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Information Technology (IT) has become the integral part of majority of businesses. Healthcare sector is also one such sector where IT adoption is increased in recent times. This adoption of IT has increased the internet exposure and hence increased the attack surface of the organisations working in healthcare sector. During covid outbreak, we have observed various cyber-attack and threats on organisations operating in healthcare sector. This paper focuses on cyber threat pattern in healthcare sector during covid-19 outbreak and post-covid-19 period. This research paper also aims to generate basic cyber awareness through generic cyber sanity checks to secure healthcare sector from malicious threat actors. The adaptation of proactive measures required to enhance the cyber hygiene of organisations becomes very essential in this sector. © 2023 IEEE.
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Teledentistry is an amalgamation of dentistry and telecommunication where the clinical information is exchanged between the patient/ caregiver with the dentist from a remote distance. The role of teledentistry came into spotlight in the pandemic Covid 19 era. This proved to be the safest method to provide health care assistance in preventing the disease transmission was cost effective and also formed a bridge between the rural and urban communities. It also helps in telediagnosis and formulation of treatment in dental emergenices. Teledentistry has improved a lot in the recent times due to advent of smart phones, widespread internet connectivity and video conferencing applications. Teledentistry could provide hassle free consultations as well the patient history can be stored in the data base for a longer period of time. This review highlights the origin, its applications, drawbacks and its role in the covid 19 and lock down phase.
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In a retrospective analysis, we recently reported findings on the detrimental motor effects of interrupted physiotherapy following the COVID-19 pandemic in parkinsonian patients. Using an extended follow-up period, we investigated the beneficial effect of reinstated physiotherapy on patients' disease severity and reversal of interruption-induced motor deterioration. Compared to before the COVID-19 outbreak, we observed persistence of motor disease worsening despite full resumption of state-of-the-art physical therapy suggesting that motor deterioration after discontinuation of physical therapy could not be compensated for. Therefore, and considering possible future crises, establishing means to safeguard continuation of physical therapy and to foster remote provision of care should be major goals.
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WHEN I (CHIARA) TOOK UP MY POSITION at Vanderbilt in 2016, I was given a one-year contract. Since I teach at a well-resourced university, there was a network of child care centers where I could enroll my child—a nice perk that many academic jobs do not include. While those with full-time or tenure-stream positions may not have had to worry about health insurance coverage or paid sick leave (Douglas-Gabriel 2020), questions continue to abound over hiring, pay freezes or cuts (Woolston 2021), parental leave policies and tenure clock extensions, as colleges and universities have scrambled to develop clear and equitable responses to the crisis. [...]the grand revelation of COVID is that, in the words of Chris Caterine, author of Leaving Academia, "All faculty are contingent.” For this reason, programs should be encouraged to work toward converting long-term contingent faculty members into tenured or tenure-track hires whenever possible, or to ensure that contingent positions have as much security and permanence as possible through the use of longer-term contracts.
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The author describes the evolution of the psychotherapy of a psychotic adolescent in the period when the pandemic induced their national authorities to impose lockdown. The difficulty of coming to terms with an ever-present reality that proved to be distressing for both the patient and the analyst, as well as with the violence and rapidity with which the external situation developed, leading to a change in the therapy setting, are at the heart of the reflections in this paper. The "choice" of whether to continue the sessions over the phone determined the emergence of some distinctive issues related to discontinuity and to the impossibility of relying on visual perception. However, to the analyst's surprise, it also favoured the possibility of working through the meaning of some autistic mental areas which, up to that moment, had never really been accessible to verbalization. Questioning the meaning of these changes, the author develops a broader reflection about the way that, for analysts and patients, modifications in the frames of our daily lives and clinical practice have enabled the deployment of undifferentiated parts of the personality which had previously been secretly deposited in the "body" of the setting and therefore were inaccessible.
