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24-epibrasinolide modulates the vase life of lisianthus cut flowers by modulating acc oxidase enzyme activity and physiological responses
(MDPI AG, 2021-05) Darvish, Mohammad; Shirzad, Habib; Asghari, Mohammadreza; Noruzi, Parviz; Alirezalu, Abolfazl; Pateiro, Mirian; Takshe, Aseel A.; Lorenzo, José Manuel
Ethylene is the most important factor playing roles in senescence and deterioration of harvested crops including cut flowers. Brassinosteroids (BRs), as natural phytohormones, have been reported to differently modulate ethylene production and related senescence processes in different crops. This study was carried out to determine the effects of different levels of 24-epibrassinolide (EBL) on ACC oxidase enzyme activity, the final enzyme in ethylene biosynthesis pathway, vase life, and senescence rate in lisianthus cut flowers. Harvested flowers were treated with EBL (at 0, 3, 6, and 9 µmol/L) and kept at 25◦C for 15 days. The ACC oxidase activity, water absorption, malondialdehyde (MDA) production and vase solution absorption rates, chlorophyll and anthocyanin contents, and the vase life of the flowers were evaluated during and at the end of storage. EBL at 3 µmol/L significantly (p ≤ 0.01) enhanced the flower vase life by decreasing the ACC oxidase activity, MDA production and senescence rates, and enhancing chlorophyll and anthocyanin biosynthesis and accumulation, relative water content, and vase solution absorption rates. By increasing the concentration, EBL negatively affected the flower vase life and postharvest quality probably via enhancing the ACC oxidase enzyme activity and subsequent ethylene production. EBL at 6 and 9 µmol/L and in a concentration dependent manner, enhanced the ACC oxidase activity and MDA production rate and decreased chlorophyll and anthocyanin accumulation and water absorption rate. The results indicate that the effects of brassinosteroids on ethylene production and physiology of lisianthus cut flowers is highly dose dependent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
A review of the environmental implications of the COVID-19 pandemic in the United Arab Emirates
(Elsevier B.V., 2022-08) Alalawi, Shaikha; Issa, Sahar T.; Takshe, Aseel A.; ElBarazi, Iffat
This paper reviews the environmental implications associated with the COVID-19 pandemic at the individual and community levels in the UAE. The positive effects emanating from the pandemic include improved air quality and reduced contamination of public spaces with pollutants. On the other hand, far-reaching negative effects include poor disposal of medical plastic waste and facemasks and the rise in unhygienic health practices amongst residents of UAE. The long-term ecological implications of the pandemic are still not well understood. The findings shed the light on the importance of addressing the consequences of the COVID-19 pandemic through preventative policies and strategies for better environmental health and readiness for future crises. Future research could assess the long-term environmental conse-quences of the pandemic on the UAE. © 2022
Analysis on severe fever with thrombocytopenia syndrome bunyavirus infection combined with atrial fibrillation under digital model detection
(Elsevier B.V., 2021) Tao, Lin; Yi, Yinping; Shan, Yu; Yu, Dan; Zhang, Jing; Qu, Yongsheng; Qin, Qingzhu; Pei, Yongju; Zhang, Hongmei; Chen, Xiongbiao; Kaddouri, Meriem; Omar, Khairi Mohamed
To heighten the diagnostic efficiency, in this study, the algebraic reconstruction technology (ART)-based echocardiography (ECG) was used to analyze severe fever with thrombocytopenia syndrome bunyavirus (SFTSV) complicated by atrial fibrillation. From Jan. 2015 to Dec. 2019, 200 elderly patients with SFTSV infection and hypertrophic cardiomyopathy (HCM) admitted to our hospital were selected as the observation group, and 20 healthy volunteers were in the control group. Then 200 patients were randomly divided into two groups with 100 people in each group. One group received routine clinical observation after surgery, and the other group received artificial intelligence atrial fibrillation monitoring. ECG displayed the left ventricular ejection fraction (LVEF), left ventricular mass index (LVMI), left ventricular end diastolic volume index (EDVI), and end systolic volume index (ESVI) of patients. The accuracy and satisfaction of different methods in observation were recorded. The risk factors of postoperative atrial fibrillation in elderly patients with HCM were evaluated, and changes in their white blood cell levels were detected. The results showed that, there was a significant difference in ECG between normal people and patients after surgery. Also, differences were noted in accuracy and satisfaction of the two methods in