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PrefaceThe 16th International Conference on the Modelling of Casting, Welding, and Advanced Solidification Processes (MCWASP XVI) was held from June 18 to 23, 2023, in Banff, Canada, at the Banff Centre for Arts and Creativity. Founded in 1933, the Centre in Treaty 7 Territory within Banff National Park—Canada's first National Park—is a learning organization built upon an extraordinary legacy of excellence in artistic and creative development. The "all-inclusive” nature of the conference and the remote setting meant that participants dined, attended oral and poster presentations, and participated in social activities as a group, fostering outstanding opportunities for networking.Given that the MCWASP community had not met in person since 2015 in Japan (the 2020 edition of MCWASP was virtual owing to COVID-19), the 2023 conference provided the opportunity to renew old friendships and make new ones as well as discuss the science of solidification and related processes—all within the backdrop of the beautiful Canadian Rocky Mountains.The technical program comprised more than 70 oral and poster presentations. In addition to content related to modelling of casting, welding, and advanced solidification processes, keynotes were invited to talk about related subjects (artificial intelligence/machine learning, and permeability modelling in shale rock) as well as the rich diversity of fossils, especially dinosaurs, found in Alberta.The oral technical program was organized with as a single session (i.e., no concurrent presentations). It featured all aspects of solidification modelling, including solidification process technologies (continuous and semi-continuous casting, shape casting, additive manufacturing, and welding), coupled multi-physics simulations, defect formation, fluid flow, micro- and macro-structure formation, numerical methods, and related experimentation, especially in-situ observation of solidification.The four-day technical program was spread over five days to give participants the opportunity to explore the stunning Canadian Rocky Mountains.In these proceedings, the papers are organized by major theme. The dominant topics are Additive Manufacturing and Welding and Microstructure Formation, followed by Continuous Casting – Shape Casting, Heat Transfer and Fluid Flow, Alloy Segregation, Defects, Imaging of Solidification, Thermomechanics, and Materials Properties. In these themes, the authors report advances in numerical modelling techniques, new scientific and process developments in solidification, and related in-situ experimentation.Although significant progress has been made over these past 16 MCWASP conferences covering 43 years, it is clear that the complexity of advanced solidification phenomena as related to conventional and emerging manufacturing technologies still attracts a great deal of scientific and industrial interest to support technological innovation.André PhillionBanff, Canada, June 2023MCWASP XVI 2023List of Peer Reviewers, Sponsors, MCWASP XVI Organizers, International Scientific Committee are available in this Pdf.
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The time-fractional advection–diffusion reaction equation (TFADRE) is a fundamental mathematical model because of its key role in describing various processes such as oil reservoir simulations, COVID-19 transmission, mass and energy transport, and global weather production. One of the prominent issues with time fractional differential equations is the design of efficient and stable computational schemes for fast and accurate numerical simulations. We construct in this paper, a simple and yet efficient modified fractional explicit group method (MFEGM) for solving the two-dimensional TFADRE with suitable initial and boundary conditions. The proposed method is established using a difference scheme based on L1 discretization in temporal direction and central difference approximations with double spacing in spatial direction. For comparison purposes, the Crank–Nicolson finite difference method (CNFDM) is proposed. The stability and convergence of the presented methods are theoretically proved and numerically affirmed. We illustrate the computational efficiency of the MFEGM by comparing it to the CNFDM for four numerical examples including fractional diffusion and fractional advection–diffusion models. The numerical results show that the MFEGM is capable of reducing iteration count and CPU timing effectively compared to the CNFDM, making it well-suited to time fractional diffusion equations. © 2023, The Author(s) under exclusive licence to Sociedade Brasileira de Matemática Aplicada e Computacional.
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These days the online social network has become a huge source of data. People are actively sharing information on these platforms. The data on online social networks can be misinformation, information, and disinformation. Because online social networks have become an important part of our lives, the information on online social networks makes a great impact on us. Here a differential epidemic model for information, misinformation, and disinformation on online social networks is proposed. The expression for basic reproduction number has been developed. Again, the stability condition for the system at both infection-free and endemic equilibriums points has been discussed. The numerical simulation has been performed to validate the theoretical results. Data available on Twitter related to COVID-19 vaccination is used to perform the experiment. Finally, the authors discuss the control strategy to minimize the misinformation and disinformation related to vaccination. © 2022 Authors. All rights reserved.