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Coronavirus , Psychoanalytic Therapy , Humans , Adolescent , Personality , Countertransference , Violence , Professional-Patient RelationsABSTRACT
The development of coastal shipping (CS) in Africa has been identified as a way to bolster the continent's freight transport network. Thus, our study examined the recent CS experiences of three regional shipping lines in sub Saharan Africa (SSA)-Ocean Africa Container Lines, Adom Mbroso Transport and United Africa Feeder Line-operating respectively in Southern, West and East Africa. We employed an in-depth case-study approach involving semi-structured interviews with senior managers, which enabled us to discover and understand the real-life phenomenon of successfully operating CS services in SSA today and how the COVID-19 pandemic has affected the three companies. Our study revealed gaps that need to be addressed in order to develop maritime transport in Africa's subregions, namely by clarifying the predicted appropriateness and credibility of different policies and which elements are more likely to generate positive behavioural change in regional shipping lines. It also revealed major barriers for CS, including customs, a lack of intra-regionally traded cargo and high tariffs and low efficiency at port. Although the establishment of the Africa Continental Free Trade Area and 2050 Africa's Integrated Maritime Strategy have clearly had positive effects, African states need to implement the policies in concert as well as improve the performance of ports. Last, concerning the pandemic, COVID-19-related restrictions have decreased transport demand for CS in SSA and limited crew changes, shore leaves and cargo operations. Although business viability has been negatively affected as a consequence, freight rates have increased across SSA and thus improved the sustainability of CS.
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The aim of the present investigation was to analyze the acute effect of different facemasks on physiological, perceptual and performance parameters in trained young women during a High Intensity Interval Training (HIIT) on cycle ergometer. Fifteen subjects participated in the study. Heart rate variability, muscle oxygen saturation, lactate concentration and comfort parameters were measured under 3 conditions: no facemask, surgical and FFP2 facemask. The use of facemasks had no effect on any variable related to oxygen saturation, heart rate variability and cycling power during the HIIT protocol. Only lactate concentration revealed significantly lower values in the No mask condition compared to FFP2 3 min after HIIT (p =.038). Regarding the overall perception and comfort, participants reported greater discomfort when wearing the FFP2 mask compared to the No mask condition (p<.05). On the contrary, the analysis of heart rate variability, revealed significant differences (p<.001) in the Pre compared to the Post exercise for all conditions. The use of surgical or FFP2 facemask during HIIT training does not affect performance during strenuous exercise while perceived comfort appears to be lower with FFP2 masks in physically trained women.
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Using microdata from Statistics Canada's Labour Force Survey (LFS) and Population Census, this paper explores how spatial characteristics are correlated with temporary employment outcomes for Canada's immigrant population. Results from ordinary least square regression models suggest that census metropolitan areas and census agglomerations (CMAs/CAs) characterized by a high share of racialized immigrants, immigrants in low-income, young, aged immigrants, unemployed immigrants, and immigrants employed in health and service occupations were positively associated with an increase in temporary employment for immigrants. Furthermore, findings from principal component regression models revealed that a combination of spatial characteristics, namely CMAs/CAs characterized by both a high share of unemployed immigrants and immigrants in poverty, had a greater likelihood of immigrants being employed temporarily. The significance of this study lies in the spatial conceptualization of temporary employment for immigrants that could better inform spatially targeted employment policies, especially in the wake of the structural shift in the nature of work brought about by the COVID-19 pandemic.
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Video conferencing applications have become an integral part of today's world for attending interviews, classes, meetings, and assorted gatherings as well in the COVID-19 era. Alongside the increased use of such applications to facilitate the process of conducting interviews, the quality interview has taken a hit overall. This is largely because prospective candidates resort to fraud by switching tabs and using their phones during the course of an interview, and so come through with flying colors despite a clear lack of skills. Consequently, deserving candidates with the requisite skill set lose out to impostors who manage to clear the interviews. In this paper, we propose an approach to make interviews straightforward and fair to all candidates. Our Online Interview Platform, a web application built using Node.js and Express.js, offers indispensable features that are prerequisites for an interview. These include a real-time collaborative code editor that uses an operational transformation algorithm which allows users to collaborate in real time, test and run code;a video/audio conferencing feature using Peer JS;a chat box for communication, and a real-time collaborative whiteboard that lets users design or draw diagrams. The features are included in the same tab, thus ensuring that the candidate does not switch tabs. Using this application, candidates will be screened based on their technical knowledge, appropriately assessed, and performance-based hiring decisions made. The proposed approach proved that the malpractices strictly restricted while comparing with existing approaches. © 2021
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This cross-sectional study aims to record post-traumatic stress (PTS) and post-traumatic growth (PTG) of the general population of China during the first wave of COVID-19 spread. Method: An online survey was distributed in China during February and March 2020 to record the general population's PTS (using the Post-traumatic Stress Disorder Checklist-Civilian Version, PCL-C) and PTG (using the Post-traumatic Growth Inventory, PTGI) due to COVID-19. Confirmatory Factor Analyses (CFAs) and a Two-Part Model (TPM) of regression analysis were conducted. Results: In total, 29,118 Chinese participants completed the survey (54.20% were in their 20s, 68% were males, and 60.30% had a university education). CFA results illustrated that bifactor models described the Chinese psychometric traits of PTS and PTG over the default models. Results of TPM suggested that female, low-educated, and middle-aged individuals were more vulnerable to PTS. Remarkably, mutual and positive correlations between the PTS and the PTG, though small in statistics, were observed through regression analyses. Conclusions: The current results presented new best-fit structural models, potential predictors, and valuable baseline information on the PTS and the PTG of the Chinese population in the context of COVID-19.
