Solar desalination/purification (solar stills, humidification-dehumidification, solar disinfection) in high altitude during COVID19: Insights of gastrointestinal manifestations and systems' mechanism.

From the starting of the pandemic different transmission routes of the pathogen was brought into the spotlight by researchers from different disciplines. This matter in high-altitudes was more boosted as the main parameters were not exactly realized. In this review we are about to highlight the possibility of consuming contaminated water generated form solar water desalination/disinfection systems in highlands. Three systems including solar still, solar disinfection (which experimented by the authors in 2019 in high altitude) and humidification-dehumidification were consider in this context. Ascribe to the risks of pathogens transmission in solar desalination/disinfection systems where the water resources are heavily polluted in every corner of the world, highlighting the risk of consuming water in high-altitude where there are many other parameters associated with spread of pathogen is of great importance. As it was reported, reliability of solar desalination and solar water disinfections systems against contaminated water by the novel coronavirus remained on the question because the virus can be transmitted by vapor in solar stills due to tiny particle size (60-140 nm) and would not be killed by solar disinfections due to low-temperature of operation <40 °C while for HDH contamination of both water and air by sars-cov-2 could be a concern. Although the SARS-CoV-2 is not a waterborne pathogen, its capability to replicate in stomach and infection of gastrointestinal glandular suggested the potential of transmission via fecal-oral. Eventually, it was concluded that using solar-based water treatment as drinking water in high altitude regions should be cautiously consider and recommendations and considerations are presented. Importantly, this critical review not only about the ongoing pandemic, but it aims is to highlight the importance of produced drinking water by systems for future epidemic/pandemic to prevent spread and entering a pathogen particularly in high-altitude regions via a new routes.

Burnout among pharmacists in Tunisia during COVID-19 pandemic

Introduction: Both public and private sector pharmacists were instrumental in containing this health crisis in Tunisia. The high workload had a considerable impact on their mental health during the outbreak of the Corona Virus. Objective(s): This study aims to assess burnout and the psychological toll of the pandemic among pharmacists in Tunisia during covid-19. Method(s): 258 Tunisian pharmacists working in the public and private sector participated in a questionnaire. Burnout was assessed by the Maslach burnout scale. Regression analysis was used to assess the impact of the pandemic on Tunisian pharmacists. Result(s): 80% of the respondents were women. Participants ranged in age from 22 to 62, 60% were married, 57% had at least one child, and 42% had been working for less than five years. The burnout scale revealed 76% burnout among them. Univariate linear regression showed that female gender (p = 0.014 <0.05) was associated with the development of burnout. Conclusion(s): The considerable prevalence of burnout among pharmacists during the COVID-19 pandemic in Tunisia can be attributed to the enormous and overwhelming responsibilities that any health care worker endured.

A Quantum-Based Chameleon Swarm for Feature Selection

The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations;so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation.

The Validation, Economic and Enhancement of Double Pipe Heat Exchanger by CuO Nanoparticles through COVID-19

Enhancement of heat transfer in industrial processes becomes amore senous challenge for economical and safety reasons. Understanding the heat transfer phenomenon in solid-liquid dispersion (nanoflmd) system is a concern of the researchers for the successful design of heat exchangers. In the present work, the heat transfer rate under solid-liquid dispersion conditions was evaluated numerically. Nanoparticles of copper oxide (CuO) of different concentrations were added to the mixing vessel containing water. In the vessel, a mixer of Rushton turbine impeller was used to disperse the nanoparticles into the liquid. The nanoflmd was pumped mto a double pipe heat exchanger through the inner tube as a hot fluid to exchange heat with cold water provided by fhe chiller m the shell The investigated range Reynolds number of nanoflmd(hot fluid) was (Reh) 19000-64000 and of cold water (Rec). The results revealed a significant enhancement m heat transfer by using nanoparticles as compared with smgle-phase cases. The heat transfer enhancement by using nanoparticles ranged 40- 114 % as compared with a smgle phase CFD simulation was performed to predict the velocity field m the agitated tank and to predict the heat transfer coefficient in the double pipe heat exchanger in the presence and absence of nanoflmd. The CFD simulation led to a successful understanding of the temperature distribution in the radial, axial, and tangential directions under turbulent flow conditions. The economic and numencal analysis prevail that the optimal conditions at Reh=64000 and nanoparticles concentrations of 2 g/L where the mmus operating cost is obtained for various oil barrel pnce before and after COVID 19 pandemic situations. © 2022 American Institute of Physics Inc.. All rights reserved.

