The effects of channel width on particle sedimentation in fluids using a coupled lattice Boltzmann-discrete element model

Understanding particle settlement in channeled fluids has wide applications, such as fine particulate matter, coronavirus particle transport, and the migration of solid particles in water. Various factors have been investigated but few studies have acknowledged the channel's effect on settlement dynamics. This study developed a coupled interpolated bounce-back lattice Boltzmann-discrete element model and examined how a channel's width affects particle settlement. A factor k denoting the ratio of the channel's width and the particle diameter was defined. The terminal settling velocity for a single particle is inversely proportional to k, and the time that the particle takes to reach the terminal velocity is positively related to k. When k is greater than 15, the channel width's effects are negligible. For dual particles of the same size, the drafting-kissing-tumbling (DKT) process occurs infinitely in a periodic pattern, with the two particles swapping positions and settling around the channel's centerline. The smaller the k, the sooner the DKT process occurs. The particles collide with the channel wall when k <= 10. For dual particles of different sizes, the DKT process occurs once so that the bigger particle leads the settlement. Both particles settle along the channel's centerline in a steady state. The bigger the k, the bigger the difference in their terminal settling velocities until k = 15. The small particle collides with the channel wall if released under the big particle when k = 6. The findings of this study are expected to inform channeling or pipeline design in relevant engineering practices.

Perception of the COVID-19 vaccine and its effect on the regular vaccination coverage of indigenous peoples of the Department of Presidente Hayes, Paraguay

Introduction: During the COVID-19 pandemic, factors such as border closures, late receipt of vaccines, limitation of population circulation, relocation of nurses from vaccination areas to areas of care for patients with COVID-19, added to the fear of contagion affected vaccination coverage in several countries. Objective: Describe the perception of the COVID-19 vaccine and its effect on the regular vaccination coverage of indigenous peoples in the department of Presidente Hayes, Paraguay 2022. Methods: Descriptive, retrospective, observational study with a qualitative-quantitative cross-sectional design. Results: The characteristics of the indigenous peoples reveals that 110 (26.44%) are between 28 and 37 years old, 276 (66.35%) are female, 133 (31.97%) are of the Angaité ethnic group, 290 (69.71%) have studies up to the primary level and 178 (42.79%) have single marital status. The perception towards the COVID-19 vaccination was favorable in 201 subjects (48%) and very unfavorable in 148 (36%). There are indigenous peoples with great influence of cultural and religious beliefs in relation to vaccines. Regular vaccination was affected. Discussion: It was observed that the COVID-19 pandemic and the appearance of the vaccine developed distrust not only in the COVID-19 vaccine but also in the rest of the vaccines in some of the towns studied. © 2023, Faculty of Medical Sciences, Santa Rosa del Aguaray Branch, National University of Asuncion. All rights reserved.

Computational Investigation of Hand Foot Mouth Disease Dynamics with Fuzziness

The first major outbreak of the severely complicated hand, foot and mouth disease (HFMD), primarily caused by enterovirus 71, was reported in Taiwan in 1998. HFMD surveillance is needed to assess the spread of HFMD. The parameters we use in mathematical models are usually classical mathematical parameters, called crisp parameters, which are taken for granted. But any biological or physical phenomenon is best explained by uncertainty. To represent a realistic situation in any mathematical model, fuzzy parameters can be very useful. Many articles have been published on how to control and prevent HFMD from the perspective of public health and statistical modeling. However, few works use fuzzy theory in building models to simulate HFMD dynamics. In this context, we examined an HFMD model with fuzzy parameters. A Non Standard Finite Difference (NSFD) scheme is developed to solve the model. The developed technique retains essential properties such as positivity and dynamic consistency. Numerical simulations are presented to support the analytical results. The convergence and consistency of the proposed method are also discussed. The proposed method converges unconditionally while the many classical methods in the literature do not possess this property. In this regard, our proposed method can be considered as a reliable tool for studying the dynamics of HFMD.

