Computation, Volume 11, Issue 10 (October 2023) – 21 articles

Vaccine hesitancy is a significant public health concern, with numerous studies demonstrating its negative impact on immunization rates. One factor that can influence vaccine hesitancy is media coverage of vaccination. The media is a significant source of immunization information and can significantly shape people’s attitudes and behaviors toward vaccine uptake. Media influences vaccination positively or negatively. Accurate coverage of the benefits and effectiveness of vaccination can encourage uptake, while coverage of safety concerns or misinformation may increase hesitancy. Our study investigated whether vaccine hesitancy acts as a mediator between information sources and vaccination uptake. We analyzed a cross-sectional online survey by the European Commission of 27,524 citizens from all EU member states between 15 and 29 March 2019. The study used structural equation modeling to conduct a mediation analysis, revealing that the influence of media on vaccine uptake is fully mediated by vaccine hesitancy, except for television, which depicted an inconsistent mediating role. In other words, the effect of different media on vaccine uptake is largely driven by the extent to which individuals are hesitant or resistant to vaccinating. Therefore, media outlets, governments, and public health organizations must work together to promote accurate and reliable information about vaccination and address vaccine hesitancy. Full article

This research aims to formulate and analyze two mathematical models describing the within-host dynamics of human immunodeficiency virus type-1 (HIV-1) in case of impaired humoral immunity. These models consist of five compartments, including healthy CD4${}^{+}$ T cells, (HIV-1)-latently infected cells, (HIV-1)-actively infected cells, HIV-1 particles, and B-cells. We make the assumption that healthy cells can become infected when exposed to: (i) HIV-1 particles resulting from viral infection (VI), (ii) (HIV-1)-latently infected cells due to latent cellular infection (CI), and (iii) (HIV-1)-actively infected cells due to active CI. In the second model, we introduce distributed time-delays. For each of these systems, we demonstrate the non-negativity and boundedness of the solutions, calculate the basic reproductive number, identify all possible equilibrium states, and establish the global asymptotic stability of these equilibria. We employ the Lyapunov method in combination with LaSalle’s invariance principle to investigate the global stability of these equilibrium points. Theoretical findings are subsequently validated through numerical simulations. Additionally, we explore the impact of B-cell impairment, time-delays, and CI on HIV-1 dynamics. Our results indicate that weakened immunity significantly contributes to disease progression. Furthermore, the presence of time-delays can markedly decrease the basic reproductive number, thereby suppressing HIV-1 replication. Conversely, the existence of latent CI spread increases the basic reproductive number, intensifying the progression of HIV-1. Consequently, neglecting latent CI spread in the HIV-1 dynamics model can lead to an underestimation of the basic reproductive number, potentially resulting in inaccurate or insufficient drug therapies for eradicating HIV-1 from the body. These findings offer valuable insights that can enhance the understanding of HIV-1 dynamics within a host. Full article

The maximum independent set problem is a classic and fundamental combinatorial challenge, where the objective is to find the largest subset of vertices in a graph such that no two vertices are adjacent. In this paper, we introduce a novel linear prioritized local algorithm tailored to address this problem on random d-regular graphs with a small and fixed degree d. Through exhaustive numerical simulations, we empirically investigated the independence ratio, i.e., the ratio between the cardinality of the independent set found and the order of the graph, which was achieved by our algorithm across random d-regular graphs with degree d ranging from 5 to 100. Remarkably, for every d within this range, our results surpassed the existing lower bounds determined by theoretical methods. Consequently, our findings suggest new conjectured lower bounds for the MIS problem on such graph structures. This finding has been obtained using a prioritized local algorithm. This algorithm is termed ‘prioritized’ because it strategically assigns priority in vertex selection, thereby iteratively adding them to the independent set. Full article

Electromembrane processes underlie the functioning of electrodialysis devices and nano- and microfluidic devices, the scope of which is steadily expanding. One of the main aspects that determine the effectiveness of membrane systems is the choice of the optimal electrical mode. The solution of this problem, along with experimental studies, requires tools for the theoretical analysis of ion-transport processes in various electrical modes. The system of Nernst–Planck–Poisson and Navier–Stokes (NPP–NS) equations is widely used to describe the overlimiting mass transfer associated with the development of electroconvection. This paper proposes a new approach to describe the electrical mode in a membrane system using the displacement current equation. The equation for the displacement current makes it possible to simulate the galvanodynamic mode, in which the electric field is determined by the given current density. On the basis of the system of Nernst–Planck, displacement current and Navier–Stokes (NPD–NS) equations, a model of the electroconvective overlimiting mass transfer in the diffusion layer at the surface of the ion-exchange membrane in the DC current mode was constructed. Mathematical models based on the NPP–NS and NPD–NS equations, formulated to describe the same physical situation of mass transfer in the membrane system, differ in the peculiarities of numerical solution. At overlimiting currents, the required accuracy of the numerical solution is achieved in the approach based on the NPP–NS equations with a smaller time step than the NPD–NS equation approach. The accuracy of calculating the current density at the boundaries parallel to the membrane surface is higher for the model based on the NPD–NS equations compared to the model based on the NPP–NS equations. Full article