observation group (P < 0.05). The atrial fibrillation group and the non-atrial fibrillation group showed notable differences in smoking history and age (P < 0.05); the white blood cell content of the atrial fibrillation group was 8.64 × 109, and that in non-atrial fibrillation group was 3.25 × 109. The content of ST-2 in postoperative atrial fibrillation group was 21.3 g/mL, and the content of Gal-3 was 9.57 g/mL, while the content of ST-2 in non-atrial fibrillation group was 17.24 g/mL, and the content of Gal-3 was 5.21 g/mL. There was no significant difference in LVEF, LVMI, EDVI, ESVI between postoperative atrial fibrillation and the non-atrial fibrillation group. In summary, ECG can effectively detect HCM, and the digital model demonstrated superb capabilities in monitoring atrial fibrillation after cardiac sympathetic block. The atrial fibrillation group showed a higher level of white blood cells after surgery and was more likely to develop SFTSV infection. Measures should be taken to prevent infection. © 2021 The Authors
Antifungal activity of some indigenous lactic acid bacteria isolated from soft wheat
(Journal of Pure and Applied Microbiology, 2018) Djaaboub, Serra; Abdallah, Moussaoui; Meddah, Boumedien; Makhloufi, Souad; Gouri, Saif; El Khatib, Rami
The objective of this study was to find an alternative to chemical control of pathogenic fungi in wheat, using microorganisms that are safe and that can be isolated from the same biotopes of the pathogens. Lactic acid bacteria isolated from soft wheat grains were screened for their antifungal activity against Fusarium graminearum Schwab, Aspergillus flavus Link and Aspergillus parasiticus Speare, using two techniques (overlay and co-culture) on De Man, Rogosa, and Sharpe agar. The overlay method showed that out of forty-six lactic acid bacteria, five isolates showed an inhibition of radial growth range from 1% to 73.89%. According to the co-culture method, the most efficient biological agent for wheat mold growth isolate was LAB001 with an average rate of inhibition of 31.18% against A. flavus, 42.26% against A. parasiticus and 55.53% against F. graminearum. Lactic acid bacteria LAB001 was identified as Enterococcus faecium with 99.6% of similarity. E. faecium LAB001 can be considered as promising isolate for the biocontrol of pathogenic molds in small grain cereals. © 2018 Journal of Pure and Applied Microbiology. All rights reserved.
Application of machine learning risk prediction mathematical model in the diagnosis of Escherichia coli infection in patients with septic shock by cardiovascular color doppler ultrasound
(Elsevier B.V., 2021-07) Shen, Hualiang; Hu, Yinfeng; Liu, Xiatian; Jiang, Zhenzhen; Ye, Hongwei; Takshe, Aseel A.; Al Dulaimi, Saeed Hameed Kurdi
this study was to explore the diagnosis of septic shock patients with Escherichia coli (E. coli) infection based on cardiovascular color Doppler ultrasound (CCDUS) images under the machine learning risk prediction mathematical model (risk prediction model). 120 septic shock patients with Escherichia coli (E. coli) infection, admitted to xxx hospital were selected as research subjects, and they were randomly divided into experimental group and control group, including 76 males and 44 females, with an average age of (45.47 ± 11.35) years old. The prediction model, random forest mathematical model (RF model), and feature combination were trained and applied in the CCDUS. The error rate, F1-score, and area under the curve (AUC) were compared. It was found that the prediction effect of the risk prediction model was better (P < 0.05). The receiver operating characteristic curve (ROC) was drawn based on the risk prediction model, and it was found that the AUC was 0.924, and the best cutoff value was 0.247. The consistency test between the predicted death result and the actual result showed that Kappa = 0.824, which was higher than 0.75. The pathogenic microorganisms of the patients were mainly Gram-positive bacteria (GPB) in 32 cases (53.33%). There were 19 cases whose pathogenic bacteria was E. coli, and 11 cases (57.9%) of which were acquired in the intensive care unit (ICU). The patient mortality rate was 41.67%. Finally, the acute physiology and chronic health II (APACH II) score and D-dimer of the patients were substituted into the Logistic regression model. The effect of the risk prediction model was better than the RF model and feature combination; the measurement results based on the risk prediction model had good consistency; the D-dimer and APACH II score were independent factors for death of the septic shock. © 2021 The Author(s)
Application of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non-small cell lung cancer
(Elsevier B.V., 2021-07) Wei, Jing; Zhu, Ronghua; Zhang, Huai; Li, Pingwei; Okasha, Ahmad; Muttar, Ahmed K.H.
