Impact of COVID19 Forced Confinement for CO2 Emission, NO2 Concentration, and Traffic Congestion Throughout EU Nations and United Kingdom (UK)

The coronavirus outbreak has led several cities to come to the standstill and country lockdown within several locations to reduce coronavirus spread. Here we investigate CO2 emission, NO2 concentration, and mobility throughout EU nations and the United Kingdom (UK) from January 2019 until the end of August 2021. In accordance with the previous research obtained by Liu et al. and Le Quéré et al., as mentioned in the literature, our results show a reduction of CO2 emission for an extended period of 2020 and 2021 compared to the annual emission in 2019. This work obtained abrupt reductions of 10.66% and 4.36% in 2020 and 2021, respectively. Although the ratios and relationship between CO2 and NO2 were considered, we found that monthly NO2 concentration was reduced by 2–39% for ±1σ in 2020 and 13–34% for ±1σ in 2021 (until August) relative to 2019. Additionally, during confinements, the average annual mobility was substantially reduced by 36% for 2020 and 24% for 2021 (until August) relative to 2019. By discussing the role of distinct countries, the current study can contribute to comprehending the role of coronavirus as a huge disruptive factor in socio-economic activities, air quality, and city mobility.

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Since the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.

Survival and Transcriptomic Response of Clinically relevant pathogens on Surfaces in Relation to Probiotic and Traditional Cleaning Strategies

Prolonged survival of clinically relevant pathogens on inanimate surfaces represents a major concern in healthcare facilities. Contaminated surfaces can serve as reservoirs of potential pathogens and greatly hinder the prevention of healthcare-associated infections. Probiotic cleaning using environmental microorganisms to promote inter-species competition has been proposed as an alternative to traditional chemical-based cleaning using antimicrobials. Probiotic cleaning seeks to take advantage of ecological principles such as competitive exclusion and utilize benign microorganisms to inhibit viable pathogens on indoor surfaces. However, limited mechanistic study has yielded direct evidence that enables the scientific community to understand the stress response, or microbe-microbe interactions between healthcare-associated pathogens and probiotic bacteria. Therefore, to bridge this knowledge gap, we combined transcriptomics and traditional microbiology techniques to investigate the differential impact of chemical-based and probiotic surface cleaners on the survival of Acinetobacter baumannii and Klebsiella pneumoniae, two clinically important pathogens. Although probiotic Bacillus included in a commercialized All-Purpose Probiotic Cleaner persisted on surfaces for an extended period of time, surfaces contaminated with A. baumannii cleaned using chemical-based detergent with and without probiotic Bacillus showed no statistical difference in the viable colony forming units (CFUs) of A. baumannii. Similarly, for Klebsiella pneumoniae, there were negligible statistical differences in CFUs between probiotic and detergent cleaning scenarios. The transcriptome of A. baumannii with and without probiotic addition shared a high degree of similarity in overall gene expression, while the transcriptome of K. pneumoniae with and without probiotic addition differed in overall gene expression. Together, these results highlight the need to fully understand the underlying biological and ecological mechanisms for different pathogens and practical implications of probiotic indoor cleaning.

Transformer for Sub-Seasonal Extreme High Temperature Probabilistic Forecasting Over Eastern China

Sub-seasonal high temperature forecasting is significant for early warning of extreme heat weather. Currently, deep learning methods, especially Transformer, have been successfully applied to the meteorological field. Relying on the excellent global feature extraction capability in natural language processing, Transformer may be useful to improve the ability in extended periods. To explore this, we introduce the Transformer and propose a Transformer-based model, called Transformer to High Temperature (T2T). In the details of the model, we successively discuss the use of the Transformer and the position encoding in T2T to continuously optimize the model structure in an experimental manner. In the dataset, the multi-version data fusion method is proposed to further improve the prediction of the model with reasonable expansion of the dataset. The performance of well-desinged model (T2T) is verified against the European Centre for Medium-Range Weather Forecasts (ECMWF) and Multi-Layer Perceptron (MLP) at each grid of the 100.5°E to 138°E, 21°N to 54°N domain for the April to October of 2016-2019. For case study initiated from 2 June 2018, the results indicated that T2T is significantly better than ECMWF and MLP, with smaller absolute error and more reliable probabilistic forecast for the extreme high event happened during the third week. Over all, the deterministic forecast of T2T is superior to MLP and ECMWF due to ability of utilize spatial information of grids. T2T also provided a better calibrated probability of high temperature and a sharper prediction probability density function than MLP and ECMWF. All in all, T2T can meet the operational requirements for extended period forecasting of extreme high temperature. Furthermore, our research can provide experience on the development of deep learning in this field and achieve the continuous progress of seamless forecasting systems.

Impact of Training & Development and Career Planning on Employee Involvement

The purpose of this research is to investigate the impact of training and development as well as career planning in Nepalese service sector organizations. Data for this study were gathered from service organizations such as banks, insurance companies, telecommunications companies, hospitals, and colleges. In total, 502 questionnaires were distributed, and 82.97 percent of the copies that were filled out and returned were used in the study. Descriptive statistics, correlation, and multiple regression were used to analyze the data. Organizational training and development and career planning, according to the study's findings, have a significant impact on employee involvement in their jobs and performance. As a result, Nepalese service sector organizations must make provisions of the budget for additional employee training and development programs. Similarly, it is necessary to provide employees with career development opportunities so that they can stay with the company for an extended period.

