Computation, Volume 11, Issue 7 (July 2023) – 26 articles

Since cardiovascular diseases (CVDs) pose a critical global concern, identifying associated risk factors remains a pivotal research focus. This study aims to propose and optimize a fuzzy system for cardiovascular risk (CVR) classification using a multiobjective approach, addressing computational aspects such as the configuration of the fuzzy system, the optimization process, the selection of a suitable solution from the optimal Pareto front, and the interpretability of the fuzzy logic system after the optimization process. The proposed system utilizes data, including age, weight, height, gender, and systolic blood pressure to determine cardiovascular risk. The fuzzy model is based on preliminary information from the literature; therefore, to adjust the fuzzy logic system using a multiobjective approach, the body mass index (BMI) is considered as an additional output as data are available for this index, and body mass index is acknowledged as a proxy for cardiovascular risk given the propensity for these diseases attributed to surplus adipose tissue, which can elevate blood pressure, cholesterol, and triglyceride levels, leading to arterial and cardiac damage. By employing a multiobjective approach, the study aims to obtain a balance between the two outputs corresponding to cardiovascular risk classification and body mass index. For the multiobjective optimization, a set of experiments is proposed that render an optimal Pareto front, as a result, to later determine the appropriate solution. The results show an adequate optimization of the fuzzy logic system, allowing the interpretability of the fuzzy sets after carrying out the optimization process. In this way, this paper contributes to the advancement of the use of computational techniques in the medical domain. Full article

Despite low tuberculosis (TB) mortality rates in China, Europe, and the United States, many countries are still struggling to control the epidemic, including India, South Africa, and Algeria. This study aims to contribute to the body of knowledge on this topic and provide a valuable tool and evidence-based guidance for the Algerian healthcare managers in understanding the spread of TB and implementing control strategies. For this purpose, a compartmental mathematical model is proposed to analyze TB dynamics in Algeria and investigate the vaccination and treatment effects on disease breaks. A qualitative study is conducted to discuss the stability property of both disease-free equilibrium and endemic equilibrium. In order to adopt the proposed model for the Algerian case, we estimate the model parameters using Algerian TB-reported data from 1990 to 2020. The obtained results using the proposed mathematical compartmental model show that the reproduction number ($R_0$) of TB in Algeria is less than one, suggesting that the disease can be eradicated or effectively controlled through a combination of interventions, including vaccination, high-quality treatment, and isolation measures. Full article

This research analyzes electrical distribution networks using renewable generation sources based on photovoltaic (PV) sources and distribution static compensators (D-STATCOMs) in order to minimize the expected annual grid operating costs for a planning period of 20 years. The separate and simultaneous placement of PVs and D-STATCOMs is evaluated through a mixed-integer nonlinear programming model (MINLP), whose binary part pertains to selecting the nodes where these devices must be located, and whose continuous part is associated with the power flow equations and device constraints. This optimization model is solved using the vortex search algorithm for the sake of comparison. Numerical results in the IEEE 33- and 69-bus grids demonstrate that combining PV sources and D-STATCOM devices entails the maximum reduction in the expected annual grid operating costs when compared to the solutions reached separately by each device, with expected reductions of about $35.50\%$ and $35.53\%$ in the final objective function value with respect to the benchmark case. All computational validations were carried out in the MATLAB programming environment (version 2021b) with our own scripts. Full article

Modern heating furnaces use combined modes of heating the charge. At high heating temperatures, more radiation heating is used; at lower temperatures, more convection heating is used. In large heating furnaces, such as pusher furnaces, it is necessary to monitor the heating of the material zonally. Zonal heating allows the appropriate thermal regime to be set in each zone, according to the desired parameters for heating the charge. The problem for each heating furnace is to set the optimum thermal regime so that at the end of the heating, after the material has been cross-sectioned, there is a uniform temperature field with a minimum temperature differential. In order to evaluate the heating of the charge, a mathematical model was developed to calculate the heat fluxes of the moving charge (slabs) along the length of the pusher furnace. The obtained results are based on experimental measurements on a test slab on which thermocouples were installed, and data acquisition was provided by a TERMOPHIL-stor data logger placed directly on the slab. Most of the developed models focus only on energy balance assessment or external heat exchange. The results from the model created showed reserves for changing the thermal regimes in the different zones. The developed model was used to compare the heating evaluation of the slabs after the rebuilding of the pusher furnace. Changing the furnace parameters and altering the heat fluxes or heating regimes in each zone contributed to more uniform heating and a reduction in specific heat consumption. The developed mathematical heat flux model is applicable as part of the powerful tools for monitoring and controlling the thermal condition of the charge inside the furnace as well as evaluating the operating condition of such furnaces. Full article

