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The inverse Lomax distribution has been extensively used in many disciplines, including stochastic modelling, economics, actuarial sciences, and life testing. It is among the most recognizable lifetime models. The purpose of this research is to look into a new and important aspect of the inverse Lomax distribution: the calculation of the fuzzy stress-strength reliability parameter RF = P(Y < X), assuming X and Y are random independent variables that follow the inverse Lomax probability distribution. The properties of structural for the proposed reliability model are studied along with the Bayesian estimation methods, maximum product of the spacing and maximum likelihood. Extensive simulation studies are achieved to explore the performance of the various estimates. Subsequently, two sets of real data are considered to highlight the practicability of the model. [ABSTRACT FROM AUTHOR]

The post-effects of COVID-19 have begun to emerge in the long term in society. Stroke has become one of the most common side effects in the post-COVID community. In this study, to examine the relationship between COVID-19 and stroke, a fractional-order mathematical model has been constructed by considering the fear effect of being infected. The model’s positivity and boundedness have been proved, and stability has been examined for disease-free and co-existing equilibrium points to demonstrate the biological meaningfulness of the model. Subsequently, the basic reproduction number (the virus transmission potential (R0)) has been calculated. Next, the sensitivity analysis of the parameters according to R0 has been considered. Moreover, the values of the model parameters have been calculated using the parameter estimation method with real data originating from the United Kingdom. Furthermore, to underscore the benefits of fractional-order differential equations (FODEs), analyses demonstrating their relevance in memory trace and hereditary characteristics have been provided. Finally, numerical simulations have been highlighted to validate our theoretical findings and explore the system’s dynamic behavior. From the findings, we have seen that if the screening rate in the population is increased, more cases can be detected, and stroke development can be prevented. We also have concluded that if the fear in the population is removed, the infection will spread further, and the number of people suffering from a stroke may increase.

In this paper, a mathematical epidemiological model in the form of reaction diffusion is proposed for the transmission of the novel coronavirus (COVID-19). The next-generation method is utilized for calculating the threshold number R0while the least square curve fitting approach is used for estimating the parameter values. The mathematical epidemiological model without and with diffusion is simulated through the operator splitting approach based on finite difference and meshless methods. Further, for the graphical solution of the non-linear model, we have applied a one-step explicit meshless procedure. We study the numerical simulation of the proposed model under the effects of diffusion. The stability analysis of the endemic equilibrium point is investigated. The obtained numerical results are compared mutually since the exact solutions are not available.

In this study, the COVID-19 epidemic model is established by incorporating quarantine and isolation compartments with Mittag-Leffler kernel. The existence and uniqueness of the solutions for the proposed fractional model are obtained. The basic reproduction number, equilibrium points, and stability analysis of the COVID-19 model are derived. Sensitivity analysis is carried out to elaborate the influential parameters upon basic reproduction number. It is obtained that the disease transmission parameter is the most dominant parameter upon basic reproduction number. A convergent iterative scheme is taken into account to simulate the dynamical behavior of the system. We estimate the values of variables with the help of the least square curve fitting tool for the COVID-19 cases in Pakistan from 04 March to May 10, 2020, by using MATLAB. [ABSTRACT FROM AUTHOR]

The infection of coronavirus (COVID-19) is a dangerous and life-threatening disease which spread to almost all parts of the globe. We present a mathematical model for the transmission of COVID-19 with vaccination effects. The basic properties of fractional calculus are presented for the inspection of the model. We calculate the equilibria of the model and determined the reproduction number ℛ 0 . Local asymptotic stability conditions for the disease-free are obtained which determines the conditions to stabilize the exponential spread of the disease. The nonlinear least-square procedure is utilized to parameterize the model from actual cases reported in Pakistan. By fixed point theory, we prove the existence of a unique solution. We also present numerical results to simulate virus transmission and compare the results with those of the Caputo derivative. We study the solution pathways of the COVID-19 system to provide effective control policies for the infection. Significant changes have been noticed by lowering the order of fractional derivative. [ABSTRACT FROM AUTHOR]