Privacy-Preserving Social Media with a Disclosure

Social Media are an important communication tool in today's society. In recent years, many events have been held online due to COVID-19, making Social Media an even more important communication tool. However, it is difficult to explicitly imagine the recipients of messages when posting on Social Media and there is a tendency to provide information easily, leading to the existence of inappropriate postings that the user does not intend. Furthermore, it is difficult to disclose information for anonymous posting on Twitter. This cause the link problem between the posts. In our proposal, we realize a way to solve these problems by realizing a Social Media that allows both unlinkable posting and disclose posting. Specifically, unlinkable posts can be changed to named posts, and when the name is changed, it is guaranteed that the person who posted the anonymous post was really the anonymous writer and that the anonymous writer cannot be identified from the anonymous post. We introduced randomized pseudonyms to prevent the viewer from checking a post text based only on the posting name without checking the contents of the posting. We also show how to prevent the attack on our proposed scheme by using hiding property and binding property of the commitment scheme. In addition, we implement the proposed scheme and describe the changes between our proposed scheme and regular post in posting time, publication time, and verification time.

Privacy-Preserving Fast Three-Factor Authentication and Key Agreement for IoT-Based E-Health Systems

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).

The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach

We use the time-varying parameter structural vector autoregression stochastic volatility (TVP-SVAR-SV) and causality-in-quantiles methods to explore the linkage between market liquidity and efficiency in the European Union Emissions Trading Scheme (EU ETS) during Phase III. Our results show that two-way causality existed under normal and lower market conditions. Additionally, the linkage between liquidity and efficiency exhibits time-varying characteristics. Except in cases of extremely high market liquidity, the pass-through effect of liquidity on efficiency is mostly positive in the long run. The linkage is stronger in the medium and long term, but the response of liquidity to efficiency shocks is more complicated. Market efficiency has an overall inhibitory effect on liquidity in the short term and a promoting effect in the medium and long term. Furthermore, we investigate the impulse response during the COVID-19 period and the war between Russia and Ukraine and find that improvements in efficiency will permanently damage liquidity. Overall, the abilities of market makers and arbitrage traders, impacted by multiple factors, play an important role in the process by which liquidity affects market efficiency. By revealing and explaining the dynamic relationship between liquidity and efficiency, this research provides valuable information for policymakers and various market participants.

Towards a Paradigm Shift in Social Protection in Developing Countries? Analyzing the Emergence of the Ghana National Unemployment Insurance Scheme from a Multiple Streams Perspective

The COVID 19 pandemic continues to cause a lot of uncertainty around the world. At the onset of the pandemic, governments responded with policies and programs to curb its devastating effects on citizens, and Ghana was no exception. Although the Ghanaian government introduced various stop-gap measures to mitigate the effects of the pandemic, the inadequacies of the extant social welfare system was badly exposed. Consequently, as the pandemic seethed on, there were calls for reform of the existing social protection system and the introduction of new programs, especially for those in the informal sector. In response, the government introduced a new National Unemployment Insurance Scheme (NUIS). How did this happen? What led the government to accept tentatively the need to reform and transform the social welfare system after years of policy padding and the dragging of feet? Drawing on Kingdon's Multiple Streams Framework, we argue that the pandemic created a policy window, which enabled policy enntrepreneurs to push the unemployment insurance idea to reform the existing social welfare system. The introduction of a NUIS, is seen as a paradigm shift in social protection and more broadly in social policy. The objective of this paper is to examine how the NUIS got on government's agenda, and whether the NUIS is a game changer in social protection in Ghana. We sourced information mainly from secondary sources.

ACHIEVING EMPLOYMENT DIVIDEND IN THE POST-COVID-19 ERA: AN EXPLORATION FROM CHINA'S CARBON MARKET

Under the pressure of economic uncertainty and environmental protection in the post-COVID-19 era, achieving a new round of employment dividends has become one practical choice. Using the panel data of 30 Chinese provinces from 2007 to 2019, this study estimates the employment outcomes of carbon ETS pilots based on the difference-in-differences model. The findings of this study indicate the following: (1) Carbon ETS pilots can positively increase employment scales with an average effect of 7.12%. (2) This promoting effect will become more significant in provinces with high education levels, provinces with high average wages, and eastern region provinces. But there is no obvious difference between gender. (3) This positive effect can be transferred and enhanced by market competition and energy consumption. At the crossroads of green economic recovery, it will be greatly beneficial to formulate the national carbon market development roadmap under the carbon neutrality strategy.

