Computation

24 pages, 6215 KiB

Open AccessArticle

Interface Splitting Algorithm: A Parallel Solution to Diagonally Dominant Tridiagonal Systems

by Arpiruk Hokpunna

Computation 2023, 11(9), 187; https://doi.org/10.3390/computation11090187 - 21 Sep 2023

Viewed by 990

We present an interface-splitting algorithm (ITS) for solving diagonally dominant tridiagonal systems in parallel. The construction of the ITS algorithm profits from bidirectional links in modern networks, and it only needs one synchronization step to solve the system. The algorithm trades some necessary [...] Read more.

We present an interface-splitting algorithm (ITS) for solving diagonally dominant tridiagonal systems in parallel. The construction of the ITS algorithm profits from bidirectional links in modern networks, and it only needs one synchronization step to solve the system. The algorithm trades some necessary accuracy for better parallel performance. The accuracy and the performance of the ITS algorithm are evaluated on four different parallel machines of up to 2048 processors. The proposed algorithm scales very well, and it is significantly faster than the algorithm used in ScaLAPACK. The applicability of the algorithm is demonstrated in the three-dimensional simulations of turbulent channel flow at Reynolds number 41,430. Full article

(This article belongs to the Section Computational Engineering)

► Show Figures

Figure 1

13 pages, 1872 KiB

Open AccessArticle

Tire–Pavement Interaction Simulation Based on Finite Element Model and Response Surface Methodology

by Qingtao Zhang, Lingxiao Shangguan, Tao Li, Xianyong Ma, Yunfei Yin and Zejiao Dong

Computation 2023, 11(9), 186; https://doi.org/10.3390/computation11090186 - 18 Sep 2023

Viewed by 1278

Abstract

Acquiring accurate tire–pavement interaction information is crucial for pavement mechanical analysis and pavement maintenance. This paper combines the tire finite element model (FEM) and response surface methodology (RSM) to obtain tire–pavement interaction information and to analyze the pavement structure response under different loading [...] Read more.

Acquiring accurate tire–pavement interaction information is crucial for pavement mechanical analysis and pavement maintenance. This paper combines the tire finite element model (FEM) and response surface methodology (RSM) to obtain tire–pavement interaction information and to analyze the pavement structure response under different loading conditions. A set of experiments was initially designed through the Box–Behnken design (BBD) method to obtain input and output variables for RSM calibration. The resultant RSM was evaluated accurately using the analysis of variance (ANOVA) approach. Then, tire loading simulations were conducted under different magnitudes of static loading using the optimal parameter combination obtained from the RSM. The results show that the deviations between the simulations and the real test results were mostly below 5%, validating the effectiveness of the tire FEM. Additionally, three different dynamic conditions—including free rolling, full brake, and full traction—were simulated by altering the tire rolling angle and translational velocities. Finally, the pavement mechanical response under the three rolling conditions was analyzed based on the tire–pavement contact feature. Full article

(This article belongs to the Section Computational Engineering)

► Show Figures

Figure 1

10 pages, 236 KiB

Open AccessEditorial

Computation to Fight SARS-CoV-2 (COVID-19)

by Simone Brogi and Vincenzo Calderone

Computation 2023, 11(9), 185; https://doi.org/10.3390/computation11090185 - 18 Sep 2023

Viewed by 1129

Abstract

In April 2020, during the last pandemic health emergency, we launched a Special Issue hosted by Computation—section Computational Biology, entitled “Computation to Fight SARS-CoV-2 (COVID-19)” [...] Full article

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

20 pages, 4220 KiB

Open AccessArticle

Estimation of Temperature-Dependent Thermal Conductivity and Heat Capacity Given Boundary Data

by Abdulaziz Sharahy and Zaid Sawlan

Computation 2023, 11(9), 184; https://doi.org/10.3390/computation11090184 - 14 Sep 2023

Viewed by 1113

Abstract

This work aims to estimate temperature-dependent thermal conductivity and heat capacity given measurements of temperature and heat flux at the boundaries. This estimation problem has many engineering and industrial applications, such as those for the building sector and chemical reactors. Two approaches are [...] Read more.