The retrospective inverse problem for evolution equations is formulated as the reconstruction of unknown initial data by a given solution at the final time. We consider the inverse retrospective problem for a one-dimensional parabolic equation in two disconnected intervals with weak solutions in weighted Sobolev spaces. The two solutions are connected with nonstandard interface conditions, and thus this problem is solved in the whole spatial region. Such a problem, as with other inverse problems, is ill-posed, and for its numerical solution, specific techniques have to be used. The direct problem is first discretized by a difference scheme which provides a second order of approximation in space. For the resulting ordinary differential equation system, the positive coerciveness is established. Next, we develop an iterative conjugate gradient method to solve the ill-posed systems of the difference equations, which are obtained after weighted time discretization, of the inverse problem. Test examples with noisy input data are discussed. Full article

The performance of embedding methods is directly tied to the quality of the bath orbital construction. In this paper, we develop a versatile framework, enabling the investigation of the optimal construction of the orbitals of the bath. As of today, in state-of-the-art embedding methods, the orbitals of the bath are constructed by performing a Singular Value Decomposition (SVD) on the impurity-environment part of the one-body reduced density matrix, as originally presented in Density Matrix Embedding Theory. Recently, the equivalence between the SVD protocol and the use of unitary transformation, the so-called Block-Householder transformation, has been established. We present a generalization of the Block-Householder transformation by introducing additional flexible parameters. The additional parameters are optimized such that the bath-orbitals fulfill physically motivated constraints. The efficiency of the approach is discussed and exemplified in the context of the half-filled Hubbard model in one-dimension. Full article

High availability is vital for network operators to ensure reliable services. Network faults can disrupt functionality and require quick recovery. Multipath networking enhances availability through load balancing and optimal link utilization. However, equal-cost multipath (ECMP) routing has limitations in effectively using multipaths, decreasing network availability. This paper proposes a three-phase disjoint-path framework that improves availability by directing traffic flows through separate paths. The framework provides effective load balancing and meets various service requirements. It includes the Optimization phase for identifying optimal multipath solutions, the Path Separation phase for dividing the multipath into working and backup sets, and the Quality Assessment phase for evaluating the robustness of both sets using topological metrics and micro-based characteristics. The simulations demonstrate the proposed framework’s validation and effectiveness in enhancing network availability. Full article

Dynamic Mode Decomposition with Control is a powerful technique for analyzing and modeling complex dynamical systems under the influence of external control inputs. In this paper, we propose a novel approach to implement this technique that offers computational advantages over the existing method. The proposed scheme uses singular value decomposition of a lower order matrix and requires fewer matrix multiplications when determining corresponding approximation matrices. Moreover, the matrix of dynamic modes also has a simpler structure than the corresponding matrix in the standard approach. To demonstrate the efficacy of the proposed implementation, we applied it to a diverse set of numerical examples. The algorithm’s flexibility is demonstrated in tests: accurate modeling of ecological systems like Lotka-Volterra, successful control of chaotic behavior in the Lorenz system and efficient handling of large-scale stable linear systems. This showcased its versatility and efficacy across different dynamical systems. Full article

Process Variability (PV) is a significant water quality time-series measurement. It is a critical element in detecting abnormality. Typically, the quality control system should raise an alert if the PV exceeds its normal value after a proper delay time (DT). The literature does not address the relation between the extended process variability and the time delay for a warning. The current paper shows a graphical method for calibrating a Water Quality Model based on these two parameters. The amount of variability is calculated based on the Euclidean distance between records in a dataset. Typically, each multivariable process has some relation between the variability and the time delay. In the case of a short period (a few minutes), the PV may be high. However, as the relevant DT is longer, it is expected to see the PV converge to some steady state. The current paper examines a method for estimating the relationship between the two measurements (PV and DT) as a detection tool for abnormality. Given the user’s classification of the actual event for true and false events, the method shows how to build a graphical map that helps the user select the best thresholds for the model. The last section of the paper offers an implementation of the method using real-world data. Full article