In this paper, we develop a deterministic mathematical epidemic model for tuberculosis outbreaks in order to study the disease’s impact in a given population. We develop a qualitative analysis of the model by showing that the solution of the model is positive and bounded. The global stability analysis of the model uses Lyapunov functions and the threshold quantity of the model, which is the basic reproduction number is estimated. The existence and uniqueness analysis for Caputo fractional tuberculosis outbreak model is presented by transforming the deterministic model to a Caputo sense model. The deterministic model is used to predict real data from Uganda and Rwanda to see how well our model captured the dynamics of the disease in the countries considered. Furthermore, the sensitivity analysis of the parameters according to $R_0$ was considered in this study. The normalised forward sensitivity index is used to determine the most sensitive variables that are important for infection control. We simulate the Caputo fractional tuberculosis outbreak model using the Adams–Bashforth–Moulton approach to investigate the impact of treatment and vaccine rates, as well as the disease trajectory. Overall, our findings imply that increasing vaccination and especially treatment availability for infected people can reduce the prevalence and burden of tuberculosis on the human population. Full article

Healing patterns are a critical issue that influence the fracture mechanism of self-healing concrete (SHC) structures. Partial healing cracks could happen even during the normal operating conditions of the structure, such as sustainable applied loads or quick crack spreading. In this paper, the effects of two main factors that control healing patterns, the healed crack length and the interfacial cohesive properties between the solidified healing agent and the cracked surfaces on the load carrying capacity and the fracture mechanism of healed SHC samples, are computationally investigated. The proposed computational modeling framework is based on the extended finite element method (XFEM) and cohesive surface (CS) technique to model the fracture and debonding mechanism of 2D healed SHC samples under a uniaxial tensile test. The interfacial cohesive properties and the healed crack length have significant effects on the load carrying capacity, the crack initiation, the propagation, and the debonding potential of the solidified healing agent from the concrete matrix. The higher their values, the higher the load carrying capacity. The solidified healing agent will be debonded from the concrete matrix when the interfacial cohesive properties are less than 25% of the fracture properties of the solidified healing agent. Full article

Time-series analysis is a widely used method for studying past data to make future predictions. This paper focuses on utilizing time-series analysis techniques to forecast the resource needs of logistics delivery companies, enabling them to meet their objectives and ensure sustained growth. The study aims to build a model that optimizes the prediction of order volume during specific time periods and determines the staffing requirements for the company. The prediction of order volume in logistics companies involves analyzing trend and seasonality components in the data. Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) are well-established and effective in capturing these patterns, providing interpretable results. Deep-learning algorithms require more data for training, which may be limited in certain logistics scenarios. In such cases, traditional models like SARIMAX, ARIMA, and AR can still deliver reliable predictions with fewer data points. Deep-learning models like LSTM can capture complex patterns but lack interpretability, which is crucial in the logistics industry. Balancing performance and practicality, our study combined SARIMAX, ARIMA, AR, and Long Short-Term Memory (LSTM) models to provide a comprehensive analysis and insights into predicting order volume in logistics companies. A real dataset from an international shipping company, consisting of the number of orders during specific time periods, was used to generate a comprehensive time-series dataset. Additionally, new features such as holidays, off days, and sales seasons were incorporated into the dataset to assess their impact on order forecasting and workforce demands. The paper compares the performance of the four different time-series analysis methods in predicting order trends for three countries: United Arab Emirates (UAE), Kingdom of Saudi Arabia (KSA), and Kuwait (KWT), as well as across all countries. By analyzing the data and applying the SARIMAX, ARIMA, LSTM, and AR models to predict future order volume and trends, it was found that the SARIMAX model outperformed the other methods. The SARIMAX model demonstrated superior accuracy in predicting order volumes and trends in the UAE (MAPE: 0.097, RMSE: 0.134), KSA (MAPE: 0.158, RMSE: 0.199), and KWT (MAPE: 0.137, RMSE: 0.215). Full article