A Comparative Perspective of the Effects of CO2 and Non-CO2 Greenhouse Gas Emissions on Global Solar, Wind, and Geothermal Energy Investment

Greenhouse gas emissions, including carbon dioxide and non-CO2 gases, are mainly generated by human activities such as the burning of fossil fuels, deforestation, and agriculture. These emissions disrupt the natural balance of the global ecosystem and contribute to climate change. However, by investing in renewable energy, we can help mitigate these problems by reducing greenhouse gas emissions and promoting a more sustainable future. This research utilized a panel data model to explore the impact of carbon dioxide and non-CO2 greenhouse gas emissions on global investments in renewable energy. The study analyzed data from 63 countries over the period from 1990 to 2021. Firstly, the study established a relationship between greenhouse gas emissions and clean energy investments across all countries. The findings indicated that carbon dioxide had a positive effect on clean energy investments, while non-CO2 greenhouse gas emissions had a negative impact on all three types of clean energy investments. However, the impact of flood damage as a representative of climate change on renewable energy investment was uncertain. Secondly, the study employed panel data with random effects to examine the relationship between countries with lower or higher average carbon dioxide emissions and their investments in solar, wind, and geothermal energy. The results revealed that non-CO2 greenhouse gas emissions had a positive impact on investments only in wind power in less polluted countries. On the other hand, flood damage and carbon dioxide emissions were the primary deciding factors for investments in each type of clean energy in more polluted countries.

Discretization and Analysis of HIV-1 and HTLV-I Coinfection Model with Latent Reservoirs

This article formulates and analyzes a discrete-time Human immunodeficiency virus type 1 (HIV-1) and human T-lymphotropic virus type I (HTLV-I) coinfection model with latent reservoirs. We consider that the HTLV-I infect the CD4(+)T cells, while HIV-1 has two classes of target cells-CD4(+)T cells and macrophages. The discrete-time model is obtained by discretizing the original continuous-time by the non-standard finite difference (NSFD) approach. We establish that NSFD maintains the positivity and boundedness of the model's solutions. We derived four threshold parameters that determine the existence and stability of the four equilibria of the model. The Lyapunov method is used to examine the global stability of all equilibria. The analytical findings are supported via numerical simulation. The impact of latent reservoirs on the HIV-1 and HTLV-I co-dynamics is discussed. We show that incorporating the latent reservoirs into the HIV-1 and HTLV-I coinfection model will reduce the basic HIV-1 single-infection and HTLV-I single-infection reproductive numbers. We establish that neglecting the latent reservoirs will lead to overestimation of the required HIV-1 antiviral drugs. Moreover, we show that lengthening of the latent phase can suppress the progression of viral coinfection. This may draw the attention of scientists and pharmaceutical companies to create new treatments that prolong the latency period.

Numerical Investigation of Malaria Disease Dynamics in Fuzzy Environment

The application of fuzzy theory is vital in all scientific disciplines. The construction of mathematical models with fuzziness is little studied in the literature. With this in mind and for a better understanding of the disease, an SEIR model of malaria transmission with fuzziness is examined in this study by extending a classical model of malaria transmission. The parameters beta and delta, being function of the malaria virus load, are considered fuzzy numbers. Three steady states and the reproduction number of the model are analyzed in fuzzy senses. A numerical technique is developed in a fuzzy environment to solve the studied model, which retains essential properties such as positivity and dynamic consistency. Moreover, numerical simulations are carried out to illustrate the analytical results of the developed technique. Unlike most of the classical methods in the literature, the proposed approach converges unconditionally and can be considered a reliable tool for studying malaria disease dynamics.

New Trends in Fuzzy Modeling Through Numerical Techniques

Amoebiasis is a parasitic intestinal infection caused by the highly pathogenic amoeba Entamoeba histolytica. It is spread through person-to -person contact or by eating or drinking food or water contaminated with feces. Its transmission rate depends on the number of cysts present in the environment. The traditional models assumed a homogeneous and contra-dictory transmission with reality. The heterogeneity of its transmission rate is a significant factor when modeling disease dynamics. The heterogeneity of disease transmission can be described mathematically by introducing fuzzy theory. In this context, a fuzzy SEIR Amoebiasis disease model is consid-ered in this study. The equilibrium analysis and reproductive number are studied with fuzziness. Two numerical schemes forward Euler method and a nonstandard finite difference (NSFD) approach, are developed for the learned model, and the results of numerical simulations are presented. The numerical and simulation results reveal that the proposed NSFD method provides an adequate representation of the dynamics of the disease despite the uncertainty and heterogeneity. Moreover, the obtained method generates plausible predictions that regulators can use to support decision-making to design and develop control strategies.

