Computation

17 pages, 663 KiB

Open AccessArticle

Aggregating Composite Indicators through the Geometric Mean: A Penalization Approach

by Francesca Mariani and Mariateresa Ciommi

Computation 2022, 10(4), 64; https://doi.org/10.3390/computation10040064 - 18 Apr 2022

Cited by 5 | Viewed by 2884

In this paper, we introduce a penalized version of the geometric mean. In analogy with the Mazziotta Pareto Index, this composite indicator is derived as a product between the geometric mean and a penalization term to account for the unbalance among indicators. The [...] Read more.

In this paper, we introduce a penalized version of the geometric mean. In analogy with the Mazziotta Pareto Index, this composite indicator is derived as a product between the geometric mean and a penalization term to account for the unbalance among indicators. The unbalance is measured in terms of the (horizontal) variability of the normalized indicators opportunely scaled and transformed via the Box–Cox function of order zero. The penalized geometric mean is used to compute the penalized Human Development Index (HDI), and a comparison with the geometric mean approach is presented. Data come from the Human Development Data Center for 2019 and refer to the classical three dimensions of HDI. The results show that the new method does not upset the original ranking produced by the HDI but it impacts more on countries with poor performances. The paper has the merit of proposing a new reading of the Mazziotta Pareto Index in terms of the reliability of the arithmetic mean as well as of generalizing this reading to the geometric mean approach. Full article

(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation)

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12 pages, 4204 KiB

Open AccessArticle

Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images

by Tobias Leidemer, Orou Berme Herve Gonroudobou, Ha Trang Nguyen, Chiara Ferracini, Benjamin Burkhard, Yago Diez and Maximo Larry Lopez Caceres

Computation 2022, 10(4), 63; https://doi.org/10.3390/computation10040063 - 18 Apr 2022

Cited by 4 | Viewed by 2411

Abstract

Bark beetle outbreaks are responsible for the loss of large areas of forests and in recent years they appear to be increasing in frequency and magnitude as a result of climate change. The aim of this study is to develop a new standardized [...] Read more.

Bark beetle outbreaks are responsible for the loss of large areas of forests and in recent years they appear to be increasing in frequency and magnitude as a result of climate change. The aim of this study is to develop a new standardized methodology for the automatic detection of the degree of damage on single fir trees caused by bark beetle attacks using a simple GIS-based model. The classification approach is based on the degree of tree canopy defoliation observed (white pixels) in the UAV-acquired very high resolution RGB orthophotos. We defined six degrees (categories) of damage (healthy, four infested levels and dead) based on the ratio of white pixel to the total number of pixels of a given tree canopy. Category 1: <2.5% (no defoliation); Category 2: 2.5–10% (very low defoliation); Category 3: 10–25% (low defoliation); Category 4: 25–50% (medium defoliation); Category 5: 50–75% (high defoliation), and finally Category 6: >75% (dead). The definition of “white pixel” is crucial, since light conditions during image acquisition drastically affect pixel values. Thus, whiteness was defined as the ratio of red pixel value to the blue pixel value of every single pixel in relation to the ratio of the mean red and mean blue value of the whole orthomosaic. The results show that in an area of 4 ha, out of the 1376 trees, 277 were healthy, 948 were infested (Cat 2, 628; Cat 3, 244; Cat 4, 64; Cat 5, 12), and 151 were dead (Cat 6). The validation led to an average precision of 62%, with Cat 1 and Cat 6 reaching a precision of 73% and 94%, respectively. Full article

(This article belongs to the Special Issue Computation and Analysis of Remote Sensing Imagery and Image Motion)

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31 pages, 20055 KiB

Open AccessArticle

Modeling the Territorial Structure Dynamics of the Northern Part of the Volga-Akhtuba Floodplain

by Inessa I. Isaeva, Alexander A. Voronin, Alexander V. Khoperskov and Mikhail A. Kharitonov

Computation 2022, 10(4), 62; https://doi.org/10.3390/computation10040062 - 11 Apr 2022

Cited by 8 | Viewed by 2411

Abstract

The subject of our study is the tendency to reduce the floodplain area of regulated rivers and its impact on the degradation of the socio-environmental systems in the floodplain. The aim of the work is to create a new approach to the analysis [...] Read more.