Attaining sustainable development goals is a complex process that involves a range of economic, social, and environmental factors. It requires investments in infrastructure, technology, and human capital. In this case, green finance is conducive to channel investments toward sustainable projects and initiatives by providing incentives for environmentally friendly practices and technologies and by encouraging companies and investors to adopt sustainable business models. This paper aims to check the spatial spillover effect of green finance on attaining sustainable development for European Union (EU) countries for 2008–2021. The study applies the spatial Durbin model to explore the research hypothesis. The findings confirm that green finance promotes the achievement of sustainable development goals. However, the impact of green finance on attaining sustainable development is heterogeneous depending on the EU region. In this case, the EU should intensify its green finance policy considering the regional features that significantly affect the achievement of sustainable development goals by reducing greenhouse gas emissions, improving energy efficiency, and promoting renewable energy. In addition, it is necessary to develop alternative financial sources involving green bonds that could be used to fund green projects on renewable energy projects, green building construction, etc. Full article

In recent times, the global community has been faced with the unprecedented challenge of the coronavirus disease (COVID-19) pandemic, which has had a profound and enduring impact on both global health and the global economy. The utilization of mathematical modeling has become an essential instrument in the characterization and understanding of the dynamics associated with infectious illnesses. In this study, the utilization of the differential quadrature method (DQM) was employed in order to anticipate the characterization of the dynamics of COVID-19 through a fractional mathematical model. Uniform and non-uniform polynomial differential quadrature methods (PDQMs) and a discrete singular convolution method (DSCDQM) were employed in the examination of the dynamics of COVID-19 in vulnerable, exposed, deceased, asymptomatic, and recovered persons. An analysis was conducted to compare the methodologies used in this study, as well as the modified Euler method, in order to highlight the superior efficiency of the DQM approach in terms of code-execution times. The results demonstrated that the fractional order significantly influenced the outcomes. As the fractional order tended towards unity, the anticipated numbers of vulnerable, exposed, deceased, asymptomatic, and recovered individuals increased. During the initial week of the inquiry, there was a substantial rise in the number of individuals who contracted COVID-19, which was primarily attributed to the disease’s high transmission rate. As a result, there was an increase in the number of individuals who recovered, in tandem with the rise in the number of infected individuals. These results highlight the importance of the fractional order in influencing the dynamics of COVID-19. The utilization of the DQM approach, characterized by its proficient code-execution durations, provided significant insights into the dynamics of COVID-19 among diverse population cohorts and enhanced our comprehension of the evolution of the pandemic. The proposed method was efficient in dealing with ordinary differential equations (ODEs), partial differential equations (PDEs), and fractional differential equations (FDEs), in either linear or nonlinear forms. In addition, the stability of the DQM and its validity were verified during the present study. Moreover, the error analysis showed that DQM has better error percentages in many applications than other relevant techniques. Full article

This paper presents a general procedure to formulate and implement 3D elements of arbitrary order in meshes with multiple element types. This procedure includes obtaining shape functions and integration quadrature and establishing an approach for checking the generated element’s compatibility with adjacent elements’ surfaces. This procedure was implemented in Matlab, using its symbolic and graphics toolbox, and complied as a GUI interface named ShapeGen3D to provide finite element users with a tool to tailor elements according to their analysis needs. ShapeGen3D also outputs files with the element formulation needed to enable users to implement the generated elements in other programming languages or through user elements in commercial finite element software. Currently, finite element (FE) users are limited to employing element formulation available in the literature, commercial software, or existing element libraries. Thus, the developed procedure implemented in ShapeGen3D offers FEM users the possibility to employ elements beyond those readily available. The procedure was tested by generating the formulation for a brick element, a brick transition element, and higher-order hexahedron and tetrahedron elements that can be used in a spectral finite element analysis. The formulation obtained for the 20-node element was in perfect agreement with the formulation available in the literature. In addition, the results showed that the interpolation condition was met for all the generated elements, which provides confidence in the implementation of the process. Researchers and educators can use this procedure to efficiently develop and illustrate three-dimensional elements. Full article

