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The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Author Mohammad Khishe disagrees with the retraction. The other authors have not responded to correspondence regarding this retraction. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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Background: Since 1990, the maternal mortality significantly decreased at global scale as well as the North Africa and Middle East. However, estimates for mortality and morbidity by cause and age at national scale in this region are not available. Method(s): This study is part of the Global Burden of Diseases, Injuries, and Risk Factors study (GBD) 2019. Here we report maternal mortality and morbidity by age and cause across 21 countries in the region from 1990 to 2019. Result(s): Between 1990 and 2019, maternal mortality ratio (MMR) dropped from 148.8 (129.6-171.2) to 94.3 (73.4-121.1) per 100 000 live births in North Africa and Middle East. In 1990, MMR ranged from 6.0 (5.3-6.8) in Kuwait to 502.9 (375.2-655.3) per 100 000 live births in Afghanistan. Respective figures for 2019 were 5.1 (4.0-6.4) in Kuwait to 269.9 (195.8-368.6) in Afghanistan. Percentages of deaths under 25 years was 26.0% in 1990 and 23.8% in 2019. Maternal hemorrhage, indirect maternal deaths, and other maternal disorders rank 1st to 3rd in the entire region. Ultimately, there was an evident decrease in MMR along with increase in socio-demographic index from 1990 to 2019 in all countries in the region and an evident convergence across nations. Conclusion(s): MMR has significantly declined in the region since 1990 and only five countries (Afghanistan, Sudan, Yemen, Morocco, and Algeria) out of 21 nations didn't achieve the Sustainable Development Goal (SDG) target of 70 deaths per 100 000 live births in 2019. Despite the convergence in trends, there are still disparities across countries.Copyright © 2022 Academy of Medical Sciences of I.R. Iran. All rights reserved.
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Background: Hand hygiene has been long been acknowledged as one of the most simple and cost-effective method to prevent the spread of many infections. Hand hygiene is defined as the cleaning of hands to reduce microbial load. Hand hygiene can be performed either by hand washing with soap and water or by using alcohol based hand rubs. The ongoing pandemic has further stressed the importance of hand hygiene in prevention of infections including the COVID 19 infection. In fact, hand hygiene along with use of face mask and social distancing are recommended as the first-line interventions in the prevention of COVID 19. WHO has come forward with certain recommendations pertaining to hand hygiene practices to be followed in this regard. The Government of India has also organized various campaigns for the general public regarding the importance of hand hygiene and the correct techniques to be followed in doing so. Our study aims to evaluate the knowledge, attitude and practices among medical students regarding hand hygiene since non-compliance on their part to adhere to such practices could lead to further transmission of infections. Method(s): This study will be questionnaire based and is being conducted among the students of Government Medical College, Srinagar to evaluate their knowledge, attitude and practice regarding hand hygiene.
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The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), Whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to 200 × 200 pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score. © 2022 IEEE.
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The COVID-19 pandemic profoundly affected global patterns, and the period of the declared virus pandemic has had a negative influence on all aspects of life. This research focuses on categorizing and empirically investigating the role of digital platforms in learning and business processes during the COVID-19 pandemic outbreak. The main purpose of this paper is to investigate to what extent the use of electronic learning (EL) has been boosted by COVID-19's spread, and EL's effectiveness on the sustainable development of electronic commerce due to the demand for a variety of electronic devices. For this purpose, the information has been collected through an online questionnaire applied to 430 participants from the Kurdistan Region of Iraq (KRI). The results indicate that participant usage and skills with electronic devices and online software programs are increasing, as the ratio indicated a level of 68% for both genders. Thus, the significance of EL concerning electronic commercial enterprises has been openly acknowledged and influenced by numerous factors. In addition, several suggestions and steps to be undertaken by the government are highlighted. Finally, this research mentions the current limitations of EL and suggests future works to build sustainable online experiences. © 2023 by the authors.
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Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy for improving the basic HHO’s performance using the Salp algorithm’s power to select the best fitness values. The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of four performance metrics (Accuracy, Recall, Precision, F1) were employed in the studies using three classifiers (Support vector machines (SVMs), k-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98% accuracy with two classifiers, XGboost and KNN © 2022, International journal of online and biomedical engineering.All Rights Reserved.
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COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neura Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.
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The world has paid increasing attention to energy efficiency projects since the Paris agreement and UN climate summit. Recently, the COVID-19 pandemic accelerated the process of the green energy transition, which has attracted considerable attention from economists, environmentalists, and international organizations and has led to significant research in energy. This study addresses the importance of green energy practices in the post-COVID-19 era to deal with environmental deregulation using bibliometric analysis. Data were extracted from the Scopus database from 2020 to 2022. Results indicate that China gained a prominent place in publishing topic-related articles. However, Italy stands at the top position in total and average article citations. Sustainability is the most productive journal, followed by Energies and the Journal of Cleaner Production. Nazarbayev University and the University of Cambridge are the most contributing research institutes. In general, the cooperation of authors, institutes, and countries strengthens research;however, collaboration at the author level across the nation was lower than in others. The study highlights three research streams and four themes by systematically conducting a bibliometric coupling and co-occurrence network that anticipates and significantly segregates literature. Bibliometric coupling identifies three research streams of sustainable green business strategies, green infrastructure requirements, and green solutions and opportunities from COVID-19. Furthermore, the co-occurrence network proposes four main themes related to green innovation in the epidemic era, security and sustainable development goals with green practices, public health protection and green finance, and investment and risk management. The results provide insights into current research in the field of energy and will assist future work promoting environmentally friendly projects.
