Uncertain hypothesis test for uncertain differential equations

Uncertain hypothesis test is a statistical tool that uses uncertainty theory to determine whether some hypotheses are correct or not based on observed data. As an application of uncertain hypothesis test, this paper proposes a method to test whether an uncertain differential equation fits the observed data or not. In order to demonstrate the test method, some numerical examples are provided. Finally, both uncertain currency model and stochastic currency model are used to model US Dollar to Chinese Yuan (USD–CNY) exchange rates. As a result, it is shown that the uncertain currency model fits the exchange rates well, but the stochastic currency model does not.

A case study for surgical mask production during the COVID-19 pandemic: continuous improvement with Kaizen and 5S applications

PurposeWith the emergence of the COVID-19 pandemic, the production shortage of personal protective equipment (PPE), such as surgical masks, has become increasingly significant. It is vital to quickly provide high-quality, hygienic PPE during pandemic periods. This comprehensive case study aims to confirm that Kaizen and 5S applications reduce wastage rates and stoppages, which as a result, created a more efficient and sustainable workplace in a small–mediumenterprise (SME) producing PPE in Turkey.Design/methodology/approachThe method for this case is discussed with the help of a flowchart using the DMAIC cycle: D-define, M-measure, A-analyse, I-improve and C-control.FindingsThe total stoppages due to fishing line, gripper, piston and yarn welding have decreased by approximately 42.4%. As a result of eliminating wasted time and reduced changeovers, a total of 5,502 min have been saved per month. This increased production of approximately 10.55% per month, led to an addition of 506,184 units.Originality/valueThe use of lean manufacturing (LM), Six Sigma, Lean Six Sigma and continuous improvement methodologies are not common in textile SMEs. Based on the current literature reviewed, to the best of the authors' knowledge, this is the first comprehensive case study that combines statistical tools, such as hypothesis tests and LM practices, in the production process for a PPE company operating as a textile SME.

Analysis of Covid-19 using machine learning techniques

Covid-19 is caused by a newly detected coronavirus (SARS-CoV-2). It is a respiratory infection that usually spreads from individual to individual through sneezing or coughing. The disease, which was first detected in the province of Wuhan, China, had effected more than one continent and was declared as a pandemic by the World Health Organization (WHO). The pandemic has affected health, social, economic, and psychological segments of life for billions of people. Though vaccines have been developed and are made available, we are still prone to the virus, which is similar to any other flu. This chapter presents an analysis of the symptoms of the disease and identifies significant symptoms that impact the cause of the illness. Machine learning techniques like multiple regression, support vector machine (SVM), Decision Tree, Random Forest, and Logistic Regression are applied to understand the evaluation with respect to the measures like coefficient of determination, and mean-squared error. Hypothesis testing is used to determine whether at least one of the features is useful in the diagnosis of the disease. Further feature selection process is used to identify the most significant symptoms that will cause the virus. Different visualization methods are used to figure the substantial reasoning from the model's prediction and perform analysis on the results obtained. © 2023 Elsevier Inc. All rights reserved.

COVID-19 pandemic: Analyzing of infection waves in Germany in comparison with common infectious diseases by using reliability methods

Since December 2019, the world is confronted with the COVID-19 pandemic, caused by the Coronavirus SARS-CoV-2. The COVID-19 pandemic with its incredible spreading speed shows the vulnerability of a globalized and networked world. The first two years of the pandemic were characterized by several infection waves, described by length, peak, and speed. The infection waves caused a heavy burden on health systems and severe restrictions on public life, like educational system shutdown, travel restrictions, limitations regarding public life, or a comprehensive lockdown within a lot of countries. The goal of the presented research study is the analysis of the development of the six dominant infection waves in Germany within the first two years of the COVID-19 pandemic (February 2020 - February 2022). The analyses are focusing on the occurrence of infection and spreading behavior, in detail on attributes like length, peak, and speed of each wave. Furthermore, various impacts of lockdown strategies (hard, soft) or virus variants are considered. The analyses of the infection waves are based on a transfer and application of methods - especially the Weibull distribution model and statistical hypothesis tests - used in reliability engineering for analyzing the upcoming failure development within product fleets in the field. The spreading behavior of a COVID-19 infection wave can be described by the Weibull distribution model in a sound way, related to a short time interval. The interpretation of the Weibull model parameters allows the assessment of the COVID-19 infection wave characteristics and generates additional information to classical infection analysis models like the SIR model [10]. Finally, the characteristics of the COVID-19 infection waves are analyzed in the context of other common infectious diseases in Germany like Influenza or Norovirus. This study continues previous research;cf. [1-3,11,12]. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.

Uncertain Logistic Growth Model for Confirmed COVID-19 Cases in Brazil

The pandemic COVID-19 gives rise to a serious threat to people's health, economic development and social stability. This paper employs uncertain regression analysis to model the cumulative number of COVID-19 infection in Brazil. Some fundamental knowledge about the uncertain regression analysis is reviewed firstly. Then parameter estimation, residual analysis, uncertain hypothesis test and the forecast value and confidence interval are studied for confirmed COVID-19 cases in Brazil. As a byproduct, the reason for using uncertain regression analysis instead of probabilistic regression analysis is explained by analyzing the characteristics of the residual plot. All the analysis and prediction are devoted to proposing some theoretical supports for the epidemic prevention and control to some extent. © 2022 World Scientific Publishing Company.

