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Computation, Volume 11, Issue 2 (February 2023) – 28 articles

Cover Story (view full-size image): Coarse-grained (CG) modeling is an established approach of simulating simplified systems to reach greater space and time scales than expensive all-atomic (AA) molecular dynamics (MD) simulations. The development of CG models requires deriving CG interactions that match AA or experimental properties. We proposed a physics-informed machine learning (PIML) framework for CG modeling, applied it to model the SARS-CoV-2 spike glycoprotein, and determined the force-field parameters using a force-matching scheme. With our framework, CGMD validation simulations reach microsecond time scales and are 40,000 times faster than conventional AAMD. The framework achieves improved accuracy compared to traditional iterative approaches, opening avenues in illuminating protein mechanisms and complex interactions. View this paper
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30 pages, 1117 KiB  
Article
Low-Voltage Photovoltaic System Based on a Continuous Input/Output Current Converter
by Carlos Andres Ramos-Paja, Juan David Bastidas-Rodriguez and Andres Julian Saavedra-Montes
Computation 2023, 11(2), 42; https://doi.org/10.3390/computation11020042 - 20 Feb 2023
Cited by 2 | Viewed by 1863
Abstract
Low-voltage photovoltaic systems are being widely used around the world, including their introduction into the power grid. The development of these systems requires the adaptation of several power converters, their static and dynamic modeling, the design of passive elements, and the design of [...] Read more.
Low-voltage photovoltaic systems are being widely used around the world, including their introduction into the power grid. The development of these systems requires the adaptation of several power converters, their static and dynamic modeling, the design of passive elements, and the design of the controller parameters, among other actions. Today, power converters are key elements in the development of photovoltaic systems, and classical power converters such as buck converters produce discontinuous input and output currents, requiring a high input capacitance and impacting the output power quality of these systems. This paper presents a proposal for a low-voltage photovoltaic system that uses a continuous input/output current buck converter, which enhances the operation of the classical buck converter in photovoltaic systems. The methodology describes the proposed photovoltaic system, including the power converter, its detailed operation, and the analysis of its waveforms. Moreover, the methodology includes a mathematical model of the photovoltaic system’s dynamic behavior and the design of a sliding-mode controller for maximum power extraction and perturbation rejection. The photovoltaic system is validated in two ways: first, a comparison with the classical buck converter highlighting the advantages of continuous input/output currents is presented; then, an application example using commercial devices is described in detail. The application example uses a flowchart to design the power converter and the sliding-mode controller, and a circuit simulation confirms the advantages of the continuous input/output current buck converter with its controller. In the circuit simulation, the control strategy is formed by a perturb and observe algorithm that generates the voltage reference for the sliding-mode controller, which guarantees the system stability, tracks the maximum power point, and rejects the double-frequency oscillations generated by an intended microinverter. Full article
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16 pages, 4474 KiB  
Article
Composite Mould Design with Multiphysics FEM Computations Guidance
by Iñaki Garmendia, Haritz Vallejo and Usue Osés
Computation 2023, 11(2), 41; https://doi.org/10.3390/computation11020041 - 17 Feb 2023
Cited by 1 | Viewed by 2015
Abstract
Composite moulds constitute an attractive alternative to classical metallic moulds when used for components fabricated by processes such as Resin Transfer Moulding (RTM). However, there are many factors that have to be accounted for if a correct design of the moulds is sought [...] Read more.
Composite moulds constitute an attractive alternative to classical metallic moulds when used for components fabricated by processes such as Resin Transfer Moulding (RTM). However, there are many factors that have to be accounted for if a correct design of the moulds is sought after. In this paper, the Finite Element Method (FEM) is used to help in the design of the mould. To do so, a thermo-electrical simulation has been performed through MSC-Marc in the preheating phase in order to ensure that the mould is able to be heated, through the Joule’s effect, according to the thermal cycle specified under operating conditions. Mean temperatures of 120 °C and 100 °C are predicted for the lower and upper semi-mould parts, respectively. Additionally, a thermo-electrical-mechanical calculation has been completed with MSC-Marc to calculate the tensile state along the system during the preheating stage. For the filling phase, the filling process itself has been simulated through RTM-Worx. Both the uniform- and non-uniform temperature distribution approaches have been used to assess the resulting effect. It has been found that this piece of software cannot model the temperature dependency of the resin and a numerical trick must have been applied in the second case to overcome it. Results have been found to be very dependent on the approach, the filling time being 73% greater when modelling a non-uniform temperature distribution. The correct behaviour of the mould during the filling stage, as a consequence of the filling pressure, has been also proved with a specific mechanical analysis conducted with MSC-Marc. Finally, the thermo-elastic response of the mould during the curing stage has been numerically assessed. This analysis has been made through MSC-Marc, paying special attention to the curing of the resin and the exothermic reaction that takes place. For the sake of accuracy, a user subroutine to include specific curing laws has been used. Material properties employed are also described in detail following a modified version of the Scott model, with curing properties extracted from experiments. All these detailed calculations have been the cornerstone to designing the composite mould and have also unveiled some capabilities that were missed in the commercial codes employed. Future versions of these commercial codes will have to deal with these weak points but, as a whole, the Finite Element Method is shown to be an appropriate tool for helping in the design of composite moulds. Full article
(This article belongs to the Special Issue Application of Finite Element Methods)
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14 pages, 2202 KiB  
Article
First-Principles Investigation of Electronic and Related Properties of Cubic Magnesium Silicide (Mg2Si)
by Allé Dioum, Yacouba I. Diakité, Yuiry Malozovsky, Blaise Awola Ayirizia, Aboubaker Chedikh Beye and Diola Bagayoko
Computation 2023, 11(2), 40; https://doi.org/10.3390/computation11020040 - 17 Feb 2023
Cited by 2 | Viewed by 1382
Abstract
We present results from ab initio, self-consistent calculations of electronic, transport, and bulk properties of cubic magnesium silicide (Mg2Si). We employed a local density approximation (LDA) potential to perform the computation, following the Bagayoko, Zhao, and Williams (BZW) method, as improved [...] Read more.