It was to study the adoption of positron emission computed tomography (PET-CT) based on the convolutional neural networks (CNN) model in the monitoring of postoperative pneumonia virus infection in patients with non-small cell lung cancer (NSCLC). 120 patients with NSCLC were set as the research object. CNN model was constructed and applied to PET-CT images to identify lesions and screen tumor markers for detection. Then, the patients were randomly divided into group A (CT), group B (PET-CT), group C (PET-CT based on artificial neural network model), and group D (PET-CT diagnosis based on CNN model), 30 cases in each group, and infection surveillance was conducted. The result showed that the accuracy (Acc), sensitivity (Sen), and specificity (Spe) of PET-CT image recognition based on the CNN model were 99.31%, 100%, and 98.31%, respectively. The proportion of serum neutrophils, white blood cell count, and PCT content in group D three days after operation were significantly lower than those in groups B, C, and A (P < 0.05). The proportions of patients with surgical wound infection and lung infection in group D were 6.54% and 15.38% respectively, which were significantly lower than those in groups B, C, and A (P < 0.05). The complication rates of patients in groups A, B, C, and D were 32.4%, 30.2%, 28.75, and 8.7%, respectively. The complication rate of patients in group D was significantly lower than that of the other three groups (P < 0.05). In short, PET-CT images based on the CNN model had high accuracy, sensitivity, and specificity in monitoring postoperative pneumonia virus infection in NSCLC patients. Applying it to the patient's virus infection monitoring can effectively prevent the patient's lung and surgical wound infection and improve the patient's postoperative recovery effect. © 2021 The Author(s)
Assessing climate change indicators in the United Arab Emirates
(Inderscience Publishers, 2022) Kamkar, Fatma; Khawatmi, Layla; Arif, Aysha; Hamed, Hamed; Issa, Sahar T.; El Khatib, Rami; Takshe, Aseel A.; Karkain, Rashed M.
Anthropogenic and natural activities have led to a global phenomenon known as climate change, which evidence shows, is worsening. Climatic changes can sometimes be observed at a regional level by assessing certain indicators such as temperature, precipitation, and humidity. This study aims to explore climate indicators in the United Arab Emirates that play a possible role in climate change. To gain insight into the longer-term changes, we looked at regional changes by analysing trends of mean monthly temperature, maximum monthly temperature, minimum monthly temperature, mean monthly humidity, and rainfall quantities per year over the period of 2003 to 2019. The trend analysis showed an increasing trend in the maximum monthly temperature, minimum monthly temperature, mean monthly temperature, and mean monthly humidity, but a decreasing trend in rainfall quantities, which may suggest climate change at a regional level. © 2022 Inderscience Enterprises Ltd.
Awareness and preparedness of human monkeypox outbreak among university student: Time to worry or one to ignore?
(Elsevier Ltd, 2022-10) Jairoun, Ammar Abdulrahman; Al-Hemyari, Sabaa Saleh; Abdulla, Naseem Mohammed; El-Dahiyat, Faris; Shahwan, Moyad; Hassan, Nageeb; Jairoun, Obaida; Alyousef, Nuha Ghazi; Sharif, Safia; Jaber, Ammar Ali Saleh
Background: The growing number of human monkeypox cases worldwide illustrates the importance of early detection, prevention, management and quick action from healthcare authorities. The WHO confirmed a hundred of Monkeypox cases worldwide and disclosed Monkdypox as a worldwide emergency situation Objectives: To assess the knowledge about human monkeypox’ source, signs/symptoms, transmission, prevention and treatment among Al Ain university students in the UAE. Methods: This descriptive cross-sectional study aimed to assess Al Ain University students’ knowledge of Human Monkeypox. A validated questionnaire was distributed to students between lectures. The respondents’ knowledge of human Monkeypox was assessed by 21 questions that examined the participants’ knowledge of Monkeypox as follows: 5 items examined knowledge of the source, definition, and incubation time; 2items assessed the mechanism of transmission of human Monkeypox, 7 items assessed the signs and symptoms; 7 items assessed the preventative measures; and 6 items assessed the treatment modalities. A multivariate logistic regression model was used to identify the factors influencing respondents’ knowledge of human Monkeypox among university students. Results: A total of five hundred and fifty-eight (558) students participated in the study. The average knowledge score was 