Towards Secure and Privacy-Preserving Data Sharing for COVID-19 Medical Records: A Blockchain-Empowered Approach

COVID-19 is currently a major global public health challenge. In the battle against the outbreak of COVID-19, how to manage and share the COVID-19 Electric Medical Records (CEMRs) safely and effectively in the world, prevent malicious users from tampering with CEMRs, and protect the privacy of patients are very worthy of attention. In particular, the semi-trusted medical cloud platform has become the primary means of hospital medical data management and information services. Security and privacy issues in the medical cloud platform are more prominent and should be addressed with priority. To address these issues, on the basis of ciphertext policy attribute-based encryption, we propose a blockchain-empowered security and privacy protection scheme with traceable and direct revocation for COVID-19 medical records. In this scheme, we perform the blockchain for uniform identity authentication and all public keys, revocation lists, etc are stored on a blockchain. The system manager server is responsible for generating the system parameters and publishes the private keys for the COVID-19 medical practitioners and users. The cloud service provider (CSP) stores the CEMRs and generates the intermediate decryption parameters using policy matching. The user can calculate the decryption key if the user has private keys and intermediate decrypt parameters. Only when attributes are satisfied access policy and the user's identity is out of the revocation list, the user can get the intermediate parameters by CSP. The malicious users may track according to the tracking list and can be directly revoked. The security analysis demonstrates that the proposed scheme is indicated to be safe under the Decision Bilinear Diffie-Hellman (DBDH) assumption and can resist many attacks. The simulation experiment demonstrates that the communication and storage overhead is less than other schemes in the public-private key generation, CEMRs encryption, and decryption stages. Besides, we also verify that the proposed scheme works well in the blockchain in terms of both throughput and delay.

Machine Learning-Based COVID-19 Classification Using E-Adopted CT Scans

In recent years, several machine learning models were successfully deployed in various fields. However, a huge quantity of data is required for training good machine learning. Data are distributivity stored across multiple sources and centralizing those data leads to privacy and security issues. To solve this problem, the proposed federated-based method works by exchanging the parameters of three locally trained machine learning models without compromising privacy. Each machine learning model uses the e-adoption of CT scans for improving their training knowledge. The CT scans are electronically transferred between various medical centers. Proper care is taken to prevent identify loss from the e-adopted data. To normalize the parameters, a novel weighting scheme is also exchanged along with the parameters. Thus, the global model is trained with more heterogeneous samples to increase performance. Based on the experiment, the proposed algorithm has obtained 89% of accuracy, which is 32% more than the existing machine learning models.

Machine Learning-Based COVID-19 Classification Using E-Adopted CT Scans

In recent years, several machine learning models were successfully deployed in various fields. However, a huge quantity of data is required for training good machine learning. Data are distributivity stored across multiple sources and centralizing those data leads to privacy and security issues. To solve this problem, the proposed federated-based method works by exchanging the parameters of three locally trained machine learning models without compromising privacy. Each machine learning model uses the e-adoption of CT scans for improving their training knowledge. The CT scans are electronically transferred between various medical centers. Proper care is taken to prevent identify loss from the e-adopted data. To normalize the parameters, a novel weighting scheme is also exchanged along with the parameters. Thus, the global model is trained with more heterogeneous samples to increase performance. Based on the experiment, the proposed algorithm has obtained 89% of accuracy, which is 32% more than the existing machine learning models.

A numerical study of COVID-19 epidemic model with vaccination and diffusion.

