On a Novel Dynamics of a SIVR Model Using a Laplace Adomian Decomposition Based on a Vaccination Strategy

In this paper, we introduce a SIVR model using the Laplace Adomian decomposition. This model focuses on a new trend in mathematical epidemiology dedicated to studying the characteristics of vaccination of infected communities. We analyze the epidemiological parameters using equilibrium stability and numerical analysis techniques. New mathematical strategies are also applied to establish our epidemic model, which is a pandemic model as well. In addition, we mathematically establish the chance for the next wave of any pandemic disease and show that a consistent vaccination strategy could control it. Our proposal is the first model introducing a vaccination strategy to actively infected cases. We are sure this work will serve as the basis for future research on COVID-19 and pandemic diseases since our study also considers the vaccinated population. © 2023 by the authors.

16th International Conference on the Modelling of Casting, Welding, and Advanced Solidification Processes

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.

Numerical Investigation on Silicon based Hollow Core Photonic Crystal Fiber for sensing Multiple Virus

In recent years, spread of infection due to virus became whirlwind and creates threat to life in multiple ways. Hence there is in need to sense virus as early as possible in easier way. In this work we propose a multi virus sensor which senses IBV, H5N1, H9N2, and H4N6.Very low refractive index is sensed in this work with increased birefringence due to its elliptical core, where the samples are infiltrated. Numerical analysis is done using Finite Element Method. Among these 4 viruses, IBV has higher sensitivity, birefringence and lower confinement loss which belong to COVID family.88.56% of sensitivity is obtained at 1550nm with low confinement loss. © 2022 IEEE.

An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data

This article proposes a new AdaBoost method with k′k-means Bayes classifier for imbalanced data. It reduces the imbalance degree of training data through the k′k-means Bayes method and then deals with the imbalanced classification problem using multiple iterations with weight control, achieving a good effect without losing any raw data information or needing to generate more relevant data manually. The effectiveness of the proposed method is verified by comparing it with other traditional methods based on numerical experiments. In the NSL-KDD data experiment, the F-score values of each minority class are also greater than the other methods.

Total Value Adjustment of Multi-Asset Derivatives under Multivariate CGMY Processes

Counterparty credit risk (CCR) is a significant risk factor that financial institutions have to consider in today's context, and the COVID-19 pandemic and military conflicts worldwide have heightened concerns about potential default risk. In this work, we investigate the changes in the value of financial derivatives due to counterparty default risk, i.e., total value adjustment (XVA). We perform the XVA for multi-asset option based on the multivariate Carr–Geman–Madan–Yor (CGMY) processes, which can be applied to a wider range of financial derivatives, such as basket options, rainbow options, and index options. For the numerical methods, we use the Monte Carlo method in combination with the alternating direction implicit method (MC-ADI) and the two-dimensional Fourier cosine expansion method (MC-CC) to find the risk exposure and make value adjustments for multi-asset derivatives.

A PRODUCT SERVICE SUPPLY CHAIN NETWORK EQUILIBRIUM CONSIDERING RISK MANAGEMENT IN THE CONTEXT OF COVID-19 PANDEMIC

This paper studies the equilibrium decision-making problem of product service supply chain (PSSC) network under the impact of COVID-19 related risks. The PSSC is composed of service-oriented transformation of manufacturing enterprises to sell product service systems (PSSs) to customers. So, under the impact of COVID-19, the network faces dual risks of products and services. This paper constructs the PSSC network of raw material suppliers, service providers, manufacturing service integrators and demand markets. Through variational inequalities, a network equilibrium model of PSSC considering risk management was established, and their decision-making problems were discussed. Three numerical examples were used to analyse the impact of risk management on the supply chain network at various levels. The results show that the risk management of upstream and downstream enterprises will have mutual in uence, and the cost input of service risk management will benefit the entire PSSC network. Therefore, through the diversified development and improvement of services, the market demand for PSSs can be increased. © 2023,Journal of Industrial and Management Optimization. All Rights Reserved.

Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number $(R_{0})$ is calculated, which is an important parameter in the study of message propagation in OSN. If $R_{0} < 1$ , the propagation of rumor in the OSN will be minimal;nevertheless, if $R_{0} > 1$ , the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models Extensive theoretical study and computation analysis have also been used to validate the proposed model © 2013 IEEE.

A parallel approach to the fractional time delay model for predicting the spread of COVID-19

Due to the importance of forecast accuracy for diseases such as COVID-19, the existence of a mathematical model is particularly important. In this research, first, a model to describe the spread of the COVID-19 pandemic is examined. This model is based on a fractional ordinary differential equation. Then the predictor-corrector numerical method is presented to solve this model. Due to the computational challenge of numerically solving fractional models, a task-parallel approach with coarse granularity is presented to solve this model on shared memory systems. The initial data for testing the proposed approach is the data reported on December 31, 2019 by the Wuhan Municipal Commission of the outbreak of the COVID-19 pandemic in the city of Wuhan, China. The numerical results obtained from the proposed parallel approach show that the speedup of the parallel method compared to the sequential method reaches 2.76 in the prediction of 1000 days. © 2022 IEEE.

Editorial

The Italian Association for Stress Analysis (AIAS) was founded in 1971 by researchers from academia, research centers, and industry. AIAS was intended as a community where to discuss, share, and develop scientific knowledge related to all technical aspects of stress analysis. In the years, from an initial focus on experimental techniques, AIAS contributed considerably to the development of modern numerical methods and computational techniques for mechanical engineering design. In 2015, AIAS turned in the Italian Scientific Society of Mechanical Engineering Design.Today, AIAS is an institutional partner that supports the instances from academia in the subject area of mechanical engineering design. Every year, AIAS organizes a technical conference offering the possibility to present research updates, share new ideas, and foster collaborations. The AIAS conference has become a fundamental event for all those interested in current developments in mechanical engineering design and stress analysis, where to meet researchers, testing equipment, and software developers.The 51st AIAS Conference edition was held in Padova, Italy again in presence after two years of online-only events due to the COVID-19 pandemic.The response of researchers and students has been outstanding: over 200 oral contributions have been presented during the three days of the conference, with four parallel sessions. In addition to the thematic sessions on AIAS traditional subjects, special sessions on additive manufacturing, energy methods for structural analysis, circular design in mechanical engineering, and mechanical behavior under extreme conditions, have been successfully organized with the contribution of the AIAS technical committees.Among all contributions presented at the conference, 48 have been selected to be published after peer review, in this volume. This was made possible thanks to the active participation of all AIAS members, to the work of the AIAS Scientific Committee and Conference Papers Review panel (Profs Giovanni Meneghetti, AIAS Scientific Coordinator, Luciano Afferrante, Francesco Bucchi, Filippo Cianetti, Enrico Armentani, Marco Sasso). Their outstanding contribution is gratefully acknowledged.DisclaimerAIAS2022 Conference was held in presence on Sept. 7-10, 2022. Presentations were arranged in four parallel sessions with a time slot of 15 min assigned to each presenter. Approximately 250 participants attended the conference during the three days event. All sessions have been continuously monitored by the organizers to provide technical support. The conference ran smoothly, and the participants' feedback was very positive.

A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

In this work, the exponential approximation is used for the numerical simulation of a nonlinear SITR model as a system of differential equations that shows the dynamics of the new coronavirus (COVID-19). The SITR mathematical model is divided into four classes using fractal parameters for COVID-19 dynamics, namely, susceptible (S), infected (I), treatment (T), and recovered (R). The main idea of the presented method is based on the matrix representations of the exponential functions and their derivatives using collocation points. To indicate the usefulness of this method, we employ it in some cases. For error analysis of the method, the residual of the solutions is reviewed. The reported examples show that the method is reasonably efficient and accurate. © 2023 Tech Science Press. All rights reserved.