70.1%, with a 95% confidence interval (CI) of 68.9 − 71.3. Of the total participants, 111 (19.9%) had poor knowledge about human Monkeypox, 320 (57.3%) had moderate knowledge, and 127 (22.8%) had good knowledge. The results of the statistical modelling showed that Old age (OR 0.681; 95% CI 1.005–1.016), female gender (OR 1.26; 95% CI 0.813 –0.961), participants from medical colleges (OR 1.22; 95% CI 1.13 –1.32) having a history of human chickenpox infection (OR 2.6; 95% CI 2.3–2.9) and receiving information on human Monkeypox during education (OR 1.14; 95% CI 1.05–1.2) were strong determinants for good knowledge about human Monkeypox. Conclusion: knowledge of Monkeypox among the participants is relatively low, particularly regarding the epidemiology, symptoms and treatments. Therefore, increasing knowledge of Monkeypox will be key to enhancing the capacity to respond to human monkeypox cases and to relay pertinent data to a disease surveillance system. © 2022 The Author(s)
Big data-based grey forecast mathematical model to evaluate the effect of Escherichia coli infection on patients with lupus nephritis
(Elsevier B.V., 2021-07) Fan, Maoxiao; Gu, Shuaishuai; Jin, Yansheng; Ding, Lan; Ghonaem, Eman; Arbab, Ahmed Mohamed Hamad
The grey predictive mathematical model based on big data was used for analysis on the effect of Escherichia coli infection on patients with lupus nephritis (LN) in this study. Then, 156 patients diagnosed with LN infections by Wuzhong People's Hospital's information system (HIS) from October 30, 2017 to October 30, 2019 were selected as the experimental group, and 89 patients without LN infections were selected as the control group. Besides, the grey theory mathematical model was applied to process the integrated data, and feature analysis was employed to screen out disease-related bio-markers for the diagnosis of LN. The two groups were compared for affected organs, treatment, laboratory indicators, pathogenic bacteria, and recovery status. Multivariate logistic regression was used to analyze the related factors of patients with infections. The results showed that the specificity, sensitivity, and accuracy of the big data diagnosis based on the grey theory mathematical model were 78.9%, 87.6%, and 92.1, respectively; hormones, c-reactive protein, procalcitonin, and the daily antibiotic dose were positively correlated with concurrent infections (P < 0.05); 38 cases of Gram-negative bacteria were screened out, accounting for the largest proportion (37.18%); the effective rate of the experimental group was obviously lower than that of the control group (P < 0.05), suggesting that C-reactive protein (CRP), procalcitonin (PCT), antibiotics, daily dose of hormones, and serum albumin were independent risk factors for LN infection. In conclusion, the grey predictive mathematical model based on big data had high specificity, sensitivity, and accuracy in diagnosing the occurrence of infection in patients with LN; LN infection was mainly respiratory infection, and gram-negative bacteria were the main pathogen. Patients with LN infections showed higher serum creatinine, 24-hour urine protein quantification, CRP, and PCT, and lower serum albumin and recovery effect versus those without LN infections. © 2021 The Author(s)
Bioethics: A look at animal testing in medicine and cosmetics in the UK
(Tehran University of Medical Sciences, 2019) Kabene, Stefane; Baadel, Said
Using animals for cosmetics and medical tests has contributed towards a debate based on conflicting interests. Despite the efforts in justifying the value of animals in conducting analyses, this study seeks to elaborate whether or not it is rational to use animals as test subjects in medical and cosmetics fields. The value of animal life is at the core of the emotional conflicts that arise when animals become experimental subjects in medical and cosmetics fields. The aim of this study is to determine if there are ethical differences in the use of animal testing in medicine versus cosmetics. The research, through review and content analysis of the existing literature, compares and provides the outcomes of using animals in medical and cosmetics tests by examining studies conducted in the UK. The findings of this research indicated that animal testing is considered acceptable in the medical field only if there are no other alternatives, but is completely unacceptable in the cosmetics field. The study also provides recommendations in the form of alternatives that protect animals from cruelty and may benefit the different stakeholders and the society at large. © 2019 Medical Ethics and History of Medicine Research Center, Tehran University of Medical Sciences. All rights reserved.