Este estudio transversal se realizó para registrar el estrés postraumático (EPT) y el crecimiento de estrés postraumático (CPT) de la población general de China durante la primera ola de la extensión del COVID-19. Método: Se realizó una encuesta en línea en China durante febrero y marzo del año 2020 para registrar EPT de la población (utilizando el Post-traumatic Stress Disorder Checklist-Civilian Version, PCL-C) y CPT (utilizando el Post-traumatic Growth Inventory, PTGI). Se llevaron a cabo Análisis Factorial Confirmatorio (AFC) y Modelo de Dos Partes (MDP) de análisis de regresión. Resultados: En total, 29.118 chinos completaron la encuesta (54,2% de ellos tenían 20~29 años, 68,0% eran hombres, y 60,3% tenían una Educación Universitaria). Los resultados de AFC ilustraron que los modelos de bifactoriales eran mejores para descubrir los rasgos psicométricos de EPT y CPT de los participantes chinos que los modelos predeterminados. Los resultados de MDP sugirieron que las mujeres, las personas con bajo nivel educativo y de mediana edad eran más vulnerables a EPT. Se observaron correlaciones mutuas y positivas entre EPT y CPT, aunque pequeñas. Conclusiones: Los resultados actuales presentaron nuevos modelos estructurales de mejor ajuste, predictores potenciales e información de referencia valiosa de EPT y CPT de la población China en el contexto de COVID-19.
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Real-time data has evolved to become an integral part of understanding events across different timelines. Machine Learning uses different varieties of algorithms to determine the relationship between sets of data spread across timelines, visualize the current situation, and forecast the future, which is the most important aspect. Due to the breakout of COVID-19, a novel coronavirus, the entire planet is currently experiencing a disastrous crisis. At this time, the SARS-CoV-2 virus has proven to be a possible hazard to human life. The ARIMA Model i.e., Autoregressive Integrated Moving Average is compared with Facebook's Prophet and VARMAX model to foretell the future. The dataset is divided into the training and testing set. The size of the COVID-19 dataset is relatively small as it is a pandemic that occurred recently, due to which much of the data is used for training purposes and the last twelve days have been used for testing and validating the model. The model is trained and fits on the training data set. The algorithms are now ready to anticipate future forecasts after it has been tested and trained. The models also record the predicted and actual values, allowing them to improve their accuracy in the future. In this paper, the results of the ARIMA model are compared against Prophet and VARMAX which are other popular machine learning time series models. For the ease of visualization of covid trends, a dashboard is built using Python's Plotly and Dash and has been deployed using Voila. © 2022 IEEE.
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Objective: This quantitative study examines the impact of the COVID-19 pandemic on students' persistence at a minority-serving, open-access, public, urban community college in New York City. Specifically, the project looked at factors associated with mid-semester college withdrawals during spring 2020 when the college shifted to remote instruction due to the COVID-19 pandemic. Method: Utilizing data from three spring semesters (spring 2018, 2019, and 2020), four logistic regression models tested the marginal effects of student background and college program factors on mid-semester withdrawal and the moderating effect of spring 2020, the COVID-19 outbreak semester. Results: Findings indicated that the withdrawal rates were higher for new students, men, minoritized students, and part-time students across all three spring semesters. Spring 2020 disproportionally affected part-time students, men, Black students, as well as readmitted students. The greatest increase in the probability of mid-semester college withdrawal was observed for Black men who had been enrolled part-time in spring 2020. Belonging to a highly structured full-time study program protected students from leaving mid-semester, although this protection was weaker in spring 2020 and spring 2019 compared to spring 2018. Contributions: The research highlights the equity gap for Black men at the college and points to additional factors contributing to mid-semester college attrition. The work provides insights into factors that worsened during the COVID-19 pandemic. The study thereby contributes to understanding short-term risk factors for vulnerable student populations and adds to the body of literature on crisis situations in higher education. © The Author(s) 2023.