Joint Decisions of Components Replacement and Spare Parts Ordering Considering Different Supplied Product Quality

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. Note to Practitioners—This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3). IEEE

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes

An acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. The proposed method leverages the concept of topological layer to facilitate the DAG learning, and its theoretical justification in terms of exact DAG recovery is also established under mild conditions. Particularly, we show that the topological layers can be exactly reconstructed in a bottom-up fashion, and the parent-child relations among nodes can also be consistently established. The established asymptotic DAG recovery is in sharp contrast to that of many existing learning methods assuming parental faithfulness or ordered noise variances. The advantage of the proposed method is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19. ©2022 Ruixuan Zhao, Xin He, and Junhui Wang.

Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model

The SARS-CoV-2 coronavirus is the cause of the COVID-19 disease in humans. Like many coronaviruses, it can adapt to different hosts and evolve into different lineages. It is well-known that the major SARS-CoV-2 lineages are characterized by mutations that happen predominantly in the spike protein. Understanding the spike protein structure and how it can be perturbed is vital for understanding and determining if a lineage is of concern. These are crucial to identifying and controlling current outbreaks and preventing future pandemics. Machine learning (ML) methods are a viable solution to this effort, given the volume of available sequencing data, much of which is unaligned or even unassembled. However, such ML methods require fixed-length numerical feature vectors in Euclidean space to be applicable. Similarly, euclidean space is not considered the best choice when working with the classification and clustering tasks for biological sequences. For this purpose, we design a method that converts the protein (spike) sequences into the sequence similarity network (SSN). We can then use SSN as an input for the classical algorithms from the graph mining domain for the typical tasks such as classification and clustering to understand the data. We show that the proposed alignment-free method is able to outperform the current SOTA method in terms of clustering results. Similarly, we are able to achieve higher classification accuracy using well-known Node2Vec-based embedding compared to other baseline embedding approaches. © 2022 IEEE.

Stability analysis of a fractional-order SEIR epidemic model with general incidence rate and time delay

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.

2nd International Conference on Recent Advances in Mechanical Engineering Research and Development, ICRAMERD 2021

The proceedings contain 79 papers presendted at a virtual meeting. The special focus in this conference is on Recent Advances in Mechanical Engineering Research and Development. The topics include: Firmware of Indigenous and Custom-Built Flexible Robots for Indoor Assistance;Automation of AM Via IoT Towards Implementation of e-logistics in Supply Chain for Industry 4.0;Evaluation and Optimization of Process Parameter for Surface Roughness of 3D-Printed PETG Specimens Using Taguchi Method at Constant Printing Temperature;Evaluation of Preventive Activities of COVID-19 Using Multi-criteria Decision Making Method;mechanical Characterization of Concrete with Rice Husk-Based Biochar as Sustainable Cementitious Admixture;Ranking of Barriers for SSCM Implementation in Indian Textile Industries;Framework to Monitor Vehicular GHG Footprint;solution to Real-Time Problem in Shifter Knob Assembly at Automobile Manufacturing Industry;performance of Chemical Route-Synthesized SnO2 Nanoparticles;a Numerical Study to Choose the Best Model for a Bladeless Wind Turbine;Effect of Tissue Properties on the Efficacy of MA on Lungs;effect of Process Parameters and Coolant Application on Cutting Performance of Centrifugal Cast Single Point Cutting Tools;Study and Analysis of Thermal Barrier Application of Lanthanum Oxide Coated SS-304 Steel;recovery of Iron Values from Blast Furnace Gas Cleaning Process Sludge by Medium Intensity Magnetic Separation Method;fatigue Analysis of Rectangular Plate with a Circular Cut-Out;protection of Vital Facilities from the Threat of External Explosion Using D3o Material;investigation on Coefficient of Heat Transfer Through Impact of Engine Vibration;electrical Modulus and Conductivity Study of Styrene-Butadiene Rubber/Barium Hexaferrite Flexible Polymer Dielectrics;preface.