Background: In epidemiology, indicators such as the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S) are commonly used to assess additive interactions between two variables. However, the results of these indicators are sometimes inconsistent in real world applications and it may be difficult to draw conclusions from them., Method: Based on the relationship between the RERI, AP, and S, we propose a method with consistent results, which are achieved by constraining e θ 3 - e θ 1 - e θ 2 + 1 = 0 , and the interpretation of the results is simple and clear. We present two pathways to achieve this end: one is to complete the constraint by adding a regular penalty term to the model likelihood function; the other is to use model selection., Result: Using simulated and real data, our proposed methods effectively identified additive interactions and proved to be applicable to real-world data. Simulations were used to evaluate the performance of the methods in scenarios with and without additive interactions. The penalty term converged to 0 with increasing λ, and the final models matched the expected interaction status, demonstrating that regularized estimation could effectively identify additive interactions. Model selection was compared with classical methods (delta and bootstrap) across various scenarios with different interaction strengths, and the additive interactions were closely observed and the results aligned closely with bootstrap results. The coefficients in the model without interaction adhered to a simplifying equation, reinforcing that there was no significant interaction between smoking and alcohol use on oral cancer risk., Conclusion: In summary, the model selection method based on the Hannan-Quinn criterion (HQ) appears to be a competitive alternative to the bootstrap method for identifying additive interactions. Furthermore, when using RERI, AP, and S to assess the additive interaction, the results are more consistent and the results are simple and easy to understand., Competing Interests: The authors declare that they have no competing interests., (© 2024 Hu et al.)

The virus that begins from Wuhan China, known as COVID-19 or coronavirus is still a huge panic for humans around the globe. The elimination of this virus from our society needs proper attentions to follows the rule suggested by World Health Organization (WHO). A vast literature on the modeling of this infection in various perspective is available. In the present work, we design a new mathematical model for COVID-19 pandemic by utilizing the real infected cases reported from Kingdom of Saudi Arabia. Initially, we formulate the model with the help of classical integer order nonlinear differential equations. The treatment class is considered the model to analyze the impact of treatment on the disease dynamics. The Caputo-Fabrizio derivative with the non-singular exponential kernel is applied in order to reformulate the proposed COVID-19 transmission model with a fractional order. The biologically important parameter called the basic reproductive number is investigated both theoretically and numerically. The estimated values of R0 for the selected period are approximated to be 1.63. Further, by making use of the Picard Lindelöf theorem we provide the existence and uniqueness of the COVID-19 fractional epidemic model. Moreover, the fractional model is solved numerically and a number of simulation results are depicted using the real estimated parameters. The impact of various model parameters and memory index are shown graphically. We conclude that the fractional order epidemic models are more appropriate and provide deep insights into the disease dynamics.

IntroductionElectronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research.ObjectivesThis study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism.MethodsThe MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k.ResultsThe average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected.ConclusionThe experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.

This research article investigates the application of Lévy noise to understand the dynamic aspects of measles epidemic modeling and seeks to explain the impact of vaccines on the spread of the disease. After model formulation, the study utilises uniqueness and existence techniques to derive a positive solution to the underlying stochastic model. The Lyapunov function is used to investigate the stability results associated with the proposed stochastic model. The model’s dynamic characteristics are analyzed in the vicinity of the infection-free and endemic states of the associated ODEs model. The stochastic threshold Rs that ensures disease’s extinction whenever Rs<1 is calculated. We utilized data from Pakistan in 2019 to estimate the parameters of the model and conducted simulations to forecast the future behavior of the disease. The results were compared to actual data using standard curve fitting tools.