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Sensor technology has become an integral part of the diagnosis, monitoring, therapeutic and surgical areas of medical science. Various sensors like glucose biosensors for diagnosis of diabetes mellitus or fluorescent sensors for gene expression and protein localization have become a common part of the biomedical field. Due to their widespread applications, various advances and improvements have taken place in medical sensor technology which has led to an increase in the ease and accuracy of diagnosis as well as treatment of diseases. This review article aims at studying various novel and innovative developments in biosensors, fibre optic sensors, sensors used for microelectromechanical systems, flexible sensors and wearable sensors. This article also explores new sensing methodologies and techniques in different medical domains like dentistry, robotic surgery and diagnosis of severe life-threatening diseases like cancer and diabetes. Various sensors and systems used for rapid detection of the SARS-CoV-2 virus which is responsible for the COVID-19 pandemic have also been discussed in this article. Comparison of novel sensor-based systems for detection of various medical parameters with traditional techniques is included. Further research is necessary to develop low cost, highly accurate and easy-to-use medical devices with the help of these innovative sensor technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The need for mobile-based solutions for healthcare after COVID pandemic is more obvious than ever as mobile itself is an integral part of everyday life. m-Health is not an unfamiliar phenomenon, but despite the progress that has been made in this area, it is still difficult for m-health platforms to enter and stabilize in the market, especially in developing countries. So, in this study, we tried to prioritize the factors affecting the commercialization of m-Health and platforms. By reviewing related researches to the field of mobile health commercialization, 30 main effective indicators in mobile health commercialization were identified. After surveying experts and conducting exploratory factor analysis, these 30 indicators have been prioritized in 6 dimensions of efficiency and effectiveness, market, organizational and legal, technology and infrastructure, property and project management, and macro contexts. According to experts, the most important indicator is the timeliness of technology, and least important factor is the index of technology convergence with the laws and regulations in the field of health and treatment of the country. © 2022 IEEE.
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The COVID-19 pandemic has triggered changes in higher education and put to light the question of the future of education. The goal of the research was to gather the opinions of students, who are the main stakeholders of education, concerning their perception of various forms of education in the context of distance learning and their preferences and predictions on the future shape of education. 1005 students of the Cracow University of Economics, Poland, share their opinion participating in the survey. The results point to a diversification of the student population as far as their perception of distance learning is concerned: students with greater experience in traditional higher education and part time students favored distance learning more and foresee it as the future of education, which is in line with the necessity of facilitating life-long learning. Hence, universities have to adjust their offer to different student preferences in order to survive. © 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.
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This study investigates the perspectives of part-time students and academics on the uses of blogs within Higher Education. It examines blogging within a socio-cultural framework through the theoretical lens of connectivism (Siemens, 2009, 2018;Downes, 2012). Qualitative methodologies are utilised in the interpretivist paradigm to understand the challenges and benefits of using a blog. This research reports Academic and student views regarding the usefulness of blogging for educational purposes, describes how and why blogs are used and reveals why uptake for some students is limited.A small-scale research project, using thematic analysis to investigate samples of student blogs and examine interview data, involved the analysis of the contents of 12 students' blogs, followed by interviews with students (n=8) and academics (n=4). This research took place at two universities in the East Midlands, and focussed on two professional education courses during the first term of the first year of study. The findings identified benefits for students, both in their academic and reflective writing and in synthesising theory with their professional practice. However, the need for appropriate training to combine pedagogical design with collaborative technologies, accessible to both staff and students, emerges as an essential priority. Moreover, it was important to understand the broader context of multiple online platforms and face-to-face communication that students are already accessing. Finally, traditional delivery models within practices and concepts of academic and student roles, i.e., expert and novice, limit the role of the 'More Knowledgeable Other' (MKO) to the academic alone, which influences how the blog was viewed, used and valued within student groups.The findings further developed Garcia et al. (2013) model of connectivism and supports that learning occurs within a fluid and dynamic context online. In this evolved model, the various students can be centrally vii active or more passive at different times but still engaged. All the actors have agency in this sense, even when they choose to behave as 'lurkers'. The findings suggest that this new model recognises the vital importance of the expert within the system and argues that, for blogs to achieve maximum benefit, the academic needs to play a central role (at least initially).Recommendations are contextualised as part of a set of potential responses to the current COVID-19 pandemic and post-pandemic climate, as blogging could play an important role in a range of online teaching scenarios in higher education (HE). (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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Online Social Networks (OSN s) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer's fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events. © 2022 IEEE.
