ABSTRACT
Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.
ABSTRACT
The Reactive Power Reserve (RPR) is a very important indicator for voltage stability and is sensitive to the operating conditions of power systems. Thorough understanding of RPR, specifically Effective Reactive Reserve (ERR) under intermittent Wind Power (WP) and uncertain demand is essential and key focus of this research. Hence, a stochastic multivariate ERR assessment and optimization problem is introduced here. The proposed problem is solved in three stages: modeling of multivariate uncertainty, studying the stochastic behavior of ERR and optimizing ERR. The volatilities associated with WP generation and consumer demand are modeled explicitly, and their probability distribution function is discretized to accommodate structural uncertainty. A combined load modeling approach is introduced and extended further to accommodate multi-variability. The impact of these uncertainties on ERR is assessed thoroughly on modified IEEE 30 and modified Indian 62 bus system. A non-linear dynamic stochastic optimization problem is formulated to maximize the expected value of ERR and is solved using ‘Coronavirus Herd Immunity Optimizer (CHIO)'. The impact of the proposed strategy on stability indices like the L-index, Proximity Indicator (PI) are analyzed through various case studies. Further, the effectiveness of the proposed approach is also compared with the existing mean value approach. Additionally, the performance of CHIO is confirmed through exhaustive case studies and comparisons. © 2023 Elsevier B.V.
ABSTRACT
The aim of the study is to assess the consequences of the COVID-19 pandemic on the incomes of households located in various national economies in 2021. The survey of representatives of the economically active adult population (18-64 years old) was conducted in 47 countries in Europe, Asia, Africa, Latin America, and North America within the framework of the Global Entrepreneurship Monitoring Project. The development of mathematical models included the construction of normal distribution density functions in accordance with the authors' methodology. It was proved that almost half of the households (46.6%) had a certain decrease in household income due to the pandemic. Slightly less (45.6%) was the proportion of households in which the income remained stable. An absolute minority (7.8%) of households experienced income growth. © 2022 Universidad Católica de Colombia.
ABSTRACT
Mathematical modelling of the COVID-19 epidemic is based on system dynamics and SIR models, which are not considered adequate. To overcome the shortcomings of modelling, a non-classical discipline, epidemic dynamics, is proposed. The epidemic should be viewed as an open, self-replicating dynamic system in epidemic dynamics. Epidemic dynamics models are based on a dynamic system model with an extended network of inverse relationship. This non-classical approach allows the tools of non-linear and non-equilibrium dynamics to be used and models of epidemic dynamics to be represented in the form of non-linear and non-stationary differential equations. The solutions of the equations are special COVID-19 distribution functions – functions of the flows and accumulation levels of the infected and the dead. The COVID-19 distribution functions show high accuracy in approximating the statistics, demonstrating the excellent adequacy of these functions in principle. The application of COVID-19 distribution functions makes it possible to quantitatively describe the basic concepts of an epidemic to carry out comparative parametric analysis of the distribution of diseases and predict the development of an epidemic. © 2022 Copyright for this paper by its authors.
ABSTRACT
Supply chains SC increasing complexity has made it more and more vulnerable to an array of disruptions and disturbances especially in an unstable and volatile environment. Transportation and distribution functions being an integral part of the supply chain and the last phase to touch the costumers, can be impacted high negatively by some risks. That's has been exposed by the arrival of the coronavirus pandemic which crippled the whole SC both globally and locally. This paper presents a case study based on a survey that addresses supply chains risks which are generated in both transportation and distribution systems of 50 Moroccan companies and the ones which are implemented in the Moroccan territory in the Covid-19 era. The study reveals some measures and strategies that companies have and can implement to withstand and cope with the risks caused by the health crisis disruptions such as the rising importance of integration, digitalization and the need to revisit the meaning of efficiency in transportation and distribution management. © 2022 IEEE.
ABSTRACT
The exponentiated generalized Gull alpha power exponential distribution is an extension of the exponential distribution that can model data characterized by various shapes of the hazard function. However, change point problem has not been studied for this distribution. In this study, the change point detection of the parameters of the exponentiated generalized Gull alpha power exponential distribution is studied using the modified information criterion. In addition, the binary segmentation procedure is used to identify multiple change point locations. The assumption is that all the parameters of the EGGAPE distributions are considered changeable. Simulation study is conducted to illustrate the power of the modified information criterion in detecting change point in the parameters with different sample sizes. Three applications related to COVID-19 data are used to demonstrate the applicability of the MIC in detecting change point in real life scenario.
