Pub Date : 2023-11-02DOI: 10.3390/computation11110217
Elsayed Dahy, Ahmed M. Elaiw, Aeshah A. Raezah, Hamdy Z. Zidan, Abd Elsattar A. Abdellatif
In this paper, we study a model that enhances our understanding of cytokine-influenced HIV-1 infection. The impact of adaptive immune response (cytotoxic T lymphocytes (CTLs) and antibodies) and time delay on HIV-1 infection is included. The model takes into account two types of distributional delays, (i) the delay in the HIV-1 infection of CD4+T cells and (ii) the maturation delay of new virions. We first investigated the fundamental characteristics of the system, then found the system’s equilibria. We derived five threshold parameters, ℜi, i = 0, 1,…, 4, which completely determine the existence and stability of the equilibria. The Lyapunov method was used to prove the global asymptotic stability for all equilibria. We illustrate the theoretical results by performing numerical simulations. We also performed a sensitivity analysis on the basic reproduction number ℜ0 and identified the most-sensitive parameters. We found that pyroptosis contributes to the number ℜ0, and then, neglecting it will make ℜ0 underevaluated. Necrosulfonamide and highly active antiretroviral drug therapy (HAART) can be effective in preventing pyroptosis and at reducing viral replication. Further, it was also found that increasing time delays can effectively decrease ℜ0 and, then, inhibit HIV-1 replication. Furthermore, it is shown that both CTLs and antibody immune responses have no effect on ℜ0, while this can result in less HIV-1 infection.
在本文中，我们研究了一个模型，提高了我们对细胞因子影响的HIV-1感染的理解。适应性免疫反应(细胞毒性T淋巴细胞(ctl)和抗体)和时间延迟对HIV-1感染的影响也包括在内。该模型考虑了两种类型的分布延迟，(i) CD4+T细胞HIV-1感染的延迟和(ii)新病毒粒子的成熟延迟。我们首先研究了系统的基本特征，然后找到了系统的平衡点。我们导出了5个阈值参数，即i, i = 0,1，…，4，它们完全决定了平衡点的存在性和稳定性。利用Lyapunov方法证明了所有平衡点的全局渐近稳定性。我们通过数值模拟来说明理论结果。我们还对基本繁殖数进行了敏感性分析，并确定了最敏感的参数。我们发现，焦亡作用是导致数为0的原因之一，忽略焦亡作用会导致对数为0的评价被低估。坏死性磺胺和高效抗逆转录病毒药物治疗(HAART)可以有效防止焦亡和减少病毒复制。此外，研究还发现，增加时间延迟可以有效地减少r0，从而抑制HIV-1的复制。此外，研究表明ctl和抗体免疫反应对免疫球蛋白o没有影响，而这可以减少HIV-1感染。
{"title":"Global Properties of Cytokine-Enhanced HIV-1 Dynamics Model with Adaptive Immunity and Distributed Delays","authors":"Elsayed Dahy, Ahmed M. Elaiw, Aeshah A. Raezah, Hamdy Z. Zidan, Abd Elsattar A. Abdellatif","doi":"10.3390/computation11110217","DOIUrl":"https://doi.org/10.3390/computation11110217","url":null,"abstract":"In this paper, we study a model that enhances our understanding of cytokine-influenced HIV-1 infection. The impact of adaptive immune response (cytotoxic T lymphocytes (CTLs) and antibodies) and time delay on HIV-1 infection is included. The model takes into account two types of distributional delays, (i) the delay in the HIV-1 infection of CD4+T cells and (ii) the maturation delay of new virions. We first investigated the fundamental characteristics of the system, then found the system’s equilibria. We derived five threshold parameters, ℜi, i = 0, 1,…, 4, which completely determine the existence and stability of the equilibria. The Lyapunov method was used to prove the global asymptotic stability for all equilibria. We illustrate the theoretical results by performing numerical simulations. We also performed a sensitivity analysis on the basic reproduction number ℜ0 and identified the most-sensitive parameters. We found that pyroptosis contributes to the number ℜ0, and then, neglecting it will make ℜ0 underevaluated. Necrosulfonamide and highly active antiretroviral drug therapy (HAART) can be effective in preventing pyroptosis and at reducing viral replication. Further, it was also found that increasing time delays can effectively decrease ℜ0 and, then, inhibit HIV-1 replication. Furthermore, it is shown that both CTLs and antibody immune responses have no effect on ℜ0, while this can result in less HIV-1 infection.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.3390/computation11110214
Seyed Iman Taheri, Mohammadreza Davoodi, Mohd. Hasan Ali
Conventional evolutionary optimization techniques often struggle with finding global optima, getting stuck in local optima instead, and can be sensitive to initial conditions and parameter settings. Efficient Distributed Generation (DG) allocation in distribution systems hinges on streamlined optimization algorithms that handle complex energy operations, support real-time decisions, adapt to dynamics, and improve system performance, considering cost and power quality. This paper proposes the Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm to efficiently allocate DGs within a distribution test system. The study focuses on wind turbines, photovoltaic units, and fuel cells as prominent DG due to their growing usage trends. The optimization goals include minimizing voltage losses, reducing costs, and mitigating greenhouse gas emissions in the distribution system. The proposed algorithm is implemented and evaluated on the IEEE 70-bus test system, with a comparative analysis conducted against other evolutionary methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Honey Bee Mating Optimization (HBMO), and Teaching-Learning-Based Optimization (TLBO) algorithms. Results indicate that the proposed algorithm is effective in allocating the DGs. Statistical testing confirms significant results (probability < 0.1), indicating superior optimization capabilities for this specific problem. Crucially, the proposed algorithm excels in both accuracy and computational speed compared to other methods studied.
