COVID-19

La pandémie de Covid -19 s’est propagée dans le monde entier, envoyant des millions de personnes en quarantaine. Parmi les mesures décidées par le gouvernement jordanien pour endiguer l’avancée de cette pandémie se trouve la suspension des cours dans toutes les universités et le passage à l’enseignement à distance afin d’assurer la continuité pédagogique. Cet article vise à proposer, à travers l’expérience vécue par les étudiants, une analyse de l’impact du nouveau contexte sur l’apprentissage de la langue française en milieu universitaire durant la période de confinement liée à la pandémie de Covid-19. D’après les résultats de l’enquête, il est clair que ce modèle d’apprentissage présente des avantages tels que le facteur de temps gagné, la prise en charge de l’apprentissage et la diminution de la barrière de peur concernant la compétence de l’expression orale, mais le manque d’interaction, la baisse de motivation et le progrès minime réalisé en compétence d’expression écrite sont les défis les plus identifiés.

COVID-19 is a new variant of SARS-COV-2 which can lead to mild to severe infection in humans. Despite the remarkable efforts to contain the epidemic, the virus spread rapidly around the world and its prevalence continued with different degrees of clinical symptoms in many countries. Although common strategies including prevention, diagnosis, and care are necessary to curb this epidemic, early and accurate diagnosis can play an important role in reducing the speed of the epidemic. In this regard, the use of technologies based on artificial intelligence can be of great help. For this reason, since the outbreak of COVID-19, many researchers have tried to use machine learning techniques as a subset of artificial intelligence for the early diagnosis of COVID-19. Considering the importance and role of using clinical and laboratory data in the diagnosis of people with covid-19, in this paper K-NN, SVM, decision tree, random forest, Naive Bayes, neural network and XGBoost models are the most common machine learning models, and a dataset containing 1354 records consisting of clinical and laboratory data of patients in Imam Hossein Hospital in Tehran has been used to diagnose patients with covid-19. The results of this research indicate that based on the evaluation criteria, XGBoost and K-NN models have the most accuracy among the mentioned models and can be considered suitable predictive models for the diagnosis of COVID-19.

Since the outbreak of Covid19 virus to date, various methods have been introduced in order to diagnose the virus infection. One of the most reliable tests is assessing frontal Chest X-Ray(CXR) images. As the virus causes inflammation in the infected patient's lung, it is possible to diagnose whether one is infected or not using his/her CXR image. in contrast to other tests which mostly are based on the virus genome, this test is not time-consuming and it is reliable against new strains of the virus. However, this test requires a specialist to assess the CXR images. As the datasets of  Covid19 patient CXR images are increasing in number, it is possible to use machine learning techniques in order to assess CXR images automatically and even online. In this study, we used deep learning approaches and we fine-tuned the Alexent in order to automatically classify CXR images and label the whether "Covid" or "Normal". The data we used in this study include about 10,000 chest images, half of which are related to CXR images and the other half are related to patients with Covid19 infection. The model proved to be very reliable with 99.26% accuracy in diagnosis and 95% sensitivity and 99.7% specificity.

INTRODUCTION: The imposition of quarantine in pandemics brings about negative psychological consequences. Sufficient understanding based on environmental and behavioral sciences leads to the prediction of needs and preparedness. Given the role of housing and the environment on health, awareness of their effects on the COVID-19 pandemic will facilitate future actions and risk management planning. METHODS: The present cross-sectional exploratory study was conducted based on a mixed-method approach. In the qualitative section, participants who were selected by non-probability sampling method were interviewed via phone, and data collection continued until data saturation using the snowball method. A number of 27 telephone interviews were conducted and after the content analysis of unorganized data, the questionnaire was developed in three parts, the initial pilot was conducted on 30 participants, and Cronbach's alpha was calculated at 0.76. Moreover, 144 online questionnaires were completed in What’sApp virtual groups by purposive and available sampling method, followed by statistical analysis. FINDINGS: As evidenced by the obtained results, in house and interior design scales, surface area; furniture; color; opening and outdoor space as well as living space and in neighborhood scale, density indicators; shared accesses and spaces have positively affected the tolerance of quarantine, reduction of anxiety, and health improvement. Furthermore, stress-reducing places were found to be different in different age groups. In addition, some motivations have generated some new behaviors and needs which are supported by such affordances as openings and windows to open spaces for the provision of natural daylight and ventilation, as well as a specific open space for movement, nature enjoyment, activity, physical activity, and contact with the natural environment. CONCLUSION: There was a reciprocal relationship between behavioral orientations and environmental-physical aspects of housing in the current health crisis. The residential environment can influence disease prevention and the promotion of health-related behaviors.

