2种侧围加强内框总成检具结构对比分析

概述了侧围加强内框总成与周边零件的匹配关系,从检具操作的人机工程出发,描述了侧围加强内框总成检具的2种定位方式和结构设计,对比了2种定位方式对于检测结果的影响,总结了其优点和弊端,结果表明侧围加强内框总成检具采用车内方向朝下的定位方式效果更好。     

Lake charr (Salvelinus namaycush) clotting response may act as a plasma biomarker of sea lamprey (Petromyzon marinus) parasitism: Implications for management and wound assessment

Sea lampreys (Petromyzon marinus) have decimated the Great Lakes fisheries over the past century, and their control is central to protecting native fish populations. Wounding data collected from host fish, including classifying wounds as Type A (penetrating the integument) or Type B (superficial), is an integral part of sea lamprey monitoring efforts and helps inform management decisions; however, wound assessment is subjective and error-prone. This study aimed to determine if protein biomarkers of parasitism could be quantified in host fish plasma to serve as a potential objective aide in current wound assessment practices. Male siscowet lake charr (Salvelinus namaycush) were parasitized in a lab setting for four days, after which the sea lamprey was removed, host blood was collected, and the wound Type recorded. A second blood sample was collected from host fish with Type A wounds 7 months later. The plasma proteome was quantified using iTRAQ, and the relative abundances of 169 proteins were compared between parasitized and non-parasitized control fish. Three functional classes of proteins were modified by sea lamprey parasitism: immune response, lipid transport, and blood coagulation. A major finding was evidence of a concerted anticoagulation response in fish with Type A wounds, including changes in protein components and regulators of fibrin clot formation, some of which did not fully recover within 7 months. A modified clotting assay yielded a smaller thrombin-induced fibrin clot from parasitized fish, supporting the proteomic results. Therefore, measuring blood clottability could improve sea lamprey damage estimates by providing a more objective and quantitative index of parasitism than is offered by wounding data alone.     

Design,Synthesis, and in vitro Evaluation of P2X7 Antagonists

The P2X7 receptor is a promising target for the treatment of various diseases due to its significant role in inflammation and immune cell signaling. This work describes the design, synthesis, and in vitro evaluation of a series of novel derivatives bearing diverse scaffolds as potent P2X7 antagonists. Our approach was based on structural modifications of reported (adamantan-1-yl)methylbenzamides able to inhibit the receptor activation. The adamantane moieties and the amide bond were replaced, and the replacements were evaluated by a ligand-based pharmacophore model. The antagonistic potency of the synthesized analogues was assessed by two-electrode voltage clamp experiments, using Xenopus laevis oocytes that express the human P2X7 receptor. SAR studies suggested that the replacement of the adamantane ring by an aryl-cyclohexyl moiety afforded the most potent antagonists against the activation of the P2X7 cation channel, with analogue 2-chloro-N-[1-(3-(nitrooxymethyl)phenyl)cyclohexyl)methyl]benzamide ( 56 ) exhibiting the best potency with an IC₅₀ value of 0.39 μM.     

公民水素养评价表征因素模型构建与分析

针对我国水资源紧缺、水生态损害、水环境污染等相关水问题,以提升全民爱水惜水节水护水的意识和能力为目标,从水素养概念出发,通过扎根理论质性研究方法对公民水素养评价的表征因素进行筛选和提炼,最终得出包含水知识、水态度、水行为为主范畴的10个表征因素并运用解释结构模型(ISM)深入剖析各因素之间的内在联系。在此基础上建立了3级多层次递阶结构模型,进一步精确描述系统各要素两两之间的直接关系。结果表明:公民水素养评价的表征因素自上而下形成了表象层、中间层和根源层等3个层级,可直观、清晰地反映表征因素之间的递阶、层级关系;水态度在各表征因素中具有调节作用以及各表征因素之间存在内在逻辑关系。研究成果为提高公民水素养水平提供一定的科学依据和政策参考。     