A case study for surgical mask production during the COVID-19 pandemic: continuous improvement with Kaizen and 5S applications

PurposeWith the emergence of the COVID-19 pandemic, the production shortage of personal protective equipment (PPE), such as surgical masks, has become increasingly significant. It is vital to quickly provide high-quality, hygienic PPE during pandemic periods. This comprehensive case study aims to confirm that Kaizen and 5S applications reduce wastage rates and stoppages, which as a result, created a more efficient and sustainable workplace in a small-mediumenterprise (SME) producing PPE in Turkey. Design/methodology/approachThe method for this case is discussed with the help of a flowchart using the DMAIC cycle: D-define, M-measure, A-analyse, I-improve and C-control. FindingsThe total stoppages due to fishing line, gripper, piston and yarn welding have decreased by approximately 42.4%. As a result of eliminating wasted time and reduced changeovers, a total of 5,502 min have been saved per month. This increased production of approximately 10.55% per month, led to an addition of 506,184 units. Originality/valueThe use of lean manufacturing (LM), Six Sigma, Lean Six Sigma and continuous improvement methodologies are not common in textile SMEs. Based on the current literature reviewed, to the best of the authors' knowledge, this is the first comprehensive case study that combines statistical tools, such as hypothesis tests and LM practices, in the production process for a PPE company operating as a textile SME.

To curb research misreporting, replace significance and confidence by compatibility: A Preventive Medicine Golden Jubilee article.

It is well known that the statistical analyses in health-science and medical journals are frequently misleading or even wrong. Despite many decades of reform efforts by hundreds of scientists and statisticians, attempts to fix the problem by avoiding obvious error and encouraging good practice have not altered this basic situation. Statistical teaching and reporting remain mired in damaging yet editorially enforced jargon of "significance", "confidence", and imbalanced focus on null (no-effect or "nil") hypotheses, leading to flawed attempts to simplify descriptions of results in ordinary terms. A positive development amidst all this has been the introduction of interval estimates alongside or in place of significance tests and P-values, but intervals have been beset by similar misinterpretations. Attempts to remedy this situation by calling for replacement of traditional statistics with competitors (such as pure-likelihood or Bayesian methods) have had little impact. Thus, rather than ban or replace P-values or confidence intervals, we propose to replace traditional jargon with more accurate and modest ordinary-language labels that describe these statistics as measures of compatibility between data and hypotheses or models, which have long been in use in the statistical modeling literature. Such descriptions emphasize the full range of possibilities compatible with observations. Additionally, a simple transform of the P-value called the surprisal or S-value provides a sense of how much or how little information the data supply against those possibilities. We illustrate these reforms using some examples from a highly charged topic: trials of ivermectin treatment for Covid-19.

Weigh-in-Motion: Lightweight Real-Time Identification of Gbps Wireless Traffic.

Distinguishing between wireless and wired traffic in a network middlebox is an essential ingredient for numerous applications including security monitoring and quality-of-service (QoS) provisioning. The majority of existing approaches have exploited the greater delay statistics, such as round-trip-time and inter-packet arrival time, observed in wireless traffic to infer whether the traffic is originated from Ethernet (i.e., wired) or Wi-Fi (i.e., wireless) based on the assumption that the capacity of the wireless link is much slower than that of the wired link. However, this underlying assumption is no longer valid due to increases in wireless data rates over Gbps enabled by recent Wi-Fi technologies such as 802.11ac/ax. In this paper, we revisit the problem of identifying Wi-Fi traffic in network middleboxes as the wireless link capacity approaches the capacity of the wired. We present Weigh-in-Motion, a lightweight online detection scheme, that analyzes the traffic patterns observed at the middleboxes and infers whether the traffic is originated from high-speed Wi-Fi devices. To this end, we introduce the concept of ACKBunch that captures the unique characteristics of high-speed Wi-Fi, which is further utilized to distinguish whether the observed traffic is originated from a wired or wireless device. The effectiveness of the proposed scheme is evaluated via extensive real experiments, demonstrating its capability of accurately identifying wireless traffic from/to Gigabit 802.11 devices.

Uncertain regression model with autoregressive time series errors.

Uncertain regression model is a powerful analytical tool for exploring the relationship between explanatory variables and response variables. It is assumed that the errors of regression equations are independent. However, in many cases, the error terms are highly positively autocorrelated. Assuming that the errors have an autoregressive structure, this paper first proposes an uncertain regression model with autoregressive time series errors. Then, the principle of least squares is used to estimate the unknown parameters in the model. Besides, this new methodology is used to analyze and predict the cumulative number of confirmed COVID-19 cases in China. Finally, this paper gives a comparative analysis of uncertain regression model, difference plus uncertain autoregressive model, and uncertain regression model with autoregressive time series errors. From the comparison, it is concluded that the uncertain regression model with autoregressive time series errors can improve the accuracy of predictions compared with the uncertain regression model.

Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death.

"Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code-"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months - January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.