The subject of our study is the tendency to reduce the floodplain area of regulated rivers and its impact on the degradation of the socio-environmental systems in the floodplain. The aim of the work is to create a new approach to the analysis and forecasting of the multidimensional degradation processes of floodplain territories under the influence of natural and technogenic factors. This approach uses methods of hydrodynamic and geoinformation modeling, statistical analysis of observational data and results of high-performance computational experiments. The basis of our approach is the dynamics model of the complex structure of the floodplain. This structure combines the characteristics of the frequency ranges of flooding and the socio-environmental features of various sites (cadastral data of land use). Modeling of the hydrological regime is based on numerical shallow water models. The regression model of the technogenic dynamics of the riverbed allowed us to calculate corrections to the parameters of real floods that imitate the effect of this factor. This made it possible to use digital maps of the modern topography for hydrodynamic modeling and the construction of floods maps for past and future decades. The technological basis of our study is a set of algorithms and software, consisting of three modules. The data module includes, first of all, the cadastres of the territory of the Volga-Akhtuba floodplain (VAF, this floodplain is the interfluve of the Volga and Akhtuba rivers for the last 400 km before flowing into the Caspian Sea), satellite and natural observation data, spatial distributions of parameters of geoinformation and hydrodynamic models. The second module provides the construction of a multilayer digital model of the floodplain area, digital maps of floods and their aggregated characteristics. The third module calculates a complex territorial structure, criteria for the state of the environmental and socio-economic system (ESES) and a forecast of its changes. We have shown that the degradation of the ESES of the northern part of the VAF is caused by the negative dynamics of the hydrological structure of its territory, due to the technogenic influence the hydroelectric power station on the Volga riverbed. This dynamic manifests itself in a decrease in the stable flooded area and an increase in the unflooded and unstable flooded areas. An important result is the forecast of the complex territorial structure and criteria for the state of the interfluve until 2050. Full article

(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation)

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28 pages, 1611 KiB

Open AccessArticle

Co-Design of the Control and Power Stages of a Boost-Based Rectifier with Power Factor Correction Depending on Performance Criteria

by Carlos Andres Ramos-Paja, Andres Julian Saavedra-Montes and Juan David Bastidas-Rodriguez

Computation 2022, 10(4), 61; https://doi.org/10.3390/computation10040061 - 11 Apr 2022

Cited by 5 | Viewed by 3076

Abstract

Rectifiers with power factor correction are key devices to supply DC loads from AC sources, guaranteeing a power factor close to one and low total harmonic distortion. Boost-based power factor correction rectifiers are the most widely used topology and they are formed by [...] Read more.

Rectifiers with power factor correction are key devices to supply DC loads from AC sources, guaranteeing a power factor close to one and low total harmonic distortion. Boost-based power factor correction rectifiers are the most widely used topology and they are formed by a power stage (diode bridge and Boost converter) and a control system. However, there is a relevant control problem, because controllers are designed with linearized models of the converters for a specific operating point; consequently, the required dynamic performance and stability of the whole system for different operating points are not guaranteed. Another weak and common practice is to design the power and control stages independently. This paper proposes a co-design procedure for both the power stage and the control system of a Boost-based PFC rectifier, which is focused on guaranteeing the system’s stability in any operating conditions. Moreover, the design procedure assures a maximum switching frequency and the fulfillment of different design requirements for the output voltage: maximum overshoot and settling time before load disturbances, maximum ripple, and the desired damping ratio. The proposed control has a cascade structure, where the inner loop is a sliding-mode controller (SMC) to track the inductor current reference, and the outer loop is an adaptive PI regulator of the output voltage, which manipulates the amplitude of the inductor current reference. The paper includes the stability analysis of the SMC, the design procedure of the inductor to guarantee the system stability, and the design of the adaptive PI controller parameters and the capacitor to achieve the desired dynamic performance of the output voltage. The proposed rectifier is simulated in PSIM and the results validate the co-design procedures and show that the proposed system is stable for any operating conditions and satisfies the design requirements. Full article

(This article belongs to the Section Computational Engineering)

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21 pages, 4806 KiB

Open AccessArticle

Natural Convection Flow over a Vertical Permeable Circular Cone with Uniform Surface Heat Flux in Temperature-Dependent Viscosity with Three-Fold Solutions within the Boundary Layer

by Md Farhad Hasan, Md. Mamun Molla, Md. Kamrujjaman and Sadia Siddiqa

Computation 2022, 10(4), 60; https://doi.org/10.3390/computation10040060 - 09 Apr 2022

Cited by 9 | Viewed by 2096

Abstract

The aim of this study is to investigate the effects of temperature-dependent viscosity on the natural convection flow from a vertical permeable circular cone with uniform heat flux. As part of numerical computation, the governing boundary layer equations are transformed into a non-dimensional [...] Read more.