{"title":"A Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm for Distributed Generation Allocation","authors":"Seyed Iman Taheri, Mohammadreza Davoodi, Mohd. Hasan Ali","doi":"10.3390/computation11110214","DOIUrl":"https://doi.org/10.3390/computation11110214","url":null,"abstract":"Conventional evolutionary optimization techniques often struggle with finding global optima, getting stuck in local optima instead, and can be sensitive to initial conditions and parameter settings. Efficient Distributed Generation (DG) allocation in distribution systems hinges on streamlined optimization algorithms that handle complex energy operations, support real-time decisions, adapt to dynamics, and improve system performance, considering cost and power quality. This paper proposes the Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm to efficiently allocate DGs within a distribution test system. The study focuses on wind turbines, photovoltaic units, and fuel cells as prominent DG due to their growing usage trends. The optimization goals include minimizing voltage losses, reducing costs, and mitigating greenhouse gas emissions in the distribution system. The proposed algorithm is implemented and evaluated on the IEEE 70-bus test system, with a comparative analysis conducted against other evolutionary methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Honey Bee Mating Optimization (HBMO), and Teaching-Learning-Based Optimization (TLBO) algorithms. Results indicate that the proposed algorithm is effective in allocating the DGs. Statistical testing confirms significant results (probability < 0.1), indicating superior optimization capabilities for this specific problem. Crucially, the proposed algorithm excels in both accuracy and computational speed compared to other methods studied.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"10 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.3390/computation11110218
Li Shang, Zi Zhang, Fujian Tang, Qi Cao, Nita Yodo, Hong Pan, Zhibin Lin
Welded joints in metallic pipelines and other structures are used to connect metallic structures. Welding defects, such as cracks and lack of fusion, are vulnerable to initiating early-age cracking and corrosion. The present damage identification techniques use ultrasonic-guided wave procedures, which depend on the change in the physical characteristics of waveforms as they propagate to determine damage states. However, the complexity of geometry and material discontinuity (e.g., the roughness of a weldment with or without defects) could lead to complicated wave reflection and scatters, thus increasing the difficulty in the signal processing. Artificial intelligence and machine learning exhibit their capability for data fusion, including processing signals originally from ultrasonic-guided waves. This study aims to utilize deep learning approaches, including a convolutional neural network (CNN), Long-short term memory network (LSTM), or hybrid CNN-LSTM model, to demonstrate the capability in automation for damage detection for pipes with welded joints embedded in soil. The damage features in terms of welding defect types and severity as well as multiple defects are used to understand the effectiveness of the hybrid CNN-LSTM model, which is further compared to the two commonly used deep learning approaches, CNN and LSTM. The results showed the hybrid CNN-LSTM model has much higher classification accuracy for damage states under all scenarios in comparison with the CNN and LSTM models. Furthermore, the impacts of the pipelines embedded in different types of materials, ranging from loose sand to stiff soil, on signal processing and data classification were further calibrated. The results demonstrated these deep learning approaches can still perform well to detect various pipeline damage under varying embedment conditions. However, the results demonstrate when concrete is used as an embedding material, high attention to absorbing the signal energy of concrete could pose a challenge for the signal processing, particularly under high noise levels.