Since its introduction, researching malware has had two main goals. On the one hand, malware writers have been focused on developing software that can cause more damage to a targeted host for as long as possible. On the other hand, malware analysts have as one of their main purposes the development of tools such as malware detection systems (MDS) or network intrusion detection systems (NIDS) to prevent and detect possible threats to the informatic systems. Obfuscation techniques, such as the encryption of the virus’s code lines, have been developed to avoid their detection. In contrast, shallow machine learning and deep learning algorithms have recently been introduced to detect them. This paper is devoted to some theoretical implications derived from these investigations. We prove that hidden algebraic structures as equipped posets and their categories of representations are behind the research of some infections. Properties of these categories are given to provide a better understanding of different infection techniques. Full article

In a nuclear power plant (NPP), the used tools are visually inspected to ensure their integrity before and after their use in the nuclear reactor. The manual inspection is usually performed by qualified technicians and takes a large amount of time (weeks up to months). In this work, we propose an automated tool inspection that uses a classification model for anomaly detection. The deep learning model classifies the computed tomography (CT) images as defective (with missing components) or defect-free. Moreover, the proposed algorithm enables incremental learning (IL) using a proposed thresholding technique to ensure a high prediction confidence by continuous online training of the deployed online anomaly detection model. The proposed algorithm is tested with existing state-of-the-art IL methods showing that it helps the model quickly learn the anomaly patterns. In addition, it enhances the classification model confidence while preserving a desired minimal performance. Full article

This paper is devoted to the comparison of discrete velocity models used for simulation of compressible flows with arbitrary specific heat ratios in the lattice Boltzmann method. The stability of the governing equations is analyzed for the steady flow regime. A technique for the construction of stability domains in parametric space based on the analysis of eigenvalues is proposed. A comparison of stability domains for different models is performed. It is demonstrated that the maximum value of macrovelocity, which defines instability initiation, is dependent on the values of relaxation time, and plots of this dependence are constructed. For double-distribution-function models, it is demonstrated that the value of the Prantdl number does not seriously affect stability. The off-lattice parametric finite-difference scheme is proposed for the practical realization of the considered kinetic models. The Riemann problems and the problem of Kelvin–Helmholtz instability simulation are numerically solved. It is demonstrated that different models lead to close numerical results. The proposed technique of stability investigation can be used as an effective tool for the theoretical comparison of different kinetic models used in applications of the lattice Boltzmann method. Full article

This paper presents a comprehensive approach for minimizing makespan in the challenging two-stage hybrid flowshop with dedicated machines, a problem known to be strongly NP-hard. This study proposed a constraint programming approach, a novel heuristic based on a priority rule, and Tabu search procedures to tackle this optimization problem. The constraint programming model, implemented using a commercial solver, serves as the exact resolution method, while the heuristic and Tabu search explore approximate solutions simultaneously. The motivation behind this research is the need to address the complexities of scheduling problems in the context of two-stage hybrid flowshop with dedicated machines. This problem presents significant challenges due to its NP-hard nature and the need for efficient optimization techniques. The contribution of this study lies in the development of an integrated approach that combines constraint programming, a novel heuristic, and Tabu search to provide a comprehensive and efficient solution. The proposed constraint programming model offers exact resolution capabilities, while the heuristic and Tabu search provide approximate solutions, offering a balance between accuracy and efficiency. To enhance the search process, the research introduces effective elimination rules, which reduce the search space and simplify the search effort. This approach improves the overall optimization performance and contributes to finding high-quality solutions. The results demonstrate the effectiveness of the proposed approach. The heuristic approach achieves complete success in solving all instances for specific classes, showcasing its practical applicability. Furthermore, the constraint programming model exhibits exceptional efficiency, successfully solving problems with up to $n=500$ jobs. This efficiency is noteworthy compared to instances solved by other exact solution approaches, indicating the scalability and effectiveness of the proposed method. Full article