The aim of this study is to investigate the effects of temperature-dependent viscosity on the natural convection flow from a vertical permeable circular cone with uniform heat flux. As part of numerical computation, the governing boundary layer equations are transformed into a non-dimensional form. The resulting nonlinear system of partial differential equations is then reduced to local non-similarity equations which are solved computationally by three different solution methodologies, namely, (i) perturbation solution for small transpiration parameter (ξ), (ii) asymptotic solution for large ξ, and (iii) the implicit finite difference method together with a Keller box scheme for all ξ. The numerical results of the velocity and viscosity profiles of the fluid are displayed graphically with heat transfer characteristics. The shearing stress in terms of the local skin-friction coefficient and the rate of heat transfer in terms of the local Nusselt number (Nu) are given in tabular form for the viscosity parameter (ε) and the Prandtl number (Pr). The viscosity is a linear function of temperature which is valid for small Prandtl numbers (Pr). The three-fold solutions were compared as part of the validations with various ranges of Pr numbers. Overall, good agreements were established. The major finding of the research provides a better demonstration of how temperature-dependent viscosity affects the natural convective flow. It was found that increasing Pr, ξ, and ε decrease the local skin-friction coefficient, but ξ has more influence on increasing the rate of heat transfer, as the effect of ε was erratic at small and large ξ. Furthermore, at the variable Pr, a large ξ increased the local maxima of viscosity at large extents, particularly at low Pr, but the effect on temperature distribution was found to be less significant under the same condition. However, at variable ε and fixed Pr, the temperature distribution was observed to be more influenced by ε at small ξ, whereas large ξ dominated this scheme significantly regardless of the variation in ε. The validations through three-fold solutions act as evidence of the accuracy and versatility of the current approach. Full article

(This article belongs to the Special Issue Applications of Computational Mathematics to Simulation and Data Analysis)

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17 pages, 24788 KiB

Open AccessArticle

An Alternative Methodology to Compute the Geometric Tortuosity in 2D Porous Media Using the A-Star Pathfinding Algorithm

by Mayken Espinoza-Andaluz, Javier Pagalo, Joseph Ávila and Julio Barzola-Monteses

Computation 2022, 10(4), 59; https://doi.org/10.3390/computation10040059 - 02 Apr 2022

Cited by 4 | Viewed by 3702

Abstract

Geometric tortuosity is an essential characteristic to consider when studying a porous medium’s morphology. Knowing the material’s tortuosity allows us to understand and estimate the different diffusion transport properties of the analyzed material. Geometric tortuosity is useful to compute parameters, such as the [...] Read more.

Geometric tortuosity is an essential characteristic to consider when studying a porous medium’s morphology. Knowing the material’s tortuosity allows us to understand and estimate the different diffusion transport properties of the analyzed material. Geometric tortuosity is useful to compute parameters, such as the effective diffusion coefficient, inertial factor, and diffusibility, which are commonly found in porous media materials. This study proposes an alternative method to estimate the geometric tortuosity of digitally created two-dimensional porous media. The porous microstructure is generated by using the PoreSpy library of Python and converted to a binary matrix for the computation of the parameters involved in this work. As a first step, porous media are digitally generated with porosity values from 0.5 to 0.9; then, the geometric tortuosity is determined using the A-star algorithm. This approach, commonly used in pathfinding problems, improves the use of computational resources and complies with the theory found in the literature. Based on the obtained results, the best geometric tortuosity–porosity correlations are proposed. The selection of the best correlation considers the coefficient of determination value (99.7%) with a confidence interval of 95%. Full article

(This article belongs to the Section Computational Engineering)

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9 pages, 906 KiB

Open AccessArticle

Understanding the Complex Impacts of Seatbelt Use on Crash Outcomes

by Mahdi Rezapour and Khaled Ksaibati

Computation 2022, 10(4), 58; https://doi.org/10.3390/computation10040058 - 01 Apr 2022

Cited by 1 | Viewed by 1865

Abstract

Despite the importance of seatbelt use in the reduction of injuries and fatalities, the majority of past studies failed to account for the complex nature of seatbelts on the safety of roadways. The complexity of seatbelt use is especially related to a possible [...] Read more.