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In this paper, we investigate the qualitative behavior of a class of fractional SEIR epidemic models with a more general incidence rate function and time delay to incorporate latent infected individuals. We first prove positivity and boundedness of solutions of the system. The basic reproduction number (Formula presented.) of the model is computed using the method of next generation matrix, and we prove that if (Formula presented.), the healthy equilibrium is locally asymptotically stable, and when (Formula presented.), the system admits a unique endemic equilibrium which is locally asymptotically stable. Moreover, using a suitable Lyapunov function and some results about the theory of stability of differential equations of delayed fractional-order type, we give a complete study of global stability for both healthy and endemic steady states. The model is used to describe the COVID-19 outbreak in Algeria at its beginning in February 2020. A numerical scheme, based on Adams–Bashforth–Moulton method, is used to run the numerical simulations and shows that the number of new infected individuals will peak around late July 2020. Further, numerical simulations show that around 90% of the population in Algeria will be infected. Compared with the WHO data, our results are much more close to real data. Our model with fractional derivative and delay can then better fit the data of Algeria at the beginning of infection and before the lock and isolation measures. The model we propose is a generalization of several SEIR other models with fractional derivative and delay in literature. © 2023 John Wiley & Sons, Ltd.
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The world experienced the life-threatening COVID-19 disease worldwide since its inversion. The whole world experienced difficult moments during the COVID-19 period, whereby most individual lives were affected by the disease socially and economically. The disease caused millions of illnesses and hundreds of thousands of deaths worldwide. To fight and control the COVID-19 disease intensity, mathematical modeling was an essential tool used to determine the potentiality and seriousness of the disease. Due to the effects of the COVID-19 disease, scientists observed that vaccination was the main option to fight against the disease for the betterment of human lives and the world economy. Unvaccinated individuals are more stressed with the disease, hence their body's immune system are affected by the disease. In this study, the SVEIHR deterministic model of COVID-19 with six compartments was proposed and analyzed. Analytically, the next-generation matrix method was used to determine the basic reproduction number (R0). Detailed stability analysis of the no-disease equilibrium (E0) of the proposed model to observe the dynamics of the system was carried out and the results showed that E0 is stable if R0<1 and unstable when R0>1. The Bayesian Markov Chain Monte Carlo (MCMC) method for the parameter identifiability was discussed. Moreover, the sensitivity analysis of R0 showed that vaccination was an essential method to control the disease. With the presence of a vaccine in our SVEIHR model, the results showed that R0=0.208, which means COVID-19 is fading out of the community and hence minimizes the transmission. Moreover, in the absence of a vaccine in our model, R0=1.7214, which means the disease is in the community and spread very fast. The numerical simulations demonstrated the importance of the proposed model because the numerical results agree with the sensitivity results of the system. The numerical simulations also focused on preventing the disease to spread in the community. © 2022 The Authors
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This study aims to structure and evaluate a new COVID-19 model which predicts vaccination effect in the Kingdom of Saudi Arabia (KSA) under Atangana-Baleanu-Caputo (ABC) fractional derivatives. On the statistical aspect, we analyze the collected statistical data of fully vaccinated people from June 01, 2021, to February 15, 2022. Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic, based on daily series data from February 16, 2022, to April 15, 2022. The results of data analysis show that the appropriate model is autoregressive integrated moving average ARIMA (1, 1, 2), and hence, a forecast about the evolution of the COVID-19 vaccination in 60 days is presented. The theoretical aspect provides equilibrium points, reproduction number R0, and biologically feasible region of the proposed model. Also, we obtain the existence and uniqueness results by using the Picard-Lindel method and the iterative scheme with the Laplace transform. On the numerical aspect, we apply the generalized scheme of the Adams-Bashforth technique in order to simulate the fractional model. Moreover, numerical simulations are performed dependent on real data of COVID-19 in KSA to show the plots of the effects of the fractional-order operator with the anticipation that the suggested model approximation will be better than that of the established traditional model. Finally, the concerned numerical simulations are compared with the exact real available date given in the statistical aspect. © 2023 Authors. All rights reserved.