The purpose of this study is to construct a store layout that increases the number of visitors in an underground shopping street located near a terminal station, where most of the passersby use the street as a transfer route for transportation. In this study, the effects of advertisements on the number of passersby are measured in the real shopping street. Based on these measurements, simulations are constructed and conducted to verify the effect of the location of advertisements on the number of customers to the store. [ABSTRACT FROM AUTHOR]

In this work, the operation of microgrids is studied, using real data for demand and renewable energy sources. A mixed integer nonlinear program to operate the microgrid is proposed, following a model predictive control methodology, which allows to enhance the economic performance by means of a predictive strategy. A comparison with other techniques without predictive feature nor forecast mismatches is made, showing that our method outperforms them by adapting the current control decision to future costly issues. The case study shows savings of more than 10%, analysing the qualitative aspects of the proposed strategy. • A MINLP problem is solved in an MPC with real data. • The inclusion of a prediction horizon is an immediate advantage, decreasing overall production costs. • Real-time two-stage MPC allows for an adequate adaptation to the real circumstances. • The real-time economic dispatch yields savings up to 10%. [ABSTRACT FROM AUTHOR]

In the present investigations, we construct a new mathematical for the transmission dynamics of corona virus (COVID‐19) using the cases reported in Kingdom of Saudi Arabia for March 02 till July 31, 2020. We investigate the parameters values of the model using the least square curve fitting and the basic reproduction number is suggested for the given data is ℛ0 ≈ 1.2937. The stability results of the model are shown when the basic reproduction number is ℛ0 < 1. The model is locally asymptotically stable when ℛ0 < 1. Further, we show some important parameters that are more sensitive to the basic reproduction number ℛ0 using the PRCC method. The sensitive parameters that act as a control parameters that can reduce and control the infection in the population are shown graphically. The suggested control parameters can reduce dramatically the infection in the Kingdom of Saudi Arabia if the proper attention is paid to the suggested controls. [ABSTRACT FROM AUTHOR]

In this paper, we consider a fractional SIS epidemic system with logistic growth demographic and saturated incidence rate for susceptibles. First, we validate our model by proving the global existence, positivity as well as boundedness of solutions. Then, we give necessary and sufficient conditions for the extinction and persistence of the disease from the population. We also study the local asymptotic stability of the unique positive equilibrium point by analyzing the corresponding characteristic equation. We find that combining logistic growth and saturated incidence for susceptibles can lead the system dynamic behavior to exhibit stability switches. By choosing the growth rate and the carrying capacity of the population as the bifurcation parameters, the stability of the positive equilibrium and the existence of Hopf bifurcation are investigated. Finally, numerical simulations are performed to verify the theoretical results, to fit real-time data from 10 June to 25 November of 2020 and also to predict the number of cumulative cases for COVID-19 in Morocco during 2021. [ABSTRACT FROM AUTHOR]

Abstract This work investigates the dissemination mechanism of pine wilt disease. The basic reproduction number is computed explicitly, and an ultimate invariable level of contagious hosts and vectors, without and with disease, is discussed by using this number. Highly effective techniques, Lyapunov functional and graph theoretic, are utilised to obtain the ultimate constant level of the whole population. The idea of complete disease eradication and reduction of endemic level is explored through the utilisation of two efficient methods. Using sensitivity analysis approach, necessary control measures are suggested to overcome the disease. Using the literature data, the robustness of control strategies is shown graphically.

Abstract COVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained R 0 ≈ 1.50 $\mathcal{R}_{0} \approx 1.50$ . Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.

We consider a new investigations to the competition model in fractional derivative of Caputo-Fabrizio type with Newton based polynomial approach. We first, give brief discussion on the model in integer case and then apply the Caputo-Fabrizio derivative for the model generalization. We explore the fixed points of the model and present their stability results. Considering the actual data of commercial and rural banks in Indonesia since 2004–2014, we obtain the real parameters. We provide many graphical illustrations for the data fitting versus model with this new approach and identify the suitable order of ρ for data fitting. Some graphical results are shown in order to show the effectiveness of the proposed new scheme.

The purpose of this work is to analyze the dynamics of dengue fever in newly introduced operator known as fractal-fractional Atangana-Baleanu. Initially, we formulate a new dengue model with hospitalization class of notified infected cases and present the model basic results. Stability results for the model are shown when R0

This study aims to describe the design and development of a STEM problem-solving activity involving mathematics and physics content. It is an activity whose starting point is a question based on a real problem: to identify the areas of the classroom where the intensity of sound is higher. In this way, the problem involves the physics of sound. However, in addition, by trying to give an answer to the initial question, students have the opportunity to develop progressively more complex mathematical models from already known mathematical concepts and procedures. The analysis of the results of the implementation based on the identification of the learning opportunities and the catalyst elements that generate them, allows us to state that the activity encourages students to enrich their mathematical models by incorporating new mathematical concepts such as the function of two variables. We conclude that the design of the activity and the choice of data acquisition technology are key, but also the interaction between the students and the teacher.