Despite the importance of seatbelt use in the reduction of injuries and fatalities, the majority of past studies failed to account for the complex nature of seatbelts on the safety of roadways. The complexity of seatbelt use is especially related to a possible association between seatbelt use and other factors at the time of crashes. Ignoring those interaction terms might result in unrealistic or biased point estimates regarding the underlying impact of seatbelt use on roadway safety. For instance, is the impact of seatbelt use on the severity of crashes stable or varies based on other factors such as gender? Or does the impact of seatbelt use changes based on whether a driver is under the influence of alcohol or not? The mixed logit model was used to model the severity of crashes. In this study we focused on interaction terms between seatbelt use and all other plausible predictors of crashes. The results highlighted that there are important and significant interaction terms between seatbelt status and other predictors such as drivers under the influence (DUI), drivers with invalid driver’s licenses, lack of attention in crashes, having a citation record, ejected drivers, and other environmental and roadway characteristics. For instance, it was found that the impact of seatbelt use on the severity of crashes varies based on the actions that drivers took before crashes, such as improper lane changes or following too close. On the other hand, seatbelt use is more effective in crash severity reduction for ejected drivers and less effective for drivers under the influence of alcohol or unattended drivers. The results provide important information to gain a better understanding regarding the effectiveness of seatbelt use. Full article

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12 pages, 570 KiB

Open AccessArticle

Applying Machine Learning Methods and Models to Explore the Structure of Traffic Accident Data

by Anton Sysoev, Vladimir Klyavin, Alexandra Dvurechenskaya, Albert Mamedov and Vladislav Shushunov

Computation 2022, 10(4), 57; https://doi.org/10.3390/computation10040057 - 31 Mar 2022

Viewed by 2268

Abstract

The problem of reducing the increasing number of road traffic accidents has become more relevant in recent years. According to the United Nations plan this number has to be halved by 2030. A very effective way to handle it is to apply the [...] Read more.

The problem of reducing the increasing number of road traffic accidents has become more relevant in recent years. According to the United Nations plan this number has to be halved by 2030. A very effective way to handle it is to apply the machine learning paradigm to retrospective road traffic accident datasets. This case study applies machine learning techniques to form typical “portraits” of drivers violating road traffic rules by clustering available data into seven homogeneous groups. The obtained results can be used in forming effective marketing campaigns for different target groups. Another relevant problem under consideration is to use available retrospective statistics on mechanical road traffic accidents without victims to estimate the probable type of road traffic accident for the driver taking into account her/his personal features (such as social characteristics, typical road traffic rule violations, driving experience, and age group). For this purpose several machine learning models were applied and the results were discussed. Full article

(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation)

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3 pages, 707 KiB

Open AccessEditorial

Computation 2020 Best Paper Awards

by Computation Editorial Office

Computation 2022, 10(4), 56; https://doi.org/10.3390/computation10040056 - 31 Mar 2022

Viewed by 2198

Abstract

Computation is instituting the Best Paper Awards to recognize outstanding papers published in the journal [...] Full article

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24 pages, 3778 KiB

Open AccessArticle

Evaluation of the Leak Detection Performance of Distributed Kalman Filter Algorithms in WSN-Based Water Pipeline Monitoring of Plastic Pipes

by Valery Nkemeni, Fabien Mieyeville and Pierre Tsafack

Computation 2022, 10(4), 55; https://doi.org/10.3390/computation10040055 - 30 Mar 2022

Cited by 2 | Viewed by 2157

Abstract

Water is a basic necessity and one of the most valuable resources for human living. Sadly, large quantities of treated water get lost daily worldwide, especially in developing countries, through leaks in the water distribution network. Wireless sensor network-based water pipeline monitoring (WWPM) [...] Read more.

