A study on the implementation of remote learning: Malaysian students' struggles and suggestions for improvement

This study seeks to explore Malaysian undergraduates' perspectives on the implementation of remote learning in their university during the period of the movement control order (MCO). Since teaching and learning activities have been impacted by the pandemic, it is imperative to consider students' perspectives on carrying out classes via the online platform as many studies claim that the pandemic has disrupted teaching and learning activities. A total of 1,028 undergraduate students participated in this voluntary study by answering an open-ended survey sent out to their student email addresses during the MCO period that restricted students and lecturers from going to the university. The qualitative responses from the students were critically analyzed for thematic patterns. The four themes emerging from the data provide future teaching and learning plans that should embed self-learning techniques that could aid students if a similar predicament should hit us in the future. Course instructors can use this information to design future lessons that could assist their learners better. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Pre- and in-hospital anticoagulation therapy in coronavirus disease 2019 patients: a propensity-matched analysis of in-hospital outcomes.

AIMS: To estimate if chronic anticoagulant (CAC) treatment is associated with morbidity and mortality outcomes of patients hospitalized for SARS-CoV-2 infection. METHODS: In this European multicentric cohort study, we included 1186 patients of whom 144 were on CAC (12.1%) with positive coronavirus disease 2019 testing between 1 February and 30 July 2020. The average treatment effect (ATE) analysis with a propensity score-matching (PSM) algorithm was used to estimate the impact of CAC on the primary outcomes defined as in-hospital death, major and minor bleeding events, cardiovascular complications (CCI), and acute kidney injury (AKI). We also investigated if different dosages of in-hospital heparin were associated with in-hospital survival. RESULTS: In unadjusted populations, primary outcomes were significantly higher among CAC patients compared with non-CAC patients: all-cause death (35% vs. 18% P < 0.001), major and minor bleeding (14% vs. 8% P = 0.026; 25% vs. 17% P = 0.014), CCI (27% vs. 14% P < 0.001), and AKI (42% vs. 19% P < 0.001). In ATE analysis with PSM, there was no significant association between CAC and primary outcomes except for an increased incidence of AKI (ATE +10.2%, 95% confidence interval 0.3-20.1%, P = 0.044). Conversely, in-hospital heparin, regardless of dose, was associated with a significantly higher survival compared with no anticoagulation. CONCLUSIONS: The use of CAC was not associated with the primary outcomes except for the increase in AKI. However, in the adjusted survival analysis, any dose of in-hospital anticoagulation was associated with significantly higher survival compared with no anticoagulation.

Novel COVID-19 Recognition Framework Based on Conic Functions Classifier

The new coronavirus has been declared as a global emergency. The first case was officially declared in Wuhan, China, during the end of 2019. Since then, the virus has spread to nearly every continent, and case numbers continue to rise. The scientists and engineers immediately responded to the virus and presented techniques, devices and treatment approaches to fight back and eliminate the virus. Machine learning is a popular scientific tool and is applied to several medical image recognition problems, involving tumour recognition, cancer detection, organ transplantation and COVID-19 diagnosis. It is proved that machine learning presents robust, fast and accurate results in various medical image recognition problems. Generally, machine learning-based frameworks consist of two stages: feature extraction and classification. In the feature extraction, overwhelmingly unsupervised learning techniques are applied to reduce the input data’s size. This step extracts appropriate features by reducing the computational time and increasing the performance of the classifiers. A classifier is the second step that aims to categorise the input. Within the proposed step, the unsupervised part relies on the feature extraction by using local binary patterns (LBP), followed by feature selection relying on factor analysis technique. The LBP is a kind of visual descriptor, mainly applied for image recognition problem. The aim of using LBP is to analyse the input COVID-19 image and extract salient features. Furthermore, factor analysis is a statistical technique applied to define variability among observed variables in less unnoticed variables named factors. The factor analysis applied to the LBP wavelet aims to select sensitive features from input data (LBP output) and reduce the size input. In the last stage, conic functions classifier is applied to classify two sets of data, categorising the extracted features by using LBP and factor analysis as positive or negative COVID-19 cases. The proposed solution aims to diagnose COVID-19 by using LBP and factor analysis, based on conic functions classifier. The conic functions classifier presents remarkable results compared with these popular classifiers and state-of-the-art studies presented in the literature. © 2022, Springer Nature Switzerland AG.

Impact of COVID 19 on FM services and strategies to overcome this situation in GCC countries

The Gulf Cooperation Council (GCC) countries) face the dual shock of a pandemic caused by the novel coronavirus (COIVD 19) and a collapse in oil prices. GCC countries many times experienced fluctuations in oil price and learnt how to deal the situation. However, the COVID-19 outbreak, being a new one, has created a lot of concern among GCC countries. This pandemic is causing turbulence to the economies of the GCC countries. Major industries that have been impacted in GCC countries due to COVID-19 pandemic include Energy, Aviation, Food & Beverage, Chemical, Retail & E-commerce, Travel & Tourism among others. Besides a major downfall in oil demand has been reported across the globe due to the effect of COVID-19. Due to this, many oil productions sites have been shut down or has to decrease production in the region. The closure of the industrial and commercial activities because of the pandemic would certainly affect their economies. Facility management (FM) constitutes a branch, jointly representing real estate market with property management and asset management. It plays a crucial role in economic activities in region as FM services are involved in all industrial and commercial activities. The Facility Management (FM) market in GCC countries has witnessed robust growth during the last few decades due to rapid economic activities in this region. It is an established fact in FM services manpower cost dominates the total cost whereas material cost plays vital role in construction industries. Majority of work forces in GCC countries in FM sector is migrant people from the Globe. CCC countries are showing actions that they are capable of acting effectively to contain the health and economic impacts of the pandemic within their own borders, albeit with marked shortcomings when it comes to protecting migrant workers. It is estimated that approximately 23 million migrant workers are living GCC countries . These millions of migrant workers across the Gulf face uncertainty as host countries lock down, employers withhold wages or mull redundancies, and strict coronavirus containment measures lead to deportations and confinement. This will have series impact on FM sector. In this paper a detail study the impact of COVID 19 on FM sector in GCC countries is reported. Strategies to overcome the crisis are listed along with the means and recommendation to implement the strategy.

Covid-19 impacts on organization infrastructure via exploiting society digital illiteracy: The rise of electronics

Globally, the COVID-19 pandemic has been the headline over the past few months and forced the institution and individuals to work remotely and practices such as social distancing. Consequently, the cybercriminals urgent implementation of technology to enable the organization to work remotely by conducting cyber-attack targeting critical organization within countries. This article discusses the different type of cyber threats and its impact to the organizations during COVID-19 pandemic by exploiting the digital and technology.