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview.

Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm.

Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19.

The longest common consecutive subsequences (LCCS) play a vital role in revealing the biological relationships between DNA/RNA sequences especially the newly discovered ones such as COVID-19. FLAT is a Fragmented local aligner technique which is an accelerated version of the local pairwise sequence alignment algorithm based on meta-heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima. This paper introduces a modified version of FLAT based on improving the performance of the BA algorithm by integration with particle swarm optimization (PSO) algorithm based on a novel infection mechanism. The proposed algorithm, named BPINF, depends on finding the best-explored solution using BA operators which can infect the agents during the exploitation phase using PSO operators to move toward it instead of moving toward the best-exploited solution. Hence, moving the solutions toward the two best solutions increase the diversity of generated solutions and avoids trapping in local optima. The infection can be propagated through the agents where each infected agent can transfer the infection to other non-infected agents which enhances the diversification of generated solutions. FLAT using the proposed technique (BPINF) was validated to detect LCCS between a set of real biological sequences with huge lengths besides COVID-19 and other well-known viruses. The performance of BPINF was compared to the enhanced versions of BA in the literature and the relevant studies of FLAT. It has a preponderance to find the LCCS with the highest percentage (88%) which is better than other state-of-the-art methods.

Psychotic relapse from COVID-19 pandemic: Clinical features

IntroductionThe COVID-19 pandemic affected today more than 76,000,000 worldwide, and more than half of humanity has been placed in quarantine. This pandemic affects mental health problems and influences the onset of symptoms.ObjectivesThe aim of this review is to analyze the impact of the COVID-19 pandemic on psychotic disorders and its interaction with the various risk factors.MethodsWe undertook a review of the impact of COVID-19 pandemic on psychosis. We carried out a systematic review of electronic databases using the keywords “COVID-19”, “pandemics”, “psychotic disorders”, and “delusions”. Relevant literature was limited to articles conducted around the world and published between January and December 2020.ResultsWe identified ten papers addressing incident cases of psychosis relapse linked to coronavirus pandemic. In multiple cases, psychotic symptoms were characterized by delusional thoughts about being infected by the coronavirus. The limited access to regular medications and psychosocial interventions was the main factor to psychotic relapse. This review included one cross-sectional clinical study comparing the impact of this pandemic on patients suffering from severe mental illness compared with healthy controls and they found that patients with mental disorders reacted to the pandemic and the lockdown restrictions with higher anxiety levels than the general public. Our study also revealed that elderly people suffering from psychosis and other chronic illness were the most vulnerable to relapse.ConclusionsPsychotic disorders can relapse during stressful events like COVID-19 pandemic. Therefore, specific attention to these vulnerable subjects is crucial to prevent relapses in times of worldwide pandemic.DisclosureNo significant relationships.

COVID-19 pandemic and first episode of psychosis: Clinical characteristics

IntroductionThe rapid spread of the SARS‐CoV‐2 pandemic among the world poses challenges to the management of both physical and mental health. This unexpected situation could predict an exacerbation of anxiety, depressions, obsessions, and even multiple cases of psychosis.ObjectivesThe aim of this literature review is to identify and analyze studies conducted in 2020 that investigate the incidence of psychotic disorders, related to COVID-19 pandemic and describe its symptoms.MethodsA systematic search in the PubMed electronic database was performed using keywords “COVID-19”, “pandemics”, “psychotic symptoms”, and “ first episode of psychosis” Relevant literature was limited to articles describing studies conducted and published in 2020.Results9 papers met the inclusion criteria. The selected studies reported 20 cases of psychosis in patients with no psychiatric history, directly triggered by stress derived from the COVID-19 pandemic and by social distancing and quarantine. All cases were characterized by sudden behavioral changes out of character, increased concern about coronavirus risk infection, anxiety, psychomotor agitation, and insomnia. In multiple cases, psychotic symptoms were characterized by thoughts of reference, persecution, and structured delusional. 5 patients were convinced that COVID-19 Pandemic was part of a conspiracy and that someone was trying to infect them by diffusing the COVID-19 or other pollutants. Half of the patients had the delusional conviction that they got infected and they were contagious.ConclusionsCOVID-19 pandemic appears to be the trigger for precipitating psychosis which has a high risk of suicidal behavior. During pandemics, mental health professionals should carry out more focused diagnostic and therapeutic strategies.DisclosureNo significant relationships.