This paper presents modern methods of mathematical modeling, which are widely used in the development of new inhalation and intranasal drugs, including those necessary for the treatment of socially significant diseases, which include: tuberculosis, bronchial asthma, and mental and behavioral disorders. Based on the conducted studies, it was revealed that the methods of mathematical modeling used in the development of drugs are fragmented, and there is no single approach that would combine the existing methods. The results presented in the work should contribute to the development of a unified multiscale model as a new approach in mathematical modeling that contributes to the accelerated development and introduction to the market of new drugs with high bioavailability and the required therapeutic efficacy. Full article

With the proliferation of the Internet of Things devices and cyber-physical systems, there is a growing demand for highly functional and high-quality software. To address this demand, it is crucial to employ effective software verification methods. The proposed method is based on the use of physical quantities defined by the International System of Units, which have specific physical dimensions. Additionally, a transformation of the physical value orientation introduced by Siano is utilized. To evaluate the effectiveness of this method, specialized software defect models have been developed. These models are based on the statistical characteristics of the open-source C/C++ code used in drone applications. The advantages of the proposed method include early detection of software defects during compile-time, reduced testing duration, cost savings by identifying a significant portion of latent defects, improved software quality by enhancing reliability, robustness, and performance, as well as complementing existing verification techniques by focusing on latent defects based on software characteristics. By implementing this method, significant reductions in testing time and improvements in both reliability and software quality can be achieved. The method aims to detect 90% of incorrect uses of software variables and over 50% of incorrect uses of operations at both compile-time and run-time. Full article

This paper investigates the asymptotic hyperstability of a single-input–single-output closed-loop system whose controlled plant is time-invariant and possesses a strongly strictly positive real transfer function that is subject to internal and external point delays. There are, in general, two controls involved, namely, the internal one that stabilizes the system with linear state feedback independent of the delay sizes and the external one that belongs to an hyperstable class and satisfies a Popov’s-type time integral inequality. Such a class of hyperstable controllers under consideration combines, in general, a regular impulse-free part with an impulsive part. Full article

Path-specific effect analysis is a powerful tool in causal inference. This paper provides a definition of causal counterfactual path-specific importance score for the structural causal model (SCM). Different from existing path-specific effect definitions, which focus on the population level, the score defined in this paper can quantify the impact of a decision variable on an outcome variable along a specific pathway at the individual level. Moreover, the score has many desirable properties, including following the chain rule and being consistent. Finally, this paper presents an algorithm that can leverage these properties and find the k-most important paths with the highest importance scores in a causal graph effectively. Full article

Risk involves identifying several options that the decision-maker can opt for while making a choice either in the direction of risk or reliability. In this approach, risk is defined as the action of the subject which will lead to the loss or guaranteed safety of what has been achieved. As the uncertainty of the external business environment increases for companies, the task of managing risks both individually and as a set of risks becomes more and more relevant. The purpose of this study is to solve the problem of managing multifactorial risks using mathematical methods for determining the optimal risk management trajectories separately for each factor. To determine the optimal risk management trajectories for each factor, a numerical method is used based on the choice of the most effective direction, which is defined as the ratio of risk change to cost change. An information system prototype has been created that can support the management of a set of risks. Approbation of the information system was carried out on an example containing two conceptual risk factors. The proposed prototype builds a three-dimensional risk map by interpolating the risk matrix entered by the risk manager using an additive–multiplicative aggregation procedure, as well as optimal risk management trajectories for all entered risk factors. Full article

New analytical solutions of the heat conduction equation obtained by utilizing a self-similar Ansatz are presented in cylindrical and spherical coordinates. Then, these solutions are reproduced with high accuracy using recent explicit and unconditionally stable finite difference methods. After this, real experimental data from the literature regarding a heated cylinder are reproduced using the explicit numerical methods as well as using Finite Element Methods (FEM) ANSYS workbench. Convection and nonlinear radiation are also considered on the boundary of the cylinder. The verification results showed that the numerical methods have a high accuracy to deal with cylindrical and spherical bodies; also, the comparison of the temperatures for all approaches showed that the explicit methods are more accurate than the commercial software. Full article