{"title":"Deep Learning Enriched Automation in Damage Detection for Sustainable Operation in Pipelines with Welding Defects under Varying Embedment Conditions","authors":"Li Shang, Zi Zhang, Fujian Tang, Qi Cao, Nita Yodo, Hong Pan, Zhibin Lin","doi":"10.3390/computation11110218","DOIUrl":"https://doi.org/10.3390/computation11110218","url":null,"abstract":"Welded joints in metallic pipelines and other structures are used to connect metallic structures. Welding defects, such as cracks and lack of fusion, are vulnerable to initiating early-age cracking and corrosion. The present damage identification techniques use ultrasonic-guided wave procedures, which depend on the change in the physical characteristics of waveforms as they propagate to determine damage states. However, the complexity of geometry and material discontinuity (e.g., the roughness of a weldment with or without defects) could lead to complicated wave reflection and scatters, thus increasing the difficulty in the signal processing. Artificial intelligence and machine learning exhibit their capability for data fusion, including processing signals originally from ultrasonic-guided waves. This study aims to utilize deep learning approaches, including a convolutional neural network (CNN), Long-short term memory network (LSTM), or hybrid CNN-LSTM model, to demonstrate the capability in automation for damage detection for pipes with welded joints embedded in soil. The damage features in terms of welding defect types and severity as well as multiple defects are used to understand the effectiveness of the hybrid CNN-LSTM model, which is further compared to the two commonly used deep learning approaches, CNN and LSTM. The results showed the hybrid CNN-LSTM model has much higher classification accuracy for damage states under all scenarios in comparison with the CNN and LSTM models. Furthermore, the impacts of the pipelines embedded in different types of materials, ranging from loose sand to stiff soil, on signal processing and data classification were further calibrated. The results demonstrated these deep learning approaches can still perform well to detect various pipeline damage under varying embedment conditions. However, the results demonstrate when concrete is used as an embedding material, high attention to absorbing the signal energy of concrete could pose a challenge for the signal processing, particularly under high noise levels.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"38 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135974228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.3390/computation11110216
Toma Sikora, Jonathan Klein Schiphorst, Riccardo Scattolini
Roboat is an autonomous surface vessel (ASV) for urban waterways, developed as a research project by the AMS Institute and MIT. The platform can provide numerous functions to a city, such as transport, dynamic infrastructure, and an autonomous waste management system. This paper presents the development of a learning-based controller for the Roboat platform with the goal of achieving robustness and generalization properties. Specifically, when subject to uncertainty in the model or external disturbances, the proposed controller should be able to track set trajectories with less tracking error than the current nonlinear model predictive controller (NMPC) used on the ASV. To achieve this, a simulation of the system dynamics was developed as part of this work, based on the research presented in the literature and on the previous research performed on the Roboat platform. The simulation process also included the modeling of the necessary uncertainties and disturbances. In this simulation, a trajectory tracking agent was trained using the proximal policy optimization (PPO) algorithm. The trajectory tracking of the trained agent was then validated and compared to the current control strategy both in simulations and in the real world.