Water is a basic necessity and one of the most valuable resources for human living. Sadly, large quantities of treated water get lost daily worldwide, especially in developing countries, through leaks in the water distribution network. Wireless sensor network-based water pipeline monitoring (WWPM) systems using low-cost micro-electro-mechanical systems (MEMS) accelerometers have become popular for real-time leak detection due to their low-cost and low power consumption, but they are plagued with high false alarm rates. Recently, the distributed Kalman filter (DKF) has been shown to improve the leak detection reliability of WWPM systems using low-cost MEMS accelerometers. However, the question of which DKF is optimal in terms of leak detection reliability and energy consumption is still unanswered. This study evaluates and compares the leak detection reliability of three DKF algorithms, selected from distributed data fusion strategies based on diffusion, gossip and consensus. In this study, we used a combined approach involving simulations and laboratory experiments. The performance metrics used for the comparison include sensitivity, specificity and accuracy. The laboratory results revealed that the event-triggered diffusion-based DKF is optimal, having a sensitivity value of 61%, a specificity value of 93%, and an accuracy of 90%. It also has a lower communication burden and is less affected by packet loss, making it more responsive to real-time leak detection. Full article

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22 pages, 9423 KiB

Open AccessArticle

Shape Optimization of a Shell in Comsol Multiphysics

by Evgenia Ermakova, Timur Elberdov and Marina Rynkovskaya

Computation 2022, 10(4), 54; https://doi.org/10.3390/computation10040054 - 30 Mar 2022

Cited by 3 | Viewed by 4611

Abstract

Optimization calculations are currently an actual and in-demand direction of computer-aided design. It allows not only the identification of the future characteristics of an object, but also the implementation of its exact model using a set of various optimization algorithms. The advent of [...] Read more.

Optimization calculations are currently an actual and in-demand direction of computer-aided design. It allows not only the identification of the future characteristics of an object, but also the implementation of its exact model using a set of various optimization algorithms. The advent of digital modeling has significantly facilitated the approach to optimization and its methods. Many software systems are equipped with capabilities not only for calculating the design, but also for finding its optimal variant. The calculation programs can include a special optimization module that can be based on one or more mathematical methods. The purpose of the present study is to explore a process of shape optimization through the calculation of two shells: the simple one (spherical dome) and complex one (helicoid) in Comsol Multiphysics using three optimization methods: MMA, SNOPT and IPOPT. Additionally, special attention is paid to the construction of a mesh for calculations and two types of selected element sizes: finer and fine. Then, the important task is to compare the obtained results and to find the most optimal method and most effective design solution for each shell. When calculating the sphere, the most suitable solution was obtained using the IPOPT method, with the help of which it was possible to achieve an optimal reduction in the dome along the z-axis. When calculating the helicoid, all methods showed approximately the same values and equally changed the angle of inclination of the surface relative to the horizontal plane. Full article

(This article belongs to the Special Issue Recent Advances in Process Modeling and Optimisation)

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18 pages, 969 KiB

Open AccessArticle

Double Adaptive PI-Structure for Regulating a Microgrid DC Bus Using a Flyback-Based Battery Charger/Discharger Converter

by Carlos Andres Ramos-Paja, Juan David Bastidas-Rodriguez and Andres Julian Saavedra-Montes

Computation 2022, 10(4), 53; https://doi.org/10.3390/computation10040053 - 29 Mar 2022

Cited by 1 | Viewed by 2154

Abstract

DC microgrids are composed of loads, renewable sources, and storage devices that require control and protection to operate safely. The flyback converter is an alternative to connect paralleled batteries with nominal voltage DC buses; however, until now, complex controllers have been proposed, making [...] Read more.