Today, health tourism is a growing phenomenon, especially in developing countries, which has been greatly affected by the Covid 19 crisis. After overcoming this crisis and re-competing in this industry, our country also needs to develop capabilities. Take your marketing step and move towards being financially agile. Agility, or the ability to adapt quickly and in a timely manner to changing international markets at low cost and high added value, is therefore cost-effective. The present study was conducted with the exploratory mixed research method and the purpose of designing and validating the agile financial marketing capability model in the health tourism industry in Iran. The statistical population includes health tourism industry experts and prominent professors in the field of finance who have worked in medical centers that provide health tourism services. The research findings led to the identification of 14 main concepts that were presented in the form of a paradigm model and the central category of agile financial marketing capabilities (specialized and structural capabilities) was empirically examined with real data and confirmed.

The Coronavirus disease (Covid-19) is one of the infectious and contagious ones called 2019-nCoV acute respiratory disease. Its outbreak was first reported on December 31, 2019, in the Chinese city of Wuhan that quickly spread throughout the country within a few weeks and spread to several other countries, including Italy, the United States, and Germany, within a month. This disease was officially reported in Iran on February 19, 2020. It is important to detect and analyze high risk zones and establish regulations according to the data and the analyses of Geographic Information System (GIS) in epidemiological situations. Meanwhile, the GIS, with its location nature, can be effective in preventing the breakdown of Covid-19 by displaying and analyzing the dangerous zones where people infected with the disease. In fact, recognizing regions based on the risk of getting the disease can influence social restriction policies and urban movement rules in order to prepare daily and weekly plans in different urban regions. In this applied and analytical research, high and low risk zones of Tehran have been identified by using the random forest algorithm which is used for both classification and regression. The algorithm builds decision trees on data samples and then predicts data from each of them, and finally chooses the best solution. In this research, 7 effective criteria have been used in the level of risk of regions toward Covid-19 virus, which is: subway paths and bus for rapid transits, hospitals, administrative and commercial complexes, passageways, population densities and urban traffic. After providing the map of high-risk zones of Covid-19, the Receiver Operating Characteristic curve (ROC) has been used for evaluation. The area under the curve (AUC) obtained from ROC shows an accuracy of 98.8%, which means the high accuracy of this algorithm in predicting high and low zones toward getting the Covid-19 disease.

In early 2020, the coronavirus outbreak compelled higher education institutions worldwide to cancel campus-based teaching and conduct a variety of electronic learning which led to assessment of the quality of e-learning and its results, such as learning and self-efficacy. Thus, this study aimed to determine the effect of problem-based e-learning (PBe-L) on the learning and academic efficacy of students during the COVID-19 pandemic in ‘An Introduction of Educational Technology Course’. The research method was quasi-experimental with pre-test/ post-test design with experimental and control groups. The statistical population of this study consisted of undergraduate students in the field of educational sciences studying in the University of Tehran. 38 students were selected as the research sample using the available sampling method and were randomly divided into experimental and control groups. PBe-L was conducted for the experimental group and the control group experienced the direct learning approach. A researcher-made learning test and Morgan-Jinks Student Efficacy Scale (1999) were used. The results showed that the mean of learning and self-efficacy of the experimental group increased from 7.92 and 57.00 in the pre-test to 18.51 and 76.57 in the post-test respectively. The results of covariance showed that the mean scores of the post-test of the two groups were statistically significant (p≥0.005). The results of this study showed that the use of PBe-L during the COVID-19 pandemic can be effective in increasing the students’ learning and academic efficacy.