{"title":"Learning Trajectory Tracking for an Autonomous Surface Vehicle in Urban Waterways","authors":"Toma Sikora, Jonathan Klein Schiphorst, Riccardo Scattolini","doi":"10.3390/computation11110216","DOIUrl":"https://doi.org/10.3390/computation11110216","url":null,"abstract":"Roboat is an autonomous surface vessel (ASV) for urban waterways, developed as a research project by the AMS Institute and MIT. The platform can provide numerous functions to a city, such as transport, dynamic infrastructure, and an autonomous waste management system. This paper presents the development of a learning-based controller for the Roboat platform with the goal of achieving robustness and generalization properties. Specifically, when subject to uncertainty in the model or external disturbances, the proposed controller should be able to track set trajectories with less tracking error than the current nonlinear model predictive controller (NMPC) used on the ASV. To achieve this, a simulation of the system dynamics was developed as part of this work, based on the research presented in the literature and on the previous research performed on the Roboat platform. The simulation process also included the modeling of the necessary uncertainties and disturbances. In this simulation, a trajectory tracking agent was trained using the proximal policy optimization (PPO) algorithm. The trajectory tracking of the trained agent was then validated and compared to the current control strategy both in simulations and in the real world.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.3390/computation11110215
Lizhen Chen, Ying Ma, Bo Ren, Guohui Zhang
An efficient modified leapfrog time-marching scheme for the diblock copolymer model is investigated in this paper. The proposed scheme offers three main advantages. Firstly, it is linear in time, requiring only a linear algebra system to be solved at each time-marching step. This leads to a significant reduction in computational cost compared to other methods. Secondly, the scheme ensures unconditional energy stability, allowing for a large time step to be used without compromising solution stability. Thirdly, the existence and uniqueness of the numerical solution at each time step is rigorously proven, ensuring the reliability and accuracy of the method. A numerical example is also included to demonstrate and validate the proposed algorithm, showing its accuracy and efficiency in practical applications.
{"title":"Numerical Approximations of Diblock Copolymer Model Using a Modified Leapfrog Time-Marching Scheme","authors":"Lizhen Chen, Ying Ma, Bo Ren, Guohui Zhang","doi":"10.3390/computation11110215","DOIUrl":"https://doi.org/10.3390/computation11110215","url":null,"abstract":"An efficient modified leapfrog time-marching scheme for the diblock copolymer model is investigated in this paper. The proposed scheme offers three main advantages. Firstly, it is linear in time, requiring only a linear algebra system to be solved at each time-marching step. This leads to a significant reduction in computational cost compared to other methods. Secondly, the scheme ensures unconditional energy stability, allowing for a large time step to be used without compromising solution stability. Thirdly, the existence and uniqueness of the numerical solution at each time step is rigorously proven, ensuring the reliability and accuracy of the method. A numerical example is also included to demonstrate and validate the proposed algorithm, showing its accuracy and efficiency in practical applications.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"6 S1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.3390/computation11110213
Ziemowit Malecha, Gideon Dsouza
This paper analyzes the interaction of wind turbines and losses in wind farms using computational fluid dynamics (CFD). The mathematical model used consisted of three-dimensional Reynolds-averaged Navier–Stokes (RANS) equations, while the presence of wind turbines in the flow was simulated as additional source terms. The novelty of the research is the definition of the source term as a velocity-dependent actuator disc model (ADM). This allowed for modeling the operation of a wind farm consisting of real wind turbines, characterized by power coefficients Cp and thrust force coefficients CT, which are a function of atmospheric wind speed. The calculations presented used a real 5 MW Gamesa turbine. Two different turbine spacings, 5D and 10D, where D is the diameter of the turbine, and two different locations corresponding to the offshore and onshore conditions were examined. The proposed model can be used to analyze wind farm losses not only in terms of the geometric distribution of individual turbines but also in terms of a specific type of wind turbine and in the entire wind speed spectrum.
