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Urban resilience, which has emerged as an important concept in cities since sustainability became a 21st-century urban paradigm, reflects the needs of the times to change and bring about a shift in existing national landscape architecture and social policies. To explore the characteristics of recognition of college students majoring in landscape architecture towards the concept of urban resilience before and after the beginning of COVID-19, this study aims to answer three research questions: to analyzes recognitions of landscape architecture majoring students on urban resilience (research question 1); to compare the differences that emerge from before and after the beginning of the COVID-19 pandemic (research question 2); and to explore latent classes according to the education pattern (research question 3). The results of this study are as follows: First, before the beginning of COVID-19, four latent classes were drawn up in relation to awareness of the concept of urban resilience, while three latent classes were examined after the start of the pandemic. Before the beginning of COVID-19, students of landscape architecture accepted the concept of urban resilience as a physical and environmental approach to overcome risk factors by creating landscape architecture and infrastructure or applying the concept of resilience in urban development and redevelopment. However, after the beginning of COVID-19, they mostly have been recognized urban resilience as a concept related to technological ability. Thirdly, the grades and educational experiences of the students were found to have a significant effect on the probability of their belonging to a specific latent class.

Recently, Internet of Things (IoT) technology has become a hot trend and is used in a wide variety of fields. For instance, in education, this technology contributes to improving learning efficiency in the class by enabling learners to interact with physical devices and providing appropriate learning content based on this interaction. Such interaction data can be collected through the physical devices to define personal data. In the meanwhile, multimedia contents in this environment usually have a wide variety of formats and standards, making it difficult for computers to understand their meaning and reuse them. This could be a serious obstacle to the effective use or sustainable management of educational contents in IoT-based educational systems. In order to solve this problem, in this paper, we propose a semantic annotation scheme for sustainable computing in the IoT environment. More specifically, we first show how to collect appropriate multimedia contents and interaction data. Next, we calculate the readability of learning materials and define the user readability level to provide appropriate contents to the learners. Finally, we describe our semantic annotation scheme and show how to annotate collected data using our scheme. We implement a prototype system and show that our scheme can achieve efficient management of various learning materials in the IoT-based educational system.

The ever-increasing development of information technologies has led to the adoption of advanced learning techniques. In this regard, e-learning and machine learning are two of the emerging instructional means for educational institutes. The current study investigates the role of the Internet of Things (IoT) and digital educational platforms (DEPs) in the adoption of machine learning. The present research additionally investigated the function of DEPs as mediators between IoT and machine learning adoption. The department chairs or heads of 310 departments at 91 Chinese institutions provided the information. In order to analyze the data, we used SPSS 25.0 and SEM (structural equation modeling). The results demonstrated how crucial an impact IoT has on DEPs and the uptake of machine learning. DEPs directly affect machine learning adoption and also act as mediators. The findings also support the mediating role of DEPs in the IoT and machine learning adoption link. The current study contributes to both theory and practical management by examining how IoT is helpful for achieving machine learning adoption. Based on the responses of 91 educational departments, this is a unique study of the mechanisms to achieve machine learning adoption through IoT and DEPs.

In the efficient design and functionality of complex systems, redundancy problems in systems play a key role. The consecutive-k-out-of-n:F structure, which has broad application in street light arrangements, vacuum systems in an accelerator, sliding window detection, relay stations for a communication system. Availability is one of the significant measures for a maintained device because availability accounts for the repair capability. A very significant feature is the steady-state availability of a repairable device. For the repairable consecutive k-out-of-n:F system with independent and identically distributed components, the Bayesian point estimate (B.P.E) of steady-state availability under squared error loss function (SELF) and confidence interval are obtained.