Morphological study of thin films: Simulation and experimental insights using horizontal visibility graph

We studied the morphological nature of various thin films such as silicon carbide (SiC), diamond (C), germanium (Ge), and gallium nitride (GaN) on silicon substrate Si(100) using the pulsed laser deposition (PLD) method and Monte Carlo simulation. We, for the first time, systematically employed the visibility algorithm graph to meticulously study the morphological features of various PLD grown thin films. These thin-film morphologies are investigated using random distribution, Gaussian distribution, patterned heights, etc. The nature of the interfacial height of individual surfaces is examined by a horizontal visibility graph (HVG). It demonstrates that the continuous interfacial height of the silicon carbide, diamond, germanium, and gallium nitride films are attributed to random distribution and Gaussian distribution in thin films. However, discrete peaks are obtained in the brush and step-like morphology of germanium thin films. Further, we have experimentally verified the morphological nature of simulated silicon carbide, diamond, germanium, and gallium nitride thin films were grown on Si(100) substrate by pulsed laser deposition (PLD) at elevated temperature. Various characterization techniques have been used to study the morphological, and electrical properties which confirmed the different nature of the deposited films on the Silicon substrate. Decent hysteresis behavior has been confirmed by current-voltage (IV) measurement in all the four deposited films. The highest current has been measured for GaN at ～60 nA and the lowest current in SiC at ～30 nA level which is quite low comparing with the expected signal level (μA). The HVG technique is suitable to understand surface features of thin films which are substantially advantageous for the energy devices, detectors, optoelectronic devices operating at high temperatures.     

碳脱氧在钢包顶渣改质中的应用

 为了进一步降低夹杂物缺陷并提高产品质量,基于碳脱氧进行了钢包顶渣改质的研究。冷轧产品的生产工艺为铁水预处理→转炉→RH精炼→连铸,为减少钢中夹杂物质量分数,需要进行钢包顶渣改质,同时降低钢包顶渣TFe质量分数。采用粒碳部分替代铝渣球的方法进行基于碳脱氧工艺的钢包顶渣改质,试验结果表明,顶渣改质效果良好,在顶渣TFe质量分数、中间包钢水游离氧明显降低的同时铸坯中Al₂O₃夹杂物得到优化;“30 kg粒渣+铝渣球”工艺降低生产成本5.16元/t(钢)。     

基于密钥共享的分层混合认证模型

随着信息时代的迅速发展,云计算数据访问安全已经成为了用户最关心的问题。身份认证技术是确保参与者在开放的网络环境中实现安全通信的一种重要手段,如何利用身份认证技术为云环境安全保驾护航,成为学者研究的热点。文中通过公钥基础设施(Public Key Infrastructure,PKI)颁发CA证书以在不同云服务间建立信任,将多个采用身份密码体制(Identity-Based Encryption,IBE)的云联合起来；采用分层身份加密体系,引入共享密钥技术,通过选取成环结构,提出一种PKI-IBE混合认证模型方案,并对方案的安全性进行分析,从理论上证明了云环境下PKI-IBE(Public Key Infrastructure-Identity-Based Encryption)同层成环模型提供服务的可行性。同时文中设计了一种基于该模型的签密技术,通过公私密钥对实现云内认证以及跨云认证。安全性理论证明与性能分析表明,该方案在计算量稍增加的前提下,保证了足够的安全性,更加满足云环境下的用户分属不同云域的认证以及用户安全访问的需求,有效解决了云环境中数据访问的安全问题。     

土木工程施工课程线上平台教学实践

土木工程施工课程作为土木工程及工程管理专业的学科基础课和核心专业课程,在疫情防控期间"停课不停教""停课不停学"的要求下,通过线上平台进行授课。由于土木工程施工课程内容庞杂、综合性强、实践性强,且章节之间关联性较弱,探究既能使学生快速适应,又能保证教学质量的在线教学方法至关重要。以华南某高校土木工程施工课程为例,基于中国大学MOOC、建筑云课、腾讯课堂、QQ群等线上平台讲授教学内容,并运用问卷调查对课程线上教学效果进行评价。结果表明,线上教学为学生提供了丰富灵活的学习方式,显著提高了学生的自主学习能力,扩大了学生的知识面,达到了较好的学习效果。     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

S6-11综放工作面回风巷支护设计优化

针对常村矿回采巷道变形严重,影响正常生产的问题,通过对矿井赋存条件、巷道现有支护形式和围岩变形过程分析,采用新型联合支护技术对S6-11回风巷道支护进行优化。现场实测巷道顶底板相对移近量最大120mm,两帮相对移近量最大140mm,均在允许正常范围之内。     

大采高综放工作面强动压端头顶板控制技术

为了解决大采高综放工作面回风侧端头顶板控制问题,针对马道头矿8211工作面的地质情况,通过对回风顺槽超前注浆加固,提高超前支护范围和支护强度,并对回风侧端头三角区煤壁和顶板进行化学浆加固的技术方案,有效控制了顺槽超前区围岩变形和工作面回风侧端头三角区煤壁片帮,保证了工作面端头区顶板安全。     

Cloud based cyber-physical systems: Network evaluation study

In recent years, the Industry 4.0 concept brings new demands and trends in different areas; one of them is distributing computational power to the cloud. This concept also introduced the Reference Architectural Model for Industry 4.0 (RAMI 4.0). The efficiency of data communications within the RAMI 4.0 model is a critical issue. Aiming to evaluate the efficiency of data communication in the Cloud Based Cyber-Physical Systems (CB-CPS), this study analyzes the periods and data amount required to communicate with individual hierarchy levels of the RAMI 4.0 model. The evaluation of the network properties of the communication protocols eligible for CB-CPS is presented. The network properties to different cloud providers and data centers’ locations have been measured and interpreted. To test the findings, an architecture for cloud control of laboratory model was proposed. It was found that the time of the day; the day of the week; and data center utilization have a negligible impact on latency. The most significant impact lies in the data center distance and the speed of the communication channel. Moreover, the communication protocol also has impact on the latency. The feasibility of controlling each level of RAMI 4.0 through cloud services was investigated. Experimental results showed that control is possible in many solutions, but these solutions mostly cannot depend just on cloud services. The intelligence on the edge of the network will play a significant role. The main contribution is a thorough evaluation of different cloud providers, locations, and communication protocols to provide recommendations sufficient for different levels of the RAMI 4.0 architecture.