We present results from ab initio, self-consistent calculations of electronic, transport, and bulk properties of cubic magnesium silicide (Mg2Si). We employed a local density approximation (LDA) potential to perform the computation, following the Bagayoko, Zhao, and Williams (BZW) method, as improved by Ekuma and Franklin (BZW-EF). The BZW-EF method guarantees the attainment of the ground state as well as the avoidance of over-complete basis sets. The ground state electronic energies, total and partial densities of states, effective masses, and the bulk modulus are investigated. As per the calculated band structures, cubic Mg2Si has an indirect band gap of 0.896 eV, from Γ to X, for the room temperature experimental lattice constant of 6.338 Å. This is in reasonable agreement with the experimental value of 0.8 eV, unlike previous ab initio DFT results of 0.5 eV or less. The predicted zero temperature band gap of 0.965 eV, from Γ to X, is obtained for the computationally determined equilibrium lattice constant of 6.218 Å. The calculated value of the bulk modulus of Mg2Si is 58.58 GPa, in excellent agreement with the experimental value of 57.03 ± 2 GPa. Full article
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13 pages, 2158 KiB  
Article
Modeling and Forecasting of nanoFeCu Treated Sewage Quality Using Recurrent Neural Network (RNN)
by Dingding Cao, MieowKee Chan and SokChoo Ng
Computation 2023, 11(2), 39; https://doi.org/10.3390/computation11020039 - 17 Feb 2023
Cited by 3 | Viewed by 1947
Abstract
Rapid industrialization and population growth cause severe water pollution and increased water demand. The use of FeCu nanoparticles (nanoFeCu) in treating sewage has been proven to be a space-efficient method. The objective of this work is to develop a recurrent neural network (RNN) [...] Read more.
Rapid industrialization and population growth cause severe water pollution and increased water demand. The use of FeCu nanoparticles (nanoFeCu) in treating sewage has been proven to be a space-efficient method. The objective of this work is to develop a recurrent neural network (RNN) model to estimate the performance of immobilized nanoFeCu in sewage treatment, thereby easing the monitoring and forecasting of sewage quality. In this work, sewage data was collected from a local sewage treatment plant. pH, nitrate, nitrite, and ammonia were used as the inputs. One-to-one and three-to-three RNN architectures were developed, optimized, and analyzed. The result showed that the one-to-one model predicted all four inputs with good accuracy, where R2 was found within a range of 0.87 to 0.98. However, the stability of the one-to-one model was not as good as the three-to-three model, as the inputs were chemically and statistically correlated in the later model. The best three-to-three model was developed by a single layer with 10 neurons and an average R2 of 0.91. In conclusion, this research provides data support for designing the neural network prediction model for sewage and provides positive significance for the exploration of smart sewage treatment plants. Full article
(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)
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13 pages, 453 KiB  
Article
Impact of Social Media on Knowledge of the COVID-19 Pandemic on Bangladeshi University Students
by Shanjida Chowdhury, Mahfujur Rahman, Indrajit Ajit Doddanavar, Nurul Mohammad Zayed, Vitalii Nitsenko, Olena Melnykovych and Oksana Holik
Computation 2023, 11(2), 38; https://doi.org/10.3390/computation11020038 - 16 Feb 2023
Cited by 2 | Viewed by 3077
Abstract
This study aimed to examine the role and impact of social media on the knowledge of the COVID-19 pandemic in Bangladesh through disseminating actual changes in health safety, trust and belief of social media’s coverage statistics, isolation, and psychological numbness among students. This [...] Read more.
This study aimed to examine the role and impact of social media on the knowledge of the COVID-19 pandemic in Bangladesh through disseminating actual changes in health safety, trust and belief of social media’s coverage statistics, isolation, and psychological numbness among students. This study used a cross-sectional design in which a quantitative approach was adopted. Data from an online survey were collected in a short period of time during the early stages of COVID-19 to determine the relationship between social media activity and knowledge of the COVID-19 pandemic with accuracy. A total of 189 respondents were interviewed using structured questionnaires during the onset of the COVID-19 outbreak in Bangladeshi university students. Exploratory factor analysis (EFA) and path analysis were performed. Out of 189 respondents, about 80% were aged between 16 and 25 years, of which nearly 60.33% were students. This study explored four factors—knowledge and health safety, trust in social media news, social distancing or quarantine, and psychological effect—using factor analysis. These four factors are also found to be positively associated in path analysis. Validation of the model was assessed, revealing that the path diagram with four latent exogenous variables fit well. Each factor coefficient was treated as a factor loading (β = 0.564 to 0.973). The results suggested that the measurement models using four elements were appropriate. The coefficient of determination was 0.98, indicating that the model provided an adequate explanation. Social media is transforming the dynamics of health issues, providing information and warnings about the adverse effects of COVID-19, having a positive impact on lockdown or quarantine, and promoting psychological wellness. This comprehensive study suggested that social media plays a positive role in enhancing knowledge about COVID-19 and other pandemic circumstances. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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17 pages, 794 KiB  
Article
Benders Decomposition Method on Adjustable Robust Counterpart Optimization Model for Internet Shopping Online Problem
by Diah Chaerani, Shenya Saksmilena, Athaya Zahrani Irmansyah, Elis Hertini, Endang Rusyaman and Erick Paulus
Computation 2023, 11(2), 37; https://doi.org/10.3390/computation11020037 - 16 Feb 2023
Cited by 2 | Viewed by 1501
Abstract
In this paper, the implementation of the Benders decomposition method to solve the Adjustable Robust Counterpart for Internet Shopping Online Problem (ARC-ISOP) is discussed. Since the ARC-ISOP is a mixed-integer linear programming (MILP) model, the discussion begins by identifying the linear variables in [...] Read more.