Natural symmetries exist in several processes of chemistry, physics, and biology. Symmetries possess interesting dynamical characteristics that cannot be seen in non-symmetric systems. The present paper investigates the competition between two banking systems, rural and commercial, in Indonesia, in parameterized fractional order Caputo derivative. A novel numerical method is used to discretize the competition system using the real data of rural and commercial banks in Indonesia for the period 2004–2014. The new scheme is more suitable and reliable for data fitting results and has good accuracy. The integer model is formulated in Caputo derivative and their stability results are presented. With the available parameters, the data for the model is analyzed using various scenarios. We shall compare the result with the previous method used in the literature and show that the present method is better than the previous method in the literature. It is shown that fractional order α and the parameter ρ involved in the numerical scheme provide excellent fitting.

Sensor drift in Wastewater Treatment Plants (WWTPs) reduces the efficiency of the plants and needs to be handled. Several studies have investigated anomaly detection and fault detection in WWTPs. However, these solutions often remain as academic projects. In this study, the gap between academia and practice is investigated by applying suggested algorithms on real WWTP data. The results show that it is difficult to detect drift in the data to a sufficient level due to missing and imprecise logs, ad hoc changes in control settings, low data quality and the equality in the patterns of some fault types and optimal operation. The challenges related to data quality raise the question of whether the data-driven approach for drift detection is the best solution, as this requires a high-quality data set. Several recommendations are suggested for utilities that wish to bridge the gap between academia and practice regarding drift detection. These include storing data and select data parameters at resolutions which positively contribute to this purpose. Furthermore, the data should be accompanied by sufficient logging of factors affecting the patterns of the data, such as changes in control settings. [ABSTRACT FROM AUTHOR]

The high growth of urban centers brings several problems for the population, such as socioeconomic and health problems due to toxins, polluting gases, delay in emergency care, and the stress to which citizens are exposed to traffic. Generally, for predicting the impact of a given action in the city, simulations are used to take into account the mobility of its inhabitants. These simulations must correspond with the environment that you want to be represented. Therefore, datasets with real data, make the simulations more reliable so that the results obtained are more satisfactory. The project aims to build a dataset with real data of user locations and traffic interventions for network simulations, optimize services for intelligent transport systems, and improve urban mobility in the city of Catanduva - SP. The results were performed on the mobile application (TIMELESS) and show that it consumes few smartphone resources (data, memory, and battery) to collect and generate the data set, compared to the use of other applications in the same segment (traffic monitoring and route suggestion). [ABSTRACT FROM AUTHOR]

In the current review, an exceptional view on the multi-scale integrated computational modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials in the framework of integrated computational materials engineering (ICME) is discussed. In the first part of the review, process simulation (P-S linkage), structure modelling (S-P linkage), property simulation (S-P linkage), and integrated modelling (PSP and PSPP linkages) are elaborated considering different physical phenomena (multi-physics) in AM and at micro/meso/macro scales (multi-scale modelling). The second part provides an extensive discussion of a data-driven framework, which involves extracting existing data from databases and texts, data pre-processing, high throughput screening, and, therefore, database construction. A data-driven workflow that integrates statistical methods, including ML, artificial intelligence (AI), and neural network (NN) models, has great potential for completing PSPP linkages. This review paper provides an insight for both academic and industrial researchers, working on the AM of metallic materials. [ABSTRACT FROM AUTHOR]

