Successful implementation and use of learning management systems (LMSs) have become a critical challenge for many higher education institutes during the Covid-19 pandemic. Although LMSs with lots of features were developed for universities, the success of those systems is highly related to a detailed understanding of challenges and factors influencing the use of the systems among their users. HELMS (Higher Education Learning Management System) is a countrywide LMS used for teaching and learning during the quarantine period caused by covid-19 in Afghanistan universities. As it was the first experience of Afghan universities in using the learning management systems during the pandemic, challenges were expected to appear. No previous research has been conducted on either studying the challenges of using the HELMS or investigating the factors influencing the use of HELMS during the Covid-19 pandemic in Afghanistan. Hence, there was no unified view of the potential challenges of using HELMS and factors influencing the use of the HELMS among the researchers. This research aims to investigate the challenges that face the use of HELMS and explore the factors influencing the use of HELMS among both lecturers and students. This study employed a qualitative research method by conducting semi-structured interviews with 100 participants including university management, lecturers, and students. Thematic analysis was used as a method for the analysis of qualitative data. The findings of this research will help policymakers, researchers, and practitioners in public and private universities to grasp knowledge on the successful implementation and use of LMSs during covid-19 and afterward.

Summary In this paper, the problem of robust distributed H∞ filtering is investigated for state‐delayed discrete‐time linear systems over a sensor network with multiple fading measurements, random time‐varying communication delays, and norm‐bounded uncertainties in all matrices of the system. The diagonal matrices, whose elements are individual independent random variables, are utilized to describe the multiple fading measurements. Furthermore, the Bernoulli‐distributed white sequences are introduced to model the random occurrence of time‐varying communication delays. In the proposed filtering approach, the stability of the estimation error system is first shown by the Lyapunov stability theory and the H∞ performance is then achieved using a linear matrix inequality method. Finally, two numerical examples are given to show the effectiveness and performance of the proposed approach.

The proliferation of fast, dense, byte-addressable nonvolatile memory suggests that data might be kept in pointer-rich "in-memory" format across program runs and even process and system crashes. For full generality, such data requires dynamic memory allocation, and while the allocator could in principle be "rolled into" each data structure, it is desirable to make it a separate abstraction.Toward this end, we introduce recoverability, a correctness criterion for persistent allocators, together with a nonblocking allocator, Ralloc, that satisfies this criterion. Ralloc is based on the LRMalloc of Leite and Rocha, with three key innovations. First, we persist just enough information during normal operation to permit correct reconstruction of the heap after a fullsystem crash. Our reconstruction mechanism performs garbage collection (GC) to identify and remedy any failure-induced memory leaks. Second, we introduce the notion of filter functions, which identify the locations of pointers within persistent blocks to mitigate the limitations of conservative GC. Third, to allow persistent regions to be mapped at an arbitrary address, we employ position-independent (offset-based) pointers for both data and metadata.Experiments show Ralloc to be performance-competitive with both Makalu, the state-of-theart lock-based persistent allocator, and such transient allocators as LRMalloc and JEMalloc. In particular, reliance on GC and offline metadata reconstruction allows Ralloc to pay almost nothing for persistence during normal operation.

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