{"title":"Modeling of Wind Turbine Interactions and Wind Farm Losses Using the Velocity-Dependent Actuator Disc Model","authors":"Ziemowit Malecha, Gideon Dsouza","doi":"10.3390/computation11110213","DOIUrl":"https://doi.org/10.3390/computation11110213","url":null,"abstract":"This paper analyzes the interaction of wind turbines and losses in wind farms using computational fluid dynamics (CFD). The mathematical model used consisted of three-dimensional Reynolds-averaged Navier–Stokes (RANS) equations, while the presence of wind turbines in the flow was simulated as additional source terms. The novelty of the research is the definition of the source term as a velocity-dependent actuator disc model (ADM). This allowed for modeling the operation of a wind farm consisting of real wind turbines, characterized by power coefficients Cp and thrust force coefficients CT, which are a function of atmospheric wind speed. The calculations presented used a real 5 MW Gamesa turbine. Two different turbine spacings, 5D and 10D, where D is the diameter of the turbine, and two different locations corresponding to the offshore and onshore conditions were examined. The proposed model can be used to analyze wind farm losses not only in terms of the geometric distribution of individual turbines but also in terms of a specific type of wind turbine and in the entire wind speed spectrum.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"20 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135326236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.3390/computation11110212
Aminah Alqahtani, Meznah Almutairy
This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on ClustalΩ, MAFFT, and MUSCLE in the context of genotyping SARS-CoV-2 for the Saudi population. Our results indicate that MAFFT outperforms the others, making it an ideal choice for large-scale genomic analyses. The comparative performance of MSAs assembled using MergeAlign demonstrates that MAFFT and MUSCLE consistently exhibit higher accuracy than ClustalΩ in both reference-based and consensus-based approaches. The evaluation of genotyping effectiveness reveals that the addition of a reference sequence, such as the SARS-CoV-2 Wuhan-Hu-1 isolate, does not significantly affect the alignment process, suggesting that using consensus sequences derived from individual MSA alignments may yield comparable genotyping outcomes. Investigating single-nucleotide polymorphisms (SNPs) and mutations highlights distinctive features of MSA programs. ClustalΩ and MAFFT show similar counts, while MUSCLE displays the highest SNP count. High-frequency SNP analysis identifies MAFFT as the most accurate MSA program, emphasizing its reliability. Comparisons between Saudi and global SARS-CoV-2 populations underscore regional genetic variations. Saudis exhibit consistently higher frequencies of high-frequency SNPs, attributed to genetic similarity within the population. Transmission dynamics analysis reveals a higher frequency of co-mutations in the Saudi dataset, suggesting shared evolutionary patterns. These findings emphasize the importance of considering regional diversity in genetic analyses.
本研究探讨了多序列比对(MSA)程序的准确性和效率，重点关注ClustalΩ、MAFFT和MUSCLE在沙特人群SARS-CoV-2基因分型的背景下。我们的结果表明，MAFFT优于其他方法，使其成为大规模基因组分析的理想选择。使用MergeAlign组装的msa的比较性能表明，在基于参考和基于共识的方法中，MAFFT和MUSCLE始终表现出比ClustalΩ更高的准确性。基因分型有效性评估显示，添加参考序列(如SARS-CoV-2武汉- hu -1分离物)不会显著影响比对过程，这表明使用来自单个MSA比对的共识序列可能产生可比较的基因分型结果。研究单核苷酸多态性(SNPs)和突变突出了MSA程序的独特特征。ClustalΩ和MAFFT显示相似的计数，而MUSCLE显示最高的SNP计数。高频SNP分析确定MAFFT是最准确的MSA程序，强调其可靠性。沙特和全球SARS-CoV-2人群的比较强调了区域遗传差异。沙特人表现出一贯较高的高频snp频率，归因于种群内的遗传相似性。传播动力学分析显示，沙特数据集中的共突变频率更高，表明共享的进化模式。这些发现强调了在遗传分析中考虑区域多样性的重要性。