Many researchers began doing studies about pandemic COVID-19 which began to spread from Wuhan, China in 2019 to all around the world and so far, numerous researches have been done around the world to control this contagious disease. In this paper, we proposed a MSIlIhR-V mathematical model to study the spreading of pandemic COVID-19. This paper is aimed to study the vaccination effect in the control of the disease propagation rate. Another goal of this paper is to find the maximum number of susceptible people, minimum number of infected people, and the best value for number of vaccination people. The Jacobian matrix was obtained in the virus absenteeism equilibrium point for the proposed dynamical system. The spectral radius method was applied to find the analytical formula for the reproductive number. Reproductive number is one of the most benefit and important tools to study of epidemic model's stability and instability. In the following, by adding a controller to the model and also using the optimal control strategy, model performance was improved. To validate of the proposed models with controller and without controller we use the real data of COVID-19 from 4 January, 2021 up to 14 June, 2021 in Iran. Maple and MATLAB software's will be used for programming. We will use Maple software for analytical parts and MATLAB software for numerical and simulation parts. [ABSTRACT FROM AUTHOR]

The present paper describes the mathematical modeling and dynamics of a novel corona virus (2019-nCoV). We describe the brief details of interaction among the bats and unknown hosts, then among the peoples and the infections reservoir (seafood market). The seafood marked are considered the main source of infection when the bats and the unknown hosts (may be wild animals) leaves the infection there. The purchasing of items from the seafood market by peoples have the ability to infect either asymptomatically or symptomatically. We reduced the model with the assumptions that the seafood market has enough source of infection that can be effective to infect people. We present the mathematical results of the model and then formulate a fractional model. We consider the available infection cases for January 21, 2020, till January 28, 2020 and parameterized the model. We compute the basic reproduction number for the data is R0≈2.4829. The fractional model is then solved numerically by presenting many graphical results, which can be helpful for the infection minimization.

To facilitate efficient cloud managed resource allocation solutions, collection of key wireless metrics from multiple access points (APs) at different locations within a given area is required. In unlicensed shared spectrum bands collection of metric data can be a challenging task for a cloud manager as independent self-interested APs can operate in these bands in the same area. We propose to design an intelligent crowdsourcing solution that incentivizes independent APs to truthfully measure/report data relating to their wireless channel utilization (CU). Our work focuses on challenging scenarios where independent APs can take advantage of recurring patterns in CU data by utilizing distribution aware strategies to obtain higher reward payments. We design truthful reporting methods that utilize logarithmic and quadratic scoring rules for reward payments to the APs. We show that when measurement computation costs are considered then under certain scenarios these scoring rules no longer ensure incentive compatibility. To address this, we present a novel reward function which incorporates a distribution aware penalty cost that charges APs for distorting reports based on recurring patterns. Along with synthetic data, we also use real CU data values crowdsourced using multiple independent measuring/reporting devices deployed by us in the University of Oulu.

Energy consumption is the key to restrict the development of electric vehicles (EV), which is heavily affected by complex driving behaviors. In this paper, we propose a classified driving behavior based energy consumption prediction model, as well as recommended mechanisms for energy-efficient driving. Firstly, utilizing six EVs, we collect real data related to driving behaviors and energy consumption of vehicle in one year. After clustering behaviors of drivers, we present an energy consumption predication model, which accurately forecast the energy consumption caused by different driving behaviors. Motivated by the model, energy-saving strategies are proposed to recommend suitable driving behaviors. The simulation results further indicate that the accuracy of the proposed model is up to 98%. Specifically, the proposed model is less dependence on data volume, which guarantees the precision of more than 96% when the data volume is very small.

The objective of this paper is to analyze the inclusion of one or more random parameters into the deterministic Lighthill-Whitham-Richards traffic flow model and use a semi-intrusive approach to quantify uncertainty propagation. To verify the validity of the method, we test it against real data coming from vehicle embedded GPS systems, provided by Autoroutes Trafic.

