Explore the vast range of titles available.

Due to the characteristics of plant electrical signals being weak, low-frequency, and susceptible to interference, this study proposes a hardware solution involving silver chloride medical adhesive electrodes and the design of conditioning circuits to amplify the plant electrical signals and reduce noise. On the software side, deep learning algorithms are proposed to extract the voltage values from a self-built plant electrical signal acquisition system. Experiments were conducted on two aloe vera plants grown in different environments. Voltage signals were synchronously collected by using a high-precision digital multimeter with anti-interference capabilities. The measured signals from the system were used as input signals for a 1D-CNN, and the synchronized high-precision digital multimeter measurements served as network labels. The 1D-CNN network was then trained by using deep learning algorithms to fit the voltage values from the acquisition system to those of the high-precision digital multimeter. This approach effectively reduces noise and extracts accurate voltage values in the self-built measurement system. By combining hardware and software, the precision of the measurements is improved, providing a new method for measuring plant electrical signals.

Examining potential drug–target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network’s topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network’s node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision–recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.

Constructed wetlands (CWs) are a sustainable, nature-based solution for wastewater treatment, where pollutants are removed through contact with microorganisms attached to substrates and plant roots. Efficient hydraulic performance is critical for CWs, since poor hydraulic performance can reduce treatment efficiency by altering the actual residence time relative to the design value. Two methods to evaluate the Residence Time Distribution (RTD) within the CW system are the tracer method and numerical modelling. This study provides a comprehensive review of experimental methodologies and numerical models used to investigate hydraulic processes in CWs, outlining available techniques to assist researchers in selecting the most suitable approach based on their research needs and wetland characteristics. For experimental procedures, this review focuses on the selection of tracers, indicators for hydraulic performance assessment, and water quality responses to changing hydrological conditions. The advantages and disadvantages of existing numerical models, their suitability, and future research direction are also discussed. Understanding these methodologies and their application is crucial for advancing our knowledge of the hydraulic features of CWs and improving their design and operation. Ultimately, improving hydraulic performance through appropriate experimental and modelling techniques supports the sustainable development and operation of CW systems for long-term wastewater treatment applications.

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment.

Constructed wetlands (CWs) are cost-effective and sustainable systems for wastewater treatment, but their hydraulic performance remains a critical challenge. In this study, a lab-scale baffled horizontal subsurface flow constructed wetland was modeled using Computational Fluid Dynamics to investigate the effects of aeration strategies on hydraulic performance, focusing on aeration rates and positions. A gas–liquid two-phase flow system was modeled using the Euler–Euler approach with the Darcy–Forchheimer model in OpenFOAM, simulating 15 cases with varying aeration rates (0.1–0.3 m3/day) and positions (middle of channels vs. bends at the ends of baffles). Results show that the introduction of aeration influenced hydraulic efficiency (HE) and the Morrill Dispersion Index (MDI). Without aeration, the baseline HE was already high (HE = 0.9297) due to the optimized baffle configuration. However, aeration further improved performance, with HE increasing to 0.9594 and MDI decreasing from 1.6087 to 1.4000 when aeration was applied at bends (Position C) at 0.3 m3/day. Aeration at bends was more effective than mid-channel aeration, promoting uniform flow distribution and reducing short-circuiting. These findings highlight the importance of aeration positioning and provide insights for optimizing CW design to balance energy consumption and hydraulic performance.

Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees.

