Prophages domesticated by bacteria promote the adaptability of bacterial cells

Prophages are temperate phages integrated into the host bacterial genome. They play an important role in the adaptation and the pathogenicity of bacteria, especially pathogenic bacteria. In this review, we described the distribution of prophages in different hosts and different environments, and focused on the significance of prophages. At the singlecell level, prophages can help the host adapt to harsh external environments by directly carrying virulence genes, encoding regulatory factors and activating lysogeny. At the population level, prophages can influence the overall evolutionary direction and ecological function of the host bacterial community. This review will help us understand the important role of prophages as unique organisms in individual bacteria and microbial populations.     

煤矿科室工作信息管理系统设计

为了提高了煤矿工作者的工作效率和矿井安全程度，设计了煤矿科室工作信息管理系统，分析了系统的关键技术，主要为J2EE技术和B/S和C/S的体系结构；根据现场调研，对系统需求进行了分析，然后设计了系统总体架构和系统功能模块。煤矿科室工作信息管理系统的应用，减少了人力资源的浪费，确保了信息的保存和查询的安全性。     

Motion Planning by T‐RRT with Potential Function for Vertical Articulated Robots

RRT (rapidly exploring random tree) with random sampling is an effective method for path planning, and is often used for robot manipulators. The RRT has many modified methods for applying various problems and conditions. Particularly, T‐RRT (Transition‐based RRT) one of those has advantage that it is able to adopt arbitrary evaluation function. In this paper, a novel path planning method based on the T‐RRT is proposed for ensuring “quality” of a generated path. Then, its effectiveness is evaluated via comparison with other sampling‐based methods using simulation of the industrial robot having seven DOFs.     

基于模糊推理的碟式太阳能跟踪系统改进研究

在碟式太阳能系统的长期实验数据中发现,太阳辐照度较低时系统集热效率非常低。为使碟式太阳能系统始终运行在高集热效率状态,提出模糊推理方法改进跟踪系统的启停判定条件,并根据集热器出口气流温度的影响因素建立模糊推理系统。仿真结果表明模糊系统在已知太阳辐照度、辐照变化度和空气流量的前提下,可智能控制系统在集热效率高时运行,集热效率低时待机,证明了该方法的可行性和有效性。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

具有光照鲁棒的图像匹配方法

针对现有的基于局部特征的图像匹配算法对光照变化敏感、匹配正确率低等问题，提出一种具有光照鲁棒性的图像匹配算法。首先使用实时对比保留去色（RTCP）算法灰度化图像，然后利用对比拉伸函数模拟不同光照变换对图像的影响从而提取抗光照变换特征点，最后采用局部强度顺序模式建立特征点描述符，根据待匹配图像局部特征点描述符的欧氏距离判断是否为成对匹配点。在公开数据集上，所提算法与尺度不变特征变换（SIFT）算法、加速鲁棒特征（SURF）算法、"风"（KAZE）算法和ORB算法在匹配速度和匹配正确率上进行了对比实验。实验结果表明：随着图像亮度差异的增加，SIFT算法、SURF算法、"风"（KAZE）算法和ORB算法匹配正确率下降迅速，所提算法下降缓慢并且正确率均高于80%；所提算法特征点检测较慢和描述符维数较高，平均耗时为23.47 s，匹配速度不及另外四种算法，但匹配质量却远超过它们。对实时性要求不高的系统中，所提算法可以克服光照变化对图像匹配造成的影响。     

Statistics of ceramic strength: Use ordinary Weibull distribution function or Weibull statistical fracture theory?

“Weibull statistics” for strength distribution analysis refers to either the ordinary Weibull distribution function or the Weibull statistical fracture theory. The ordinary Weibull distribution function is an empirical distribution function on an equal footing with other type of classical empirical distributions such as normal and log-normal distributions for fitting the statistical data of various random variables nonexclusive to materials strength. It has no explicit physical meaning and cannot be used for size scaling and prediction of strength. The Weibull statistical fracture theory is a weakest-link statistical fracture model for a solid with the strength distribution of an elemental volume being described by the ordinary Weibull distribution function. It has the capability of size scaling and prediction of strength for specimens with different geometries and different loading modes. The three-parameter Weibull statistical fracture theory in uniaxial flexure of prismatic beams is reformulated and validated by both numerical and real strength experiments of different ceramics.     

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

Abstract

The expected longer service life of modified asphalt can be jeopardized by different environmental factors, such as moisture, oxidation, etc. which affect the desired properties by altering the adhesive property. An insight into knowledge of the adhesive property of the asphalt can help in providing more durable asphalt pavement. The study attempted to develop different models of adhesive properties of polymers and carbon nanotubes (CNTs) modified asphalt binders. The polymer-CNT modified asphalt is processed to prepare different types of samples, by simulating the damage due to moisture and oxidization, following the corresponding standard method. An Atomic Force Microscopy (AFM) was employed to assess the nanoscale adhesion force of the tested samples following the existing functional group in asphalt. Finally, the study has developed Radial Basis Function Neural Network (RBFNN) as a function of different parameters including; asphalt chemistry (i.e. AFM tip type and constant), type and percentages of polymers and CNTs and different environmental exposures (oxidation, moisture, etc.) to predict the nano adhesion force of asphalt. It is observed that the adhesive property of the Styrene–Butadiene modified asphalt is more consistent compared to the Styrene–Butadiene–Styrene modified asphalt, while the presence of Single-Wall Nanotubes (SWNT) is observed to affect the adhesive properties of asphalt significantly as compared to Multi-Wall Nanotubes (MWNT). The higher accuracy level of RBFNN model also indicates that the functional group (tip-type) adding with the percentages and types of polymers and CNTs significantly affect the adhesive properties of asphalt.     

Investigating hydrogen evolution reaction properties of a new honeycomb 2D AlC

The hydrogen due to its high mass energy density is a new renewable, economically viable and clean resource. The most eco-friendly and economical approaches for the generation of hydrogen through hydrogen evolution is electrochemical water splitting. The two-dimensional (2D) nanomaterials have been recently found as potential candidates as non-noble metal catalyst for hydrogen evolution. In this work, we have systematically studied the structural and electronic properties of the newly predicted hexagonal-aluminium carbide monolayer (h-AlC ML) under the framework of dispersion-corrected density functional theory (DFT) calculations. The calculated electronic total density of states (TDOS) of h-AlC ML predict its metallic nature in contrast to other polar honeycomb 2D materials which are either semiconducting or semimetallic. The metallic behavior of h-AlC monolayer which motivates us to investigate its HER activity results due to the presence of delocalized charge density near Fermi level. Thus, we have investigated the HER activity of h-AlC ML by calculating hydrogen (H) adsorption energy (ΔE_H) and Gibbs free energy (ΔG_H) at three different sites of the 3 × 3 and 4 × 4 supercells of h-AlC ML; top of carbon atom (E_H-C), top of aluminium atom (E_H-Al) and hollow site (E_H-Hollow). Our results show that the hollow site is most catalytically active site in both supercells of h-AlC ML. We believe that our results will inspire experimentalists to fabricate this new 2D material for achieving the desired range of HER activity.