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Auxiliary information is an essential component in the field of survey sampling since it enables precise estimation of population parameters like mean, variance, distribution function, and so on, which in turn guarantees the best possible outcomes. In order to estimate the population mean of a study variable, this study makes use of auxiliary information in a two-fold approach. Through a stratified random sampling scheme, we introduce a novel class of estimators that utilize auxiliary information and their corresponding ranks. By conducting a thorough evaluation based on metrics such as mean square error and percentage relative efficiency, these proposed estimators have been shown to be effective in the estimation process. Empirical validation is conducted using a real dataset sourced from the domain of real estate. Exploring the relationship between Assessed Value (X) and Sale Amount (Y) during a five-year period extending from 2017 to 2021 is the primary emphasis of the empirical validation process, which is carried out with the assistance of a real dataset of real estate data. Furthermore, in order to demonstrate that our suggested estimator is superior to conventional unbiased estimators, as well as traditional regression estimators and other estimators that have been considered in the literature, a full simulation analysis is carried out. Our proposed estimator appears to be the most effective choice after being subjected to a comparison study against a variety of preexisting approaches. The findings of this study not only make a significant contribution to the development of the methodology of survey sampling but also offer vital insights for predictive modeling within the real estate sector. [ABSTRACT FROM AUTHOR]

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]

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]

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. [ABSTRACT FROM AUTHOR]

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 R 0 < 1. We estimate the parameters for the model considering the infected cases occur in East Java Indonesia for the year 2018. We estimated that the basic reproduction number for this model is approximately equal to R 0 ≈ 1.8463. We show the application of the fractal-fractional Atangana-Baleanu operator to the dengue model and present its numerical solution with a new method. We show the fractal-fractional model versus model fitting for different values of fractal and fractional orders and suggest that the fractal-fractional model provides better fitting to the infected cases of dengue infection. Some numerical results for different orders of fractal and fractional are shown for the validity and the applicability of the newly introduced operator. [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 R 0 ≈ 2.4829. The fractional model is then solved numerically by presenting many graphical results, which can be helpful for the infection minimization. [ABSTRACT FROM AUTHOR]

Falls among older people remain a very important public healthcare issue. In the majority of fall events external support is imperative in order to avoid major consequences. Therefore, the ability to automatically detect these fall events could help reducing the response time and significantly improve the prognosis of fall victims. This paper presents a practical real time fall detection system running on a smartwatch (F2D). A decision module takes into account the rebound after the fall and the residual movement of the user, matching a detected fall pattern to an actual fall. The last module of F2D is the location module which makes our system very useful for nursing homes that host elderly people. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner who is responsible for the commercialization of our system. By testing with real data and achieving an accuracy of 96.01% we have a fall detection system ready to be deployed on the market and by adding the location module we can provide it to nursing homes for elderly people. [ABSTRACT FROM AUTHOR]

In this work, we evaluate the effectiveness of using a Full Waveform Inversion (FWI) algorithm to image CO 2 accumulated in thin layers at the Sleipner field. In a first stage, a feasibility study is carried out on synthetic data to assess the capability of FWI to resolve thin CO 2 layers. The results suggest that the method can be useful to improve the resolution of the velocity model and help in better characterizing both velocities and thicknesses of the CO 2 bearing layers. In a second stage, the method is applied to the Sleipner real data. The results derived from FWI, using frequencies up to 40 Hz, provide clear indications about the geometry and the lateral extent of the CO 2 plume. In a third stage, the inversion results are used to perform pre-stack depth migration. [ABSTRACT FROM AUTHOR]

In this work, we apply full waveform inversion (FWI) for CO2 quantification at the Sleipner field. A feasibility study is first carried out on synthetic data to evaluate the capability of FWI to resolve thin CO2 layers. The results show that FWI can improve the resolution of the velocity model at Sleipner and help in better characterizing the CO2 bearing layers velocities and thicknesses. The method is then applied to the 2008 vintage. The inversion results provide clear indications about the geometry and the lateral extent of the CO2 plume. In addition, strong coherency is observed when compared to the migrated image. [ABSTRACT FROM AUTHOR]

For make-to-stock production systems the production plans and activities are based mainly on demand forecasting, which is one of the key causes of the bullwhip effect. The bullwhip effect (BE) is the inherent increase in demand fluctuation up the supply chain. In the paper we have illustrated the phenomenon of bullwhip effect using real demand data in three simulation cases of a supply chain with different level constraints. We experimented with a simple three-stage supply chain using seasonal and deseasonalized time series of the market demand data in order to identify, illustrate and discuss the impacts of different level constraints on the BE. The results are presented for different overall equipment effectiveness (OEE) and constrained inventory policies. At higher OEE level there is less variability in production processes, but at inventory limitations the impact is more complex. The goals of this paper are to discuss the causes of the BE and investigate the potential benefit that information sharing offers for overcoming these problems. [ABSTRACT FROM AUTHOR]