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Worldwide students are having their education disrupted by the 2019 coronavirus disease (COVID-19) pandemic. Due to this, numerous contact courses have recently been moved to the online format in academic reforms. Microelectronics, Nanoelectronics, Nano-Electro-Mechanical Systems (NEMS) and Micro-Electro-Mechanical Systems (MEMS), in general, become challenging to educate and learn for both lecturers and students, respectively in this pandemic scenario. The epidemic has also offered a stimulus to increase the use of educational tools. The primary goal is to use Project-Based Learning (PBL) to explore conceptual online tools and generate attention in the field of MEMS and NEMS. This research describes a teaching style that combines PBL with NEMS and MEMS online courses for undergraduate students. This research work delves into the principles and ideas of Micro- and Nano-electronic models. Teaching styles develop understanding, skills, and values relative to the subject. The basics of MOSFETs, cantilever beams, biosensors, comb drive, piezoelectric devices, etc. are also examined clearly through assignments. The online platform is designed to develop creative concepts and model devices for future applications. Using the PBL technique, this research work fosters both academics and students' self-learning, resulting in more sophisticated studies on subjects such as NEMS, MEMS, and Bio-MEMS. This work displays its text description as well as numerical simulations. In a controlled experiment, two sets of pre- and post-evaluation analyses have been conducted to look at the impact of PBL utilizing numerical simulation tools on fundamental theory learning. The analytical assessment demonstrates that combining numerical simulation with PBL results in more efficient understanding of fundamental MEMS and NEMS ideas. It will be a potential teaching modality for the development of online courses in this area. © 2022 IEEE.
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Currently, the entire planet is suffering from a contagious epidemic infection, 2019-nCOV due to newly detected coronavirus. This is a lethal infectious virus that has destroyed thousands of lives all over the world. The important aim of this study is to investigate a susceptible-infected-treatment-recovered (SITR) model of coronavirus (2019-nCOV) with bi-modal virus spread in a susceptible population. The considered 2019-nCOV model is analyzed by two fractional derivatives: the Caputo and Atangana-Baleanu-Caputo (ABC). For the Caputo model, we present a few basic mathematical characteristics such as existence, positivity, boundedness and stability result for disease-free equilibria. The fixed-point principle is used to establish the existence and uniqueness conditions for the ABC model solution. We employed the Adams-Bashforth-Moulton (ABM) numerical technique for the Caputo model solution and the Toufik-Atangana (TA) numerical approach for the ABC model solution. Finally, using MATLAB, the simulation results are shown to highlight the impact of arbitrarily chosen fractional-order and model parameters on infection dynamics. © 2022 The Author(s).
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Safe social distance is an important parameter for the prevention and treatment of a virus transmitted through droplets. However, the distance selected is not suitable for all air environments, and the calculation method of fluid research is time-consuming. Therefore, rapid and accurate prediction of safe social distance is the key to epidemic prevention and control. However, it is difficult for the existing fluid research to obtain the safe social distance rapidly. In this study, we set up a simple and effective numerical model and develop codes that combine the effects of evaporation, drag force, and gravity. We further conducted numerical simulations to investigate the motion of droplets in various air conditions. We completed a single case simulation with only one core and within several minutes, and determined that the resistance time and velocity evolution directly impact the transmission distance. We also observed two-stage regularity of motions in both the vertical and horizontal directions, and competition between evaporation and vertical falling. In addition, we derived a set of analytical solutions to describe the evaporation time and vertical and horizontal distances. The results demonstrated the good accuracy of the predicted data. Herein, we obtained an approximate rather than an accurate solution and, together with empirical coefficients, fitted it based on our numerical simulation. The proposed method can provide a rapid and accurate estimation of safe social distances for various environmental conditions. Common clinical cases were also analyzed, and prevention and control recommendations were provided based on the outcomes of the study. © 2023 Elsevier Ltd
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This article is devoted to investigate a mathematical model consisting on susceptible, exposed, infected, quarantined, vaccinated, and recovered compartments of COVID-19. The concerned model describes the transmission mechanism of the disease dynamics with therapeutic measures of vaccination of susceptible people along with the cure of the infected population. In the said study, we use the fractal-fractional order derivative to understand the dynamics of all compartments of the proposed model in more detail. Therefore, the first model is formulated. Then, two equilibrium points disease-free (DF) and endemic are computed. Furthermore, the basic threshold number is also derived. Some sufficient conditions for global asymptotical stability are also established. By using the next-generation matrix method, local stability analysis is developed. We also attempt the sensitivity analysis of the parameters of the proposed model. Finally, for the numerical simulations, the Adams-Bashforth method is used. Using some available data, the results are displayed graphically using various fractal-fractional orders to understand the mechanism of the dynamics. In addition, we compare our numerical simulation with real data in the case of reported infected cases. © 2022 Kamal Shah et al.