This work aims to estimate temperature-dependent thermal conductivity and heat capacity given measurements of temperature and heat flux at the boundaries. This estimation problem has many engineering and industrial applications, such as those for the building sector and chemical reactors. Two approaches are proposed to address this problem. The first method uses an integral approach and a polynomial approximation of the temperature profile. The second method uses a numerical solver for the nonlinear heat equation and an optimization algorithm. The performance of the two methods is compared using synthetic data generated with different boundary conditions and configurations. The results demonstrate that the integral approach works in limited scenarios, whereas the numerical approach is effective in estimating temperature-dependent thermal properties. The second method is also extended to account for noisy measurements and a comprehensive uncertainty quantification framework is developed. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Engineering)

► Show Figures

Figure 1

14 pages, 2480 KiB

Open AccessArticle

Theoretical Modeling of B₁₂N₁₂ Nanocage for the Effective Removal of Paracetamol from Drinking Water

by Kainat, Sana Gul, Qaisar Ali, Momin Khan, Munir Ur Rehman, Mohammad Ibrahim, Abdullah F. AlAsmari, Fawaz Alasmari and Metab Alharbi

Computation 2023, 11(9), 183; https://doi.org/10.3390/computation11090183 - 14 Sep 2023

Cited by 3 | Viewed by 1164

Abstract

In our current investigation, we employed a B₁₂N₁₂ nanocage to extract paracetamol from water utilizing a DFT approach. We explored three distinct positions of paracetamol concerning its interaction with the B₁₂N₁₂ nanocage, designated as complex-1 (BNP-1), complex-2 [...] Read more.

In our current investigation, we employed a B₁₂N₁₂ nanocage to extract paracetamol from water utilizing a DFT approach. We explored three distinct positions of paracetamol concerning its interaction with the B₁₂N₁₂ nanocage, designated as complex-1 (BNP-1), complex-2 (BNP-2), and complex-3 (BNP-3), under both aqueous and gaseous conditions. The optimized bond distances exhibited strong interactions between the nanocage and the paracetamol drug in BNP-1 and BNP-3. Notably, BNP-1 and BNP-3 displayed substantial chemisorption energies, measuring at −27.94 and −15.31 kcal/mol in the gas phase and −30.69 and −15.60 kcal/mol in the aqueous medium, respectively. In contrast, BNP-2 displayed a physiosorbed nature, indicating weaker interactions with values of −6.97 kcal/mol in the gas phase and −4.98 kcal/mol in the aqueous medium. Our analysis of charge transfer revealed significant charge transfer between the B₁₂N₁₂ nanocage and paracetamol. Additionally, a Quantum Theory of Atoms in Molecules (QTAIM) analysis confirmed that the O─B bond within BNP-1 and BNP-3 exhibited a strong covalent and partial bond, encompassing both covalent and electrostatic interactions. In contrast, the H─N bond within BNP-2 displayed a weaker hydrogen bond. Further investigation through Noncovalent Interaction (NCI) and Reduced Density Gradient (RDG) analyses reinforced the presence of strong interactions in BNP-1 and BNP-3, while indicating weaker interactions in BNP-2. The decrease in the electronic band gap (E_g) demonstrated the potential of B₁₂N₁₂ as a promising adsorbent for paracetamol. Examining thermodynamics, the negative values of ∆H (enthalpy change) and ∆G (Gibbs free energy change) pointed out the exothermic and spontaneous nature of the adsorption process. Overall, our study underscores the potential of B₁₂N₁₂ as an effective adsorbent for eliminating paracetamol from wastewater. Full article

► Show Figures

Graphical abstract

31 pages, 7813 KiB

Open AccessArticle

Agile Software Development Lifecycle and Containerization Technology for CubeSat Command and Data Handling Module Implementation

by Oleksandr Liubimov, Ihor Turkin, Vladimir Pavlikov and Lina Volobuyeva

Computation 2023, 11(9), 182; https://doi.org/10.3390/computation11090182 - 14 Sep 2023

Cited by 1 | Viewed by 1336

Abstract

As a subclass of nanosatellites, CubeSats have changed the game’s rules in the scientific research industry and the development of new space technologies. The main success factors are their cost effectiveness, relative ease of production, and predictable life cycle. CubeSats are very important [...] Read more.