In this paper, the implementation of the Benders decomposition method to solve the Adjustable Robust Counterpart for Internet Shopping Online Problem (ARC-ISOP) is discussed. Since the ARC-ISOP is a mixed-integer linear programming (MILP) model, the discussion begins by identifying the linear variables in the form of continuous variables and nonlinear variables in the form of integer variables. In terms of Benders decomposition, the ARC-ISOP model can be solved by partitioning them into smaller subproblems (the master problem and inner problem), which makes it easier for computational calculations. Pseudo-codes in Python programming language are presented in this paper. An example case is presented for the ARC-ISOP to determine the optimal total cost (including product price and shipping cost) and delivery time. Numerical simulations were carried out using Python programming language with case studies in the form of five products purchased from six shops. Full article
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26 pages, 3023 KiB  
Article
Mathematical Modeling of SARS-CoV-2 Omicron Wave under Vaccination Effects
by Gilberto González-Parra and Abraham J. Arenas
Computation 2023, 11(2), 36; https://doi.org/10.3390/computation11020036 - 15 Feb 2023
Cited by 10 | Viewed by 1713
Abstract
Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron [...] Read more.
Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron variant has caused a large amount of infected cases in the US and worldwide. The average number of deaths during the Omicron wave toll increased in comparison with previous SARS-CoV-2 waves. We studied the Omicron wave by using a highly nonlinear mathematical model for the COVID-19 pandemic. The novel model includes individuals who are vaccinated and asymptomatic, which influences the dynamics of SARS-CoV-2. Moreover, the model considers the waning of the immunity and efficacy of the vaccine against the Omicron strain. This study uses the facts that the Omicron strain has a higher transmissibility than the previous circulating SARS-CoV-2 strain but is less deadly. Preliminary studies have found that Omicron has a lower case fatality rate compared to previous circulating SARS-CoV-2 strains. The simulation results show that even if the Omicron strain is less deadly it might cause more deaths, hospitalizations and infections. We provide a variety of scenarios that help to obtain insight about the Omicron wave and its consequences. The proposed mathematical model, in conjunction with the simulations, provides an explanation for a large Omicron wave under various conditions related to vaccines and transmissibility. These results provide an awareness that new SARS-CoV-2 variants can cause more deaths even if their fatality rate is lower. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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16 pages, 5136 KiB  
Article
Algorithm for Determining Three Components of the Velocity Vector of Highly Maneuverable Aircraft
by Volodymyr Pavlikov, Eduard Tserne, Oleksii Odokiienko, Nataliia Sydorenko, Maksym Peretiatko, Olha Kosolapova, Ihor Prokofiiev, Andrii Humennyi and Konstantin Belousov
Computation 2023, 11(2), 35; https://doi.org/10.3390/computation11020035 - 15 Feb 2023
Cited by 1 | Viewed by 1504
Abstract
We developed a signal processing algorithm to determine three components of the velocity vector of a highly maneuverable aircraft. We developed an equation of the distance from an aircraft to an underlying surface. This equation describes a general case of random spatial aircraft [...] Read more.
We developed a signal processing algorithm to determine three components of the velocity vector of a highly maneuverable aircraft. We developed an equation of the distance from an aircraft to an underlying surface. This equation describes a general case of random spatial aircraft positions. Particularly, this equation considers distance changes according to an aircraft flight velocity variation. We also determined the relationship between radial velocity measured within the radiation pattern beam, the signal frequency Doppler shift, and the law of the range changing within the irradiated surface area. The models of the emitted and received signals were substantiated. The proposed equation of the received signal assumes that a reflection occurs not from a point object, but from a spatial area of an underlying surface. It fully corresponds to the real interaction process between an electromagnetic field and surface. The considered solution allowed us to synthesize the optimal algorithm to estimate the current range and three components {Vx,Vy,Vz} of the aircraft’s velocity vector V. In accordance with the synthesized algorithm, we propose a radar structural diagram. The developed radar structural diagram consists of three channels for transmitting and receiving signals. This number of channels is necessary to estimate the full set of the velocity and altitude vector components. We studied several aircraft flight trajectories via simulations. We analyzed straight-line uniform flights; flights with changes in yaw, roll, and attack angles; vertical rises; and landings on a glide path and lining up with the correct yaw, pitch, and roll angles. The simulation results confirmed the correctness of the obtained solution. Full article
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11 pages, 5085 KiB  
Article
Performance Analysis of SiGe-Cladded Silicon MMI Coupler in Presence of Stress
by Sneha Kumari, Akhilesh Kumar Pathak, Rahul Kumar Gangwar and Sumanta Gupta
Computation 2023, 11(2), 34; https://doi.org/10.3390/computation11020034 - 14 Feb 2023
Cited by 1 | Viewed by 1397
Abstract
In this study, we demonstrate the influence of operating temperature variation and stress-induced effects on a silicon-on-insulator (SOI)-based multi-mode interference coupler (MMI). Here, SiGe is introduced as the cladding layer to analyze its effect on the optical performance of the MMI coupler. SiGe [...] Read more.
In this study, we demonstrate the influence of operating temperature variation and stress-induced effects on a silicon-on-insulator (SOI)-based multi-mode interference coupler (MMI). Here, SiGe is introduced as the cladding layer to analyze its effect on the optical performance of the MMI coupler. SiGe cladding thickness is varied from 5 nm to 40 nm. Characterization of the MMI coupler for ridge waveguides with both rectangular and trapezoidal sidewall slope angle cross-sections is reviewed in terms of power splitting ratio and birefringence. Stress-induced birefringence as a function of operating temperature and cladding thickness for fundamental mode have been calculated. A trapezoidal waveguide with 40 nm of cladding thickness induces more stress and, therefore, affects birefringence more than a rectangular waveguide of any thickness. Simulation results using the finite element method (FEM) confirmed that operating temperature variation, upper cladding thickness, and its stress effect are significant parameters that drastically modify the performance of an MMI coupler. Full article
(This article belongs to the Special Issue Application of Finite Element Methods)
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15 pages, 1280 KiB  
Article
Analyzing the Passenger Waiting Tolerance during Urban Rail Transit Service Interruption: Using Stated Preference Data in Chongqing, China
by Binbin Li, Zhefan Ye, Jue Li, Siyuan Shao and Chenlu Wang
Computation 2023, 11(2), 33; https://doi.org/10.3390/computation11020033 - 14 Feb 2023
Cited by 2 | Viewed by 1306
Abstract
To reduce traffic congestion and pollution, urban rail transit in China has been in a stage of rapid development in recent years. As a result, rail transit service interruption events are becoming more common, seriously affecting the resilience of the transportation system and [...] Read more.