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A mathematical model can describe a problem of spreading of a disease outbreak and can further predict the number of cases for the future. As we have faced COVID-19 outbreak since Desember 2019, the mathematics formula can be used by the authorities to prevent and as well as prepare the spreading of the disease. This paper uses the SEIRV method to simulate the data of COVID-19 in West Sumatera Province. Runge-Kutta 4th Order numeric method was used to simulate the model due to its higher accuracy compared to other numeric methods. The simulation also compares the cases between tha vaccines that available in West Sumatera Province. In conclusion, the simulation has shown that the number of COVID-19 patients has sharply decreased compared to without having the vaccination. Furthermore, moderna and pfizer which are having high efficacy, has been proved in the simulation can sharply decrease COVID-19 cases number compared to sinovac © 2022 IEEE.
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This study aims to structure and evaluate a new COVID-19 model which predicts vaccination effect in the Kingdom of Saudi Arabia (KSA) under Atangana-Baleanu-Caputo (ABC) fractional derivatives. On the statistical aspect, we analyze the collected statistical data of fully vaccinated people from June 01, 2021, to February 15, 2022. Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic, based on daily series data from February 16, 2022, to April 15, 2022. The results of data analysis show that the appropriate model is autoregressive integrated moving average ARIMA (1, 1, 2), and hence, a forecast about the evolution of the COVID-19 vaccination in 60 days is presented. The theoretical aspect provides equilibrium points, reproduction number R0, and biologically feasible region of the proposed model. Also, we obtain the existence and uniqueness results by using the Picard-Lindel method and the iterative scheme with the Laplace transform. On the numerical aspect, we apply the generalized scheme of the Adams-Bashforth technique in order to simulate the fractional model. Moreover, numerical simulations are performed dependent on real data of COVID-19 in KSA to show the plots of the effects of the fractional-order operator with the anticipation that the suggested model approximation will be better than that of the established traditional model. Finally, the concerned numerical simulations are compared with the exact real available date given in the statistical aspect. © 2023 Authors. All rights reserved.
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We propose a theoretical study investigating the spread of the novel coronavirus (COVID-19) reported in Wuhan City of China in 2019. We develop a mathematical model based on the novel corona virus’s characteristics and then use fractional calculus to fractionalize it. Various fractional order epidemic models have been formulated and analyzed using a number of iterative and numerical approaches while the complications arise due to singular kernel. We use the well-known Caputo-Fabrizio operator for the purposes of fictionalization because this operator is based on the non-singular kernel. Moreover, to analyze the existence and uniqueness, we will use the well-known fixed point theory. We also prove that the considered model has positive and bounded solutions. We also draw some numerical simulations to verify the theoretical work via graphical representations. We believe that the proposed epidemic model will be helpful for health officials to take some positive steps to control contagious diseases. © 2023 Authors. All rights reserved.
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A fundamentally new multiphase compartmental mathematical model for predicting the spread of several waves of coronavirus infection has been developed. Quality indicators in comparison with existing single-phase models are analyzed. The developed model will allow to model several waves of the process of spreading new coronavirus infections, to predict the process of loading the medical system, as well as the needs for staff, equipment and hospital beds during pandemics. © 2022 IEEE.
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Contacts between people are the main drivers of contagious respiratory infections. For this reason, limiting and tracking contacts is a key strategy for controlling the COVID-19 epidemic. Digital contact tracing has been proposed as an automated solution to scale up traditional contact tracing. However, the required penetration of contact tracing apps within a population to achieve a desired target in controlling the epidemic is currently under discussion within the research community. In order to understand the effects of digital contact tracing, several mathematical models have been studied. In this article, we propose a novel compartmental SEIR model with which it is possible, differently from the models in the related literature, to derive closed-form conditions regarding the control of the epidemic. These conditions are a function of the penetration of contact tracing applications and testing efficiency. Closed-form conditions are crucial for the understandability of models, and thus for decision makers (including digital contact tracing designers) to correctly assess the dependencies within the epidemic. Feeding COVID-19 data to our model, we find that digital contact tracing alone can rarely tame the epidemic: for unrestrained COVID-19, this would require a testing turnaround of around 1 day and app uptake above 80% of the population, which are very difficult to achieve in practice. However, digital contact tracing can still be effective if complemented with other mitigation strategies, such as social distancing and mask-wearing.