Abstract: We introduce a hybrid discrete choice framework to model the decisions of investors in stock markets. More specifically, we model the decision to buy or sell stocks using a binary logit model with latent classes, characterizing the perception of risk. The model considers the dynamic nature of the underlying decision process and is estimated from the data of a Swiss bank containing 25989 transactional observations from January 2005 to September 2010 for 6 different portfolios. The predictive performance of the model is tested: a cross-validation analysis is performed and the forecasting accuracy of the model is studied in details. Parameters of the model are interpretable and quantify interesting behavioral mechanisms related to investors' decisions. The predictive capabilities of the model in a real context makes it practicable. [Copyright &y& Elsevier]

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 R 0 < 1 are shown. In addition, we frame the model with the vaccination class. We look at the mathematical results with the details of the vaccine model. Additionally, we are considering setting controls to minimize infection spread and control. We consider four different controls, such as prevention, vaccination control, rapid screening of people in the exposed category, and people who are identified as infected without screening. Using the suggested controls, we develop an optimal control model and derive mathematical results from it. In addition, the mathematical model with control and without control is resolved by the forward–backward Runge–Kutta method and presents the results graphically. The results obtained through optimal control suggest that controls can be useful for minimizing infected individuals and improving population health. [ABSTRACT FROM AUTHOR]

The carsharing market has never been as competitive as it is now, and during the last years, we have been witnessing a boom in the number of carsharing organizations that appear, often accompanied by an also booming number of companies that disappear. Designing a viable carsharing system is challenging and often depends on local conditions as well as on a myriad of operational decisions that need to be supported by suitable decision-support systems. Therefore, carsharing is being increasingly studied in the Operations Management (OM) literature. Nevertheless, often due to the limited transparency of this highly competitive sector and the recency of this business, there is still a "gap of understanding" of the scientific community concerning the business practices and contexts, often resulting in over-simplifications and relevant problems being overlooked. In this paper, we aim to close this "gap of understanding" by describing, conceptualizing, and analyzing the reality of 34 business-to-consumer carsharing organizations. With the data collected, we propose a detailed description of the current business practices, such as the ones concerning pricing. From this, we highlight relevant "research insights" and structure all collected data organized by different OM topics, enabling knowledge to be further developed in this field. • We collect and systemize data of 34 carsharing operators based on various OM topics. • We analyze the market real data and draw insights that boost research in the field. • Real pricing plans are complex and built to attract different customers segments. • Fleet heterogeneity is key, especially considering the growth of electric vehicles. • We propose a new metric to evaluate financial performance with fleet utilization. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 ( R 0 ) 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). [ABSTRACT FROM AUTHOR]

The novel coronavirus disease or COVID-19 is still posing an alarming situation around the globe. The whole world is facing the second wave of this novel pandemic. Recently, the researchers are focused to study the complex dynamics and possible control of this global infection. Mathematical modeling is a useful tool and gains much interest in this regard. In this paper, a fractional-order transmission model is considered to study its dynamical behavior using the real cases reported in Saudia Arabia. The classical Caputo type derivative of fractional order is used in order to formulate the model. The transmission of the infection through the environment is taken into consideration. The documented data since March 02, 2020 up to July 31, 2020 are considered for estimation of parameters of system. We have the estimated basic reproduction number (R 0) for the data is 1.2937. The Banach fixed point analysis has been used for the existence and uniqueness of the solution. The stability analysis at infection free equilibrium and at the endemic state are presented in details via a nonlinear Lyapunov function in conjunction with LaSalle Invariance Principle. An efficient numerical scheme of Adams-Molten type is implemented for the iterative solution of the model, which plays an important role in determining the impact of control measures and also sensitive parameters that can reduce the infection in the general public and thereby reduce the spread of pandemic as shown graphically. We present some graphical results for the model and the effect of the important sensitive parameters for possible infection minimization in the population. [ABSTRACT FROM AUTHOR]

The Coronavirus disease or COVID-19 is an infectious disease caused by a newly discovered coronavirus. The COVID-19 pandemic is an inciting panic for human health and economy as there is no vaccine or effective treatment so far. Different mathematical modeling approaches have been suggested to analyze the transmission patterns of this novel infection. this paper, we investigate the dynamics of COVID-19 using the classical Caputo fractional derivative. Initially, we formulate the mathematical model and then explore some the basic and necessary analysis including the stability results of the model for the case when R 0 < 1. Despite the basic analysis, we consider the real cases of coronavirus in China from January 11, 2020 to April 9, 2020 and estimated the basic reproduction number as R 0 ≈ 4.95. The present findings show that the reported data is accurately fit the proposed model and consequently, we obtain more realistic and suitable parameters. Finally, the fractional model is solved numerically using a numerical approach and depicts many graphical results for the fractional order of Caputo operator. Furthermore, some key parameters and their impact on the disease dynamics are shown graphically. [ABSTRACT FROM AUTHOR]