To reduce traffic congestion and pollution, urban rail transit in China has been in a stage of rapid development in recent years. As a result, rail transit service interruption events are becoming more common, seriously affecting the resilience of the transportation system and user satisfaction. Therefore, determining the changing mechanism of the passenger waiting tolerance, which helps establish a scientific and effective emergency plan, is urgent. First, the variables and levels of the urban rail service interruption scenarios were screened and determined, and the stated preference questionnaire was designed using the orthogonal design method. Further, the data of the waiting tolerance of passengers during service interruptions were obtained through questionnaires. Second, combined with the questionnaire data, an accelerated failure time model that obeys the exponential distribution was constructed. The results indicate that factors such as the service interruption duration, travel distance, bus bridging, information accuracy, attention to operation information, travel frequency and interruption experience affect the waiting tolerance of passengers during service interruptions. Finally, combined with the sensitivity analysis of the key influencing factors, the policy analysis and suggestions are summarized to provide theoretical support for the urban rail operation and management department to capture the passenger waiting tolerance accurately during service interruptions and formulate an efficient, high-quality emergency organization plan. Full article
(This article belongs to the Special Issue Algorithm to Compute Urban Road Network Resilience)
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15 pages, 1870 KiB  
Article
An Improved Multilabel k-Nearest Neighbor Algorithm Based on Value and Weight
by Zhe Wang, Hao Xu, Pan Zhou and Gang Xiao
Computation 2023, 11(2), 32; https://doi.org/10.3390/computation11020032 - 13 Feb 2023
Cited by 3 | Viewed by 2852
Abstract
Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (ML-kNN) algorithm, has poor performance on imbalanced multilabel data. To address [...] Read more.
Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (ML-kNN) algorithm, has poor performance on imbalanced multilabel data. To address this problem, this study proposes an improved ML-kNN algorithm based on value and weight. In this improved algorithm, labels are divided into minority and majority, and different strategies are adopted for different labels. By considering the label of latent information carried by the nearest neighbors, a value calculation method is proposed and used to directly classify majority labels. Additionally, to address the misclassification problem caused by a lack of nearest neighbor information for minority labels, weight calculation is proposed. The proposed weight calculation converts distance information with and without label sets in the nearest neighbors into weights. The experimental results on multilabel datasets from different benchmarks demonstrate the performance of the algorithm, especially for datasets with high imbalance. Different evaluation metrics show that the results are improved by approximately 2–10%. The verified algorithm could be applied to a multilabel classification of various fields involving label imbalance, such as drug molecule identification, building identification, and text categorization. Full article
(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)
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20 pages, 7600 KiB  
Article
Computational Triangulation in Mathematics Teacher Education
by Sergei Abramovich
Computation 2023, 11(2), 31; https://doi.org/10.3390/computation11020031 - 10 Feb 2023
Cited by 2 | Viewed by 1801
Abstract
The paper is written to demonstrate the applicability of the notion of triangulation typically used in social sciences research to computationally enhance the mathematics education of future K-12 teachers. The paper starts with the so-called Brain Teaser used as background for (what is [...] Read more.
The paper is written to demonstrate the applicability of the notion of triangulation typically used in social sciences research to computationally enhance the mathematics education of future K-12 teachers. The paper starts with the so-called Brain Teaser used as background for (what is called in the paper) computational triangulation in the context of four digital tools. Computational problem solving and problem formulating are presented as two sides of the same coin. By revealing the hidden mathematics of Fibonacci numbers included in the Brain Teaser, the paper discusses the role of computational thinking in the use of the well-ordering principle, the generating function method, digital fabrication, difference equations, and continued fractions in the development of computational algorithms. These algorithms eventually lead to a generalized Golden Ratio in the form of a string of numbers independently generated by digital tools used in the paper. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems)
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20 pages, 1664 KiB  
Article
Pricing and Hedging Index Options under Mean-Variance Criteria in Incomplete Markets
by Pornnapat Yamphram, Phiraphat Sutthimat and Udomsak Rakwongwan
Computation 2023, 11(2), 30; https://doi.org/10.3390/computation11020030 - 7 Feb 2023
Viewed by 1914
Abstract
This paper studies the portfolio selection problem where tradable assets are a bank account, and standard put and call options are written on the S&P 500 index in incomplete markets in which there exist bid–ask spreads and finite liquidity. The problem is mathematically [...] Read more.
This paper studies the portfolio selection problem where tradable assets are a bank account, and standard put and call options are written on the S&P 500 index in incomplete markets in which there exist bid–ask spreads and finite liquidity. The problem is mathematically formulated as an optimization problem where the variance of the portfolio is perceived as a risk. The task is to find the portfolio which has a satisfactory return but has the minimum variance. The underlying is modeled by a variance gamma process which can explain the extreme price movement of the asset. We also study how the optimized portfolio changes subject to a user’s views of the future asset price. Moreover, the optimization model is extended for asset pricing and hedging. To illustrate the technique, we compute indifference prices for buying and selling six options namely a European call option, a quadratic option, a sine option, a butterfly spread option, a digital option, and a log option, and propose the hedging portfolios, which are the portfolios one needs to hold to minimize risk from selling or buying such options, for all the options. The sensitivity of the price from modeling parameters is also investigated. Our hedging strategies are decent with the symmetry property of the kernel density estimation of the portfolio payout. The payouts of the hedging portfolios are very close to those of the bought or sold options. The results shown in this study are just illustrations of the techniques. The approach can also be used for other derivatives products with known payoffs in other financial markets. Full article
(This article belongs to the Special Issue Quantitative Finance and Risk Management Research)
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17 pages, 1940 KiB  
Article
Measuring the Recovery Performance of a Portfolio of NPLs
by Alessandra Carleo, Roberto Rocci and Maria Sole Staffa
Computation 2023, 11(2), 29; https://doi.org/10.3390/computation11020029 - 7 Feb 2023
Cited by 3 | Viewed by 2010
Abstract
The objective of the present paper is to propose a new method to measure the recovery performance of a portfolio of non-performing loans (NPLs) in terms of recovery rate and time to liquidate. The fundamental idea is to draw a curve representing the [...] Read more.
