The Function of CBM32 in Alginate Lyase VxAly7B on the Activity on Both Soluble Sodium Alginate and Alginate Gel

Carbohydrate-binding modules (CBMs), as an important auxiliary module, play a key role in degrading soluble alginate by alginate lyase, but the function on alginate gel has not been elucidated. Recently, we reported alginate lyase VxAly7B containing a CBM32 and a polysaccharide lyase family 7 (PL7). To investigate the specific function of CBM32, we characterized the full-length alginate lyase VxAly7B (VxAly7B-FL) and truncated mutants VxAly7B-CM (PL7) and VxAly7B-CBM (CBM32). Both VxAly7B-FL and native VxAly7B can spontaneously cleavage between CBM32 and PL7. The substrate-binding capacity and activity of VxAly7B-CM to soluble alginate were 0.86- and 1.97-fold those of VxAly7B-FL, respectively. Moreover, CBM32 could accelerate the expansion and cleavage of alginate gel beads, and the degradation rate of VxAly7B-FL to alginate gel beads was threefold that of VxAly7B-CM. Results showed that CBM32 is not conducive to the degradation of soluble alginate by VxAly7B but is helpful for binding and degradation of insoluble alginate gel. This study provides new insights into the function of CBM32 on alginate gel, which may inspire the application strategy of CBMs in insoluble substrates.

Adaptive restart scheme based on active injection current ratio for half-bridge MMC-HVDC with overhead transmission lines

Modular multilevel converter for high voltage DC system has great potential as the key role in global energy interconnection. Adaptive reclosing or restarting after suffering from short-circuit faults is a critical segment to realize safe and stable operation. An adaptive restart scheme for the half-bridge sub-module transmission system is proposed, which is based on the active current injection of the auxiliary fault discriminate module. Combined with the engineering experience, the transient characteristics of the line current during the fault restart stage are analyzed. The ratio of the current peaks at both ends of the fault point is used as the criterion. Moreover, the restart current suppression strategy is introduced into the adaptive restart scheme. It provides a decision-making basis for discriminate the property of the fault and improves the success rate of fault restart. The simulation results show that after the external auxiliary module is introduced into the adaptive restart stage, the restart current does not exceed the rated value. Finally, The permanent fault and the transient fault can be successfully distinguished, providing a reliable guarantee for the subsequent action decision of the protection devices.

Bi-Objective Design and Management of Reconfigurable Manufacturing Systems to Optimize Technical and Ergonomic Performances

In the last decades, Reconfigurable Manufacturing Systems (RMSs) rose as an emerging manufacturing strategy matching the modern industrial and market requirements asking for a wide variety of products in flexible batches. A traditional reconfigurable manufacturing environment consists of dynamic cells, called Reconfigurable Machine Cells (RMCs), including a set of machines called Reconfigurable Machine Tools (RMTs). Such machines are characterized by fixed elements, i.e., basic modules, and dynamic elements, i.e., auxiliary modules, allowing them to perform different operations. Despite their automation level, these systems require the intervention of the human operators in performing specific tasks, e.g., handling of the auxiliary modules from the warehouse to the RMTs and their assembly/disassembly to/from the RMTs. This issue rises relevant ergonomic and safety questions due to the human–machine collaboration. Following this stream, this paper proposes and applies a bi-objective optimization model for the design and management of RMSs. The technical objective function minimizes the reconfiguration time, i.e., the time needed to equip the RMTs with the required auxiliary modules, and the part and auxiliary module travel time among the RMCs. The ergonomic objective function minimizes the repetitive movements performed by the human operators during the working activities according to the ISO 11228-3 standard. Results show the existence of a good trade-off between the two objective functions, proving the possibility to improve the ergonomic conditions of the human operators without excessively increasing the total time needed for RMTs reconfiguration and for part and auxiliary module travelling.

DEVELOPMENT OF SUBSYSTEM SOFTWARE FOR RESEARCH OF EXPERIMENTAL CONSTRUCTIONS FOR THE FEMUR REINFORCING

Bone reinforcement is one of the most effective surgical interventions. In addition, development, improvement and production of implants aimed at creating high quality, reliable structures that can retain their functional properties for a long time. One of the most important stages in the development and design of implantable power structures is the biomechanical justification of their performance and reliability. The article presents the development and testing of a software subsystem for the study of experimental structures for preventive reinforcement of the femur, as well as the study of the peculiarities of the creation of such systems. The design subsystem, which is proposed in the article, is an auxiliary module for Ansys software, written in the Python programming language in the PyScripter environment. It allows you to build a qualitative picture of the stress-strain state in a selected volume of the femoral neck, to refine the grid of finite elements and set boundary conditions, which in turn used for mathematical calculation of the stress state. The study includes the calculation of stress states in the intact bilayer bone to identify critical points of onset of destruction of the cortical layer of bone. The created calculation module facilitates interaction with the software, allowing specifying more precisely necessary conditions of carrying out experiment. Based on the results obtained during the experiments, we can conclude about the usefulness of using both the created module and the created models. The results of the numerical experiment show better characteristics of the reinforced bone in contrast to the intact.

Deep Global Features for Point Cloud Alignment

Point cloud registration is a key problem in computer vision applications and involves finding a rigid transform from a point cloud into another such that they align together. The iterative closest point (ICP) method is a simple and effective solution that converges to a local optimum. However, despite the fact that point cloud registration or alignment is addressed in learning-based methods, such as PointNetLK, they do not offer good generalizability for point clouds. In this stud, we proposed a learning-based approach that addressed existing problems, such as finding local optima for ICP and achieving minimum generalizability. The proposed model consisted of three main parts: an encoding network, an auxiliary module that weighed the contribution of each input point cloud, and feature alignment to achieve the final transform. The proposed architecture offered greater generalization among the categories. Experiments were performed on ModelNet40 with different configurations and the results indicated that the proposed approach significantly outperformed the state-of-the-art point cloud alignment methods.

OF-MSRN: Optical Flow-Auxiliary Multi-Task Regression Network for Direct Quantitative Measurement, Segmentation and Motion Estimation

Comprehensively analyzing the carotid artery is critically significant to diagnosing and treating cardiovascular diseases. The object of this work is to simultaneously achieve direct quantitative measurement and automated segmentation of the lumen diameter and intima-media thickness as well as the motion estimation of the carotid wall. No work has simultaneously achieved the comprehensive analysis of carotid artery due to three intractable challenges: 1) Tiny intima-media is more challenging to measure and segment; 2) Artifact generated by radial motion restrict the accuracy of measurement and segmentation; 3) Occlusions on diseased carotid walls generate dynamic complexity and indeterminacy. In this paper, we propose a novel optical flow-auxiliary multi-task regression network named OF-MSRN to overcome these challenges. We concatenate multi-scale features to a regression network to simultaneously achieve measurement and segmentation, which makes full use of the potential correlation between the two tasks. More importantly, we creatively explore an optical flow auxiliary module to take advantage of the co-promotion of segmentation and motion estimation to overcome the restrictions of the radial motion. Besides, we evaluate consistency between forward and backward optical flow to improve the accuracy of motion estimation of the diseased carotid wall. Extensive experiments on US sequences of 101 patients demonstrate the superior performance of OF-MSRN on the comprehensive analysis of the carotid artery by utilizing the dual optimization of the optical flow auxiliary module.