When Emotions Rule Knowledge

Although emotions play an important role in human behavior and knowledge studies, knowledge management (KM) research considers them from specific angles and, to date, has lacked a comprehensive understanding of the emotions dominating KM. To offer a holistic view, this study investigates the presence of emotions in KM publications by applying a sentiment analysis. The authors present a sentiment dictionary tailored to KM, apply it to KM publications to determine where and how emotions occur, and categorize them on an emotion scale. The considerable amount of positive and negative emotions expressed in KM studies prove their relevance to and dominance in KM. There is high term diversity but also a need to consolidate terms and emotion categories in KM. This study's results provide new insights into the relevance of emotions in KM research, while practitioners can use this method to detect emotion-laden language and successfully implement KM initiatives.

A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission

In view of the characteristics of the guidance, navigation and control (GNC) system of the lunar orbit rendezvous and docking (RVD), we design an auxiliary safety prediction system based on the human–machine collaboration framework. The system contains two parts, including the construction of the rendezvous and docking safety rule knowledge base by the use of machine learning methods, and the prediction of safety by the use of the base. First, in the ground semi-physical simulation test environment, feature extraction and matching are performed on the images taken by the navigation surveillance camera. Then, the matched features and the rendezvous and docking deviation are used to form training sample pairs, which are further used to construct the safety rule knowledge base by using the decision tree method. Finally, the safety rule knowledge base is used to predict the safety of the subsequent process of the rendezvous and docking based on the current images taken by the surveillance camera, and the probability of success is obtained. Semi-physical experiments on the ground show that the system can improve the level of intelligence in the flight control process and effectively assist ground flight controllers in data monitoring and mission decision-making.

Bicycle Rider Behavior and Crash Involvement in Australia

This research investigated how behaviours and attitudes of bicycle riders influence crash frequency and severity. The study recruited 1102 Australian bicycle riders for an online survey. The survey comprised questions on demographics, frequency of riding and the number and severity of traffic crashes during the last five years. The survey included the Cycling Behaviour Questionnaire and the Cyclist Risk Perception and Regulation Scale. Overall, there were low levels of errors and violations reported by participants indicating that these behaviours were on average never or rarely exhibited while riding a bicycle. Conversely, participants reported high levels of engagement in positive behaviours and reported high levels of traffic rule knowledge and risk perception. Higher rates of violations and errors were associated with increased crash likelihood, while higher rates of positive behaviours were associated with reduced rates of crash involvement in a period of 5 years. The findings highlight the relationship between errors, total crashes and crash severity Further promotion of positive behaviours amongst riders may also help to reduce the risk of crashes.

Application of fuzzy intelligent reasoning method in art color measurement

Under the influence of the COVID-19 epidemic situation, many countries have taken many measures to control the flow of people. The inability of people to gather makes art color measurement a problem. Color matching and coordination and color space conversion have always been the research focus of art color measurement. This paper studies a method of fuzzy intelligent reasoning in art color measurement. Based on case-based reasoning and rule-based reasoning, this method is an important self-learning and self-maintenance method in the field of artificial intelligence and expert system. On the basis of expounding the basic characteristics of color and color space, this paper designs the process principle of case-based reasoning and the process of rule-based reasoning. In this paper, case and rule knowledge representation method, case retrieval technology and rule conflict resolution strategy are established. Based on the above strategy, the color case base is established. In addition, the rule table is established by combining color-brewer with rule reasoning and referring to color-brewer. The rule table has a certain reference value for the application of intelligent reasoning method in the field of art color measurement under the influence of COVID-19 epidemic.

Introduction

As technocracy has spread, critical analysis of it has stagnated. In part, this is because the critics take an external perspective on technocracy, condemning it for being anti-democratic. This perspective discourages critical theorizing about whether technocrats possess the knowledge that, from an internal perspective, qualifies them to rule: knowledge of the costs and benefits of public policies designed to address people’s social and economic problems. However, once we thus recognize technocracy as an inherently epistemic enterprise, we discover that there is a democratic version of technocracy: ordinary citizens often assume that they, too, know the costs and benefits of policies aimed at solving social and economic problems. Indeed, we discover that much of modern mass politics revolves around competing claims about these costs and benefits. An internal critique of technocracy, then, will have to challenge the knowledge claims of both “technocrats” in the ordinary usage—epistocrats—and those of members of the mass public, or “citizen-technocrats.”

The relationship between prosocial behaviours, aggression types and moral—social rule knowledge in preschool children

The objective of the present study was to investigate prosocial behaviour, aggression types and moral and social rule knowledge perceptions of 4–5 year old preschool children. The study sample included 310 children (154 female and 156 male), who were attending a pre-school education institution during the 2017–2018 academic year. A personal information form, Pre-School Social Behavior Scale-Teacher Form and Moral and Social Rule Knowledge Perception Scale were used as data collection instruments. The data were analysed with descriptive analysis, Pearson product-moment correlation coefficient and stepwise regression analysis. In the study, it was determined that there were moderate significant correlations between prosocial behaviour, aggression types and moral and social rule knowledge perceptions of 4–5 year old preschool children. It was also determined that prosocial behaviour and aggression types scores of the children significantly predicted their moral and social rule knowledge perception scores. Keywords: Prosocial behaviours, aggression types, moral rule knowledge, social rule knowledge.