径流过程分维数与其主要影响因素的定量研究

为研究分维数与其主要影响因素的定量关系,收集泾河张家山水文站的径流资料和该流域长武、环县、崆峒气象站日降雨数据以及ＮＤＶＩ遥感影像,分别选取降水不均匀系数为代表降水年内分配不均匀程度的指标和ＮＤＶＩ为反映人类活动对流域植被覆盖影响的指标,经过归一化处理后,使用ＭＴＡＬＡＢ软件得出以年为单位的泾河径流过程分维数与该流域降水不均匀系数和ＮＤＶＩ的定量关系式。结果表明分维数与降水不均匀系数呈正相关（Ｒ＝０．５５７１）,而与ＮＤＶＩ呈负相关（Ｒ＝０．２２７８）。说明降水年内分配越不均匀,植被覆盖越小,径流过程越复杂,分维数就越大,符合平常认知;泾河流域的降水对径流过程的影响较流域植被覆盖更大。     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

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.     

Language learners' digital literacies: Focus on students' information literacy and reading practices online

The widespread use of technology and the Internet have changed many of language learners' everyday practices, including literacies. While there have been many studies with the focus on language learners' digital literacies, few, however, have explored language learners' digital information literacy and online reading practices with the use of social bookmarking tools, especially in a community college setting. We address this gap by investigating community-college language learners' digital literacies when social bookmarking with the focus on digital information and online reading practices from an ecological perspective. In this qualitative multiple-case study, the focal participants were five English learners, students in an English as a Second Language writing course in a community college in the northeastern United States. Data collection included interviews, observations, and researchers' e-journals. Thorough within- and cross-case analysis of data shows that language learners searched for digital texts and evaluated them based on relevance, reliability, interest, language, and importance for them and their learning community in the social bookmarking tool. The participants struggled with the number of results in search engines, keywords, and evaluation of digital texts for relevance and reliability. We show the need for more instruction, support, and guidance of language learners' digital information literacy practices as well as the benefits of providing students with opportunities to read digital texts. Our suggestions for future research include investigating the role of multimodality and other factors that influence language learners' evaluation practices when they look for and read information online.     

Land‐use dynamic discovery based on heterogeneous mobility sources

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport‐network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time‐invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.     

Creativity in non‐routine jobs: The role of transformational leadership and organizational identification

An increasing number of individuals work in jobs with little standardization and repetition, that is, with high levels of job non‐routinization. At the same time, demands for creativity are high, which raises the question of how employees can use job non‐routinization to develop creativity. Acknowledging the importance of social processes for creativity, we propose that transformational leaders raise feelings of organizational identification in followers and that this form of identification then helps individuals to develop creativity in jobs with little routinization. This is because organizational members evaluate and promote those ideas as more creative, which are in line with a shared understanding of creativity within the organization. To investigate these relationships, we calculated a mediated moderation model with 173 leader–follower dyads from China. Results confirm our hypotheses that transformational leadership moderates the relationship between job non‐routinization on employee creativity through organizational identification. We conclude that raising feelings of social identity is a key task for leaders today, especially when working in uncertain and fast developing environments with little repetition and the constant need to develop creative ideas.     

基于计算机视觉的油封尺寸检测系统设计

针对油封的大批量、多品种、流水线作业的生产特点以及"零废品率"的检测要求,利用机器视觉检测和计算机图象处理技术,研究开发了适应大批量生产和在线实时检测的油封尺寸机器视觉检测系统;建立了系统软硬件结构,分析了系统的组成和工作原理及工作过程,并对图像处理算法进行了设计;系统标定后的实验检测结果证明,该检测系统能实现产品100%检测的生产目标,具有非接触、在线实时、精度适中、成本低等特点,具有较好的生产通用性。     

非传统信息安全下的搜索引擎

搜索引擎已成为当今人们上网获取信息所不可缺少的一种手段,在得到自己所需要信息的同时也在暴露着自己的行踪,从而引出了保护个人隐私的大讨论。本文利用社会工程学原理从非传统信息安全角度分析,以通过QQ号入手,利用现有的搜索引擎的例子,来揭示搜索引擎在非传统信息安全下带来的危害,阐明社会工程学原理对非传统信息安全的重大影响,并提出相应的防范对策。     

社保系统个人信息隐私保护算法

针对社会保障信息系统中公民的个人信息隐私保护越来越困难的现状,介绍了社保系统的隐私保护难题包括如何保护信息安全和如何界定隐私信息2个方面,分析了隐私信息保护的2个根源性问题在于个人信息的不当采集和不当使用,在此基础上提出了利用转换原始数据的方式保护隐私信息的算法,阻止了信息拥有者对隐私信息直接或间接的获取,为社保系统中个人信息隐私保护的实现提供了一定的研究思路。     

分形压缩感知高维信号重构方法

压缩感知理论改变了香农采样定理的信号处理思路,具有十分重要的科研应用价值。压缩感知框架下信号重构是获取数字终端产品的关键性环节,典型的重构方法是以基追踪(BP)算法为代表,核心是解决L₁范数最小化问题,但是BP算法在高维的信号重构中表现不佳。因此,本文提出一种基于分形维度的压缩感知高维信号重构方法,采用分形中的Minkowski维度代替L₁范数作为重构问题的目标函数。实验的可视化结果和信噪比均表明,分形压缩感知信号重构方法既保持了BP算法的优点又改善了其维度的广延性。