Evolution to Online Education around the globe during a SARS-CoV-2 Coronavirus (COVID-19) Pandemic: Do develop and underdeveloped cope alike?

Background: Educational institutes around the globe in this 21st century is facing challenges of SARS-CoV-2 Coronavirus infectious disease. They are required to conduct online learning to avoid face to face contact in emergency scenarios such as COVID-19 pandemic and continuing academic year while keeping social distancing. Students need to adapt to new roles of learning through information technology to succeed in academics amid COVID-19. Objective: However, access to the impact of access & use of online learning resources, to what extent, these students are satisfied with online learning amid COVID-19 particularly in handling new challenges are critical to explore. Therefore, in this paper, we aimed to assess and compare the access & use of online learning of Bruneians and Pakistanis amid enforced lockdown imposed by the governments using a five-items satisfaction scale underlying existing literature. Method: For this, a cross-sectional study was done in the first half of June 2020 after the pandemic situation among 320 students' across Pakistan and Brunei and strata with a pre-defined questionnaire. Data were analyzed with statistical software package SPSS 2.0. Results: The finding showed that there is a relationship between students' satisfaction and access & use of online learning. Outcomes of the survey suggest that Bruneian are more satisfied (50%) with the use of online learning amid lockdown as compared to Pakistanis (35.9%). Living in the Urban area as compared to a rural area is also a major factor contributing to satisfaction with the access and use of online learning for both Bruneian and Pakistanis. Moreover, previous experience with the use of online learning is observed prevalent among Bruneians (P=.000), while among friends and family is using online learning (P=.000) were encouraging factors contributed to satisfaction with the use of online learning among Pakistanis amid COVID-19. Correlation results suggest that access and use factors of online learning amid COVID-19 were positively associated with satisfaction among both populations amid the COVID-19 pandemic. However, Bruneian is more satisfied with Internet access (r=.437,

Carbon capture, utilization and storage opportunities to mitigate greenhouse gases

Carbon capture, utilization and storage (CCUS) technologies are utmost need of the modern era. CCUS technologies adoption is compulsory to keep global warming below 1.5 °C. Mineral carbonation (MC) is considered one of the safest and most viable methods to sequester anthropogenic carbon dioxide (CO2). MC is an exothermic reaction and occur naturally in the subsurface because of fluid-rock interactions with serpentinite. In serpentine carbonation, CO2 reacts with magnesium to produce carbonates. This article covers CO2 mitigation technologies especially mineral carbonation, mineral carbonation by natural and industrial materials, mineral carbonation feedstock availability in Pakistan, detailed characterization of serpentine from Skardu serpentinite belt, geo sequestration, oceanic sequestration, CO2 to urea and CO2 to methanol and other chemicals. Advantages, disadvantages, and suitability of these technologies is discussed. These technologies are utmost necessary for Pakistan as recent climate change induced flooding devastated one third of Pakistan affecting millions of families. Hence, Pakistan must store CO2 through various CCUS technologies

Enhancing business intelligence by means of suggestive reviews

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers’ choices and designers’ understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.Published versio

Pattern prediction of crude oil using regression moderated with Markov switching model

Scarcity of refined products for long time does contribute to economic backwardness and de-industrialization of most sub-Saharan African and the Organization of Petroleum Exporting Countries (OPEC). Institutional quality and managerial transparency absence impacted the downstream sub-sector negatively and induces Nigeria to import 80% refined products despite its huge crude oil exports. We have used Markov-switching models, a stochastic technique capable of capturing statistical knowledge to moderate Newton Series Polynomial generated on a best linear slope equation. And postulate that with less than 70% annual refinery utilization and undemocratic institutional performance, the Nigerian state will continue to experience resource curse syndrome. Continuation of Nigeria's mono-product economic structure having demonstrated a dismal performance to the economy may curse doom for the nation if it ignores calls for more domestically refined products. This paper offers an oil utilization directional guide to economic development in oil rich nations as against historical illustration of resources rich nations' failure to develop fast. However, if Nigeria chooses to maintain its current crude oil exports status industrialization is foregone

Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review

The aim of this systematic literature review (SLR) is to identify and critically evaluate current research advancements with respect to small data and the use of data augmentation methods to increase the amount of data available for deep learning classifiers for sound (including voice, speech, and related audio signals) classification. Methodology: This SLR was carried out based on the standard SLR guidelines based on PRISMA, and three bibliographic databases were examined, namely, Web of Science, SCOPUS, and IEEE Xplore. Findings. The initial search findings using the variety of keyword combinations in the last five years (2017–2021) resulted in a total of 131 papers. To select relevant articles that are within the scope of this study, we adopted some screening exclusion criteria and snowballing (forward and backward snowballing) which resulted in 56 selected articles. Originality: Shortcomings of previous research studies include the lack of sufficient data, weakly labelled data, unbalanced datasets, noisy datasets, poor representations of sound features, and the lack of effective augmentation approach affecting the overall performance of classifiers, which we discuss in this article. Following the analysis of identified articles, we overview the sound datasets, feature extraction methods, data augmentation techniques, and its applications in different areas in the sound classification research problem. Finally, we conclude with the summary of SLR, answers to research questions, and recommendations for the sound classification task