ABSTRACT
This research shows that function words can be useful as features for machine learning models tasked with detecting conspiratorial content in COVID-19 related Twitter posts. A significance test exposes that the distribution of function words between fake and legitimate content varies greatly. Further, a support vector machine classifier is demonstrated to perform above chance when using function word-only features, achieving a Matthews correlation coefficient of 0.139 on unseen test data. Copyright 2021 for this paper by its authors.
ABSTRACT
An infinite-server queueing model with state-dependent arrival process and exponential distribution of service time is analyzed. It is assumed that the difference between the value of the arrival rate and total service rate becomes positive starting from a certain value of the number of customers in the system. In this paper, time until reaching this value by the number of customers in the system is called the pseudo steady-state period (PSSP). Distribution of duration of PSSP, its raw moments and its simple approximation under a certain scaling of the number of customers in the system are analyzed. Novelty of the considered problem consists of an arbitrary dependence of the rate of customer arrival on the current number of customers in the system and analysis of time until reaching from below a certain level by the number of customers in the system. The relevant existing papers focus on the analysis of time interval since exceeding a certain level until the number of customers goes down to this level (congestion period). Our main contribution consists of the derivation of a simple approximation of the considered time distribution by the exponential distribution. Numerical examples are presented, which confirm good quality of the proposed approximation.
ABSTRACT
In this paper, the main aim is to define a statistical distribution that can be used to model COVID-19 data in Mexico and Canada. Using the method of exponentiation on the gull alpha exponential distribution introduces a new distribution with three parameters called the exponentiated gull alpha power exponential (EGAPE) distribution. The distribution has the benefit of being able to represent monotonic and nonmonotonic failure rates, both of which are often seen in dependability issues. It is possible to determine the quantile function as well as the skewness, kurtosis, and order statistics of the suggested distribution. The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. In conclusion, the flexibility of the new distribution is demonstrated by modeling COVID-19 data. From the practical application, we can conclude that the proposed model outperformed the competing models and therefore can be used as a better option for modeling COVID-19 and other related datasets.
ABSTRACT
Engineering human-centric urban transport systems should be carried out using information technology in forecasting traffic and passenger flows. One of the most important objects of urban transport systems’ progress is modeling patterns of transport flows and their distribution on the road network. These patterns are determined by the subjective choice of city residents of traffic routes using public and private transport. This study aimed to form a sequence of stages of modeling transport and passenger flows in human-centric urban transport systems and passenger flows in the human-centric urban intelligent transport systems and to determine the patterns of change to the gravity function of employees of municipal services. It was revealed that the trip distribution function of workers of urban service enterprises can be described by the attributes of the structure of the city, socio-economic data, and attributes characterizing the zones and its residents.
ABSTRACT
Motivation: Due to the significant impact of the COVID-19 pandemic on the exit of entrepreneurs from their businesses, the study of this problem in different countries is relevant. Purpose: The aim of the study is to assess the reasons for the exit of entrepreneurs from their businesses in national economies in 2020. Approach: The assessment of five indicators characterizing the opinions of entrepreneurs who have left their businesses about the positive and negative reasons was considered. In addition, an assessment was made of the share of entrepreneurs who stopped working due to the influence of COVID-19 in the total number of economically active population in 2020. The initial data were the results of a survey of the economically active population in 39 countries during the implementation of the Global Entrepreneurship Monitoring project. Five indicators were evaluated using the density functions of the normal distribution. Results: It is proved that the share of people who stopped entrepreneurial activity in 2020 amounted to about 6 percent of the total economically active population on average in the countries under consideration. It is shown that 0.7 percent of the total economically active population stopped entrepreneurial activity for positive reasons. It is proved that about five out of every six entrepreneurs who have gone out of business have stopped their activities for negative reasons. It is shown that about one-third of entrepreneurs who left their business in 2020 for negative reasons did so due to the consequences of the coronavirus pandemic. Conclusion: The results of our research have a certain theoretical and practical significance for governments, entrepreneurs, and the economically active population. The methodological approach presented in the article can be used to assess the impact of the COVID-19 pandemic on the exit of entrepreneurs from their businesses in 2021. Consistency: The pandemic has significantly increased the risks and uncertainty in the activities of entrepreneurs, so the new knowledge gained is of interest to a wide range of government organizations and entrepreneurs in various countries.