This paper presents a detailed investigation of a stochastic model that rules the spreading behavior of the measles virus while accounting for the white noises and the influence of immunizations. It is hypothesized that the perturbations of the model are nonlinear, and that a person may lose the resistance after vaccination, implying that vaccination might create temporary protection against the disease. Initially, the deterministic model is formulated, and then it has been expanded to a stochastic system, and it is well-founded that the stochastic model is both theoretically and practically viable by demonstrating that the model has a global solution, which is positive and stochastically confined. Next, we infer adequate criteria for the disease’s elimination and permanence. Furthermore, the presence of a stationary distribution is examined by developing an appropriate Lyapunov function, wherein we noticed that the disease will persist for R0s>1 and that the illness will vanish from the community when R0s<1. We tested the model against the accessible data of measles in Pakistan during the first ten months of 2019, using the conventional curve fitting methods and the values of the parameters were calculated accordingly. The values obtained were employed in running the model, and the conceptual findings of the research were evaluated by simulations and conclusions were made. Simulations imply that, in order to fully understand the dynamic behavior of measles epidemic, time-delay must be included in such analyses, and that advancements in every vaccine campaign are inevitable for the control of the disease.

We are considering a new COVID-19 model with an optimal control analysis when vaccination is present. Firstly, we formulate the vaccine-free model and present the associated mathematical results involved. Stability results for R0

Natural symmetry exists in several phenomena in physics, chemistry, and biology. Incorporating these symmetries in the differential equations used to characterize these processes is thus a valid modeling assumption. The present study investigates COVID-19 infection through the stochastic model. We consider the real infection data of COVID-19 in Saudi Arabia and present its detailed mathematical results. We first present the existence and uniqueness of the deterministic model and later study the dynamical properties of the deterministic model and determine the global asymptotic stability of the system for R0≤1. We then study the dynamic properties of the stochastic model and present its global unique solution for the model. We further study the extinction of the stochastic model. Further, we use the nonlinear least-square fitting technique to fit the data to the model for the deterministic and stochastic case and the estimated basic reproduction number is R0≈1.1367. We show that the stochastic model provides a good fitting to the real data. We use the numerical approach to solve the stochastic system by presenting the results graphically. The sensitive parameters that significantly impact the model dynamics and reduce the number of infected cases in the future are shown graphically.

Objective: Strengthen the legal, compliant, and rational use of medical equipment and further guide the rationalization of medical behaviors., Methods: By utilizing the Internet of Things (IoT) and image analysis technology, collect real-time operation data of the equipment, establish a real-time running database for medical equipment, and cooperate with the 12 key links of the "whole life" of the equipment and the 8+6 management system framework to implement lean management of the efficiency, benefit, and effectiveness of medical equipment usage., Results: It realizes the improvement of the quality and efficiency of medical equipment, cost reduction and cost control, and provides data support for scientific decision-making., Conclusion: This study innovates the management model for the entire life cycle of medical equipment, providing a scientific approach to the management of hospital equipment.

The paper considers the design of a multimodal and multilayer inbound logistics network for iron ore to be delivered from suppliers to steel plants. To make cost-efficient logistics and transportation planning decisions, steel companies should account for uncertainty in the demand for iron ore and the economies of scale during transshipment process in the port, which are two critical elements affecting decisions. To this end, we propose a two-stage nonlinear stochastic programming model with the first stage determining the choice of the port to perform transshipment operations and the needed capacity, and the second stage selecting transportation modes after demand uncertainty has been realized. To solve this problem, we first reformulate and linearize the model based on a quantity discount policy applied to the transshipment ports, and then, we develop a scenario-based decomposition algorithm. We conduct a case study based on the data of a steel company in China to illustrate the applicability of our proposed model. Moreover, we perform numerical experiments to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The virus that begins from Wuhan China, known as COVID-19 or coronavirus is still a huge panic for humans around the globe. The elimination of this virus from our society needs proper attentions to follows the rule suggested by World Health Organization (WHO). A vast literature on the modeling of this infection in various perspective is available. In the present work, we design a new mathematical model for COVID-19 pandemic by utilizing the real infected cases reported from Kingdom of Saudi Arabia. Initially, we formulate the model with the help of classical integer order nonlinear differential equations. The treatment class is considered the model to analyze the impact of treatment on the disease dynamics. The Caputo-Fabrizio derivative with the non-singular exponential kernel is applied in order to reformulate the proposed COVID-19 transmission model with a fractional order. The biologically important parameter called the basic reproductive number is investigated both theoretically and numerically. The estimated values of R 0 for the selected period are approximated to be 1.63. Further, by making use of the Picard Lindelöf theorem we provide the existence and uniqueness of the COVID-19 fractional epidemic model. Moreover, the fractional model is solved numerically and a number of simulation results are depicted using the real estimated parameters. The impact of various model parameters and memory index are shown graphically. We conclude that the fractional order epidemic models are more appropriate and provide deep insights into the disease dynamics. [ABSTRACT FROM AUTHOR]