{"title":"Evaluating the Performance of Multiple Sequence Alignment Programs with Application to Genotyping SARS-CoV-2 in the Saudi Population","authors":"Aminah Alqahtani, Meznah Almutairy","doi":"10.3390/computation11110212","DOIUrl":"https://doi.org/10.3390/computation11110212","url":null,"abstract":"This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on ClustalΩ, MAFFT, and MUSCLE in the context of genotyping SARS-CoV-2 for the Saudi population. Our results indicate that MAFFT outperforms the others, making it an ideal choice for large-scale genomic analyses. The comparative performance of MSAs assembled using MergeAlign demonstrates that MAFFT and MUSCLE consistently exhibit higher accuracy than ClustalΩ in both reference-based and consensus-based approaches. The evaluation of genotyping effectiveness reveals that the addition of a reference sequence, such as the SARS-CoV-2 Wuhan-Hu-1 isolate, does not significantly affect the alignment process, suggesting that using consensus sequences derived from individual MSA alignments may yield comparable genotyping outcomes. Investigating single-nucleotide polymorphisms (SNPs) and mutations highlights distinctive features of MSA programs. ClustalΩ and MAFFT show similar counts, while MUSCLE displays the highest SNP count. High-frequency SNP analysis identifies MAFFT as the most accurate MSA program, emphasizing its reliability. Comparisons between Saudi and global SARS-CoV-2 populations underscore regional genetic variations. Saudis exhibit consistently higher frequencies of high-frequency SNPs, attributed to genetic similarity within the population. Transmission dynamics analysis reveals a higher frequency of co-mutations in the Saudi dataset, suggesting shared evolutionary patterns. These findings emphasize the importance of considering regional diversity in genetic analyses.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Identifying the relationship between human mobility, air pollution, and communicable disease poses a challenge for impact evaluation and public health planning. Specifically, Coronavirus disease 2019 (COVID-19) and air pollution from fine particulates (PM2.5), by which human mobility is mediated in a public health emergency. To describe the interplay between human mobility and PM2.5 during the spread of COVID-19, we proposed a nonlinear model of the time-dependent transmission rate as a function of these factors. A compartmental epidemic model, together with daily confirmed case data in Bangkok, Thailand during 2020–2021, was used to estimate the intrinsic parameters that can determine the impact on the transmission dynamic of the two earlier outbreaks. The results suggested a positive association between mobility and transmission, but this was strongly dependent on the context and the temporal characteristics of the data. For the ascending phase of an epidemic, the estimated coefficient of mobility variable in the second wave was greater than in the first wave, but the value of the mobility component in the transmission rate was smaller. Due to the influence of the baseline value and PM2.5, the estimated basic reproduction number of the second wave was higher than that of the first wave, even though mobility had a greater influence. For the descending phase, the value of the mobility component in the second wave was greater, due to the negative value of the estimated mobility coefficient. Despite this scaling effect, the results suggest a negative association between PM2.5 and the transmission rates. Although this conclusion agrees with some previous studies, the true effect of PM2.5 remains inconclusive and requires further investigation.