DC microgrids are composed of loads, renewable sources, and storage devices that require control and protection to operate safely. The flyback converter is an alternative to connect paralleled batteries with nominal voltage DC buses; however, until now, complex controllers have been proposed, making difficult their implementation. On the other hand, when the voltage of a DC microgrid is not properly controlled, the loads may be damaged due to the voltage outside of the safe range. Therefore, proposed in this paper are two adaptive PI-structures to control a battery charger based on a flyback converter to be used in DC microgrids. The first adaptive current controller regulates the magnetizing current for stabilizing the system, and the second adaptive voltage controller regulates the voltage of the DC bus to protect the elements of the microgrid. The methodology to design the adaptive parameters of the PI-structures is developed as follows: first, the power stage of the flyback converter is introduced to derive a control-oriented model. The battery and the DC bus of the microgrid, which are interfaced by the flyback converter, are represented with widely accepted approaches. The second step is focused on modeling the system. The flyback converter, which includes a capacitance to model the DC microgrid, is represented by a dynamic model. The differential equations are averaged, and several transfer functions of the main variables are obtained. In the third step, the transfer functions are used to design the PI adaptive current controller and the PI adaptive voltage controller. In the last step, several recommendations are made to implement the power and control stages in low-cost hardware. An application example with realistic parameters is carried out in PSIM to validate the controller loops design. A battery of 12 V is connected to a DC microgrid of 48 V through a flyback converter with a switching frequency of 50 kHz. The settling time and deviation of the DC microgrid voltage, after a perturbation, are 0.845 ms and 2.04 V respectively, while the maximum values are adjusted to be 1 ms and 2.4 V. The simulation results validate the proposed procedure and the effectiveness of the PI-structures in regulating the magnetizing current and the DC bus voltage. Full article

(This article belongs to the Section Computational Engineering)

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20 pages, 4326 KiB

Open AccessArticle

Continuous Simulation of the Power Flow in AC–DC Hybrid Microgrids Using Simplified Modelling

by Oswaldo López-Santos, María C. Salas-Castaño and Diego F. Salazar-Dantonio

Computation 2022, 10(4), 52; https://doi.org/10.3390/computation10040052 - 29 Mar 2022

Cited by 2 | Viewed by 2548

Abstract

This paper reports the development of a model for continuous simulation of the power flow into AC–DC hybrid microgrids operating for different generation–consumption scenarios. The proposed application was assembled using a multiple-input multiple-output model which was built using blocks containing simplified models of [...] Read more.

This paper reports the development of a model for continuous simulation of the power flow into AC–DC hybrid microgrids operating for different generation–consumption scenarios. The proposed application was assembled using a multiple-input multiple-output model which was built using blocks containing simplified models of photovoltaic (PV) modules, wind turbines (WT), battery arrays (energy storage units, ESU), and power loads. The average power was used as the input/output variable of the blocks, allowing flexibility for easy reconfiguration of the microgrid and its control. By defining a generation profile, PV and WT were modeled considering environmental conditions and efficiency profiles of the maximum power point tracking (MPPT) algorithms. ESUs were modeled from intrinsic characteristics of the batteries, considering a constant power charge regime and using the State of Energy (SoE) approach to compute autonomy. To define a consumption profile, DC and AC loads were modeled as a constant real power. As an innovative characteristic, unidirectional and bidirectional power conversion stages were modeled using efficiency profiles, which can be obtained from experiments applied to the real converters. The outputs of the models of generation, consumption, and storage units were integrated as inputs of the mathematical expressions computing the power balance of the buses of the microgrid. The proposed model is suitable to analyze efficiency for different configurations of the same microgrid architecture, and can be extended by integrating additional elements. The model was implemented in LabVIEW software and three examples were developed to test its correct operation. Full article

(This article belongs to the Topic Artificial Intelligence and Computational Methods: Modeling, Simulations and Optimization of Complex Systems)

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25 pages, 2545 KiB

Open AccessArticle

In Silico Analysis of the Multi-Targeted Mode of Action of Ivermectin and Related Compounds

by Maral Aminpour, Marco Cannariato, Jordane Preto, M. Ehsan Safaeeardebili, Alexia Moracchiato, Domiziano Doria, Francesca Donato, Eric Adriano Zizzi, Marco Agostino Deriu, David E. Scheim, Alessandro D. Santin and Jack Adam Tuszynski

Computation 2022, 10(4), 51; https://doi.org/10.3390/computation10040051 - 25 Mar 2022

Cited by 8 | Viewed by 6647

Abstract

Some clinical studies have indicated activity of ivermectin, a macrocyclic lactone, against COVID-19, but a biological mechanism initially proposed for this anti-viral effect is not applicable at physiological concentrations. This in silico investigation explores potential modes of action of ivermectin and 14 related [...] Read more.

