Representation of effective facial mask design as a novel dimensionless number—Implications for COVID 19 pandemic

A dimensionless number Nr for the effective design of facial masks was derived and compared with other dimensionless numbers of fluid mechanics. Nr is found to closely resemble Euler's number (Eu). Nr is equal to the logarithmic function of the ratio of inertial force (Fi) of the air to the pressure force (Fp) of the air through the porous membrane of the mask. Nr is then introduced as a novel dimensionless number (Habib number) Ha in which the air flow through a facial mask is derived with parameters for an effective barrier from the COVID-19 virus (SARS COV 2). The introduction of Ha was not only for a comparison reason with other dimensionless numbers of fluid mechanics but also the number Ha is an essential extension of an early work on "Fluid mechanics of facial masks as personal protection equipment (PPE) of COVID-19 virus"[Rev. Sci. Instrum. 92, 074101 (2021)], in which the air flow through the mask is in its optimum design conditions to shield against the COVID-19 virus. As a result, an optimum Nr of expressing the flow of the O2 and N2 gases through the porous membrane was determined (Nr = NO2 = NN2 = Ha = -4.4). This was obtained when the N95 mask with specifications of a = 20 nm, l = 30 μm, and ϵ = 30% was used, with respect to the pressure gradient of the human lungs during exhaled and inhaled conditions, PAverage = 20 mm Hg (g cm-1 S-2), and to the size of the COVID-19 virus of about 125 nm (0.125 μm). In addition, a range of values of Nr was analyzed with respect to the optimum (Ha) value of Nr. On the one hand, when the range value of Nr falls between 0 ≥ Nr ≥ Ha, the mask has less resistance than Nr < -4.4, but not necessarily its optimum design conditions. On the other hand, when Nr = 0, the flow through the mask has no resistance at all, as if the mask does not exist. © 2023 Author(s).

Logarithmic cubic aggregation operators and their application in online study effect during Covid-19

The aims of this study is to define a cubic fuzzy set based logarithmic decision -making strategy for dealing with uncertainty. Firstly, we illustrate some logarithmic operations for cubic numbers (CNs). The cubic set implements a more pragmatic technique to communicate the uncertainties in the data to cope with decision-making difficulties as the observation of the set. In fuzzy decision making situations, cubic aggregation operators are extremely important. Many aggregation operations based on the algebraic t-norm and t-conorm have been developed to cope with aggregate uncertainty expressed in the form of cubic sets. Logarithmic operational guidelines are factors that help to aggregate unclear and inaccurate data. We define a series of logarithmic averaging and geometric aggregation operators. Finally, applying cubic fuzzy information, a creative algorithm technique for analyzing multi-attribute group decision making (MAGDM) problems was proposed. We compare the suggested aggregation operators to existing methods to prove their superiority and validity, and we find that our proposed method is more effective and reliable as a result of the comparison and sensitivity analysis.

High Monocyte Count Associated with Human Cytomegalovirus Replication In Vivo and Glucocorticoid Therapy May Be a Hallmark of Disease.

Cytomegalovirus (CMV) syndrome and infectious disease are defined as pathogen detection with appropriate clinical symptoms, but there are not pathognomonic signs of CMV disease. Although the prodrome of acute minor viral infections leukopenia (lymphopenia and neutropenia) is noted with onset of fever, followed by monocytosis, the role of monocytosis in CMV disease has not been described. Furthermore, under influence of corticosteroid therapy, CMV reactivation and monocytosis are described, but without a strict relationship with steroids dose. In the study, the monocyte level was investigated during the CMV infectious process. Regrettably, a non-selected group of 160 patients with high CMV viremia showed high dispersion of monocyte level and comparable with the median value for healthy subjects. Therefore, we investigated monocyte level in CMV-infected patients in relation to the logarithmic phase of the infectious process. Samples from patients with active CMV replication (exponential growth of CMV viremia) were tested. Significant monocytosis (above 1200/µL) during the logarithmic phase of CMV infection (with exponent between 3.23 and 5.77) was observed. Increased count and percentage of monocytes correlated with viral replication in several clinical situations except when there was a rapid recovery without relapse. Furthermore, glucocorticoids equivalent to 10 and 20 mg of dexamethasone during a 2-3-week period caused monocytosis-significant increase (to 1604 and 2214/µL, respectively). Conclusion: In light of the logarithmic increase of viral load, high monocytosis is a hallmark of CMV replication. In the COVID-19 era, presence of high virus level, especially part of virome (CMV) in the molecular technique, is not sufficient for the definition of either proven or probable CMV replication at any site. These preliminary observations merit additional studies to establish whether this clinical response is mediated by monocyte production or by decrease of differentiation to macrophages.