The objective of the present paper is to propose a new method to measure the recovery performance of a portfolio of non-performing loans (NPLs) in terms of recovery rate and time to liquidate. The fundamental idea is to draw a curve representing the recovery rates over time, here assumed discretized, for example, in years. In this way, the user can get simultaneously information about recovery rate and time to liquidate of the portfolio. In particular, it is discussed how to estimate such a curve in the presence of right-censored data, e.g., when the NPLs composing the portfolio have been observed in different time periods, with a method based on an algorithm that is usually used in the construction of survival curves. The curves obtained are smoothed with nonparametric statistical learning techniques. The effectiveness of the proposal is shown by applying the method to simulated and real financial data. The latter are about some portfolios of Italian unsecured NPLs taken over by a specialized operator. Full article
(This article belongs to the Special Issue Computational Issues in Insurance and Finance)
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13 pages, 322 KiB  
Article
Nonparametric Estimation of Range Value at Risk
by Suparna Biswas and Rituparna Sen
Computation 2023, 11(2), 28; https://doi.org/10.3390/computation11020028 - 6 Feb 2023
Cited by 3 | Viewed by 1864
Abstract
Range value at risk (RVaR) is a quantile-based risk measure with two parameters. As special examples, the value at risk (VaR) and the expected shortfall (ES), two well-known but competing regulatory risk measures, are both members of the RVaR family. The estimation of [...] Read more.
Range value at risk (RVaR) is a quantile-based risk measure with two parameters. As special examples, the value at risk (VaR) and the expected shortfall (ES), two well-known but competing regulatory risk measures, are both members of the RVaR family. The estimation of RVaR is a critical issue in the financial sector. Several nonparametric RVaR estimators are described here. We examine these estimators’ accuracy in various scenarios using Monte Carlo simulations. Our simulations shed light on how changing p and q with respect to n affects the effectiveness of RVaR estimators that are nonparametric, with n representing the total number of samples. Finally, we perform a backtesting exercise of RVaR based on Acerbi and Szekely’s test. Full article
(This article belongs to the Special Issue Computational Issues in Insurance and Finance)
23 pages, 8506 KiB  
Article
Operation of Gate-Controlled Irrigation System Using HEC-RAS 2D for Spring Flood Hazard Reduction
by Farida Akiyanova, Nurlan Ongdas, Nurlybek Zinabdin, Yergali Karakulov, Adlet Nazhbiyev, Zhanbota Mussagaliyeva and Aksholpan Atalikhova
Computation 2023, 11(2), 27; https://doi.org/10.3390/computation11020027 - 6 Feb 2023
Cited by 4 | Viewed by 2051
Abstract
Flooding events have been negatively affecting the Republic of Kazakhstan, with higher occurrence in flat parts of the country during spring snowmelt in snow-fed rivers. The current project aims to assess the flood hazard reduction capacity of Alva irrigation system, which is located [...] Read more.
Flooding events have been negatively affecting the Republic of Kazakhstan, with higher occurrence in flat parts of the country during spring snowmelt in snow-fed rivers. The current project aims to assess the flood hazard reduction capacity of Alva irrigation system, which is located in the interfluve area of Yesil and Nura Rivers. The assessment is performed by simulating spring floods using HEC-RAS 2D and controlling the gates of the existing system. A digital elevation model of the study domain was generated by integration of Sentinel-1 radar images with the data obtained from bathymetrical survey and aerial photography. Comparison of the simulated inundation area with a remote sensing image of spring flood in April 2019 indicated that the main reason for differences was due to local snowmelt in the study domain. Exclusion of areas flooded by local snowmelt, which were identified using the updated DEM, from comparison increased the model similarity to 70%. Further simulations of different exceedance probability hydrographs enabled classification of the study area according to maximum flood depth and flood duration. Theoretical changes on the dam crest as well as additional gates were proposed to improve the system capacity by flooding agriculturally important areas, which were not flooded during the simulation of the current system. The developed model could be used by local authorities for further development of flood mitigation measures and assessment of different development plans of the irrigation system. Full article
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26 pages, 679 KiB  
Article
A New Extension of the Kumaraswamy Generated Family of Distributions with Applications to Real Data
by Salma Abbas, Mustapha Muhammad, Farrukh Jamal, Christophe Chesneau, Isyaku Muhammad and Mouna Bouchane
Computation 2023, 11(2), 26; https://doi.org/10.3390/computation11020026 - 5 Feb 2023
Cited by 3 | Viewed by 1819
Abstract
In this paper, we develop the new extended Kumaraswamy generated (NEKwG) family of distributions. It aims to improve the modeling capability of the standard Kumaraswamy family by using a one-parameter exponential-logarithmic transformation. Mathematical developments of the NEKwG family are provided, such as the [...] Read more.