Organizational anomie (OA) is defined as the lack of law, as well as violations or weaknesses in the implementation of laws and norms in the organization, which can have detrimental effects on the performance of the organization and its members. The main focus of this study is to establish and validate an organizational anomie scale that can be employed to implement and operate this tool in field studies. The present research is applied-exploratory in terms of purpose and descriptive-survey in terms of method. To conduct this research, issues related to organizational anomie using factor analysis approach and content analysis method of scientific texts as well as from the perspective of qualified and experienced thinkers and experts and a researcher-made questionnaire on organizational anomie in COVID-19 pandemic are analyzed. it placed. Completed by experts and managers. The reliability coefficient of the test was calculated to be 0.85 using Cronbach's alpha test. Exploratory factor analysis of organizational questions revealed that the components of this questionnaire may explain more than 78.512 % of the desired variance. After one rotation of Varimax factors, the correlation between each item with each component was determined in the best situation and accordingly, twelve components were selected and extracted with the most weight: conflicting goals of the individual and organization, weakness in communication, standard selection ,ambiguity in goals, level of trustworthiness, negative attitude of employees towards themselves, feeling useless, understanding justice, weak control, common norms, organizational indifference, lack of socialization. According to the research findings, it can be concluded that organizational anomie is a multidimensional concept with many dimensions and components and cannot be studied with limited dimensions and components.

The expansion of mobile wireless technology into education in recent decades has offered an incredible opportunity to design learning differently and to enhance students' learning experiences that extend beyond the typical teacher-led classrooms. Following the outbreak of COVID-19 and the subsequent closure of educational institutions around the world, distance/mobile learning has become a widely accepted form of information and communication technology-enhanced education. Given the infancy of technology-enhanced education at Iraqi educational institutions, this study was conducted to determine how Iraqi EFL learners preferred information and communication technology-enhanced education and how ready they were to adopt mobile learning for English learning purposes in higher education. This study also examined whether there were any significant differences in the learners' preferences and readiness for mobile learning considering their gender. Methodological triangulation was undertaken using a questionnaire and semi-structured interviews with some Iraqi EFL learners at four public universities so as to seek the research objectives. Descriptive analysis was used to analyze the data from the questionnaire. The data of the interviews were then analyzed using thematic analysis to identify and report the common themes of the two coders of the interviews. The findings revealed a plethora of evidence indicating learners' positive attitudes towards using mobile learning as a viable medium for language learning purposes in intra- and extramural situations, as it expanded the boundaries of learning beyond the limitations of traditional pedantic educational settings. Besides, no significant differences were found in the preferences and readiness of Iraqi EFL learners considering their gender for the adoption of mobile learning in higher education, though females liked mobile learning better than males.  

The aim of this study was to identify the challenges of the business model of small and medium enterprises in the path of digital transformation during the Covid 19 epidemic. The research is applied in terms of purpose and descriptive-survey in terms of method. For this purpose, first, using the literature review, the challenges of the business model in the path of digital evolution during the Covet 19 epidemic have been identified, then in order to sift and consensus of the components extracted from the literature, the Delphi method was used in two rounds. The Delphi panel consisted of 30 experts in the field of research who were selected by purposive sampling. In the next step, in order to ensure the factor structure and validity of the structures, and to fit the model using the field method and distribution of questionnaires, a survey of senior managers of small and medium companies in Tehran was conducted. The sample size was determined using the sample size method required for modeling structural equations, 200 people, and finally 162 questionnaires were selected using a simple random method. Smart PLS software was used to analyze the data. Findings indicate 23 sub-components in 6 main components of change challenges, market challenges, financial, organizational, individual and environmental. The results also showed that the factor of "lack of necessary infrastructure" has the highest priority,"lack of government support during the Pandemic" and "lack of intangible resources(experience,knowledge,flexible manpower) for digital transformation" are in the next ranks.