Development of cavitary lung disease as a long-term complication of coronavirus disease 2019 in a young previously healthy patient: a case report.

BACKGROUND: Cavities are frequent manifestations of a wide variety of pathological processes involving the lung. There has been a growing body of evidence of coronavirus disease 2019 leading to a cavitary pulmonary disease. CASE PRESENTATION: A healthy 29-year-old Filipino male presented to the hospital a couple of months after convalescence from coronavirus disease 2019 with severe pleuritic chest pain, fever, chills, and shortness of breath, and was found to have a cavitary lung lesion on chest computed tomography. While conservative management alone failed to improve the patient's condition, he ultimately underwent left lung video-assisted thoracoscopic surgery decortication. Even though the surgical pathology revealed only necrosis with dense acute inflammation and granulation tissue with no microorganisms, he gradually improved with medical therapy adjunct with surgical therapy. CONCLUSION: Documented cases of cavitary lung disease secondary to coronavirus disease 2019 have been mostly reported in the acute or subacute phase of the infection. However, clinicians should recognize this entity as a late complication of coronavirus disease 2019, even in previously healthy individuals.

Impact of COVID-19 on the outcomes of gastrointestinal surgery

COVID-19 pandemic has brought a paradigm shift in the treatment of various surgical gastrointestinal disorders. Given the increasing number of patients requiring hospitalization and intensive care for SARS-CoV-2 infections, various surgical departments worldwide were forced to stop or postpone elective surgeries to save the health resources for COVID-19 patients. Since the declaration of the COVID-19 pandemic by the World Health Organization on 12th March 2020, the recommendations from the surgical societies kept evolving to help the surgeons in making informed decisions regarding patient care. Moreover, various socio-economic and epidemiological factors have come into play while deciding the optimal approach towards patients requiring gastrointestinal surgery. Surgeries for many abdominal diseases such as acute appendicitis and acute calculous cholecystitis were postponed. Elective surgeries were triaged based on the urgency of performing the surgical procedure, the hospital burden of COVID-19 patients, and the availability of healthcare resources. Various measures were adopted such as preoperative screening for SARS-CoV-2 infection, use of personal protective equipment, and the COVID-19-free surgical pathway to prevent perioperative SARS-CoV-2 transmission. In this article, we have reviewed the recent studies reporting the outcomes of various gastrointestinal surgeries in the COVID-19 pandemic era and the recommendations from various surgical societies on the safety precautions to be followed during gastrointestinal surgery.

Centralized Multi-agent Mobile Robots SLAM and Navigation for COVID-19 Field Hospitals

In this paper we focus on the proof of concept prototype of fully autonomous centralized Multi-Robot System (MRS) consisting of a Hexapod walking robot and a six wheeled mobile robot. Recently, there has been an increasing demand for such systems as they can be involved in several tasks such as collaborative search and rescue, surveillance, monitoring, and disinfecting Field hospitals. To name a few, COVID-19 pandemic showed the weak points in the medical sector around the world, including those in the most advanced nations that had to go through hard decisions due to the lack of medical supplies and personal protective equipment. The developed system was rapidly adjusted due to COVID-19 pandemic to perform additional tasks like disinfection and remote body temperature detection. The developed system abide by ISO 13482 safety requirements for personal care robots, meaning it will be used and deployed in Field hospitals. We implemented the proposed approach in a game setting of a field hospital where the Hexapod is used to scan and draw a map of the Field hospital environment and to draw a path then the six wheeled mobile robot acts as a medical cargo delivery that enters based on the predefined map and path. © 2020 IEEE.

Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil.

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.

Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks.

SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.