In this paper, we develop the new extended Kumaraswamy generated (NEKwG) family of distributions. It aims to improve the modeling capability of the standard Kumaraswamy family by using a one-parameter exponential-logarithmic transformation. Mathematical developments of the NEKwG family are provided, such as the probability density function series representation, moments, information measure, and order statistics, along with asymptotic distribution results. Two special distributions are highlighted and discussed, namely, the new extended Kumaraswamy uniform (NEKwU) and the new extended Kumaraswamy exponential (NEKwE) distributions. They differ in support, but both have the features to generate models that accommodate versatile skewed data and non-monotone failure rates. We employ maximum likelihood, least-squares estimation, and Bayes estimation methods for parameter estimation. The performance of these methods is discussed using simulation studies. Finally, two real data applications are used to show the flexibility and importance of the NEKwU and NEKwE models in practice. Full article
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8 pages, 1876 KiB  
Article
On Volatility Transmission between Gold and Silver Markets: Evidence from A Long-Term Historical Period
by Alexandros Koulis and Constantinos Kyriakopoulos
Computation 2023, 11(2), 25; https://doi.org/10.3390/computation11020025 - 3 Feb 2023
Cited by 3 | Viewed by 1397
Abstract
Several studies estimate the volatility spillover effects between gold and silver returns, but none of them used the implied volatility to evaluate the long-term relationship between these two metal markets. Our paper aims to fill this gap in the existing literature. This paper [...] Read more.
Several studies estimate the volatility spillover effects between gold and silver returns, but none of them used the implied volatility to evaluate the long-term relationship between these two metal markets. Our paper aims to fill this gap in the existing literature. This paper investigates the long-term volatility transmission between gold and silver; by using GARCH and VAR modelling, it finds that the volatility transmission from gold to silver is unidirectional. Volatility strategies using options can be designed to take advantage of this especially in times where the volatility transmission is not captured by the markets. Additionally, the results appear to be useful for gaining better portfolio diversification benefits. Investors, for instance, could use the results of this study for making proper investment decisions during the period of economic down-turns or inflation surges. Full article
(This article belongs to the Special Issue Quantitative Finance and Risk Management Research)
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18 pages, 7783 KiB  
Article
Coarse-Grained Modeling of the SARS-CoV-2 Spike Glycoprotein by Physics-Informed Machine Learning
by David Liang, Ziji Zhang, Miriam Rafailovich, Marcia Simon, Yuefan Deng and Peng Zhang
Computation 2023, 11(2), 24; https://doi.org/10.3390/computation11020024 - 2 Feb 2023
Cited by 2 | Viewed by 2116
Abstract
Coarse-grained (CG) modeling has defined a well-established approach to accessing greater space and time scales inaccessible to the computationally expensive all-atomic (AA) molecular dynamics (MD) simulations. Popular methods of CG follow a bottom-up architecture to match properties of fine-grained or experimental data whose [...] Read more.
Coarse-grained (CG) modeling has defined a well-established approach to accessing greater space and time scales inaccessible to the computationally expensive all-atomic (AA) molecular dynamics (MD) simulations. Popular methods of CG follow a bottom-up architecture to match properties of fine-grained or experimental data whose development is a daunting challenge for requiring the derivation of a new set of parameters in potential calculation. We proposed a novel physics-informed machine learning (PIML) framework for a CG model and applied it, as a verification, for modeling the SARS-CoV-2 spike glycoprotein. The PIML in the proposed framework employs a force-matching scheme with which we determined the force-field parameters. Our PIML framework defines its trainable parameters as the CG force-field parameters and predicts the instantaneous forces on each CG bead, learning the force field parameters to best match the predicted forces with the reference forces. Using the learned interaction parameters, CGMD validation simulations reach the microsecond time scale with stability, at a simulation speed 40,000 times faster than the conventional AAMD. Compared with the traditional iterative approach, our framework matches the AA reference structure with better accuracy. The improved efficiency enhances the timeliness of research and development in producing long-term simulations of SARS-CoV-2 and opens avenues to help illuminate protein mechanisms and predict its environmental changes. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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13 pages, 6475 KiB  
Article
A Novel Computational Model for Traction Performance Characterization of Footwear Outsoles with Horizontal Tread Channels
by Shubham Gupta, Subhodip Chatterjee, Ayush Malviya, Gurpreet Singh and Arnab Chanda
Computation 2023, 11(2), 23; https://doi.org/10.3390/computation11020023 - 2 Feb 2023
Cited by 7 | Viewed by 1579
Abstract
Slips and falls are among the most serious public safety hazards. Adequate friction at the shoe–floor contact is necessary to reduce these risks. In the presence of slippery fluids such as water or oil, the footwear outsole is crucial for ensuring appropriate shoe–floor [...] Read more.
Slips and falls are among the most serious public safety hazards. Adequate friction at the shoe–floor contact is necessary to reduce these risks. In the presence of slippery fluids such as water or oil, the footwear outsole is crucial for ensuring appropriate shoe–floor traction. While the influence of flooring and contaminants on footwear traction has been extensively studied across several outsole surfaces, limited studies have investigated the science of outsole design and how it affects footwear traction performance. In this work, the tread channels of a commonly found outsole pattern, i.e., horizontally oriented treads, was varied parametrically across the widths (i.e., 2, 4, 6 mm) and gaps (i.e., 2, 3, 4 mm). Nine outsole designs were developed and their traction, fluid pressures, and fluid flow rates during slipping were estimated using a mechanical slip testing and a CFD-based computational framework. Outsoles which had wider tread (i.e., 6 mm) surfaces showed increased slip risks on wet flooring. Outsoles with large gaps (i.e., 4 mm) exhibited increased traction performance when slipped on wet flooring (R2 = 0.86). These novel results are anticipated to provide valuable insights into the science of footwear traction and provide important guidelines for the footwear manufacturers to optimize outsole surface design to reduce the risk of slips and falls. In addition to this, the presented CFD-based computational framework could help develop better outsole designs to further solve this problem. Full article
(This article belongs to the Special Issue Application of Finite Element Methods)
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13 pages, 3359 KiB  
Article
Optimized Packing Titanium Alloy Powder Particles
by Zoia Duriagina, Alexander Pankratov, Tetyana Romanova, Igor Litvinchev, Julia Bennell, Igor Lemishka and Sergiy Maximov
Computation 2023, 11(2), 22; https://doi.org/10.3390/computation11020022 - 1 Feb 2023
Cited by 4 | Viewed by 1473
Abstract
To obtain high-quality and durable parts by 3D printing, specific characteristics (porosity and proportion of various sizes of particles) in the mixture used for printing or sintering must be assured. To predict these characteristics, a mathematical model of optimized packing polyhedral objects (particles [...] Read more.