The Analytic visualization suggests representing knowledge during a visual type that may be charts, graphs, lists, or maps. The COVID 19 detection and analysis of spreading is very important for countries. Database management with respect to virus deep analysis is a critical task to the researcher through conventional algorithms. The RNA, DNA, and biological data are helping to the bio-inspired algorithm but its implementation can be complex by software tools. Therefore, an effective technique is required to cross over the above limitations. So that covid 19 pandemic data analysis is performed through FCNN (Fully conventional Neural Network) pre-training network. The dataset is collected from social media, Kaggle, and GitHub databases. At 1st stage, the auto stack encoding process is applied later same data is processed with FCNN deep learning classifier. In this research work, covid-pandemic affects parameters like infected persons, deaths, active cases, and recovering cases. The FCNN is take care of feature extraction, training, testing, and classification. Finally using a confusion matrix accuracy of 98.34%, sensitivity 97.63%, Recall 98.26%, and F measure 98.83% had been estimated.

The worldwide Covid-19 epidemic while affecting numerous places, has had a profound effect on virtual advertising and advertising and, globally, in the provinces and at the neighborhood level. except, this effect for the most element become positive, in contrast to what has seen in exclusive sectors which include economy, human sources, etc., whilst contamination reasons a variety of incapacity amongst clients and advertisers alike, in phrases of welfare, social work, inflation, business, and many others., the equal shifted conduct goes as a long way as running. , investing strength, getting into self-schooling, adopting new programs from the internet, expanding social and hygiene concerns, retaining distance strategies from complete regions, internet-based media willpower, get right of entry to online sources, etc., and this has greatly impacted the display and endorsed efforts. The moral movement has moved past the PC and digital international, which places open doors for advertisers and products to connect with clients more efficiently than ever before. With the arrival of expanded online media and the call for pc-generated content material, the evolved Media have given advertisers a part of the monetary freedom. At the equal time, this has, in turn, enabling advertisers to be extra proactive and to engage with the public at the same time as appearing excessive excellent demonstration programs. The purpose of this study is to explore, investigate, and recognize the effect of coronavirus on the digital market and businesses.

The purpose of this study was to evaluate the effectiveness of time perspective therapy on adolescent suicidal ideation in the outbreak of Covid-19. This research was quasi-experimental (pre-test, post-test and follow-up with control group). The statistical population of this study consisted of all girls’ students from 7th to 12th grades in Rasht city, Iran. The statistical sample consisted of 24 adolescents with suicidal ideation who were randomly assigned in experimental and control groups. Beck's Suicidal Ideation Scale (BSIS) was used to collect data. The experimental group received time perspective therapy (Sword et al., 2014) for 8 sessions, one 90-minute session per week. Analysis of variance with repeated measure and Tukey post-hoc tests were used for data analysis. The results showed that time perspective therapy was effective in reducing suicidal ideation of girls’ adolescents (p<0.05). Based on the results, time perspective therapy has an effect on suicidal ideation and time perspective therapy is suggested to reduce adolescent suicidal ideation in conditions such as covid-19 pandemic.

During the pandemic of COVID-19, face to face class sessions, in education systems, have been suspended due to various recommendations of governments. To continue teaching and learning, universities have switched to online learning. In the past two years, implementation of online learning, despite its benefits for higher education, has had its drawbacks and students have faced to a variety of challenges. The primary purpose of this study is to investigate the perceptions and opinions of undergraduate engineering students at the University of Tehran about the existing challenges, and their suggestions for making the online learning more efficient in engineering education. This research is performed using a mixed model. A conceptual research model was designed that categorized the main elements of effective online learning into three categories: learners, instructors, and content, and then based on this conceptual model, a questionnaire including Likert and open-ended questions was developed to determine the challenges facing engineering students in online learning in the Corona era. By distributing this questionnaire among incoming engineering students of Tehran University in 2019 and 2020 and analyzing quantitative and qualitative data, to two questions, "What have been the challenges of engineering students in online learning during the Corona era?" And "How has the quality of online engineering education been during the corona pandemic?", was answered. For analysis, qualitative data were coded and categorized using the thematic analysis method and using MAXQDA, and the frequency of each category and code was determined. Quantitative data were also analyzed by statistical analysis methods including descriptive statistics and t-test of independent groups in the SPSS, and then two sets of findings obtained from quantitative and qualitative data were summarized and combined. Based on the results, students' challenges during the pandemic were categorized into five groups: personal challenges, limited social interaction, technology problems, evaluation issues, and concerns about content and teaching methods. Students’ feedback is an important tool in assessing the quality of online courses but other stakeholders’ conceptions and feedbacks should be studied in future research as well.