The novel coronavirus infectious disease (or COVID-19) almost spread widely around the world and causes a huge panic in the human population. To explore the complex dynamics of this novel infection, several mathematical epidemic models have been adopted and simulated using the statistical data of COVID-19 in various regions. In this paper, we present a new nonlinear fractional order model in the Caputo sense to analyze and simulate the dynamics of this viral disease with a case study of Algeria. Initially, after the model formulation, we utilize the well-known least square approach to estimate the model parameters from the reported COVID-19 cases in Algeria for a selected period of time. We perform the existence and uniqueness of the model solution which are proved via the Picard-Lindelöf method. We further compute the basic reproduction numbers and equilibrium points, then we explore the local and global stability of both the disease-free equilibrium point and the endemic equilibrium point. Finally, numerical results and graphical simulation are given to demonstrate the impact of various model parameters and fractional order on the disease dynamics and control.

The novel Coronavirus infection disease is becoming more complex for the humans society by giving death and infected cases throughout the world. Due to this infection, many countries of the world suffers from great economic loss. The researchers around the world are very active to make a plan and policy for its early eradication. The government officials have taken full action for the eradication of this virus using different possible control strategies. It is the first priority of the researchers to develop safe vaccine against this deadly disease to minimize the infection. Different approaches have been made in this regards for its elimination. In this study, we formulate a mathematical epidemic model to analyze the dynamical behavior and transmission patterns of this new pandemic. We consider the environmental viral concentration in the model to better study the disease incidence in a community. Initially, the model is constructed with the derivative of integer-order. The classical epidemic model is then reconstructed with the fractional order operator in the form of Atangana–Baleanu derivative with the nonsingular and nonlocal kernel in order to analyze the dynamics of Coronavirus infection in a better way. A well-known estimation approach is used to estimate model parameters from the COVID-19 cases reported in Saudi Arabia from March 1 till August 20, 2020. After the procedure of parameters estimation, we explore some basic mathematical analysis of the fractional model. The stability results are provided for the disease free case using fractional stability concepts. Further, the uniqueness and existence results will be shown using the Picard–Lendelof approach. Moreover, an efficient numerical scheme has been proposed to obtain the solution of the model numerically. Finally, using the real fitted parameters, we depict many simulation results in order to demonstrate the importance of various model parameters and the memory index on disease dynamics and possible eradication.

Statistical data is used in the media and on online platforms, often with an interpretation that serves some commercial or political purpose. Primary preservice teachers must be able to understand statistical data and analyze it critically. Only in this way will they be able to contribute as future teachers to the development of a critical spirit in the community. We present the process of construction on a teacher‐training program of an activity for first‐year students (aged 19) based on data published by Eurostat. The activity, structured around the basic cycle of learning from data, provided real data on different topics in the form of tables, graphs and infographics, and its final goal required students to generate a brief news report based on what they had learned from the data. Finally, we note that this activity can be used at various levels, from secondary education to the introductory tertiary. [ABSTRACT FROM AUTHOR]