Branding hospitals on social media through health professionals
(Equinox Publishing Ltd, 2019) Aguerrebere, Pablo Medina
Hospitals are facing a constantly changing context in which patients are becoming more demanding, public health education initiatives are regarded as increasingly important and hospital business models have to take account of constantly developing medical technologies. In order to better interact with internal and external stakeholders, hospitals try to reinforce their corporate communication strategies as well as their brand reputation by means that include using social media platforms. This literature review paper aims to better understand why health professionals have the potential to play a key role in hospitals’ branding initiatives through social media. First, I report the findings from studies of concepts related to corporate communication, branding and the connection between social media and personal branding; and, second, I propose a communication model – what I call the PMA branding model – to help hospitals build a brand reputation based on health professionals’ participation in corporate communication initiatives led by hospitals on social media. The paper concludes by showing that the PMA branding model consists of organisational tools based on a rigorous methodology that will help health professionals participate in branding initiatives led by the hospital through these platforms. Copyright © 2020 Equinox Publishing Ltd.
A clustering approach for autistic trait classification
(Taylor and Francis Ltd, 2020-07-02) Baadel, Said; Thabtah, Fadi; Lu, Joan
Machine learning (ML) techniques can be utilized by physicians, clinicians, as well as other users, to discover Autism Spectrum Disorder (ASD) symptoms based on historical cases and controls to enhance autism screening efficiency and accuracy. The aim of this study is to improve the performance of detecting ASD traits by reducing data dimensionality and eliminating redundancy in the autism dataset. To achieve this, a new semi-supervised ML framework approach called Clustering-based Autistic Trait Classification (CATC) is proposed that uses a clustering technique and that validates classifiers using classification techniques. The proposed method identifies potential autism cases based on their similarity traits as opposed to a scoring function used by many ASD screening tools. Empirical results on different datasets involving children, adolescents, and adults were verified and compared to other common machine learning classification techniques. The results showed that CATC offers classifiers with higher predictive accuracy, sensitivity, and specificity rates than those of other intelligent classification approaches such as Artificial Neural Network (ANN), Random Forest, Random Trees, and Rule Induction. These classifiers are useful as they are exploited by diagnosticians and other stakeholders involved in ASD screening. © 2020 Taylor & Francis Group, LLC.
Compressed feature vector-based effective object recognition model in detection of COVID-19
(Elsevier B.V., 2022-02) Chen, Chao; Mao, Jinhong; Liu, Xinzhi; Tan, Yi; Abaido, Ghada M; Alsayed, Hamdy
To better understand the structure of the COVID-19, and to improve the recognition speed, an effective recognition model based on compressed feature vector is proposed. Object recognition plays an important role in computer vison aera. To improve the recognition accuracy, most recent approaches always adopt a set of complicated hand-craft feature vectors and build the complex classifiers. Although such approaches achieve the favourable performance on recognition accuracy, they are inefficient. To raise the recognition speed without decreasing the accuracy loss, this paper proposed an efficient recognition modeltrained witha kind of compressed feature vectors. Firstly, we propose a kind of compressed feature vector based on the theory of compressive sensing. A sparse matrix is adopted to compress feature vector from very high dimensions to very low dimensions, which reduces the computation complexity and saves enough information for model training and predicting. Moreover, to improve the inference efficiency during the classification stage, an efficient recognition model is built by a novel optimization approach, which reduces the support vectors of kernel-support vector machine (kernel SVM). The SVM model is established with whether the subject is infected with the COVID-19 as the dependent variable, and the age, gender, nationality, and other factors as independent variables. The proposed approach iteratively builds a compact set of the support vectors from the original kernel SVM, and then the new generated model achieves approximate recognition accuracy with the original kernel SVM. Additionally, with the reduction of support vectors, the recognition time of new generated is greatly improved. Finally, the COVID-19 patients have specific epidemiological characteristics, and the SVM recognition model has strong fitting ability. From the extensive experimental results conducted on two datasets, the proposed object recognition model achieves favourable performance not only on recognition accuracy but also on recognition speed. © 2021
Correlation between HPV infection and ovarian epithelial cancer diagnosed by Dynamic Contrast-enhanced magnetic resonance imaging information technology under exponential distribution mathematical model
(Elsevier B.V., 2021-07) Liang, Bo; He, Han; Zeng, Lingyu; Pan, Min; Huang, Tingting; Wang, Xinmin; Kabene, Stefane Mostefa; Arbab, Ahmed Mohamed Hamad