{"title":"Modeling the Dynamic Effects of Human Mobility and Airborne Particulate Matter on the Spread of COVID-19","authors":"Klot Patanarapeelert, Rossanan Chandumrong, Nichaphat Patanarapeelert","doi":"10.3390/computation11110211","DOIUrl":"https://doi.org/10.3390/computation11110211","url":null,"abstract":"Identifying the relationship between human mobility, air pollution, and communicable disease poses a challenge for impact evaluation and public health planning. Specifically, Coronavirus disease 2019 (COVID-19) and air pollution from fine particulates (PM2.5), by which human mobility is mediated in a public health emergency. To describe the interplay between human mobility and PM2.5 during the spread of COVID-19, we proposed a nonlinear model of the time-dependent transmission rate as a function of these factors. A compartmental epidemic model, together with daily confirmed case data in Bangkok, Thailand during 2020–2021, was used to estimate the intrinsic parameters that can determine the impact on the transmission dynamic of the two earlier outbreaks. The results suggested a positive association between mobility and transmission, but this was strongly dependent on the context and the temporal characteristics of the data. For the ascending phase of an epidemic, the estimated coefficient of mobility variable in the second wave was greater than in the first wave, but the value of the mobility component in the transmission rate was smaller. Due to the influence of the baseline value and PM2.5, the estimated basic reproduction number of the second wave was higher than that of the first wave, even though mobility had a greater influence. For the descending phase, the value of the mobility component in the second wave was greater, due to the negative value of the estimated mobility coefficient. Despite this scaling effect, the results suggest a negative association between PM2.5 and the transmission rates. Although this conclusion agrees with some previous studies, the true effect of PM2.5 remains inconclusive and requires further investigation.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"23 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-29DOI: 10.3390/computation11110210
Alexandru Grigoraș, Florin Leon
The paper focuses on predicting the next purchase day (NPD) for customers in e-commerce, a task with applications in marketing, inventory management, and customer retention. A novel transformer-based model for NPD prediction is introduced and compared to traditional methods such as ARIMA, XGBoost, and LSTM. Transformers offer advantages in capturing long-term dependencies within time series data through self-attention mechanisms. This adaptability to various time series patterns, including trends, seasonality, and irregularities, makes them a promising choice for NPD prediction. The transformer model demonstrates improvements in prediction accuracy compared to the baselines. Additionally, a clustered transformer model is proposed, which further enhances accuracy, emphasizing the potential of this architecture for NPD prediction.
{"title":"Transformer-Based Model for Predicting Customers’ Next Purchase Day in e-Commerce","authors":"Alexandru Grigoraș, Florin Leon","doi":"10.3390/computation11110210","DOIUrl":"https://doi.org/10.3390/computation11110210","url":null,"abstract":"The paper focuses on predicting the next purchase day (NPD) for customers in e-commerce, a task with applications in marketing, inventory management, and customer retention. A novel transformer-based model for NPD prediction is introduced and compared to traditional methods such as ARIMA, XGBoost, and LSTM. Transformers offer advantages in capturing long-term dependencies within time series data through self-attention mechanisms. This adaptability to various time series patterns, including trends, seasonality, and irregularities, makes them a promising choice for NPD prediction. The transformer model demonstrates improvements in prediction accuracy compared to the baselines. Additionally, a clustered transformer model is proposed, which further enhances accuracy, emphasizing the potential of this architecture for NPD prediction.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"58 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136136360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.3390/computation11110209
Alexander Musaev, Andrey Makshanov, Dmitry Grigoriev
The authors suggest a methodology that involves conducting a preliminary analysis of inertia in financial time series. Inertia here means the manifestation of some kind of long-term memory. Such effects may take place in complex processes of a stochastic kind. If the decision is negative, they do not recommend using predictive management strategies based on trend analysis. The study uses computational schemes to detect and confirm trends in financial market data. The effectiveness of these schemes is evaluated by analyzing the frequency of trend confirmation over different time intervals and with different levels of trend confirmation. Furthermore, the study highlights the limitations of using smoothed curves for trend analysis due to the lag in the dynamics of the curve, emphasizing the importance of considering real-time data in trend analysis for more accurate predictions.
{"title":"Exploring the Quotation Inertia in International Currency Markets","authors":"Alexander Musaev, Andrey Makshanov, Dmitry Grigoriev","doi":"10.3390/computation11110209","DOIUrl":"https://doi.org/10.3390/computation11110209","url":null,"abstract":"The authors suggest a methodology that involves conducting a preliminary analysis of inertia in financial time series. Inertia here means the manifestation of some kind of long-term memory. Such effects may take place in complex processes of a stochastic kind. If the decision is negative, they do not recommend using predictive management strategies based on trend analysis. The study uses computational schemes to detect and confirm trends in financial market data. The effectiveness of these schemes is evaluated by analyzing the frequency of trend confirmation over different time intervals and with different levels of trend confirmation. Furthermore, the study highlights the limitations of using smoothed curves for trend analysis due to the lag in the dynamics of the curve, emphasizing the importance of considering real-time data in trend analysis for more accurate predictions.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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