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The poor in Sub-Saharan Africa (SSA) are in a worse predicament than their counterparts in other regions. The goal of this study was to establish the key drivers of poverty in SSA by looking at how economic variables affect growth and poverty. Data from ten SSA nations—upper-middle-income countries (UMIC), lower-middle-income countries (LMIC), and low-income countries (LIC)—were analyzed based on historical values from 2015 to 2019. From the six economic variables studied, the best model reveals that 78% of the differences in poverty can be accounted for using a methodical, statistical approach. Poverty and unemployment rates have a substantial positive relationship (p = 0.001662). The gross domestic product (GDP) growth rate and poverty have a slight link, which is significant at the 10% level (p = 0.067) but is not a significant contributor to poverty alleviation. The secondary school enrolment rate has no bearing on poverty variation (p = 0.33). Increased GDP does not necessarily correspond to poverty reduction. Unemployment, on the other hand, is a major contributor to poverty in the region. Moreover, education (secondary school ennoblement) plays a less important role in reducing poverty, whereas per capita personal consumer spending and GDP growth rate have a bigger impact on poverty reduction. The proposed theoretical and numerical model works on general indicators and trends;it does not guarantee that people in the UMIC, LMIC, and LIC countries may not fall below the international poverty line ($1.90 per day). The poverty rates are predicted to climb by more than 2% by 2030, postponing poverty elimination in the SSA region by almost five years. This signifies that more than half of the SSA population will remain poor.
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In spite of the significant number of studies focused on the 1755 earthquake and tsunami, there are still many unknowns regarding this event in Lisbon, Portugal. Thus, in this research the authors compiled historical documents, including some that had never been analyzed, complemented with a field survey and tsunami numerical modeling at the historical civil parish of Santo Estevão, Lisbon. It was possible to identify 13 buildings, including three religious buildings and five palaces. Furthermore, the new data showed that contradicting the general idea, the earthquake caused significant damage to the selected territory because the number of households decreased by 52%. The number of residents decreased to about 51%, and in 1756, 1041 residents were still living in 297 temporary shelters. There were more than 44 dead and 1122 residents were unaccounted for. The fire did not hit the area, and the tsunami numerical model results were validated by the historical accounts and cartography, which indicate that the coastal area of the studied area was not significantly inundated by the tsunami. The consultation of historical documents that had never been analyzed by contemporary researchers provides a breakthrough in the knowledge of the event since it allowed a very detailed analysis of the disaster impact.
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This work has started from the necessity of improving the accuracy of numerical simulations of COVID-19 transmission. Coughing is one of the most effective ways to transmit SARS-CoV-2, the strain of coronavirus that causes COVID-19. Cough is a spontaneous reflex that helps to protect the lungs and airways from unwanted irritants and pathogens and it involves droplet expulsion at speeds close to 50 miles/h. Unfortunately, it’s also one of the most efficient ways to spread diseases, especially respiratory viruses that need host cells in which to reproduce. Computational Fluid Dynamics (CFD) are a powerful way to simulate droplets expelled by mouth and nose when people are coughing and/or sneezing. As with all numerical models, the models for coughing and sneezing introduce uncertainty through the selection of scales and parameters. Considering these uncertainties is essential for the acceptance of any numerical simulation. Numerical forecasting models often use Data Assimilation (DA) methods for uncertainty quantification in the medium to long-term analysis. DA is the approximation of the true state of some physical system at a given time by combining time-distributed observations with a dynamic model in an optimal way. DA incorporates observational data into a prediction model to improve numerically forecast results. In this paper, we develop a Variational Data Assimilation model to assimilate direct observation of the physical mechanisms of droplet formation at the exit of the mouth during coughing. Specifically, we use high-speed imaging, from prior research work, which directly examines the fluid fragmentation at the exit of the mouths of healthy subjects in a sneezing condition. We show the impact of the proposed approach in terms of accuracy with respect to CFD simulations. © 2022, Springer Nature Switzerland AG.
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During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination.
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Recent studies highlight the fragility of the Mediterranean basin against climate stresses and the difficulties of managing the sustainable development of groundwater resources. In this work, the main issues related to groundwater management have been identified from the stakeholder’s perspective in the following four representative water-stressed Mediterranean areas: the coastal aquifer of Comacchio (Italy), the Alto Guadalentín aquifer (Spain), the alluvial aquifer of the Gediz River basin (Turkey), and the Azraq aquifer (Azraq Wetland Reserve, Jordan). This has been achieved by designing a methodology to involve and engage a representative set of stakeholders, including a questionnaire to learn their point of view concerning the current management of aquifer systems and their experience with the already available tools for groundwater resource management, such as monitoring networks and numerical models. The outcome of the survey has allowed us to identify both particular and common challenges among the four study sites and among the various groups of stakeholders. This information provides valuable insights to improve the transfer of scientific knowledge from the research centers to the authorities managing the groundwater resources and it will help to plan more effective research activities on aquifer management. The proposed methodology could be applied in other aquifers facing similar problems.