This research aims to analyze the correlation between human papillomavirus (HPV) infection and ovarian epithelial cancer based on exponential distribution mathematical model, so as to provide an experimental basis for the early diagnosis of ovarian epithelial cancer by magnetic resonance imaging (MRI) in the future. In this study, 124 patients with ovarian epithelial cancer tissues pathologically confirmed in our hospital from March 31, 2017 to February 20, 2019 were selected as the experimental group, and 64 patients with normal ovarian tissues were selected as the control group, and the positive rate of HPV infection was detected by the computer cell test (CCT) system. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was constructed and adopted to analyze the scanning images. The correlation between HPV infection and pathological type, clinical stage, tissue differentiation degree, and CA125 in serum was studied by exponential distribution mathematical model. The results showed that the positive rate of HPV infection in the ovarian tissues of the experimental group was significantly higher than that of the control group (P < 0.05). Highly differentiated patients accounted for 25.97%, moderately differentiated patients accounted for 43.81%, and lowly differentiated patients accounted for 30.22%. Patients with clinical stage I-II accounted for 24.72%, patients with stage III accounted for 49.11%, and patients with stage IV accounted for 26.17%. HPV infection was significantly correlated with clinical stage III and moderate tissue differentiation by MRI of patients (P < 0.05), and extremely significantly correlated with clinical stage IV and low tissue differentiation by MRI (P < 0.001). The above findings show that the information technology of dynamic contrast-enhanced magnetic resonance imaging can clearly display the lesion metastasis of ovarian epithelial cancer patients with high soft tissue resolution. © 2021 The Author(s)
Costs and benefits of private tutoring programs: the South Korean case
(Emerald Group Holdings Ltd., 2021) Hultberg, Patrik T.; Calonge, David Santandreu; Choi, Ty
Purpose: The purpose of the study is twofold: to offer a theoretical model that illuminates families' motivation to invest in private tutoring and to consider the implications of such investments in the context of South Korea. Given that parents invest in private tutoring for their child if the perceived expected benefits, at the time of enrollment, are greater than the direct and indirect costs of such tutoring, the study explores how private tutoring may affect educational inequities and possibly lead to inferior social outcomes. Design/methodology/approach: A theoretical model based on the human capital approach was developed. Three questions based on stylized facts were addressed: (1) Why would a household send a child to private tutoring? (2) Why do different households invest in different amounts of private tutoring? (3) Why may a household over-invest in private tutoring? Findings: The findings of this study indicate that the demand for private tutoring services decreases with the costs of private tutoring, while increasing as levels of academic readiness and aptitude, levels of household education, levels of current wealth and expected returns to private tutoring increase. These findings imply that private tutoring may exacerbate social inequities and cause an inferior social outcome, but that a government can influence the demand for tutoring through taxation. Research limitations/implications: This study did not address the non-pecuniary benefits that may be derived from private tutoring. The most important limitation and potential source of weakness of the study is that the model is theoretical. These results therefore need to be interpreted with caution. Practical implications: The study indicates the need for private households, as well as government officials, to carefully consider the costs and benefits of private tutoring in South Korea. Although the study focuses on South Korea, the findings may apply to other countries in which private tutoring offerings are prominent. Social implications: The educational choices that families make for their children have important financial and social implications in all countries, but especially in South Korea. The important implication is that private tutoring will tend to aggravate educational and social inequality. Originality/value: The existing body of research on private tutoring investment in South Korea suggests that the phenomenon is ubiquitous, growing and spreading to other countries. Furthermore, the motivation behind households' decisions to invest in private tutoring for their children is not always addressed in the published literature. Also, far too little attention has been paid to the economic impact private tutoring has on households and children, as well as society in general. © 2021, Emerald Publishing Limited.
COVID-19: Virus or Viral Conspiracy Theories?
(BiomedGrid LLC, 2020-03-16) Abaido, Ghada M.; Takshe, Aseel A.
A novel coronavirus called SARS-CoV2 has attracted considerable attention in the past three months, unlike its sisters the Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS), and the disease it causes has been termed “coronavirus disease 2019” (COVID-19). The mortality rate of COVID-19, however, is lesser than that of SARS and MERS. Then why does COVID-19 seem to be a scarier pandemic than any before? Is it a serious virus outbreak or a sort of violence that has perpetrated across communities? The outbreak of the virus itself feels like it’s happening in your own home. This article attempts to understand the reasons for the widespread attention received by COVID-19. To do so, it briefly presents what is known so far about the SARS-CoV2 virus. After that, it explores whether the media has played a role in the widespread and perhaps exaggerated attention directed at COVID-19. At the dawn of 2020, several pneumonia cases were reported in the city of Wuhan, China, that were caused by a novel coronavirus.