The Effects of L1 Interference on the Occurrence of Code-Switching in the Algerian Context

The sociolinguistic phenomenon of Code-Switching (CS) was addressed in dramatically different academic contexts where English is spoken as a first language (L1) (i.e., inner circle), as a second language (i.e., outer circle), as well as where English is spoken as a foreign language (EFL) (i.e., expanding circle). Nevertheless, very few studies examined the issue of CS among undergraduate students in expanding circle countries such as Algeria. Basically, this study sought to find answers that would, firstly, help apprehend the overriding reason (s) that stimulate the occurrence of CS in the third year students' oral production, secondly, identify the communicative functions of English-Arabic CS in the students' class interaction, and thirdly, gauge its practicality and effectiveness in multilingual classes. Following a qualitative research approach, a case study design was adopted with a purposively (deliberately) chosen sample. Accordingly, data were collected by means of two tools of inquiry, namely observation and an unstructured questionnaire. The findings revealed that the underlying factor that prompted the occurrence of language-switching was the linguistic interference that germinated from the students' L1, among other subsidiary linguistic factors. Furthermore, it was found that CS grants its appliers the opportunity to reiterate what they exactly said in another way, to hold the floor and continue speaking for an extended period, and to insist on what was being communicated. Regarding CS technique, it was concluded that it might be considered as a productive and, simultaneously, a detrimental communication strategy to develop EFL students’ speaking competence. Finally, the findings of this study supported the initially formulated hypotheses, and, thus, reported positive results.

Impact of the Global Financial Crisis on IT Sector. The Case of Greece

The impact of the Information and Technology (IT) sector on the countries’ innovation development has been recognized as crucial in prior and recent research studies. Moreover, firms’ innovativeness affects positively countries’ economies. Nevertheless, the global economic crisis of the last decade constituted a significant barrier to the development of country economies and had a negative effect on firms’ performance. Specifically, the negative consequences of the global crisis became harder for Southern Europe Countries. More specifically the Greek economy was suffered by an extended period of crisis with harder consequences than those of other European countries. The main purpose of this study was to examine the financial performance of Greek IT firms in the early years of crisis. Our findings have been relevant to those of previous studies which observed negative effects of the financial recession on firms profitability.

ESG Scores and Bank Performance During COVID-19

This chapter examines the impact of ESG scores on bank stock returns as a response to the COVID-19 pandemic. The authors use a sample of 73 publicly listed banks from 15 developed European countries. They perform the analysis using two different periods that cover the pandemic: the first major wave period of COVID-19 (February-April 2020) and an extended period (February 2020-April 2021). The findings reveal the negative influence of the COVID-19 pandemic on bank stock returns during the first wave of the pandemic. They further find that, during the first wave, stock returns of banks with higher ESG scores were more resilient to the pandemic. However, when they use the extended time period (from February 2020-April 2021), the influence of both COVID-19 and ESG scores becomes insignificant. The chapter's findings have important policy implications during unprecedented crisis times such as COVID-19.

The Impacts of the Socratic Method and Andragogy on Adult Bible Classes

This chapter addresses the potential use of the Socratic method and andragogical teaching methods in adult Bible classes in parish education. There exist many correlations between Socratic teaching strategies and andragogical philosophy, most notably the assumption that adult learners trend towards thinking of themselves as self-directed learners with a variety of experiences and the capacity to learn through dialogue with facilitators and each other. This chapter first outlines adult Bible classes in the isolated context of Christian parish education as community or non-formal education. Then, the chapter highlights the methods of a particular study in which pastors were trained in Socratic and andragogical methods and then observed for an extended period of time. Conclusions from the study include how Socratic and andragogical methods potentially help to increase learner self-reflection and engagement in similar contexts to the featured study.

Meteorological Drought Assessment in Sharjah, UAE Using Drought Indices

Drought is a natural disaster that significantly affects environmental and socio-economic conditions. It occurs when there is a period of below average precipitation in a region, and it results in water supply shortages affecting various sectors and life adversely. Droughts impact the ecosystems, crop production, and erode livelihoods. Monitoring drought is essential especially in the United Arab Emirates (UAE) due to the scarcity of rainfall for an extended period of time. In this study, drought is assessed in Sharjah UAE using monthly precipitation and average temperature data recorded for 35 years (1981-2015) at the Sharjah International Airport. The standardized precipitation Index (SPI), and the Reconnaissance Drought Index (RDI) are selected to predict future droughts in the region. SPI and RDI are fitted to the statistical distribution functions (gamma and lognormal) in an annual time scale and then, a trend analysis of index values is carried out using Mann-Kendal test. The correlation between SPI and RDI indices was found to be high where both showed high drought frequencies and a tendency to get drier over time, thus indicating the need of appropriate drought management and monitoring.