The coronavirus infectious disease (or COVID-19) is a severe respiratory illness. Although the infection incidence decreased significantly, still it remains a major panic for human health and the global economy. The spatial movement of the population from one region to another remains one of the major causes of the spread of the infection. In the literature, most of the COVID-19 models have been constructed with only temporal effects. In this paper, a vaccinated spatio-temporal COVID-19 mathematical model is developed to study the impact of vaccines and other interventions on the disease dynamics in a spatially heterogeneous environment. Initially, some of the basic mathematical properties including existence, uniqueness, positivity, and boundedness of the diffusive vaccinated models are analyzed. The model equilibria and the basic reproductive number are presented. Further, based upon the uniform and non-uniform initial conditions, the spatio-temporal COVID-19 mathematical model is solved numerically using finite difference operator-splitting scheme. Furthermore, detailed simulation results are presented in order to visualize the impact of vaccination and other model key parameters with and without diffusion on the pandemic incidence. The obtained results reveal that the suggested intervention with diffusion has a significant impact on the disease dynamics and its control.

Fractional order epidemiological model of SARS-CoV-2 dynamism involving Alzheimer's disease

In this paper, we study a Caputo-Fabrizio fractional order epidemiological model for the transmission dynamism of the severe acute respiratory syndrome coronavirus 2 pandemic and its relationship with Alzheimer's disease. Alzheimer's disease is incorporated into the model by evaluating its relevance to the quarantine strategy. We use functional techniques to demonstrate the proposed model stability under the Ulam-Hyres condition. The Adams-Bashforth method is used to determine the numerical solution for our proposed model. According to our numerical results, we notice that an increase in the quarantine parameter has minimal effect on the Alzheimer's disease compartment.Copyright © 2022 The Author(s)

Emergency response scheme selection with T-spherical hesitant probabilistic fuzzy TODIM-TPZSG approach

This paper aims to investigate an innovative framework to handle emergency response scheme selection (ERSS) issues by integrating TODIM and TPZSG (two-person zero-sum game) methods under novel T-spherical hesitant probabilistic fuzzy set (T-SHPFS) environments. First, T-SHPFS is defined as an extension of the existing tools, which can depict the complex assessment information including several possible values of the various membership functions' degrees and the associated statistical uncertainty information. Concomitantly, T-SHPFS's normalization method, comparison laws, operation rules, cross-entropy measure and Hausdorff distance are explored. Then, an objective attribute weight determining model is constructed, considering the credibility of T-SHPF evaluations and the divergence degrees between attribute assessments simultaneously. Next, an integrated TODIM-TPZSG decision-making approach is developed to select the most desirable emergency response scheme. Finally, an illustrative example concerning the selection of the best medical waste disposal method during the COVID-19 epidemic is conducted to verify the effectiveness of the proposed TODIM-TPZSG method. Sensitivity analysis and comparisons between the TODIM-TPZSG and other representative methods are also provided to demonstrate the superiorities of the proposed method. The results reveal that the developed T-SHPFSs give DMs more assessment freedom;the proposed TODIM-TPZSG approach considers the decision makers' psychological behaviors;the ranking results of the proposed method can reflect the specific divergence degrees among the alternatives;and the needed computation burden and computational complexity are low and less affected by the number of alternatives and criteria than most current ERSS methods. © 2023

Efficient numerical simulations based on an explicit group approach for the time fractional advection–diffusion reaction equation

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.

A case study of Covid-19 epidemic in India via new generalised Caputo type fractional derivatives.

The first symptomatic infected individuals of coronavirus (Covid-19) was confirmed in December 2020 in the city of Wuhan, China. In India, the first reported case of Covid-19 was confirmed on 30 January 2020. Today, coronavirus has been spread out all over the world. In this manuscript, we studied the coronavirus epidemic model with a true data of India by using Predictor-Corrector scheme. For the proposed model of Covid-19, the numerical and graphical simulations are performed in a framework of the new generalised Caputo sense non-integer order derivative. We analysed the existence and uniqueness of solution of the given fractional model by the definition of Chebyshev norm, Banach space, Schauder's second fixed point theorem, Arzel's-Ascoli theorem, uniform boundedness, equicontinuity and Weissinger's fixed point theorem. A new analysis of the given model with the true data is given to analyse the dynamics of the model in fractional sense. Graphical simulations show the structure of the given classes of the non-linear model with respect to the time variable. We investigated that the mentioned method is copiously strong and smooth to implement on the systems of non-linear fractional differential equation systems. The stability results for the projected algorithm is also performed with the applications of some important lemmas. The present study gives the applicability of this new generalised version of Caputo type non-integer operator in mathematical epidemiology. We compared that the fractional order results are more credible to the integer order results.