ABSTRACT
While it is well understood that the emerging Social Internet of Things (SIoT) offers a description of a new world of billions of humans which are intelligently communicate and interact with each other. SIoT presents new challenges for suggesting useful objects with certain services for people. This is due to the limitation of social networks between human and objects, such as the evaluation of the various patterns inherent in human walk in cities. In this study we focus services on the problem of recommendation on SIoT which is very important for many applications such as urban computing, smart cities, and health care. The optimized results of swarm of certain infected people COViD-19 introduced in this paper aims at finding a given region of interest. Guided by a fitness function, the particle swarm optimization (PSO) algorithm has proved its efficiency to explore the search space and find the optimal solution. However, in real world scenarios in which the peoples are simulated as particles, there are practical constraints that should be taken into considerations. The most two significant constraints are (1) given the social-distance, the measurement of input variable fluctuations and their possibility of occurring via probability distribution function over the whole particles. (2) given the limited the communication range of particle/people/users, therefore, the spread of the diseases are simulated and evaluated using neighborhood particle swarm optimization (NPSO). © 2021 IEEE.
ABSTRACT
Research was conducted in an effort to study the physics of high-energy electron beams generated in a dense plasma focus (DPF). The effort consisted of both theoretical and experimental approaches. Theoretical calculations were performed using various non-local thermodynamic equilibrium (non-LTE) kinetic models to identify line candidates that would exhibit a measureable degree of polarization. Experiments carried out on Hawk tested different gases in the gas-puff nozzle at varying pressures. A convex crystal spectrometer was benchmarked using different crystals and filters, and was mounted on the Hawk chamber for collection of spectroscopic data. This memorandum summarizes the overall results of the project.
ABSTRACT
Data-driven approaches in materials science demand the collection of large amounts of data on the target materials at synchrotron beamlines. To accurately gather suitable experimental data, it is essential to establish fully automated measurement systems to reduce the workload of the beamline staff. Moreover, the recent COVID-19 pandemic has further emphasized the necessity of automated and/or remote measurements at synchrotron beamlines. Here, the installation of a new sample changer combined with a high-temperature furnace and a fully automated alignment system on beamline BL04B2 at SPring-8 is reported. The system allows X-ray total scattering measurements of up to 21 samples at different temperatures (from room temperature to 1200°C) to be conducted without any human assistance.
Subject(s)
COVID-19 , Robotics , Humans , Pandemics , SARS-CoV-2 , Synchrotrons , Temperature , X-RaysABSTRACT
In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the model has been followed by different statistical tools like the value of logarithm of likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian information criterion. Moreover, likelihood ratio test and empirical posterior probability analysis are performed to show its suitability. The maximum-likelihood and asymptotic confidence intervals for the parameters are derived from Fisher information matrix. We use the Markov Chain Monte Carlo technique to generate samples from the posterior density function. Based on generated samples, we can compute the Bayes estimates of the unknown parameters and can also construct highest posterior density credible intervals. Further we discuss the Bayesian prediction for future observation based on the observed sample. The Gibbs sampling technique has been used for estimating the posterior predictive density and also for constructing predictive intervals of the order statistics from the future sample.
ABSTRACT
This paper aims to introduce a superior discrete statistical model for the coronavirus disease 2019 (COVID-19) mortality numbers in Saudi Arabia and Latvia. We introduced an optimal and superior statistical model to provide optimal modeling for the death numbers due to the COVID-19 infections. This new statistical model possesses three parameters. This model is formulated by combining both the exponential distribution and extended odd Weibull family to formulate the discrete extended odd Weibull exponential (DEOWE) distribution. We introduced some of statistical properties for the new distribution, such as linear representation and quantile function. The maximum likelihood estimation (MLE) method is applied to estimate the unknown parameters of the DEOWE distribution. Also, we have used three datasets as an application on the COVID-19 mortality data in Saudi Arabia and Latvia. These three real data examples were used for introducing the importance of our distribution for fitting and modeling this kind of discrete data. Also, we provide a graphical plot for the data to ensure our results.