A fuzzy Einstein-based decision support system for public transportation management at times of pandemic

Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) methodology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision-makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria;high consistency of expert comparisons;and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy.

USE THE BEST FITTED MODEL FOR ESTIMATING WITH THE NUMBER OF INFECTIONS WITH THE COVID-19 VIRUS UNDER FUZZY ENVIRONMENT

In this article, a set of common statistical models, namely, linear, logarithmic, inverse, quadratic, cube, complex, power, exponential, and logistic model have been fitted to data representing the number of infections with Covid-19 virus in Iraq from the beginning of the disease until now by using the principle of fuzziness by forming a fuzzy information system (FIS) by generating values belonging to the set of infected numbers to produce a classical set that takes into account the inaccuracy (certainty) in data collection, then testing the significance of the models that were appropriate using the F-test and the probabilistic value sigma, and the comparison between these models using the coefficient of determination R-2 and MSE to reach the best model that represents the data of infection with the Covid-19 virus. Then estimate the best among those models and to calculate the estimated values for the number of infections with the virus. It was concluded that the use of the principle of fuzziness in the fitting of the models led to an increase in the accuracy of these models and the mean squares error (MSE) for all the models that have been fitted is reduced. We also note that the best model in representing the data of infections with the Covid-19 virus is the Power model, which recorded the lowest MSE among all the models, followed by the Logistic, Compound, Exponential models with the same strength of fit, with the same MSE at all alpha-cut coefficients (0.0, 0.1, 0.5, 0.8) and that the models Cubic, Quadratic, Linear, Logarithmic, Inverse are not suitable for data on the number of infections with Covid-19 virus, and we also note that the best model that achieved a fit for the data was at the alpha-cut = 0.8 (MSE=0.223) and that the value of the coefficient of the determination R-2 of the Power model decreases as the cut-off factor increases and this indicates the accuracy of the appropriate model. We also notice that increase in one unit of time led to increase infection with Covid-19 with 1.456.

COVI3D: Automatic COVID-19 CT Image-Based Classification and Visualization Platform Utilizing Virtual and Augmented Reality Technologies.

Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information.

Research on Price Elasticity of Mobile Phone Sales on Taobao Based on Logarithmic Function Model

As people become more accustomed to online buying, an increasing number of individuals are purchasing cell phones over the internet. In this paper, the price elasticity of mobile phones from Taobao is estimated using a logarithmic function model, and the link between price elasticity and market share, online sales, and pricing is empirically investigated. This study used data analytic to discover that as the price of a brand with a significant market share drops, the sales volume increases. In addition, this study examines elasticity changes caused by COVID-19 and shows practical implications with proposed business solutions;this study found that The price drop will affect the sales volume, and the sales volume will change with the price change. The findings will give helpful information to internet cell phone vendors. © 2021 IEEE.

A novel LT-LBP based prediction model for COVID-CT images with Machine Learning

Logarithmic Transformation of Local Binary Pattern (LT-LBP) is introduced with machine learning algorithms for the classification of covid CT images. Preprocessing the input information plays a key role in machine learning as well as deep learning models. CT images and Chest X-rays are significant in diagnosing the disease. Most of the medical images are greyscale images. Texture analysis is one of the ways to obtain information from medical images and Local binary pattern is an efficient texture operator. With the texture pattern LBP, a novel method as Logarithmic Transformation of Local Binary Pattern (LT-LBP) is proposed in this paper. We applied 2695 CT images and 115 Italian COVID Positive CT images and 5 COVID positive CT images from the Chennai region. The CT-Scans and Chest X-ray images have endured for preprocessing and texture analysis with Local Binary Pattern (LBP) and trained with Support Vector Machine (SVM), K-nearest neighbors (KNN), Random Forest (RF), Logistic Regression (LR) as machine learning algorithms. The LT-LBP gives a better result when compared with normal LBP when combined with SVM and RF. The retrospective study gives the result as accuracy percentage of 95.7 with LT-LBP combined with SVM and also 91.4 percent of accuracy results for LT-LBP with RF © 2021 IEEE.

Effectiveness of Logarithmic Entropy Measures for Pythagorean Fuzzy Sets in diseases related to Post COVID Implications under TOPSIS Approach

Following the second wave of Covid-19 infections in India, individuals are now arriving to hospitals with a variety of symptoms, not simply for mucormycosis, a fungal infection. The most common symptoms are extreme tiredness, drowsiness, body and joint pain, mental fog, and fever, but pneumonia, collapsed lungs, heart attacks, and strokes have all been reported. Pythagorean fuzzy sets (PFSs) proposed by Yager [42] offers a novel technique to characterize uncertainty and ambiguity with greater precision and accuracy. The idea was developed specifically to describe uncertainty and ambiguity mathematically and to provide a codified tool for dealing with imprecision in real-world circumstances. This article addresses novel logarithmic entropy measures under PFSs. Additionally, numerical illustration is utilized to ascertain the strength and validity of the proposed entropy measures. Application of the measures is used in detecting diseases related to Post COVID 19 implications through TOPSIS method. Comparison of the suggested measures with the existing ones is also demonstrated. © 2021, Ismail Saritas. All rights reserved.

