Comparing technology acceptance of K-12 teachers with and without prior experience of learning management systems: A Covid-19 pandemic study

Covid-19 pandemic has caused a massive transformation in K-12 settings towards online education. It is important to explore the factors that facilitate online teaching technology adoption of teachers during the pandemic. The aim of this study was to compare Learning Management System (LMS) acceptance of Finnish K-12 teachers who have been using a specific LMS as part of their regular teaching before the Covid-19 pandemic (experienced group) and teachers who started using it for emergency remote teaching during the pandemic (inexperienced group). Based on the Unified Theory of Acceptance and Use of Technology framework, a self-report questionnaire was administered to 196 teachers (n_experienced = 127; n_{inexperienced} = 69). Our findings showed no difference between the two groups of teachers in terms of performance expectancy, effort expectancy, LMS self-efficacy and satisfaction. However, the experienced group had higher behavioural intention to use LMS in the future, reported receiving higher online teaching support and displayed higher online teaching self-efficacy in terms of student engagement, classroom management, instructional strategies and ICT skills. For the experienced group, the most significant predictor of satisfaction with LMS was performance expectancy whereas for the inexperienced group, it was the effort expectancy. In terms of behavioural intention to use LMS in the future, the most significant predictor was the performance expectancy for both groups. Further, support was also a significant predictor of behavioural intention for the inexperienced group. Overall, our findings indicate that teachers should not be regarded as a unified profile when managing technology adoption in schools.     

Co‐evolving supportive networks and perceived community resilience across disaster‐damaged areas after the Great East Japan Earthquake: Selection,influence, or both?

After the Great East Japan Earthquake and Tsunami in March 2011, the new community currency experiment for supporting disaster recovery, Fukkou Ouen Chiiki Tsuka, was introduced by community‐based organizations in these earthquake‐damaged areas. However, little is known about how perceived community resilience coevolves with interactions in the disaster recovery process. Using Simultaneous Investigation for Empirical Network Analysis techniques, this study shows the coevolutionary dynamics between perceptions of community resilience and the formation of supportive links among residents through a community currency (“Domo”) in Kamaishi. This study also provides policy implications for how mutual reinforcement between community residents’ engagement in network establishments and building a sense of community resilience among those affected functions as a potential mechanism for facilitating disaster recovery.     

COVID TCL: A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification, combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss (COVID-TCL). Triplet loss can increase inter-class discreteness, and center loss can improve intra-class compactness. Therefore, COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period. Meanwhile, we use the extreme gradient boosting (XGBoost) as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture, to further improve the CNN-based model’s classification effect and operation efficiency. The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%, and the sensitivity is 97.61%, which is higher than the other 7 reference models. The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model, the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.     

Lobbying and emergency management in the United States: Issues,priorities and implications

Recent scholarship has explored the impact of interest groups on policy in the United States. However, little remains known about lobbying efforts and their effects in emergency management. Through analysis of a large data set of declared political activities from 1999 to 2020, we describe lobbying efforts in disaster planning and emergencies. Our findings suggest that lobbying efforts and expenditure are positively associated with appropriations (but not disaster incidence or severity), that corporations and trade associations are the organizations most involved in lobbying and that many of these efforts appear to be aimed at impacting legislation and the procurement of public funds for recovery efforts. We also find that only a minuscule number of lobbying efforts are related to socially vulnerable populations or social equity concerns. Collectively, these insights raise important questions about this process, demonstrating the need for further research to better understand lobbying and emergency management in the United States across all phases of the disaster life cycle.     

Path-based approach to integrated planning and control for robotic systems: A path-based approach provides an analytical integration of robot planning and control. As a result, the task level control can be achieved, and the system is capable of coping with unexpected or uncertain events

Our newly developed event-based planning and control theory is applied to robotic systems. It Introduces a suitable action or motion reference variable other than time, but directly related to the desired and measurable systems output, called event. Here the event is the length of the path tracked by a robot. It enables the construction of an integrated planning and control system where planning becomes a real-time closed-loop process. The path-based integration planning and control scheme is exemplified by a single-arm tracking problem. Time and energy optimal motion plans combined with nonlinear feedback control are derived in closed form. To the best of our knowledge, this closed-form solution was not obtained before. The equivalence of path-based and time-based representations of nonlinear feedback control is shown, and an overall system stability criterion has also been obtained. The application of event-based integrated planning and control provides the robotic systems the capability to cope with unexpected and uncertain events in real time, without the need for replanning. The theoretical results are illustrated and verified by experiments.