Some clinical studies have indicated activity of ivermectin, a macrocyclic lactone, against COVID-19, but a biological mechanism initially proposed for this anti-viral effect is not applicable at physiological concentrations. This in silico investigation explores potential modes of action of ivermectin and 14 related compounds, by which the infectivity and morbidity of the SARS-CoV-2 virus may be limited. Binding affinity computations were performed for these agents on several docking sites each for models of (1) the spike glycoprotein of the virus, (2) the CD147 receptor, which has been identified as a secondary attachment point for the virus, and (3) the alpha-7 nicotinic acetylcholine receptor (α7nAChr), an indicated point of viral penetration of neuronal tissue as well as an activation site for the cholinergic anti-inflammatory pathway controlled by the vagus nerve. Binding affinities were calculated for these multiple docking sites and binding modes of each compound. Our results indicate the high affinity of ivermectin, and even higher affinities for some of the other compounds evaluated, for all three of these molecular targets. These results suggest biological mechanisms by which ivermectin may limit the infectivity and morbidity of the SARS-CoV-2 virus and stimulate an α7nAChr-mediated anti-inflammatory pathway that could limit cytokine production by immune cells. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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14 pages, 1299 KiB

Open AccessReview

Mapping in the Topic of Mathematical Model in Paddy Agricultural Insurance Based on Bibliometric Analysis: A Systematic Review Approach

by Riaman, Sukono, Sudradjat Supian and Noriszura Ismail

Computation 2022, 10(4), 50; https://doi.org/10.3390/computation10040050 - 24 Mar 2022

Cited by 8 | Viewed by 2839

Abstract

Bibliometric analysis is the quantitative study of bibliographic material. In this paper, a systematic review of papers, authors, and journals is carried out. This is necessary to determine and set targets to be achieved in further research. The main objective of this study [...] Read more.

Bibliometric analysis is the quantitative study of bibliographic material. In this paper, a systematic review of papers, authors, and journals is carried out. This is necessary to determine and set targets to be achieved in further research. The main objective of this study is to identify some of the most relevant research and the latest trends according to the information found in the Google Scholar, Publish or Perish, Science Direct, and Dimension databases. The methods used are classification, analysis of the most cited journals of all time, and the most prolific and influential authors. The results are information on the number of papers, citations, researchers, h-index, g-index, major reference journals, and visualization of research roadmaps for the topic of agricultural insurance mathematical models. The research findings identify the core themes, which are used as research gaps for future research. Full article

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13 pages, 2606 KiB

Open AccessArticle

Multiple Imputation of Missing Data in Educational Production Functions

by Amira Elasra

Computation 2022, 10(4), 49; https://doi.org/10.3390/computation10040049 - 24 Mar 2022

Cited by 4 | Viewed by 2966

Abstract

Educational production functions rely mostly on longitudinal data that almost always exhibit missing data. This paper contributes to a number of avenues in the literature on the economics of education and applied statistics by reviewing the theoretical foundation of missing data analysis with [...] Read more.

Educational production functions rely mostly on longitudinal data that almost always exhibit missing data. This paper contributes to a number of avenues in the literature on the economics of education and applied statistics by reviewing the theoretical foundation of missing data analysis with a special focus on the application of multiple imputation to educational longitudinal studies. Multiple imputation is one of the most prominent methods to surmount this problem. Not only does it account for all available information in the predictors, but it also takes into account the uncertainty generated by the missing data themselves. This paper applies a multiple imputation technique using a fully conditional specification method based on an iterative Markov chain Monte Carlo (MCMC) simulation using a Gibbs sampler algorithm. Previous attempts to use MCMC simulation were applied on relatively small datasets with small numbers of variables. Therefore, another contribution of this paper is its application and comparison of the imputation technique on a large longitudinal English educational study for three iteration specifications. The results of the simulation proved the convergence of the algorithm. Full article

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19 pages, 4386 KiB

Open AccessArticle

Cost-Benefit Analysis of a Standby Retrial System with an Unreliable Server and Switching Failure

by Tzu-Hsin Liu, Ya-Ling Huang, Yih-Bey Lin and Fu-Min Chang

Computation 2022, 10(4), 48; https://doi.org/10.3390/computation10040048 - 23 Mar 2022

Viewed by 1745

Abstract

In many industries and plants, a stable power supply system with acceptable cost/benefit is essential. This paper investigates the cost-effectiveness of an unreliable retrial system that includes standby generators and experiences the switchover failures of standby generators. Four different standby retrial configurations are [...] Read more.

