduction, and growth performance enhancement in Pacific white shrimp (Penaeus vannamei) prenursery aquaculture systems. Four treatments were tested: a clear water control (CW), ozonated clear water (CW + O), biofloc (FLOC), and biofloc with ozone (FLOC + O). The FLOC + O group significantly improved water quality, reducing total ammonia nitrogen (TAN) by 61%, nitrite nitrogen (NO₂⁻-N) by 78% compared to CW, and total suspended solids (TSS) by 21% compared to FLOC (p = 0.0015). Ozone application (maintained above 0.3 mg/L, 15 min/day) demonstrated robust pathogen suppression, achieving a sharp reduction in Muscle Necrosis Virus (MNV), a 99.5% inhibition of Vibrio spp. (from 228,885 to 107 CFU/mL), and the clearance of Epistylis spp., as determined via optical microscope. These enhancements directly translated to superior biological outcomes, with the FLOC + O group exhibiting an 82% survival rate (vs. 40% in CW) and 13% higher final body weight (11.65 mg vs. 10.32 mg in CW). The integration of ozone and BFT also accelerated larval development and improved the Zoea II to Mysis I metamorphosis success rate. By maintaining stable microbial communities and reducing organic waste, the combined system lowered the water exchange frequency by 40% and eliminated the need for prophylactic antibiotics. These results demonstrate that ozone–BFT integration effectively addresses key challenges in shrimp prenursery—enhancing disease resistance, optimizing water conditions, and improving growth efficiency. The technology offers a sustainable strategy for the intensive prenursery of Pacific white shrimp, balancing ecological resilience with production scalability. Full article

Obtaining precise seasonal yield estimates is challenging, with weather forecast accuracy being a key factor. This study examines the impact of different weather data forecasting methods on yield estimation. Initially, we evaluated the suitability of the WOFOST model for highland barley (HB) and wheat on the northeastern Tibetan Plateau. Yield forecasts were conducted using nine historical weather selection methods under two scenarios, differing in their use of 10-day TIGGE data. The results showed that different weather data fusion methods led to varying forecasted yields. For HB, sequential selection and an improved KNN algorithm were optimal, while for wheat, sequential selection performed best. Early-season forecasts had lower accuracy, while predictions after flowering were more reliable. Incorporating TIGGE short-term forecasts into historical weather data improved HB yield forecasts, with 98.2% of days having an average relative error (ARE) below 20%. For wheat, using only historical weather data provided more stable yield forecasts, with 93.1% of days having an ARE below 20%. The weather data fusion strategy for yield forecasts offered reliable prediction accuracy without the need for full-cycle weather observation. Full article