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Ultrasonographic Options that come with Hypertrophic Interureteral Cristae throughout Transabdominal and also Transrectal Ultrasonography.
Anaemia in persistent renal ailment maternity.
Medical link between put together Preserflo Microshunt implantation and cataract surgery inside open-angle glaucoma sufferers.
What kind of affective phenomenon is religious zeal and how does it relate to other affective phenomena, such as moral anger, hatred, and love? In this paper, I argue that religious zeal can be both, and be presented and interpreted as both, a love-like passion and an anger-like emotion. As a passion, religious zeal consists of the loving devotion to a transcendent religious object or idea such as God. E3 Ligase modulator It is a relatively enduring attachment that is constitutive of who the zealot is, and it expresses itself in a distinctive set of mental and behavioral dispositions. Most importantly, it motivates uncompromising actions and involves intense, hot, and deep emotions. As an anger-like emotion, religious zeal is an occurrent affective state of mind that is intentionally directed towards a specific (immanent) object, characteristically a person or group of persons. It condemns the violation of a religious norm that is taken to be of absolute validity and general applicability. It motivates an action aiming at vengeance and retaliation, and it involves intense and hot feelings of hostility towards its object. link2 I argue that rather than reducing the complex phenomenon of religious zeal to one of these two manifestations, we should reflect upon the question of how the two distinct conceptualizations relate to each other (and are interwoven with political interests).The COVID-19 pandemic has brought lots of losses to the global economy. Within the context of COVID-19 outbreak, many emergency decision-making problems with uncertain information arose and a number of individuals were involved to solve such complicated problems. For instance, the selection of the first entry point to China is important for oversea flights during the epidemic outbreak given that reducing imported virus from abroad becomes the top priority of China since China has achieved remarkable achievements regarding the epidemic control. In such a large-scale group decision making problem, the non-cooperative behaviors of experts are common due to the different backgrounds of the experts. The non-cooperative behaviors of experts have a negative impact on the efficiency of a decision-making process in terms of decision time and cost. Given that the non-cooperative behaviors of experts were rarely considered in existing large-scale group decision making methods, this study aims to propose a novel consensus model to manage the non-cooperative behaviors of experts in large-scale group decision making problems. link3 A group consistency index simultaneously considering fuzzy preference values and cooperation degrees is introduced to detect the non-cooperative behaviors of experts. We combine the cooperation degrees and fuzzy preference similarities of experts when clustering experts. E3 Ligase modulator To reduce the negative influence of the experts with low degrees of cooperation on the quality of a decision-making process, we implement a dynamic weight punishment mechanism to non-cooperative experts so as to improve the consensus level of a group. An illustrative example about the selection of the first point of entry for the flights entering Beijing from Toronto during the COVID-19 outbreak is presented to show the validity of the proposed model.COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors' knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. E3 Ligase modulator Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. link2 In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. link2 We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries.Classification of COVID-19 X-ray images to determine the patient's health condition is a critical issue these days since X-ray images provide more information about the patient's lung status. To determine the COVID-19 case from other normal and abnormal cases, this work proposes an alternative method that extracted the informative features from X-ray images, leveraging on a new feature selection method to determine the relevant features. As such, an enhanced cuckoo search optimization algorithm (CS) is proposed using fractional-order calculus (FO) and four different heavy-tailed distributions in place of the Lévy flight to strengthen the algorithm performance during dealing with COVID-19 multi-class classification optimization task. The classification process includes three classes, called normal patients, COVID-19 infected patients, and pneumonia patients. The distributions used are Mittag-Leffler distribution, Cauchy distribution, Pareto distribution, and Weibull distribution. link3 The proposed FO-CS variants have been validated with eighteen UCI data-sets as the first series of experiments. For the second series of experiments, two data-sets for COVID-19 X-ray images are considered. The proposed approach results have been compared with well-regarded optimization algorithms. The outcomes assess the superiority of the proposed approach for providing accurate results for UCI and COVID-19 data-sets with remarkable improvements in the convergence curves, especially with applying Weibull distribution instead of Lévy flight.Virus diseases are a continued threat to human health in both community and healthcare settings. The current virus disease COVID-19 outbreak raises an unparalleled public health issue for the world at large. Wuhan is the city in China from where this virus came first and, after some time the whole world was affected by this severe disease. It is a challenge for every country's people and higher authorities to fight with this battle due to the insufficient number of resources. On-going assessment of the epidemiological features and future impacts of the COVID-19 disease is required to stay up-to-date of any changes to its spread dynamics and foresee needed resources and consequences in different aspects as social or economic ones. This paper proposes a prediction model of confirmed and death cases of COVID-19. The model is based on a deep learning algorithm with two long short-term memory (LSTM) layers. We consider the available infection cases of COVID-19 in India from January 22, 2020, till October 9, 2020, and parameterize the model. The proposed model is an inference to obtain predicted coronavirus cases and deaths for the next 30 days, taking the data of the previous 260 days of duration of the pandemic. The proposed deep learning model has been compared with other popular prediction methods (Support Vector Machine, Decision Tree and Random Forest) showing a lower normalized RMSE. This work also compares COVID-19 with other previous diseases (SARS, MERS, h1n1, Ebola, and 2019-nCoV). link3 Based on the mortality rate and virus spread, this study concludes that the novel coronavirus (COVID-19) is more dangerous than other diseases.In the aftermath of the COVID-19 pandemic, supply chains experienced an unprecedented challenge to fulfill consumers' demand. As a vital operational component, manual order picking operations are highly prone to infection spread among the workers, and thus, susceptible to interruption. This study revisits the well-known order batching problem by considering a new overlap objective that measures the time pickers work in close vicinity of each other and acts as a proxy of infection spread risk. For this purpose, a multi-objective optimization model and three multi-objective metaheuristics with an effective seeding procedure are proposed and are tested on the data obtained from a major US-based logistics company. Through extensive numerical experiments and comparison with the company's current practices, the results are discussed, and some managerial insights are offered. It is found that the picking capacity can have a determining impact on reducing the risk of infection spread through minimizing the picking overlap.The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug-virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug-virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug-virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug-virus network, which integrates the known drug-virus interactions, drug chemical structures, and virus genomic sequences.
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