We address a novel ride‐sharing model aiming to improve the quality of carpool services, by means of incorporation of passengers' efforts. In densely populated areas, locating and picking up passengers are often time costly. These phenomena, which are rarely considered in literature, will hurdle the service efficiency of ride‐sharing businesses by a significant amount, and contribute to the performance gap between theoretical estimation and practical performance. We notice that major platforms, such as DiDi, Uber, and Lyft are all starting innovative ways to encourage passengers to take efforts to walk to the assigned stations. We present a static optimization model for the planning stage of the service and establish an estimation of the carpooling rate for each station together with robustness consideration. In addition, we provide simple and efficient optimization algorithms with theoretical performance guarantee. At the same time, we conduct simulation studies based on both random generated data and open accessible data from DiDi. By applying our model and algorithm to the data of DiDi, we can achieve carpooling rates of 90% while passengers are located within an average radius of 250 m spending an extra waiting time of less than 3 min for their shared rides during rush hour. [ABSTRACT FROM AUTHOR]

This work investigates the dissemination mechanism of pine wilt disease. The basic reproduction number is computed explicitly, and an ultimate invariable level of contagious hosts and vectors, without and with disease, is discussed by using this number. Highly effective techniques, Lyapunov functional and graph theoretic, are utilised to obtain the ultimate constant level of the whole population. The idea of complete disease eradication and reduction of endemic level is explored through the utilisation of two efficient methods. Using sensitivity analysis approach, necessary control measures are suggested to overcome the disease. Using the literature data, the robustness of control strategies is shown graphically. [ABSTRACT FROM AUTHOR]

The three-dimensional macroscopic fundamental diagram (3D-MFD) provides a comprehensive understanding of the relationship between network efficiency and accumulation of cars and buses in transportation networks. In this paper, we propose a novel approach to partition multi-modal urban transportation networks into several homogeneous sub-regions to obtain well-shaped 3D-MFDs for each sub-region. The proposed approach consists of three stages: an initial partitioning process using the Symmetric Non-negative Matrix Factorization (SNMF) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), iterative merging of the initial partitioned sub-regions based on SNMF and TOPSIS, and optimization of the sub-regional boundaries, considering the traffic network's link hierarchy, to accurately capture its physical characteristics. Comparative experiments utilizing real data from the Zurich traffic network demonstrate that our proposed method achieves remarkable partitioning results, as supported by evaluation metrics. Furthermore, the partitioning results obtained through our proposed approach exhibit more favorable network physical characteristics, providing a solid foundation for implementing control strategies to enhance network efficiency. [ABSTRACT FROM AUTHOR]

The 2016 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report emphasized six recommendations to teach introductory courses in statistics. Among them: use of real data with context and purpose. Many educators have created databases consisting of multiple datasets for use in class; sometimes making hundreds of datasets available. Yet “the context and purpose” component of the data may remain elusive if just a generic database is made available. We describe the use of open data in introductory courses. Countries and cities continue to share data through open data portals. Hence, educators can find regional data that engage their students more effectively. We present excerpts from case studies that show the application of statistical methods to data on: crime, housing, rainfall, tourist travel, and others. Data wrangling and discussion of results are recognized as important case study components. Thus, the open data based case studies attend most GAISE College Report recommendations. Reproducible R code is made available for each case study. Example uses of open data in more advanced courses in statistics are also described. Supplementary materials for this article are available online.

One of the greatest challenges facing the humankind nowadays is to confront that emerging virus, which is the Coronavirus (COVID-19), and therefore all organizations have to unite in order to tackle that the transmission risk of this virus. From this standpoint, the scientific researchers have to find good mathematical models that do describe the transmission of such virus and contribute to reducing it in one way or another, where the study of COVID-19 transmission dynamics by mathematical models is very important for analyzing and controlling this disease propagation. Thus, in the current work, we present a new fractional-order mathematical model that describes the dynamics of COVID-19. In the proposed model, the total population is divided into eight classes, in addition to three compartments used to estimate the parameters and initial values. The effective reproduction number (R0) is derived by next generation matrix (NGM) method and all possible equilibrium points and their stability are investigated in details. We used the reported data (from January 23, 2020, to November 21, 2020) from the National Health Commission (NHC) of China to estimate the parameters and initial conditions (ICs) which suggested for our model. Simulation outcomes demonstrate that the fractional order model (FOM) represents behaviors that follow the real data more accurately than the integer-order model. The current work enhances the recent reported results of Zu et al. published in THE LANCET (doi:10.2139/ssrn.3539669).