Rapid fabrication of ultra-smooth Y-TZP bioceramic surfaces by dual-axis wheel polishing : process development and tribological characterization

The existing artificial joint implants using bioceramic materials face problems of difficulty in manufacturing and premature failure due to wear. This paper investigated a rapid preparing process of ultra-smooth surfaces of yttria-stabilized tetragonal zirconia polycrystal bioceramics based on the dual-axis wheel polishing (DAWP) system. Friction and wear tests were conducted to prove that the prepared ultra-smooth surface can effectively reduce wear. The effects of process parameters on polishing performances were investigated. The XRD and SEM analysis and micro-hardness testing were used to characterize the prepared surface in material behaviors. Tribological tests were carried out on a ball-on-plate reciprocating tribometer to comparatively study the tribological behavior and wear mechanism of the prepared ultra-smooth surfaces and the conventional surface at sub-microscale. The used finishing technology can steadily achieve fast preparation of ultra-smooth bioceramic surfaces, and with a high material removal rate (the highest value was 1.14 mg/min). Besides, in contrast to the conventional surface (Ra 129 nm), the prepared ultra-smooth surface (Ra 0.38 nm) achieved a much smaller friction coefficient, and much less wear volume, indicating that the wear resistance of the ultra-smooth surface was significantly improved

Ship structure collision experiments and simplified numerical calculation method

ObjectivesAlthough the fluid-structure interaction calculation method can better simulate the ship collision process, it requires a longer calculation time. To address this problem, a simplified numerical calculation method is proposed. MethodsThe local cabin section of a ship is taken as the object to carry out water collision experiments under various operating conditions. Force sensors and non-contact measurement based on high-speed photography technology are used to obtain the collision force and motion time history data of the ship. The collision contact force and acceleration response data are then analyzed, and arbitrary Lagrange−Euler (ALE) coupled fluid-solid numerical computational analysis is carried out on the experimental process. The effect of the water on the impacting ship in the collision process is then simplified to the equivalent mass, and the effect on the impacted ship is simplified to the equivalent resistance, which acts on the non-impacting side of the impacted ship in the form of surface force to hinder the movement of the impacted ship. Numerical calculations that do not involve the water-structure coupling process are then carried out on the basis of the simplified method. ResultsThe results show that the errors between the peak collision force and the experimental values for each condition obtained by the simplified calculation method are within 5%, and the calculation time required by this method is much smaller than that of the ALE fluid-structure interaction algorithm ConclusionsThe proposed simplified numerical calculation method can provide useful references for realizing the efficient calculation of ship structure collision response

False Negative/Positive Control for SAM on Noisy Medical Images

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code will be released soon

PLS3 Missense Variants Affecting the Actin-Binding Domains Cause X-Linked Congenital Diaphragmatic Hernia and Body-Wall Defects

Congenital diaphragmatic hernia (CDH) is a relatively common and genetically heterogeneous structural birth defect associated with high mortality and morbidity. We describe eight unrelated families with an X-linked condition characterized by diaphragm defects, variable anterior body-wall anomalies, and/or facial dysmorphism. Using linkage analysis and exome or genome sequencing, we found that missense variants in plastin 3 (PLS3), a gene encoding an actin bundling protein, co-segregate with disease in all families. Loss-of-function variants in PLS3 have been previously associated with X-linked osteoporosis (MIM: 300910), so we used in silico protein modeling and a mouse model to address these seemingly disparate clinical phenotypes. The missense variants in individuals with CDH are located within the actin-binding domains of the protein but are not predicted to affect protein structure, whereas the variants in individuals with osteoporosis are predicted to result in loss of function. A mouse knockin model of a variant identified in one of the CDH-affected families, c.1497G\u3eC (p.Trp499Cys), shows partial perinatal lethality and recapitulates the key findings of the human phenotype, including diaphragm and abdominal-wall defects. Both the mouse model and one adult human male with a CDH-associated PLS3 variant were observed to have increased rather than decreased bone mineral density. Together, these clinical and functional data in humans and mice reveal that specific missense variants affecting the actin-binding domains of PLS3 might have a gain-of-function effect and cause a Mendelian congenital disorder

YOLOv5-lotus an efficient object detection method for lotus seedpod in a natural environment

Accurate detection of lotus seedpods in a nature environment is essential for agronomic applications for automated harvesting and yield mapping. Traditional detection methods are based on grower’s experience, which is inefficient for the large-scale production. To improve the efficiency of harvesting lotus seedpods, this study proposes a YOLOv5-lotus method to effectively detect overripe lotus seedpods. The lotus seedpods image dataset is firstly created. An improved YOLOv5 network model based on coordinate attention (CA) module is then presented, namely YOLOv5-lotus model, where CA module is developed to strengthen the model inter-channel relationships and capture long-range dependencies with precise positional information, thus improving the detection accuracy of the algorithm. In order to reveal the feasibility and robustness of the proposed method, a number of case studies are presented on the detection of overripe lotus seedpods in various scenarios, including different poses, illuminations and degrees of occlusion. Compared with the classical YOLOv5s network, the average precision of YOLOv5-lotus model is increased by 0.7 % and average detection time is reduced by 0.7 ms. Compared to other state-of-the-art networks, our detection model is able to achieve the highest average precision value, faster efficient detection speed and higher F1 score, with the average precision being 98.3 %, the recall rate being 96.3 %, the precision rate being 97.3 %, F1 score being 0.968 and average detection time being 19.4 ms. Through case studies and comparisons, the effectiveness and superiority of the proposed approach are demonstrated. These research results can be applied to the detection of upwardly-growing conical fruit. It creates a prerequisite for the development of automatic harvesting equipment