The purpose of the present study is to investigate the way teachers evaluate students' performance in the CoVid-19 Pandemic and to identify challenges and solutions to overcome problems using lived experiences of parents, teachers and professionals. The current research is qualitative. To obtain the findings, the phenomenological method (semi-structured interview) was used. In this study, 25 primary school teachers and 25 parents of students in Ardabil city and 14 experts in this field across the country were selected by purposeful sampling method and theoretical saturation criteria. The results showed that during the CoVid-19 Pandemic, teachers used different methods for evaluation and faced many challenges, and were presented with suggestions to overcome them. Parents also expressed their opinions about how to evaluate teachers and provided solutions to solve the challenges. Later, in relation to the identified challenges, experts were interviewed, and in order to overcome challenges, strategies such as expansion of combined education, elucidating philosophy of evaluation and its ethical aspect to learners and parents, the development of communication infrastructures, use of research results, conducting continuous activity-based evaluations and in a practical and inferential manner, no restrictions on the use of networks, monitoring the work of teachers, emphasizing evaluation as a tool for learning not a means of stress, evaluating high levels of students' knowledge and avoiding emphasizing the evaluation of the low level of knowledge, and various other solutions that were presented in the findings section. Therefore, it is necessary for education practitioners to take advantage of the suggestions of parents, teachers, and especially experts in order to identify the challenges in the evaluation of students during the CoVid-19 Pandemic and overcome them according to the presented suggestions.

Virtual education will be an effective educational system in the effective learning of students during the outbreak of Covid-19 and after its eradication. This study aims to identify the challenges of distance education from the view point of Islamic Azad University students under Covid-19 pandemic So the research method is qualitative(grounded theory). In total, 40 MSc students of Electronic Management in the second semester of 2019-2020 were selected by purposive sampling in order to participate in structured interviews. Thematic analysis of the data obtained from the interviews was performed, and the approaches of member check and peer debriefing were used to approve the results’ credibility. Notably, the results were modified in several steps. In addition, the reliability coefficient between the encoders (75%) was applied to evaluate reliability. Data analysis was performed in Maxqda 12. According to the results, challenges of distance education for students included two individual and organizational dimensions, seven educational (11 items), communication (9 items), managerial (11 items), structural (6 items), technological (9 items), cultural (8 items), and personality component (6 items), and 60 indexes for distance learning during COVID-19 pandemic.

Cognitive Training (CT) refers to programs designed to enhance the efficiency of cognitive and brain mechanisms through practice and/or intentional instruction. A highly controversial issue in the field of Computerized Cognitive Training (CCT) is its possible transferability to non-trained areas; one untouched discipline is second language (L2) learning. Hence, due to the facilitative role of attention and the predictive strength of working memory in L2 development and comprehension, CCT seems necessary for English language learners. Furthermore, few studies have investigated users’ perceptions of potential improvements in their cognitive functioning. To fill such inherent gaps and overcome the imposed barriers of conducting intervention studies during the COVID-19 pandemic era, this study adopted an interdisciplinary approach to explore English language learners’ self-perceived Far Transfer (FT) effects of Remote Adaptive Multi-Domain Computerized Cognitive Training (RAMCCT) in general cognitive functioning and L2-specific cognitive functioning. Thus, online observations of L2 receptive skills courses (reading and listening) and synchronous semi-structured interviews were undertaken with a convenience sample of 11 intermediate EFL learners who completed eight weeks of RAMCCT. Thematic Analysis (TA) revealed perceived advances in working memory, attention, multitasking, processing speed, hand-eye/ear coordination in general cognitive functioning, and improvements in either one or both of the L2 receptive skills in terms of attention and comprehension and speed. The results are discussed by the corresponding links between attention, working memory, and multitasking, together with the automatization of the core cognitive processes. Implications address game designers, L2 teachers, teacher training programs, and researchers.