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In 1984, the film "The Terminator” predicted that a hostile Artificial Intelligence (AI) will threaten to extinguish humankind by 2029. Even though the real present is quite far from this post-apocalyptic scenario where AI rebels against its creator, a growing concern about the lack of ethical considerations in the use of AI is rapidly spreading, leading to the current "ethics crisis”. The lack of clear regulations is even more alarming considering that AI is becoming an integral part of new educational platforms. This follows the wave of digital transformation mainly induced by the Fourth Industrial Revolution, with advances in digitalization strategies, and the COVID-19 crisis, which forced education institutions worldwide to switch to e-learning. The appeal of AI is its potential to answer the needs of both educators and learners. For example, it can provide help grading assignments, enable tutoring opportunities, develop smart content, personalize and ultimately boost on-line learning. Although the "AI revolution” has great potential to improve and boost digital education, there are no clear regulations in place to ensure an ethical and fair use of AI. Therefore, this work aims to provide a comprehensive overview of the current concerns regarding fairness, accountability, transparency and ethics in AI applied to education, with specific focus on virtual laboratories. The main aspects that this work aims to discuss, and provide possible suggestions for, are: (i) ethical concerns, fairness, bias, equity, and inclusion;(ii) data transparency and digital rights, including data availability, collection, and protection;and, (iii) collaborative approach between disciplines. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. All rights reserved.
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Creative industries were thought to be the most difficult avenue for Computer Science to enter and to perform well at. Fashion is an integral part of day to day life, one necessary both for displaying style, feelings and conveying artistic emotions, and for simply serving the purely functional purpose of keeping our bodies warm and protected from external factors. The Covid-19 pandemic has accelerated several trends that had been forming in the clothing and textile industry. With the large-scale adoption of Artificial Intelligence (AI) and Deep Learning technologies, the fashion industry is at a turning point. AI is now in charge of supervising the supply chain, manufacturing, delivery, marketing and targeted advertising for clothes and wearable and could soon replace designers too. Clothing design for purely digital environments such as the Metaverse, different games and other on-line specific activities is a niche with a huge potential for market growth. This article wishes to explain the way in which Big Data and Machine Learning are used to solve important issues in the fashion industry in the post-Covid context and to explore the future of clothing and apparel design via artificial generative design. We aim to explore the new opportunities offered to the development of the fashion industry and textile patterns by using of the generative models. The article focuses especially on Generative Adversarial Networks (GAN) but also briefly analyzes other generative models, their advantages and shortcomings. To this regard, we undertook several experiments that highlighted some disadvantages of GANs. Finally, we suggest future research niches and possible hindrances that an end user might face when trying to generate their own fashion models using generative deep learning technologies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Since January 2020, COVID-19 has been spreading over the world and has been declared a pandemic. Nation and society are growing scared of it as fresh instances and mass deaths increase daily. India was one of the major countries to suffer the consequences of COVID-19 during that phase as multiple waves hit India. Many social media channels were being used by people from all over the country to discuss this pandemic and its aftereffects. One of the most popular ways to share opinions or judgments today is through social media. Therefore, machines are continuously being developed to analyze what people post on social networking sites like Twitter, Facebook, Instagram, and other platforms thanks to advancements in current computing technology. Based on their mood, these ideas or points of view can be grouped and examined. In this paper, we used tweets collected from Twitter to analyze the sentiment that people conveyed on social media after the second wave of Corona Virus. The sentiment of the tweets has been divided into five categories: "Strongly Negative", "Negative", "Neutral", "Positive"and "Strongly Positive". First, we classify data using Python's Vader. We have trained a model using our own labeled dataset and evaluated its performance using Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). © 2022 IEEE.