基于两电极体系的水质检测分析平台设计

为实现对水质的多参数检测,设计了一个基于两电极体系的水质检测分析平台。该检测平台通过最小化极化电流的思想,采用辅助电极面积远大于工作电极的电极参数,设计了基于两电极体系的集成电极和软硬件系统,摈弃了传统三电极体系的参比电极,同时克服了传统三电极体系容易污染样品的缺点,能实现电化学工作站的诸多功能。本研究在该检测平台上开展了水样的电导率(EC)、pH和重金属浓度的检测研究,获得了较好的结果,可用于实验室及工业现场的水质监测。     

ExtJS框架下表格组件的应用与改进

在油田措施效果评价系统开发中,使用了ASP.NET技术并结合ExtJS框架,设计并实现了一个基于B/S模式的Web应用系统,解决开发中的技术问题,着重对ExtJS中的表格组件进行了改进和扩展,实现了同时显示锁定部分表格列与支持多行表头的复杂表格,对系统中常用的几种表格形态及深入应用作了介绍与分析。     

软件定义车联网中缓存辅助的NOMA功率分配方案研究

由于对丰富多媒体服务的需求日益增长,车联网需要提供海量的设备连接以满足高频谱效率和低延迟的需求。软件定义网络（SDN）、缓存和非正交多址接入（NOMA）被认为是有效解决这些关键挑战的潜在技术。针对软件定义车联网,提出了一种缓存辅助的NOMA功率分配方案。首先,针对车联网中车辆总是处于高速运动状态的特点,提出了一种新的簇头选择算法,到达的道路交通将借助SDN进行预测,实现自适应车辆分簇。其次,引入了缓存辅助的NOMA方案,每个车辆在文件缓存阶段使用NOMA原理缓存和请求文件。再次,针对双Nakagami-m衰落条件下的两个簇头车辆通信场景,提出了一种最优功率分配策略,将优化问题公式化为找到每辆车的最佳功率曲线,从而最大化地在每辆车上成功解码目标文件的概率。最后,数值仿真和理论分析表明,所提缓存辅助NOMA功率分配方案,性能明显优于传统的NOMA和缓存辅助的OMA。     

A factor mining model with optimized random forest for concrete dam deformation monitoring

The unique structure of a dam complicates safety monitoring. Deformation can provide important information about dam evolution. In contrast to model prediction, actual dam response monitoring data can be used for diagnosis and early warning. Given the poor data mining ability of the conventional methods, it is essential to develop a method for extracting the factors influencing a dam. In this study, a data mining method and a model for evaluation of concrete dam deformation were developed using the evidence theory and a random forest. The model has the advantages of being easily understood, visualization with low complexity of training time, and accurate prediction. The model was applied to an actual concrete dam. The results indicated that the proposed random forest model could be used in analysis of concrete dams.     

Potential applications of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts

Theknowledge transfer problem in artificial intelligence consists of finding effective ways to elicit information from a human expert and represent it in a form suitable for use by an expert system. One approach to formalizing and guiding this knowledge transfer process for certain types of expert systems is to use psychometric scaling methods to analyze data on how the human expert compares or groups solutions. For example, Butler and Corter [1] obtained judgments of thesubstitutability of solutions from an expert, then analyzed the resulting data via techniques for fitting trees and extended trees [2]. The expert's interpretation of certain aspects of the solutions were directly encoded as production rules, allowing rapid prototyping. In this paper we consider the problem of combining information from multiple experts. We propose the use of three-way or individual differences multidimensional scaling, tree-fitting, and unfolding models to analyze two types of data obtainable from the multiple experts: judgments of the substitutability of pairs of solutions, and judgments of the appropriateness of specific solutions to specific problems. An application is described in which substitutability data were obtained from three experts and analyzed using the SINDSCAL program [3] for three-way multidimensional scaling [4].     