基于卷积神经网络的单目深度估计

针对利用深度学习方法对街道图像进行深度估计，提出采用语义分割的方法解决深度图出现边界模糊等问题；估计深度通过左右视角图生成视差图进行无监督的训练。在网络模型中添加语义分割层，采取多个空洞卷积并行的结构增加感受野，同时减少了图像下采样的次数，降低了由于下采样带来的信息损失，使得的结果更加准确。这也是在深度估计中首次与空洞卷积相结合增加准确率。通过对KITTI街道数据集进行训练，与现有结果相比，除了增加检测准确性，降低错误率之外，使得效果图中的物体更加清晰，并且在效果图中还保留了一些原模型中被忽视掉的细节信息，将原始图像更加完整的表现出来。     

面向连续查询的敏感语义位置隐私保护方案

针对连续查询位置服务中构造匿名区域未考虑语义位置信息导致敏感隐私泄露问题，通过设计[(K,θ)]-隐私模型，提出一种路网环境下面向连续查询的敏感语义位置隐私保护方案。该方案利用Voronoi图将城市路网预先划分为独立的Voronoi单元，依据用户的移动路径和移动速度，选择具有相似特性的其他[K-1]个用户，构建匿名用户集；利用匿名用户集用户设定的敏感语义位置类型和语义安全阈值，以及用户所处语义位置的Voronoi单元，构建满足[(K,θ)]-隐私模型的语义安全匿名区域，可以同时防止连续查询追踪攻击和语义推断攻击。实验结果表明，与SCPA算法相比，该方案在隐私保护程度上提升约15%，系统开销上降低约20%。     

Muddling between science and engineering: an epistemic strategy for developing human factors and ergonomics as a hybrid discipline

Human Factors and Ergonomics (HFE) recognises itself as a design-driven, systemic and scientific discipline geared towards well-being and performance. Being a scientific discipline and design-oriented requires that the epistemic basis of science and design/engineering be fully comprehended. In interdisciplinary research where these two viewpoints meet, there are often dilemmas posed in terms of knowledge construction and labelling of activity. Therefore, this article scrutinises these two orientations and addresses the differences and commonalities, using case studies from engineering and psychological science (both constituents of HFE). Based on these insights, a way forward is suggested in terms of (1) a reflexive engagement with epistemic concepts and methods; (2) finding a conceptual space for balancing and bridging the science-engineering divide; (3) comprehending ‘design-thinking/design knowledge’ and not treating it as an application of science; (4) providing emphasis on problem formulation and practices of HFE focusing on developing them in systemic terms.     

Sequence-Dependent Nanofiber Structures of Phenylalanine and Isoleucine Tripeptides

The molecular design of short peptides to achieve a tailor-made functional architecture has attracted attention during the past decade but remains challenging as a result of insufficient understanding of the relationship between peptide sequence and assembled supramolecular structures. We report a hybrid-resolution model to computationally explore the sequence–structure relationship of self-assembly for tripeptides containing only phenylalanine and isoleucine. We found that all these tripeptides have a tendency to assemble into nanofibers composed of laterally associated filaments. Molecular arrangements within the assemblies are diverse and vary depending on the sequences. This structural diversity originates from (1) distinct conformations of peptide building blocks that lead to different surface geometries of the filaments and (2) unique sidechain arrangements at the filament interfaces for each sequence. Many conformations are available for tripeptides in solution, but only an extended β-strand and another resembling a right-handed turn are observed in assemblies. It was found that the sequence dependence of these conformations and the packing of resulting filaments are determined by multiple competing noncovalent forces, with hydrophobic interactions involving Phe being particularly important. The sequence pattern for each type of assembly conformation and packing has been identified. These results highlight the importance of the interplay between conformation, molecular packing, and sequences for determining detailed nanostructures of peptides and provide a detailed insight to support a more precise design of peptide-based nanomaterials.     

Data envelopment analysis based on team reasoning

Existing approaches to data envelopment analysis focus mainly on the derivation of the efficiency of the individual decision‐making unit (DMU) or on the calculation of the weights of multiple inputs or outputs, but pay little attention to the team interest of all the DMUs. Motivated by the idea of team reasoning, in which the benefit of the team is of higher importance than that of each individual, this paper considers all the DMUs as a team and introduces the team indexes including the overall efficiencies, variance, boundaries of all the DMUs, and relationships between DMUs. Several models are first developed to estimate values of the team indexes based on which decision makers can provide their preferences regarding them. Then, models are established to obtain the interval efficiencies of individual DMUs under the condition that the team indexes are satisfied. Several examples are given to illustrate the proposed approaches and verify their applicability.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.