The geometric Brownian motion of indosat telecommunications daily stock price during the Covid-19 pandemic in Indonesia

One of the major telecommunication and network service providers in Indonesia is PT Indosat Tbk. During the coronavirus (COVID-19) pandemic, the daily stock price of that company was influenced by government policies. This study addresses stock data movement from February 5, 2020 to February 5, 2021, resulted in 243 data, using the Geometric Brownian motion (GBM). The stochastic process realization of this stock price fluctuates and increases exponentially, especially in the 40 latest data. Because of this situation, the realization is transformed into log 10 and calculated its return. As a result, weak stationary in variance is obtained. Furthermore, only data from December 7, 2020 to February 5, 2021 fulfill the GBM assumption of stock price return, as Rt1∗, t1∗ = 1, 2, 3, …, 40. The main idea of this study is adding datum one by one as much as 10% - 15% of the total data Rt1∗, starting from December 4, 2020 backwards. Following this procedure, and based on the 3% < p-value < 10%, the study shows that its datum can be included in Rt1∗, so t1∗ = −4. −3, −2, …, 40 and form five other data groups, Rt2∗, …, Rt6∗. Considering Mean Absolute Percentage Error (MAPE) and amount of data from each group, Rt6∗ is selected for modelling. Thus, GBM succeeded in representing the stock price movement of the second most popular Indonesian telecommunication company during COVID-19 pandemic. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

Anticipating trajectories of exponential growth.

Humans grossly underestimate exponential growth, but are at the same time overconfident in their (poor) judgement. The so-called 'exponential growth bias' is of new relevance in the context of COVID-19, because it explains why humans have fundamental difficulties to grasp the magnitude of a spreading epidemic. Here, we addressed the question, whether logarithmic scaling and contextual framing of epidemiological data affect the anticipation of exponential growth. Our findings show that underestimations were most pronounced when growth curves were linearly scaled and framed in the context of a more advanced epidemic progression. For logarithmic scaling, estimates were much more accurate, on target for growth rates around 31%, and not affected by contextual framing. We conclude that the logarithmic depiction is conducive for detecting exponential growth during an early phase as well as resurgences of exponential growth.

Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory.

Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach.

Effects of non-pharmaceutical interventions against COVID-19: A cross-country analysis.

Prolonging non-pharmaceutical interventions (NPIs) used in the control of pandemics can cause a devastating effect on the overall economic and social welfare levels. Therefore, policymakers are facing a difficult duty in terms of implementing economically and socially sustainable and acceptable measures. The aim of this study is to investigate the effectiveness of NPIs implemented to control the COVID-19 pandemic. To this end, eight NPI measures were analysed, and their effects on the number of cases were investigated for France, Spain, China, and South Korea. In the study, the treatment effect of these mechanisms on the daily increase rate of the total number of cases during a certain period was analysed by using logarithmic linear regression with a dummy variables model. The findings indicate that the measures are effective against the spread of the pandemic at different levels. The findings also suggest that the most effective measure in decreasing the number of cases is workplace closure. An analysis comparing the effectiveness of countrywide measures and regional measures shows that school closing is the most effective measure to decrease the number of cases when implemented countrywide as opposed to regional implementation.

Comparing length of hospital stay during COVID-19 pandemic in the USA, Italy and Germany.

BACKGROUND: COVID-19 is the most informative pandemic in history. These unprecedented recorded data give rise to some novel concepts, discussions and models. Macroscopic modeling of the period of hospitalization is one of these new issues. METHODS: Modeling of the lag between diagnosis and death is done by using two classes of macroscopic analytical methods: the correlation-based methods based on Pearson, Spearman and Kendall correlation coefficients, and the logarithmic methods of two types. Also, we apply eight weighted average methods to smooth the time series before calculating the distance. We consider five lags with the least distance. All the computations are conducted on Matlab R2015b. RESULTS: The length of hospitalization for the fatal cases in the USA, Italy and Germany are 2-10, 1-6 and 5-19 days, respectively. Overall, this length in the USA is 2 days more than that in Italy and 5 days less than that in Germany. CONCLUSION: We take the distance between the diagnosis and death as the length of hospitalization. There is a negative association between the length of hospitalization and the case fatality rate. Therefore, the estimation of the length of hospitalization by using these macroscopic mathematical methods can be introduced as an indicator to scale the success of the countries fighting the ongoing pandemic.