After publishing a research article in the year 2019, a cam-shaped cylinder was introduced, and the results expressed its ability to prevent the vortex from shedding. This makes the cam-shaped cylinder a better performer than the circular cylinder. This work is an extension of past work with the aim of further reducing drag by attaching a backward splitter plate to a cam-shaped cylinder. In an attempt to decrease drag and regulate the wake regime more efficiently than the traditional splitter plate control devices, a splitter plate flow departure control device is presented in this paper for a low Reynolds number flow range (Re = 50–200). It has been noted that when plate length increases, integral parameters like drag, lift, and Strouhal number do not change monotonically. The Strouhal number (St) increases with a drop in D₂/D_eq, but the average drag reduces with a rise in Re and a decrease in D₂/D_eq, respectively. In terms of decreased drag, the current cam-shaped cylinders attached to a rearward splitter plate have shown their superiority to other bluff bodies. Full article

Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance quantitative analysis and refine diagnostic capabilities, precise segmentation of the cervical cytoplasm and nucleus using deep learning techniques holds immense promise. This research focuses on addressing the primary challenge of achieving accurate segmentation in the presence of noisy data commonly encountered in Pap smear images. Poisson noise, a prevalent type of noise, corrupts these images, impairing the precise delineation of the cytoplasm and nucleus. Consequently, segmentation boundaries become indistinct, leading to compromised overall accuracy. To overcome these limitations, the utilization of U-Net, a deep learning architecture specifically designed for automatic segmentation, has been proposed. This approach aims to mitigate the adverse effects of Poisson noise on the digitized Pap smear slides. The evaluation of the proposed methodology involved a dataset of 110 Pap smear slides. The experimental results demonstrate that the proposed approach successfully achieves precise segmentation of the nucleus and cytoplasm in noise-free images. By preserving the boundaries of both cellular components, the method facilitates accurate feature extraction, thus contributing to improved diagnostic capabilities. Comparative analysis between noisy and noise-free images reveals the superiority of the presented approach in terms of segmentation accuracy, as measured by various metrics, including the Dice coefficient, specificity, sensitivity, and intersection over union (IoU). The findings of this study underline the potential of deep-learning-based segmentation techniques to enhance cervical cancer diagnosis and pave the way for improved quantitative analysis in this critical field of women’s health. Full article

The computational capabilities of computer tools expand the student’s search capabilities. Conducting computational experiments in the classroom is no longer an organizational problem. This raises the “black box” problem, when the student perceives the computational module as a magician’s box and loses conceptual control over the computational process. This article analyses the use of various computer tools, both existing and specially created for “key” computational experiments, that aim at revealing the essential aspects of the introduced concepts using specific examples. This article deals with a number of topics of algebra and calculus that are transitional from school to university, and it shows how computational experiments in the form of a “transparent” box can be used. Full article

This study examines the mechanism of nonlinear supratransmission (NST), which involves the transfer of disturbance to discrete media at frequencies not supported by the structure. We considered a model crystal with A3B stoichiometry. The investigation was carried out using atomistic modeling through molecular dynamics. The interatomic interaction was determined by a potential obtained through the embedded atom method, which approximates the properties of the Pt₃Al crystal. The effect of NST is an important property of many discrete structures. Its existence requires the discreteness and nonlinearity of the medium, as well as the presence of a forbidden zone in its spectrum. This work focuses on the differences in the NST effect due to the anisotropy of crystallographic directions. Three planes along which the disturbance caused by NST propagated were considered: (100), (110), and (111). It was found that the intensity of the disturbance along the (100) plane is an order of magnitude lower than for more densely packed directions. Differences in the shape of solitary waves depending on the propagation direction were shown. Moreover, all waves can be described by a single equation, being a solution of the discrete variational equations of macroscopic and microscopic displacements, with different parameters, emphasizing the unified nature of the waves and the contribution of crystal anisotropy to their properties. Studying the NST phenomenon is essential due to numerous applications of the latter, such as implications in information transmission and signal processing. Understanding how disturbances propagate in discrete media could lead to advancements in communication technologies, data storage, and signal amplification where the earlier mentioned ability to describe it with analytical equations is of particular importance. Full article

