Trusted computing in power distribution IoT: A fuzzy set theory based analysis

As emerging technologies in information security protection, trusted computing and blockchain can guarantee a safe and reliable operating environment and management mechanism in the Power Distribution IoT. Relying on the comprehensive evaluation algorithm of fuzzy mathematics set theory in this paper, a blockchain-based distributed decision-making and collaborative autonomy model for the power distribution IoT is built, and the “manageable and controllable, precise protection, visible and credible, and smart defense” security protection model is established to carry out trusted computing and privacy protection of computer blockchain IoT nodes, and the information security defense level of the distribution network is improved comprehensively.

Parents still matter: the influence of parental enforcement of bedtime on adolescents’ depressive symptoms

Study Objectives The aim of the current study was to test a multilevel mediation model that examined how adolescent sleep duration might be linked to depressive symptoms via their daytime energy levels. Furthermore, the study examined how parents’ enforcement of various types of bedtime rules predicted the duration of adolescent sleep. Methods A total of 193 adolescent (ages 14–17; Mage = 15.7 years old, SD = .94; 54.4% female; 71% Caucasian) and parent dyads completed baseline, online surveys, and adolescents also completed online 7-day, twice-daily (i.e. morning and evening) reports of their sleep duration (morning diary) and their energy levels and depressive symptoms throughout the day (evening diary). Parents (Mage = 47.6 years old, SD = 5.4; 80% female) completed assessments of enforcement of bedtime-related rules (i.e. bedtime, cessation of electronic media usage, prohibiting afternoon/evening caffeine consumption). Multilevel modeling enabled the testing of the mediation model both at the between-person level and within individuals. Results Results suggested that adolescents’ energy levels mediated the association between adolescents’ sleep duration and depressive symptoms. Furthermore, both greater enforcement of bedtimes and later school start times predicted longer sleep durations for adolescents, and were indirectly associated with adolescents’ depressive symptoms. Conclusions These findings underscore the importance of adolescents obtaining sufficient sleep to support their mental health and suggest a critical point of intervention for preventing or decreasing insufficient sleep. Given the diverse threats to adolescents’ sleep as well as adolescents’ desire for greater independence, collaborative, autonomy-promoting bedtime limit-setting is recommended to support adolescents’ well-being.

Fostering Collaborative Autonomy: The Roles of Cooperative and Collaborative Learning

The distinction between Cooperative and Collaborative Learning approaches is not a clear one. Some use the terms interchangeably while others consider Cooperative Learning to be a type of Collaborative Learning. Still others clearly differentiate between them, characterizing Cooperative Learning as more highly structured in its procedures, involving a great deal of intervention by the teacher to plan and orchestrate group interactions. Collaborative Learning, on the other hand, presupposes some degree of learner autonomy－that groups can work effectively toward shared goals and monitor their own progress. This paper takes the view that the distinction between Cooperative and Collaborative Learning is a useful one and that both approaches can play valuable roles in fostering autonomous interaction. It argues that while Collaborative Learning formations may be the ultimate goal for teachers wishing to develop learner autonomy, Cooperative Learning is a valuable means for modeling the skills and abilities to help students get there. The discussion begins with an overview of the two approaches, focusing on their implementation in the Japanese educational context. It then presents seven highly structured Cooperative Learning activities and shows how they can be modified and extended over time to encourage more autonomous interaction.

Adaptive human-in-the-loop multi-target recognition improved by learning

Machine learning algorithms have been designed to address the challenge of multi-target recognition in dynamic and complex environments. However, sufficient high-quality samples are not always available for training an accurate multi-target recognition classifier. In this article, we propose a generic human-in-the-loop multi-target recognition framework that has four collaborative autonomy levels, and it allows adaptive autonomy level adjustment based on the recognition task complexity as well as the human operator’s performance. The human operator can intervene to relabel the collected data and guarantee the recognition accuracy when the trained classifier is not good enough. Meanwhile, the relabeled data are used for online learning which further improves the performance of the classifier. Experiments have been carried out to validate the proposed approach.