Critical care nurses' reasons for working or not working overtime
(American Association of Critical Care Nurses, 2018) Lobo, Vanessa M.; Ploeg, Jenny; Fisher, Anita L.; Peachey, Gladys; Akhtar-Danesh, Noori
BACKGROUND Around the world, registered nurses are working increasing amounts of overtime. This is particularly true in critical care environments, which experience unpredictable fluctuations in patient volume and acuity, combined with a need for more specialized nurses. OBJECTIVE To explore critical care nurses' reasons for working or not working overtime. METHODS A semistructured interview guide was used to interview 28 frontline nurses from 11 critical care units in Ontario, Canada. Analysis was guided by Thorne's interpretive description methodology. RESULTS Participants' reasons for working overtime included (1) financial gain (96% of participants); (2) helping and being with colleagues (68%); (3) continuity for nurses and patients (39%); and (4) accelerated career development (39%). Their reasons for not working overtime were (1) feeling tired and tired of being at work (50%); (2) having established plans (71%); and (3) not receiving enough notice (61%). CONCLUSIONS Findings from this study provide important variations and extension of existing literature on the topic, and appear to be the first reported in Canadian critical care units. Additional research is required to understand administrative decision-making processes that lead to the use of overtime. © 2018 American Association of Critical-Care Nurses.
A Critical Examination of the Arab Undergraduate Students’ Perceptions of their Academic Arabic Proficiency in Three EMI Universities in the UAE
(Canadian Center of Science and Education, 2019) Masri, Taghreed Ibrahim
The overwhelming power that English enjoys has become a threat to many indigenous languages that are losing the battle against English dominance and hegemony. One facet for this threat is the use of English as a medium of instruction (EMI). The EMI policy has been a naturalized and taken-for-granted practice without questioning or problematizing. As a result, academic Arabic is almost absent from the academic scene in the UAE higher education. This study aims to problematize the use of English as medium of instruction at three universities in the United Arab Emirates. It also aims to critically explore the perceptions of Arab university students who were in Arabic schools, of their Arabic proficiency after studying at university. Based on critical theoretical framework and approached from interpretive and critical paradigms, the study used a mixed-methods approach of quantitative and qualitative data collection methods. 268 surveys and 20 semi-structured interviews showed that students were aware of the decline in their Arabic proficiency due to studying via English. Results also indicated that students showed symptoms of Academic language attrition. c2019 by the author(s).
Cyberbullying on social media platforms among university students in the United Arab Emirates
(Routledge, 2020-12-31) Abaido, Ghada M.
With the increased utilization of the internet and social media platforms, it is not surprising that youth are using these tools to inflict harm upon each other. Previous studies have outlined the negative impacts of cyberbullying, yet few research studies have been conducted in Arab communities examining its different forms and characteristics. Reporting incidents of cyberbullying is also a big problem, considering the social and cultural constraints of these societies. The purpose of this paper is to explore the pervasiveness of cyberbullying among university students in an Arab community, its nature and venues, and their attitudes towards reporting cyberbullying in contrast to remaining silent. Data were collected from 200 students in the UAE. 91% of the study sample confirmed the existence of acts of cyberbullying on social media with Instagram (55.5%) and Facebook (38%) in the lead. Calls for smartphone applications, stricter legal actions and proactive measures are discussed. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Cybersecurity awareness: A critical analysis of education and law enforcement methods
(Slovene Society Informatika, 2021) Baadel, Said; Thabtah, Fadi; Lu, Joan
According to the international Anti-Phishing Work Group (APWG), phishing activities have abruptly risen over the last few years, and users are becoming more susceptible to online and mobile fraud. Machine Learning techniques have potential for building technical anti-phishing models, with a handful already implemented in the real time environment. However, majority of them have yet to be applied in a real time environment and require domain experts to interpret the results. This gives conventional techniques a vital role as supportive tools for a wider audience, especially novice users. This paper reviews in-depth, common, phishing countermeasures including legislation, law enforcement, hands-on training, and education among others. A complete prevention layer based on the aforementioned approaches is suggested to increase awareness and report phishing to different stakeholders, including organizations, novice users, researchers, and computer security experts. Therefore, these stakeholders can understand the upsides and downsides of the current conventional approaches and the ways forward for improving them. © 2021 Slovene Society Informatika. All rights reserved.

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