The precise prevention and control of forest pests and diseases has always been a research hotspot in ecological environmental protection. With the continuous advancement of sensor technology, the fine-grained identification of discolored tree crowns based on UAV technology has become increasingly important in forest monitoring. Existing deep learning models face challenges such as prolonged training time and low recognition accuracy when identifying discolored tree crowns caused by pests or diseases from airborne images. To address these issues, this study improves the Faster-RCNN model by using Inception-ResNet-V2 as the feature extractor, replacing the traditional VGG16 feature extractor, aiming to enhance the accuracy of discolored tree crown recognition. Experiments and analyses were conducted using UAV aerial imagery data from Jilin Changbai Mountain. The improved model effectively identified discolored tree crowns caused by pine wood nematodes, achieving a precision of 90.22%, a mean average precision (mAP) of 83.63%, and a recall rate of 92.33%. Compared to the original RCNN model, the mAP of the improved model increased by 4.68%, precision improved by 10.11%, and recall improved by 5.23%, significantly enhancing the recognition performance of discolored tree crowns. This method provides crucial technical support and scientific basis for the prevention and control of forest pests and diseases, facilitating early detection and precise management of forest pest outbreaks.

Manual preoperative image registration for central serous chorioretinopathy (CSCR) is labor-intensive and irreproducible. While rigid registration robustly aligns images globally, it misses fine details. Non-rigid registration, though excellent for local refinement, performs poorly with large discrepancies. Therefore, this study presents a coarse-to-fine registration method for multimodal retinal images to address the aforementioned issues. First, a three-step coarse registration strategy is designed that integrates keypoint pair detection and matching via a YOLOv8-pose network, further optimizes keypoints through a post-processing technique, and achieves initial alignment via affine transformation. On this basis, a dual-component fine registration strategy is then implemented, where disentanglement learning eliminates modality-specific variations while preserving essential vessel structures required for registration, and deformable network generates optimized deformation field to refine the coarse alignment locally, ultimately enabling high-precision image registration. Comprehensive qualitative and quantitative experiments were conducted on the CSCR clinical dataset, which includes both color fundus (CF) and fundus fluorescence angiography (FFA) images, to evaluate the proposed method. With Dice and Dice s scores of 0.6759 and 0.4977, the method performs comparably to existing approaches, suggesting its potential application value for CSCR preoperative planning.

Traditional stiffness modeling methods do not consider all factors comprehensively, and the modeling methods are not unified, lacking a global stiffness model. Based on screw theory, strain energy and the virtual work principle, a static stiffness modeling method for redundant over-constrained parallel mechanisms (PMs) with clearance was proposed that considers the driving stiffness, branch deformation, redundant driving, joint clearance and joint contact deformation. First, the driving stiffness and branch deformation were considered. According to the strain energy and Castiliano’s second theorem, the global stiffness matrix of the ideal joint mechanism was obtained. The offset of the branch was analyzed according to the restraint force of each branch. The mathematical relationship between the joint clearance and joint contact deformation and the end deformation was established. Based on the probability statistical model, the uncertainty of the offset value of the clearance joint and the contact area of the joint caused by the coupling of the branch constraint force was solved. Finally, taking a 2UPR-RR-2RPU redundant PM as an example, a stiffness simulation of the mechanism was carried out using the finite element method. The research results show that the high-precision stiffness modeling method proposed in this paper is correct, and provides an effective method for evaluating the stiffness performance of the PM.

An accurate prediction model for dam deformation is crucial for ensuring the safety and operational integrity of dam structures. This study introduces a hybrid modeling approach that integrates long short-term memory (LSTM) networks with Kolmogorov–Arnold networks (KANs). Additionally, the model incorporates a dual-stage attention mechanism (DA) that includes both factor and temporal attention components, enhancing the model’s precision and interpretability. The effectiveness of the DA-LSTM-KAN model was validated through a case study involving a concrete gravity dam. A comparative analysis with traditional models, including multiple linear regression and various LSTM variants, demonstrated that the DA-LSTM-KAN model significantly outperformed these alternatives in predicting dam deformation. An interpretability analysis further revealed that the seasonal and hydrostatic components contributed significantly to the horizontal displacement, while the irreversible component had the least impact. This importance ranking was qualitatively consistent with the results obtained from the Shapley Additive Explanations (SHAP) method and the relative weight method. The enhancement of the model’s predictive and explanatory capabilities underscores the hybrid model’s utility in providing detailed and actionable intelligence for dam safety monitoring.