This paper proposes a new method of multi-axle control for numerical controller. By adopting this method, pulse series for real-time control can be easily generated, and driving velocity for multi-axle with the non-orthogonal coordinate also can be easily realize. Command pulse can be modified in real time by introducing the sensor signal into the feedback loop of controller. Simulation of six joints robot shows that this method is feasible for multi-axle numerical control.
Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle placement, brachytherapy treatment planning, and motion management. Manual segmentation during these interventions is time-consuming and subject to inter- and intraobserver variation. To address these drawbacks, we aimed to develop a deep learning-based method which integrates deep supervision into a three-dimensional (3D) patch-based V-Net for prostate segmentation.We developed a multidirectional deep-learning-based method to automatically segment the prostate for ultrasound-guided radiation therapy. A 3D supervision mechanism is integrated into the V-Net stages to deal with the optimization difficulties when training a deep network with limited training data. We combine a binary cross-entropy (BCE) loss and a batch-based Dice loss into the stage-wise hybrid loss function for a deep supervision training. During the segmentation stage, the patches are extracted from the newly acquired ultrasound image as the input of the well-trained network and the well-trained network adaptively labels the prostate tissue. The final segmented prostate volume is reconstructed using patch fusion and further refined through a contour refinement processing.Forty-four patients' TRUS images were used to test our segmentation method. Our segmentation results were compared with the manually segmented contours (ground truth). The mean prostate volume Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and residual mean surface distance (RMSD) were 0.92 ± 0.03, 3.94 ± 1.55, 0.60 ± 0.23, and 0.90 ± 0.38 mm, respectively.We developed a novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for diagnostic and therapeutic applications in prostate cancer.
Intercropping systems offer substantial benefits in crop yield nd nutrient absorption. Utilizing logistic models, we simulated the dynamic of nutrient uptake and accumulation in spring wheat and the impact of different planting patterns and compound fertilizer application rates on spring wheat yield. We conducted a field experiment involving two planting patterns: spring wheat monoculture (MS) and spring wheat-pea intercropping (MI), with five compound fertilizer applications: C0 (0 kg ha -1 ), C1 (480 kg ha -1 ), C2 (540 kg ha -1 ), C3 (600 kg ha -1 ), and C4 (660 kg ha -1 ). We assessed spring wheat yield and aboveground nitrogen (N) and phosphorus (P) accumulation under different planting patterns and fertilization treatments. Results revealed that intercropping significantly increased spike number, grains per spike, and grain yield of spring wheat by 3.7%, 6.3%, and 13.3%, respectively, compared to monoculture. Fertilization treatments notably enhanced average spring wheat grain yield, with C2 performing optimally. Logistic model analysis indicated that under intercropping, the maximum accumulated aboveground N and N uptake rate (v) of spring wheat was 11.4% and 13.2% higher, and the maximum accumulated P and maximum P uptake rate (V max ) were 11.3% and 9.5% higher, respectively, compared to monoculture. Intercropped spring wheat under C2 exhibited the highest P accumulation among all treatments. In conclusion, both intercropping and fertilization can enhance N and P uptake and accumulation in spring wheat, thereby boosting yield. Optimized yield can be achieved under C2 (540 kg h -1 ) with a 10% reduction in fertilizer application. Thus effective control of fertilizer application is pivotal for maximizing the yield advantage of the spring wheat/pea intercropping system.
Car headlight plastic as a kind of evidence often can be seen in traffic accidents and some other cases. We tested 20 brands of car headlight plastic using gel chromatography. The data were processed using the Statistical Package for the Social Sciences (SPSS) one-way analysis of variance (ANOVA) and the discrimination rate was 97.14%. This indicated that we could discriminate between different headlights by the molecular weight of their headlight plastic. Gel permeation chromatography is an effective method of discriminating between headlights, particularly in the case of a traffic accident.
The concept of Prognostic and Health Management (PHM) on machinery attracts a lot of attention during these years, as a key technique of PHM, the prediction of Remaining Useful Life (RUL) of equipment is well studied. As a method with reasonable assumption, Health Index (HI) is successfully applied in RUL prediction. However, in the scenario of large mechanical equipment, there are some disadvantages in tradition HI method. So the notion of Core State is raised and some improvements are introduced, and furthermore, a unified framework for the RUL prediction of large equipment, which mainly based on similarity metric of HI, is then presented. Finally, the proposed idea was carried out through a real-data experiment. The result indicated the signification effect of Core State in RUL prediction and verified the validity of the framework as well.
Protein and nucleic acid binding site prediction is a critical computational task that benefits a wide range of biological processes. Previous studies have shown that feature selection holds particular significance for this prediction task, making the generation of more discriminative features a key area of interest for many researchers. Recent progress has shown the power of protein language models in handling protein sequences, in leveraging the strengths of attention networks, and in successful applications to tasks such as protein structure prediction. This naturally raises the question of the applicability of protein language models in predicting protein and nucleic acid binding sites. Various approaches have explored this potential. This paper first describes the development of protein language models. Then, a systematic review of the latest methods for predicting protein and nucleic acid binding sites is conducted by covering benchmark sets, feature generation methods, performance comparisons, and feature ablation studies. These comparisons demonstrate the importance of protein language models for the prediction task. Finally, the paper discusses the challenges of protein and nucleic acid binding site prediction and proposes possible research directions and future trends. The purpose of this survey is to furnish researchers with actionable suggestions for comprehending the methodologies used in predicting protein-nucleic acid binding sites, fostering the creation of protein-centric language models, and tackling real-world obstacles encountered in this field.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)