The impact of risk and protective factors on online teaching experience in high school Italian teachers during the COVID-19 pandemic

The lockdown due to COVID-19 in Italy resulted in the sudden closure of schools, with a shift from traditional teaching to the online one. Through an online questionnaire, this survey explores teachers' experience of online teaching, the level of risk factors (e.g., stress) and protective factors (e.g., locus of control) and their impact on satisfaction levels during the social distancing. One hundred seven high school teachers from Lombardy, an Italian region very affected by the COVID-19 outbreak, participated. Results show that depression and stress are the main predictors of satisfaction levels for online teaching. In addition, coping, locus of control and self-efficacy emerge as important protective factors. Finally, although there is great satisfaction with the online teaching experience, critical elements emerged. This study is relevant because it describes the critical elements of the online teaching experience, and identifies some protective factors and the main risk factors in teachers operating in an area strongly marked by social restrictions imposed by the pandemic. High school teachers emerge as a sub-group of the general population with specific psychological reactions. Considering the results, it is possible to suggest providing high-quality educational support and crisis-psychological oriented services to teachers, and help to maintain the psychological well-being.     

基于码本模型和多特征的早期烟雾检测

提出一种基于码本模型和多特征的早期烟雾检测算法。首先,利用码本模型进行前景提取;然后,结合烟雾的颜色模型和形状特征模型检测前景中的疑似烟雾区域;最后,利用烟雾的动态特性,有效地降低误检率,提高算法的鲁棒性。通过ROC曲线对比,实验结果表明,该算法具有良好的烟雾检测能力。同时,能够满足实时性要求,具有较高的实用价值。     

阻燃型氢氧化镁制备及其影响因素探讨

以七水硫酸镁为原料、氨水为沉淀剂、选择适宜的表面活性剂。以样品XRD图中（001）面与（101）面衍射峰的相对强弱为考察指标，通过单因素实验，分别考察了反应温度、恒温反应时间、陈化时间、表面活性剂用量及氨镁摩尔比等因素对氢氧化镁样品XRD图中（001）面与（101）面衍射峰相对强弱的影响。实验结果表明，制备阻燃氩氧化镁较适宜的工艺条件为：反应温度40℃、恒温反应时间40min、陈化时间120min、表面活性剂A体积用量4ml、氨镁摩尔比6：1.     

The Role of Tumor Microenvironment Cells in Colorectal Cancer (CRC) Cachexia

Cancer cachexia (CC) is a multifactorial syndrome in patients with advanced cancer characterized by weight loss via skeletal-muscle and adipose-tissue atrophy, catabolic activity, and systemic inflammation. CC is correlated with functional impairment, reduced therapeutic responsiveness, and poor prognosis, and is a major cause of death in cancer patients. In colorectal cancer (CRC), cachexia affects around 50–61% of patients, but remains overlooked, understudied, and uncured. The mechanisms driving CC are not fully understood but are related, at least in part, to the local and systemic immune response to the tumor. Accumulating evidence demonstrates a significant role of tumor microenvironment (TME) cells (e.g., macrophages, neutrophils, and fibroblasts) in both cancer progression and tumor-induced cachexia, through the production of multiple procachectic factors. The most important role in CRC-associated cachexia is played by pro-inflammatory cytokines, including the tumor necrosis factor α (TNFα), originally known as cachectin, Interleukin (IL)-1, IL-6, and certain chemokines (e.g., IL-8). Heterogeneous CRC cells themselves also produce numerous cytokines (including chemokines), as well as novel factors called “cachexokines”. The tumor microenvironment (TME) contributes to systemic inflammation and increased oxidative stress and fibrosis. This review summarizes the current knowledge on the role of TME cellular components in CRC-associated cachexia, as well as discusses the potential role of selected mediators secreted by colorectal cancer cells in cooperation with tumor-associated immune and non-immune cells of tumor microenvironment in inducing or potentiating cancer cachexia. This knowledge serves to aid the understanding of the mechanisms of this process, as well as prevent its consequences.