To obtain high-quality and durable parts by 3D printing, specific characteristics (porosity and proportion of various sizes of particles) in the mixture used for printing or sintering must be assured. To predict these characteristics, a mathematical model of optimized packing polyhedral objects (particles of titanium alloys) in a cuboidal container is presented, and a solution algorithm is developed. Numerical experiments demonstrate that the results obtained by the algorithm are very close to experimental findings. This justifies using numerical simulation instead of expensive experimentation. Full article
(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)
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9 pages, 598 KiB  
Article
Stochastic Modeling with Applications in Supply Chain Management and ICT Systems
by Meglena Lazarova and Fatima Sapundzhi
Computation 2023, 11(2), 21; https://doi.org/10.3390/computation11020021 - 31 Jan 2023
Viewed by 4406
Abstract
Fast-growing technology and the development of IT services have yielded the idea of founding a new application of stochastic processes and their properties. We give a new connection between electronic process management and a relatively new stochastic process named the Non-central Polya-Aeppli process. [...] Read more.
Fast-growing technology and the development of IT services have yielded the idea of founding a new application of stochastic processes and their properties. We give a new connection between electronic process management and a relatively new stochastic process named the Non-central Polya-Aeppli process. This process is applied as a counting process in the mathematical construction of the given model, and it has been introduced as a counting process in electronic process management. Full article
(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)
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15 pages, 3413 KiB  
Article
Mobile Application for Tomato Plant Leaf Disease Detection Using a Dense Convolutional Network Architecture
by Intan Nurma Yulita, Naufal Ariful Amri and Akik Hidayat
Computation 2023, 11(2), 20; https://doi.org/10.3390/computation11020020 - 31 Jan 2023
Cited by 7 | Viewed by 3918
Abstract
In Indonesia, tomato is one of the horticultural products with the highest economic value. To maintain enhanced tomato plant production, it is necessary to monitor the growth of tomato plants, particularly the leaves. The quality and quantity of tomato plant production can be [...] Read more.
In Indonesia, tomato is one of the horticultural products with the highest economic value. To maintain enhanced tomato plant production, it is necessary to monitor the growth of tomato plants, particularly the leaves. The quality and quantity of tomato plant production can be preserved with the aid of computer technology. It can identify diseases in tomato plant leaves. An algorithm for deep learning with a DenseNet architecture was implemented in this study. Multiple hyperparameter tests were conducted to determine the optimal model. Using two hidden layers, a DenseNet trainable layer on dense block 5, and a dropout rate of 0.4, the optimal model was constructed. The 10-fold cross-validation evaluation of the model yielded an accuracy value of 95.7 percent and an F1-score of 95.4 percent. To recognize tomato plant leaves, the model with the best assessment results was implemented in a mobile application. Full article
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15 pages, 6881 KiB  
Article
Numerical Assessment of Terrain Relief Influence on Consequences for Humans Exposed to Gas Explosion Overpressure
by Yurii Skob, Sergiy Yakovlev, Kyryl Korobchynskyi and Mykola Kalinichenko
Computation 2023, 11(2), 19; https://doi.org/10.3390/computation11020019 - 30 Jan 2023
Cited by 2 | Viewed by 1408
Abstract
This study aims to reconstruct hazardous zones after the hydrogen explosion at a fueling station and to assess an influence of terrain landscape on harmful consequences for personnel with the use of numerical methods. These consequences are measured by fields of conditional probability [...] Read more.
This study aims to reconstruct hazardous zones after the hydrogen explosion at a fueling station and to assess an influence of terrain landscape on harmful consequences for personnel with the use of numerical methods. These consequences are measured by fields of conditional probability of lethal and ear-drum injuries for people exposed to explosion waves. An “Explosion Safety®” numerical tool is applied for non-stationary and three-dimensional reconstructions of the hazardous zone around the epicenter of the explosion of a premixed stoichiometric hemispheric hydrogen cloud. In order to define values of the explosion wave’s damaging factors (maximum overpressure and impulse of pressure phase), a three-dimensional mathematical model of chemically active gas mixture dynamics is used. This allows for controlling the current pressure in every local point of actual space, taking into account the complex terrain. This information is used locally in every computational cell to evaluate the conditional probability of such consequences for human beings, such as ear-drum rupture and lethal outcome, on the basis of probit analysis. To evaluate the influence of the landscape profile on the non-stationary three-dimensional overpressure distribution above the Earth’s surface near the epicenter of an accidental hydrogen explosion, a series of computational experiments with different variants of the terrain is carried out. Each variant differs in the level of mutual arrangement of the explosion epicenter and the places of possible location of personnel. The obtained results indicate that any change in working-place level of terrain related to the explosion’s epicenter can better protect personnel from the explosion wave than evenly leveled terrain, and deepening of the explosion epicenter level related to working place level leads to better personnel protection than vice versa. Moreover, the presented coupled computational fluid dynamics and probit analysis model can be recommended to risk-managing experts as a cost-effective and time-saving instrument to assess the efficiency of protection structures during safety procedures. Full article
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22 pages, 1181 KiB  
Review
Dental Age Estimation Using Deep Learning: A Comparative Survey
by Essraa Gamal Mohamed, Rebeca P. Díaz Redondo, Abdelrahim Koura, Mohamed Sherif EL-Mofty and Mohammed Kayed
Computation 2023, 11(2), 18; https://doi.org/10.3390/computation11020018 - 29 Jan 2023
Cited by 6 | Viewed by 5144
Abstract
The significance of age estimation arises from its applications in various fields, such as forensics, criminal investigation, and illegal immigration. Due to the increased importance of age estimation, this area of study requires more investigation and development. Several methods for age estimation using [...] Read more.