A Rolling-Based Multipeak Learning Model for COVID-19 Pandemic Predicting

The COVID-19 pandemic has led to a dramatic loss of human life and the global economy, and presents an unprecedented challenge to public health management for all countries around the world. Access to an accurate epidemic prediction model plays a crucial role in epidemic prevention, infection scale control, and medical resource allocation. In this paper, we first propose a multipeak SEIYAQURD model by using the multipeak learning algorithm to predict the COVID-19 epidemic. The model separates the total population according to characteristics of COVID-19 and can capture trend changes in the epidemic. Then, the fitting period technique and the rolling prediction strategy are proposed to improve the prediction accuracy. Numerical experiments based on the data of COVID-19 in the United States are performed to demonstrate the effectiveness of our proposed method by comparing with two benchmark methods from the literature in two cases, one has a smooth trend and the other has a significant changing trend. © 2022 IEEE.

Transforming a blended learning course in numerical methods into a flexible digital course during the Covid-19 crisis

Mobile learning has had different paces in its acceptance: fast among technology developers and slow in some educational sectors and countries. Covid-19 crisis suddenly put an end to any possible delay in adopting educative technologies. This work describes the rapid adaptation of a blended learning course in Numerical Methods in higher education during the Covid-19 confinement. Its primary intention was to determine the impact of the transformation into a flexible digital course on students' performance discussing the elements introduced, the analytical methods, and the assessment tools (grading register, learning analytics, and a final student opinion survey). The analyses also revealed social and behavioural aspects related to social learning, focused on the students' virtual contact imposed by the health contingency. Observed students' adaptations of individual and team tasks under the new learning modality impacting their performance are discussed, highlighting and supporting the belief that the context strongly influences blended learning success.

NUMERICAL MODELING OF A NOVEL STOCHASTIC CORONAVIRUS

The aim is to study the dynamics of Coronavirus model using stochastic methods. Threshold parameter R0 is obtained for the model. Afterwards, both the disease-free equilibrium (DFE) and endemic equilibrium (EE) points are acquired and the stability of the model is discussed. Both the equilibrium points are locally asymptotically stable. Euler-Maruyama, stochastic Euler scheme (SES), stochastic fourth-order Runge-Kutta scheme (SRKS) and stochastic non-standard finite difference technique (SNFDT) are applied to solve the model equations. Euler-Maruyama, SES, SRKS fail for large time step size, while, SNFDT preserves the dynamics of the proposed model for any step size. Numerical comparison of applied methods is provided using different step sizes. © 2022 The Author(s).

A Novel COVID-19 Detection Model using CT Scan

Presently, COVID-19 is considered to be the most dangerous and deadly disease to human caused by the new Coronavirus. Early discovery of COVID-19 through precise diagnosis, especially for cases without clear symptoms, can reduce the patient's mortality rate. In this work, using CT scans images, we present a new Level Set evolution method (NSDRLS), in which we propose to utilize both color intensity and the salience map as the external energy of the region to motivate an initial evolution of the Level Set function (LSF).Herein, a complete comparative quantitative study of the considered approaches was established. Various criteria were calculated to evaluate the methods of segmentation. © 2022 IEEE.

Covid-19 Variants Survivability Simulation With Genetic Algorithm

Genetic algorithm is well known for its ability to solve search and optimization problems with or without mathematical representation. The use of probabilistic transition rules makes it simpler than other algorithms that exploit complex mathematics and numerical methods. Running its iterative process will create new population of solutions that has its average fitness value increased over generations. Meanwhile the domination of one variant over another in Covid-19 spread can be seen as the implementation of elitism principle of genetic algorithm in real life. The questions are what aspects contribute to this process, what kind of relationship are there between those aspects, and what aspect(s) that contribute more to the domination of one variant over another? We choose the genetic algorithm to do simulations on searching the answer for those questions because of its nature-like behavior that explore the probabilistic transition rules. © 2022 IEEE.