We propose two accurate and efficient spectral collocation techniques based on a (novel) domain-splitting strategy to handle a nonlinear fractional system consisting of three ODEs arising in financial modeling and with chaotic behavior. One of the major numerical difficulties in designing traditional spectral methods is in the handling of model problems on a long computational domain, which usually yields to loss of accuracy. One remedy is to split the underlying domain and apply the spectral method locally in each subdomain rather than on the global domain of interest. To treat the chaotic financial system numerically, we use the generalized version of modified Bessel polynomials (GMBPs) in the collocation matrix approaches along with the domain-splitting strategy. Whereas the first matrix collocation scheme is directly applied to the financial model problem, the second one is a combination of the quasilinearization method and the direct first numerical matrix method. In the former approach, we arrive at nonlinear algebraic matrix equations while the resulting systems are linear in the latter method and can be solved more efficiently. A convergence theorem related to GMBPs is proved and an upper bound for the error is derived. Several simulation outcomes are provided to show the utility and applicability of the presented matrix collocation procedures. Full article

We studied obtaining exact solutions to a set of equations related to the SEIR (Susceptible-Exposed-Infectious-Recovered) model of epidemic spread. These solutions may be used to model epidemic waves. We transformed the SEIR model into a differential equation that contained an exponential nonlinearity. This equation was then approximated by a set of differential equations which contained polynomial nonlinearities. We solved several equations from the set using the Simple Equations Method (SEsM). In doing so, we obtained many new exact solutions to the corresponding equations. Several of these solutions can describe the evolution of epidemic waves that affect a small percentage of individuals in the population. Such waves have frequently been observed in the COVID-19 pandemic in recent years. The discussion shows that SEsM is an effective methodology for computing exact solutions to nonlinear differential equations. The exact solutions obtained can help us to understand the evolution of various processes in the modeled systems. In the specific case of the SEIR model, some of the exact solutions can help us to better understand the evolution of the quantities connected to the epidemic waves. Full article

This investigation focuses on the impact of Stefan blowing on the flow of hybrid nanoliquids over a moving slender needle with magnetohydrodynamics (MHD), thermal radiation, and entropy generation. To facilitate analysis, suitable transformations are applied to convert the governing partial differential equations into a set of ordinary differential equations, which are then solved analytically using Homotopy Analysis Method (HAM) in Mathematica. This study investigates how varying the values of Stefan blowing, magnetic field, and thermal radiation parameters impact the profiles of velocity, temperature, and concentration. Additionally, the study analyzes the outcomes of the local skin friction, local Nusselt number, and local Sherwood number. Increasing the magnetic field reduces the velocity profile. The temperature profile is enhanced by a rise in the thermal radiation parameter. Also, the results reveal that an increase in the Stefan blowing number leads to higher profiles of velocity. Full article

We utilize the travelling-wave Ansatz to obtain novel analytical solutions to the linear diffusion–reaction equation. The reaction term is a function of time and space simultaneously, firstly in a Lorentzian form and secondly in a cosine travelling-wave form. The new solutions contain the Heun functions in the first case and the Mathieu functions for the second case, and therefore are highly nontrivial. We use these solutions to test some non-conventional explicit and stable numerical methods against the standard explicit and implicit methods, where in the latter case the algebraic equation system is solved by the preconditioned conjugate gradient and the GMRES solvers. After this verification, we also calculate the transient temperature of a 2D surface subjected to the cooling effect of the wind, which is a function of space and time again. We obtain that the explicit stable methods can reach the accuracy of the implicit solvers in orders of magnitude shorter time. Full article

The dynamic analysis of structures is a computationally intensive procedure that must be considered, in order to make accurate seismic performance assessments in civil and structural engineering applications. To avoid these computationally demanding tasks, simplified methods are often used by engineers in practice, to estimate the behavior of complex structures under dynamic loading. This paper presents an assessment of several machine learning (ML) algorithms, with different characteristics, that aim to predict the dynamic analysis response of multi-story buildings. Large datasets of dynamic response analyses results were generated through standard sampling methods and conventional response spectrum modal analysis procedures. In an effort to obtain the best algorithm performance, an extensive hyper-parameter search was elaborated, followed by the corresponding feature importance. The ML model which exhibited the best performance was deployed in a web application, with the aim of providing predictions of the dynamic responses of multi-story buildings, according to their characteristics. Full article