The significance of age estimation arises from its applications in various fields, such as forensics, criminal investigation, and illegal immigration. Due to the increased importance of age estimation, this area of study requires more investigation and development. Several methods for age estimation using biometrics traits, such as the face, teeth, bones, and voice. Among then, teeth are quite convenient since they are resistant and durable and are subject to several changes from childhood to birth that can be used to derive age. In this paper, we summarize the common biometrics traits for age estimation and how this information has been used in previous research studies for age estimation. We have paid special attention to traditional machine learning methods and deep learning approaches used for dental age estimation. Thus, we summarized the advances in convolutional neural network (CNN) models to estimate dental age from radiological images, such as 3D cone-beam computed tomography (CBCT), X-ray, and orthopantomography (OPG) to estimate dental age. Finally, we also point out the main innovations that would potentially increase the performance of age estimation systems. Full article
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7 pages, 194 KiB  
Editorial
Acknowledgment to the Reviewers of Computation in 2022
by Computation Editorial Office
Computation 2023, 11(2), 17; https://doi.org/10.3390/computation11020017 - 20 Jan 2023
Viewed by 1271
Abstract
High-quality academic publishing is built on rigorous peer review [...] Full article
14 pages, 533 KiB  
Article
Efficient Data-Driven Machine Learning Models for Water Quality Prediction
by Elias Dritsas and Maria Trigka
Computation 2023, 11(2), 16; https://doi.org/10.3390/computation11020016 - 18 Jan 2023
Cited by 16 | Viewed by 3863
Abstract
Water is a valuable, necessary and unfortunately rare commodity in both developing and developed countries all over the world. It is undoubtedly the most important natural resource on the planet and constitutes an essential nutrient for human health. Geo-environmental pollution can be caused [...] Read more.
Water is a valuable, necessary and unfortunately rare commodity in both developing and developed countries all over the world. It is undoubtedly the most important natural resource on the planet and constitutes an essential nutrient for human health. Geo-environmental pollution can be caused by many different types of waste, such as municipal solid, industrial, agricultural (e.g., pesticides and fertilisers), medical, etc., making the water unsuitable for use by any living being. Therefore, finding efficient methods to automate checking of water suitability is of great importance. In the context of this research work, we leveraged a supervised learning approach in order to design as accurate as possible predictive models from a labelled training dataset for the identification of water suitability, either for consumption or other uses. We assume a set of physiochemical and microbiological parameters as input features that help represent the water’s status and determine its suitability class (namely safe or nonsafe). From a methodological perspective, the problem is treated as a binary classification task, and the machine learning models’ performance (such as Naive Bayes–NB, Logistic Regression–LR, k Nearest Neighbours–kNN, tree-based classifiers and ensemble techniques) is evaluated with and without the application of class balancing (i.e., use or nonuse of Synthetic Minority Oversampling Technique–SMOTE), comparing them in terms of Accuracy, Recall, Precision and Area Under the Curve (AUC). In our demonstration, results show that the Stacking classification model after SMOTE with 10-fold cross-validation outperforms the others with an Accuracy and Recall of 98.1%, Precision of 100% and an AUC equal to 99.9%. In conclusion, in this article, a framework is presented that can support the researchers’ efforts toward water quality prediction using machine learning (ML). Full article
(This article belongs to the Special Issue Modeling Study of Hydrodynamic Environmental Impact)
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19 pages, 446 KiB  
Article
On the Influence of Initial Stresses on the Velocity of Elastic Waves in Composites
by Alexander G. Kolpakov, Igor V. Andrianov and Sergey I. Rakin
Computation 2023, 11(2), 15; https://doi.org/10.3390/computation11020015 - 17 Jan 2023
Cited by 1 | Viewed by 1231
Abstract
The paper is devoted to the problem of propagation of elastic waves in composites with initial stresses. We suppose initial stresses are well within the elastic regime. We deal with the long-wave case and use the asymptotic homogenization technique based on the two-scale [...] Read more.
The paper is devoted to the problem of propagation of elastic waves in composites with initial stresses. We suppose initial stresses are well within the elastic regime. We deal with the long-wave case and use the asymptotic homogenization technique based on the two-scale asymptotic approach. The main problem lies in solving the local (cell) problem, i.e., boundary value problem on a periodically repeating fragment of a composite. In general, the local problem cannot be solved explicitly. In our work, it is obtained for any initial stresses formulas, which is convenient for solving by standard codes. An analytical solution is obtained for small initial stresses. Asymptotic expansions used a small parameter characterizing the smallness of the initial stresses. In the zero approximation, composites without initial stresses are considered; the first approximation takes into account their influence on waves propagation. Two particular cases are considered in detail: laminated media and frame (honeycomb cell) composites. The analyzed frame composite can be used for the modeling of porous media. We select these two cases for the following reasons. First, the laminated and porous material are widely used in practice. Second, for these materials, the homogenized coefficients may be computed in the explicit form for an arbitrary value of the initial stresses. The dependence of the velocity of elastic waves on the initial stresses in laminated and homogeneous bodies differs. The initial tension increases the velocity of elastic waves in both cases, but the quantitative effect of the increase can vary greatly. For frame composites modeling porous bodies, the initial tension can increase or decrease the velocity of elastic waves (the initial tension decreases the velocity of elastic waves in the porous body with an inverted honeycomb periodicity cell). The decrease of the velocity of elastic waves is impossible in homogeneous media. The problem under consideration is related, in particular, to the core sample analysis in the geophysics. This question is discussed in the paper. We also analyzed some features of applications of asymptotic homogenization procedure for the dynamical problem of stressed composite materials, i.e., the nonadditivity of homogenization of sum of operators. Full article
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