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Background Health inequalities in the UK and other advanced economies are receiving renewed attention by the public and governments in light of the Covid-19 pandemic. Current data on mortality and longevity in England lack precision either in space or in time. We estimated trends from 2002 to 2019 in life expectancy for all 6,791 English Middle-layer Super Output Areas (MSOAs, median population 7,985 in 2019). Methods We used data for all deaths in England from 2002 to 2019 with information on age, sex and MSOA of residence and data on population by age, sex and MSOA. We used a Bayesian hierarchical model to obtain stable estimates of age-specific death rates by sharing information across age groups, MSOAs and years. We used life-table methods to calculate life expectancy at birth by sex and MSOA. Results In 2002-2006 and 2006-2010, the vast majority of MSOAs experienced a life expectancy increase for both sexes. In 2010-2014, female life expectancy decreased in 322 (5%) of MSOAs for women. By 2014-2019, the number of MSOAs with declining life expectancy was 1,178 (17%) for women and 635 (9%) for men. The life expectancy increase from 2002 to 2019 was smaller where life expectancy had been lower in 2002, mostly northern urban MSOAs, and larger where life expectancy had been higher in 2002, mostly MSOAs in and around London. As a result of these trends, the gap between the lowest and highest MSOA life expectancy increased from 2002 to 2019, to reach 20.6 (95% credible interval 17.3-24.4) years for women and 27.2 (23.4-31.7) years for men. Conclusions This study highlights not only rising inequality, but also declining life expectancy in a large proportion of English communities. The post-Covid rebuild policies in England must include pro-equity economic and educational policies, and expansion of public health and healthcare. Key messages Between 2014 and 2019, 17% and 9% of communities in England underwent a decrease in life expectancy for women and men respectively, highlighting the immediate need for equity-focussed policies. For women, 2% of small areas in England have experienced a long-term decline in life expectancy between 2002 and 2019.
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Objectives: The SARS-CoV-2 pandemic necessitated restructuring of outpatient services with increased reliance on telemedicine. Greater use of virtual clinics (VCs) is expected to continue;However, patient and clinician satisfaction with these are poorly understood, as are their environmental and fiscal impact. Methods: The first, middle and last patients from various Urological subspeciality VC lists over a 30-day period at the peak of the pandemic were contacted. Healthcare professionals independent of initial care evaluated patient satisfaction using a custom questionnaire. Environmental and fiscal cost analyses were calculated using patient addresses, NHS tariff data and Gross Value Added (GVA) per head. Simultaneously, an online survey exploring changes to outpatient practices and clinician satisfaction with VC was distributed to UK Urologists. Results: 1146 patients underwent VC (30th March - 30th April 2020). 99 patients were contacted. 55 (56%) completed all survey questions (male: 78%, age >65: 60%, follow up: 78%). 49 (89%) were satisfied/very satisfied, with reduced time and travel having the strongest influence on responses. Approximately 5.31 tonnes of C02 emissions were avoided. Estimated cost-savings were £42,714.55 to the NHS and £62,078.82 to the economy. 86 Urologists completed the clinician survey. 83 (97%) switched some/all outpatient activity to virtual, with 69 (80%) using telephone. 24 (28%) felt satisfied/very satisfied for new referrals. 81% (70) felt satisfied/very satisfied for follow up consultations. 61 (71%) would use VC regularly. There were notable variations by subspeciality. Conclusions: VC use should be strongly considered beyond the pandemic, but may not suitable for every patient or subspeciality.
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A novel type of coronavirus, now known under the acronym COVID-19, was initially discovered in the city of Wuhan, China. Since then, it has spread across the globe and now it is affecting over 210 countries worldwide. The number of confirmed cases is rapidly increasing and has recently reached over 14 million on July 18, 2020, with over 600,000 confirmed deaths. In the research presented within this paper, a new forecasting model to predict the number of confirmed cases of COVID-19 disease is proposed. The model proposed in this paper is a hybrid between machine learning adaptive neuro-fuzzy inference system and enhanced genetic algorithm metaheuristics. The enhanced genetic algorithm is applied to determine the parameters of the adaptive neuro-fuzzy inference system and to enhance the overall quality and performances of the prediction model. Proposed hybrid method was tested by using realistic official dataset on the COVID-19 outbreak in the state of China. In this paper, proposed approach was compared against multiple existing state-of-the-art techniques that were tested in the same environment, on the same datasets. Based on the simulation results and conducted comparative analysis, it is observed that the proposed hybrid approach has outperformed other sophisticated approaches and that it can be used as a tool for other time-series prediction. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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COVID-19 is truly an unprecedented event, forcing nearly four billion people into isolation, social distancing, and requiring people to rigorously follow public health measures such as frequent hand washing and indoor face-covering. People around the world have spent months staying home-bound, enduring significant financial, social, and emotional costs. They have been feeling anxious, irritable, afraid, and ambivalent in the wake of an invisible, pervasive, and potent pandemic. A strength focus can help us mitigate unwarranted or excessive negative emotions engendered by maintaining social distancing. This paper posits that by using our strengths, we can enhance our psychological immunity through pragmatic actions to enhance our daily wellbeing. More importantly, we can reframe and reappraise challenges to build perspective in dealing with global crises such as pandemics and disasters. Strengths expressed through pragmatic actions can boost our coping skills as well as enhance our wellbeing. Consistent with the zeitgeist of our times-equity, social justice, digital connections, the paper offers easily implemented, concrete actions using character strengths in adaptive ways to reduce the likelihood that social distancing will result in overwhelming anxiety, lack of structure or stimulation, and demoralization. © 2020, International Journal of Wellbeing Charitable Trust. All rights reserved.