The emergence of new embedded system technologies, such as IoT, requires the design of new lightweight cryptosystems to meet different hardware restrictions. In this context, the concept of Finite State Machines (FSMs) can offer a robust solution when using cryptosystems based on finite automata, known as FAPKC (Finite Automaton Public Key Cryptosystems), introduced by Renji Tao. These cryptosystems have been proposed as alternatives to traditional public key cryptosystems, such as RSA. They are based on composing two private keys, which are two FSMs ${\mathcal{M}}_1$ and ${\mathcal{M}}_2$ with the property of invertibility with finite delay to obtain the composed FSM $\mathcal{M}={\mathcal{M}}_{1}o{\mathcal{M}}_2$, which is the public key. The invert process (factorizing) is hard to compute. Unfortunately, these cryptosystems have not really been adopted in real-world applications, and this is mainly due to the lack of profound studies on the FAPKC key space and a random generator program. In this paper, we first introduce an efficient algebraic method based on the notion of a testing table to compute the delay of invertibility of an FSM. Then, we carry out a statistical study on the number of invertible FSMs with finite delay by varying the number of states as well as the number of output symbols. This allows us to estimate the landscape of the space of invertible FSMs, which is considered a first step toward the design of a random generator. Full article

This article presents a study related to increasing the performance of distributed computing systems. The essence of fog computing lies in the use of so-called edge devices. These devices are low-power, so they are extremely sensitive to the computational complexity of the methods used. This article is aimed at improving the efficiency of calculations while maintaining an appropriate level of reliability by applying the methods of the Residue Number System (RNS). We are investigating methods for determining the sign of a number in the RNS based on the core function in order to develop a new, fast method. As a result, a fast method for determining the sign of a number based on the Akushsky core function, using approximate calculations, is obtained. Thus, in the course of this article, a study of methods for ensuring reliability in distributed computing is conducted. A fast method for determining the sign of a number in the RNS based on the core function using approximate calculations is also proposed. This result is interesting from the point of view of nebulous calculations, since it allows maintaining high reliability of a distributed system of edge devices with a slight increase in the computational complexity of non-modular operations. Full article

Several methodologies have successfully described the runner blade shape as a set of discrete sections joining the hub and shroud, defined by 3D geometrical forms of considerable complexity. This task requires an appropriate parametric approach for its accurate reconstruction. Among them, piecewise Bernstein polynomials have been used to create parametrizations of twisted runner blades by extracting some cross-sectional hydrofoil profiles from reference CAD data to be approximated by such polynomials. Using the interpolating polynomial coefficients as parameters, more profiles are generated by Lagrangian techniques. The generated profiles are then stacked along the spanwise direction of the blade via transfinite interpolation to obtain a smooth and continuous representation of the reference blade. This versatile approach makes the description of a range of different blade shapes possible within the required accuracy and, furthermore, the design of new blade shapes. However, even though it is possible to redefine new blade shapes using the aforementioned parametrization, a remaining question is whether the parametrized blades are suitable as a replacement for the currently used ones. In order to assess the mechanical feasibility of the new shapes, several stages of analysis are required. In this paper, bearing in mind the standard hydraulic test conditions of the hydrofoil test case of the Norwegian Hydropower Center, we present a structural stress–strain analysis of the reparametrization of a Francis blade, thus showing its adequate computational performance in two model tests. Full article

Studies of phase transition kinetics are important for such supercritical processes as supercritical drying, adsorption, micronization, etc. In supercritical technologies, “organic solvent—CO₂” systems are often formed, the properties of which strongly depend on the system parameters. In this article, the kinetic curves of phase transitions in the “2-propanol—CO₂” system were investigated experimentally and theoretically. Experimental studies were carried out in a 250 mL high-pressure apparatus at temperatures of 313 and 333 K and pressures of 6.3 and 7.8 MPa with and without the addition of alginate porous gel. Theoretical studies were carried out using the mass transfer equation, the Peng-Robinson equation of state, and the Van der Waals mixing rules, with Python being used for the calculations. The mass transfer coefficients and equilibrium concentrations of CO₂ in the liquid phase were determined using the BFGS optimization method. Full article