The climate change crisis has resulted in the need to use sustainable methods in architectural design, including building form and orientation decisions that can save a significant amount of energy consumed by a building. Several previous studies have optimized building form and envelope for energy performance, but the isolated effect of varieties of possible architectural forms for a specific climate has not been fully investigated. This paper proposes four novel office building form generation methods (the polygon that varies between pentagon and decagon; the pixels that are complex cubic forms; the letters including H, L, U, T; cross and complex cubic forms; and the round family including circular and oval forms) and evaluates their annual thermal energy use intensity (EUI) for Cairo (hot climate). Results demonstrated the applicability of the proposed methods in enhancing the energy performance of the new forms in comparison to the base case. The results of the optimizations are compared together, and the four families are discussed in reference to their different architectural aspects and performance. Scatterplots are developed for the round family (highest performance) to test the impact of each dynamic parameter on EUI. The round family optimization process takes a noticeably high calculation time in comparison to other families. Therefore, an Artificial Neural Network (ANN) prediction model is developed for the round family after simulating 1726 iterations. Training of 1200 configurations is used to predict annual EUI for the remaining 526 iterations. The ANN predicted values are compared against the trained to determine the time saved and accuracy. Full article

This research proposes a new image compression method based on the F1-transform which improves the quality of the reconstructed image without increasing the coding/decoding CPU time. The advantage of compressing color images in the YUV space is due to the fact that while the three bands Red, Green and Blue are equally perceived by the human eye, in YUV space most of the image information perceived by the human eye is contained in the Y band, as opposed to the U and V bands. Using this advantage, we construct a new color image compression algorithm based on F1-transform in which the image compression is accomplished in the YUV space, so that better-quality compressed images can be obtained without increasing the execution time. The results of tests performed on a set of color images show that our color image compression method improves the quality of the decoded images with respect to the image compression algorithms JPEG, F1-transform on the RGB color space and F-transform on the YUV color space, regardless of the selected compression rate and with comparable CPU times. Full article

Polymerase chain reaction (PCR) technique is one of the molecular methods in amplifying DNA for the detection of malaria. However, the collection and transportation of samples and the processing and dissemination of results via conventional PCR, especially when used for routine clinical practice, can hamper the technique’s sensitivity and specificity. The rampancy of such disease in the Philippines is aggravated by the limited supply of medical machinery and the poor economic state of the country; thus, the need to innovate a device for the early detection of malaria is necessary. With that, this study focuses on designing a microfluidic device that will mimic the function of a conventional genus-specific PCR based on the 18S rRNA gene to detect malaria parasites (Plasmodium falciparum) at low-grade parasitemia. The design was intended to be portable, accessible, and economical, which none from past literature has dealt with specifically for malaria detection. This in silico design is a first in the country specially crafted for such reasons. The proposed device was developed and simulated using ANSYS software for Computational Fluid Dynamics (CFD) analyses. The simulation shows that adding loops to the design increases its relative deviation but minimally compared to having only a straight path design. This indicates that looping is acceptable in designing a microfluidic device to minimize chip length. It was also found that increasing the cross-sectional area of the fluid path decreases the efficiency of the design. Lastly, among the three materials utilized, the chip made of polypropylene is the most efficient, with a relative deviation of 0.94 compared to polycarbonate and polydimethylsiloxane, which have relative deviations of 2.78 and 1.92, respectively. Future researchers may mesh the 44-cycle microfluidic chip due to the limitations of the software used in this study, and other materials, such as biocomposites, may be assessed to broaden the application of the design. Full article

In this paper, we introduce a novel approach employing two-dimensional uniform and non-uniform Haar wavelet collocation methods to effectively solve the generalized Burgers–Huxley and Burgers–Fisher equations. The demonstrated method exhibits an impressive quartic convergence rate. Several test problems are presented to exemplify the accuracy and efficiency of this proposed approach. Our results exhibit exceptional accuracy even with a minimal number of spatial divisions. Additionally, we conduct a comparative analysis of our results with existing methods. Full article

Biological networks such as protein interaction networks, gene regulation networks, and metabolic pathways are examples of complex networks that are large graphs with small-world and scale-free properties. An analysis of these networks has a profound effect on our understanding the origins of life, health, and the disease states of organisms, and it allows for the diagnosis of diseases to aid in the search for remedial processes. In this review, we describe the main analysis methods of biological networks using graph theory, by first defining the main parameters, such as clustering coefficient, modularity, and centrality. We then survey fundamental graph clustering methods and algorithms, followed by the network motif search algorithms, with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function that is carried out by that small network, and discovering such a function provides an insight into the overall function of the organism. Lastly, we review network alignment algorithms that find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor, and finding such a relationship may help researchers to derive ancestral relationships and to predict the future evolution of organisms to enable the design of new drugs. We provide a review of the research studies in all of these methods, and conclude using the current challenging areas of biological network analysis, and by using graph theory and parallel processing for high performance analysis. Full article