In many industries and plants, a stable power supply system with acceptable cost/benefit is essential. This paper investigates the cost-effectiveness of an unreliable retrial system that includes standby generators and experiences the switchover failures of standby generators. Four different standby retrial configurations are included, and each configuration includes various numbers of primary and standby generators. Upon arrival, a failed generator is repaired immediately if the server is available; otherwise, the failed generator will enter into orbit. The server is subject to breakdown even when the server is idle. The explicit expressions of the mean time-to-failure and steady-state availability for each configuration are derived and compared. We also compare the cost/benefit ratio among four configurations. The developed results can provide managers with decision reference for stable power supply system and cost reduction. Full article

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19 pages, 848 KiB

Open AccessArticle

Short-Term Mortality Fluctuations and Longevity Risk-Adjusted Age: Learning the Resilience of a Country to a Health Shock

by Gloria Polinesi, Maria Cristina Recchioni, Andrea Rimondi and Anton Sysoev

Computation 2022, 10(4), 47; https://doi.org/10.3390/computation10040047 - 22 Mar 2022

Viewed by 1983

Abstract

Recent studies have attempted to measure differences in lifestyle quality across the world. This paper contributes to this strand of literature by extending the indicator introduced in Milevsky (2020), i.e., “longevity-risk-adjusted global age” (LRaG age), to deal with the new short-term mortality fluctuation [...] Read more.

Recent studies have attempted to measure differences in lifestyle quality across the world. This paper contributes to this strand of literature by extending the indicator introduced in Milevsky (2020), i.e., “longevity-risk-adjusted global age” (LRaG age), to deal with the new short-term mortality fluctuation data series freely available from the Human Mortality Database. The new weekly data on mortality allow measuring weekly biological age. The weekly differences between biological and chronological ages across countries were used to assess country resilience to the COVID-19 pandemic in terms of excess mortality and health expenditure. Countries with a biological age lower than the chronological age had a lower excess mortality in 2020–2021 and a lower health expenditure, thus indicating some resilience to the shock of COVID-19. Full article

(This article belongs to the Special Issue Control Systems, Mathematical Modeling and Automation)

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15 pages, 2033 KiB

Open AccessArticle

Closed-Form Formula for the Conditional Moments of Log Prices under the Inhomogeneous Heston Model

by Kittisak Chumpong and Patcharee Sumritnorrapong

Computation 2022, 10(4), 46; https://doi.org/10.3390/computation10040046 - 22 Mar 2022

Cited by 4 | Viewed by 2565

Abstract

Several financial instruments have been thoroughly calculated via the price of an underlying asset, which can be regarded as a solution of a stochastic differential equation (SDE), for example the moment swap and its exotic types that encourage investors in markets to trade [...] Read more.

Several financial instruments have been thoroughly calculated via the price of an underlying asset, which can be regarded as a solution of a stochastic differential equation (SDE), for example the moment swap and its exotic types that encourage investors in markets to trade volatility on payoff and are especially beneficial for hedging on volatility risk. In the past few decades, numerous studies about conditional moments from various SDEs have been conducted. However, some existing results are not in closed forms, which are more difficult to apply than simply using Monte Carlo (MC) simulations. To overcome this issue, this paper presents an efficient closed-form formula to price generalized swaps for discrete sampling times under the inhomogeneous Heston model, which is the Heston model with time-parameter functions. The obtained formulas are based on the infinitesimal generator and solving a recurrence relation. These formulas are expressed in an explicit and general form. An investigation of the essential properties was carried out for the inhomogeneous Heston model, including conditional moments, central moments, variance, and skewness. Moreover, the closed-form formula obtained was numerically validated through MC simulations. Under this approach, the computational burden was significantly reduced. Full article

(This article belongs to the Section Computational Engineering)

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Computation, Volume 10, Issue 4 (April 2022) – 19 articles

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