As a subclass of nanosatellites, CubeSats have changed the game’s rules in the scientific research industry and the development of new space technologies. The main success factors are their cost effectiveness, relative ease of production, and predictable life cycle. CubeSats are very important for training future engineers: bachelor’s and master’s students of universities. At the same time, using CubeSats is a cost-effective method of nearest space exploration and scientific work. However, many issues are related to efficient time-limited development, software and system-level quality assurance, maintenance, and software reuse. In order to increase the flexibility and reduce the complexity of CubeSat projects, this article proposes a “hybrid” development model that combines the strengths of two approaches: the agile-a-like model for software and the waterfall model for hardware. The paper proposes a new computing platform solution, “Falco SBC/CDHM”, based on Microchip (Atmel) ATSAMV71Q21 with improved performance. This type of platform emphasizes low-cost space hardware that can compete with space-grade platforms. The paper substantiates the architecture of onboard software based on microservices and containerization to break down complex software into relatively simple components that undergraduates and graduates can handle within their Master’s studies, and postgraduates can use for scientific space projects. The checking of the effectiveness of the microservice architecture and the new proposed platform was carried out experimentally, involving the time spent on executing three typical algorithms of different algorithmic complexities. Algorithms were implemented using native C (Bare-metal) and WASM3 on FreeRTOS containers on two platforms, and performance was measured on both “Falco” and “Pi Pico” by Raspberry. The experiment confirmed the feasibility of the complex application of the “hybrid” development model and microservices and container-based architecture. The proposed approach makes it possible to develop complex software in teams of inexperienced students, minimize risks, provide reusability, and thus increase the attractiveness of CubeSat student projects. Full article

(This article belongs to the Special Issue Integrated Computer Technologies in Mechanical Engineering – Synergetic Engineering Ⅱ)

► Show Figures

Figure 1

28 pages, 13353 KiB

Open AccessArticle

In-Silico Prediction of Mechanical Behaviour of Uniform Gyroid Scaffolds Affected by Its Design Parameters for Bone Tissue Engineering Applications

by Haja-Sherief N. Musthafa, Jason Walker, Talal Rahman, Alvhild Bjørkum, Kamal Mustafa and Dhayalan Velauthapillai

Computation 2023, 11(9), 181; https://doi.org/10.3390/computation11090181 - 12 Sep 2023

Cited by 1 | Viewed by 1797

Abstract

Due to their excellent properties, triply periodic minimal surfaces (TPMS) have been applied to design scaffolds for bone tissue engineering applications. Predicting the mechanical response of bone scaffolds in different loading conditions is vital to designing scaffolds. The optimal mechanical properties can be [...] Read more.

Due to their excellent properties, triply periodic minimal surfaces (TPMS) have been applied to design scaffolds for bone tissue engineering applications. Predicting the mechanical response of bone scaffolds in different loading conditions is vital to designing scaffolds. The optimal mechanical properties can be achieved by tuning their geometrical parameters to mimic the mechanical properties of natural bone. In this study, we designed gyroid scaffolds of different user-specific pore and strut sizes using a combined TPMS and signed distance field (SDF) method to obtain varying architecture and porosities. The designed scaffolds were converted to various meshes such as surface, volume, and finite element (FE) volume meshes to create FE models with different boundary and loading conditions. The designed scaffolds under compressive loading were numerically evaluated using a finite element method (FEM) to predict and compare effective elastic moduli. The effective elastic moduli range from 0.05 GPa to 1.93 GPa was predicted for scaffolds of different architectures comparable to human trabecular bone. The results assert that the optimal mechanical properties of the scaffolds can be achieved by tuning their design and morphological parameters to match the mechanical properties of human bone. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Engineering)

► Show Figures

Figure 1

14 pages, 2530 KiB

Open AccessArticle

Regional Contribution in Electrophysiological-Based Classifications of Attention Deficit Hyperactive Disorder (ADHD) Using Machine Learning

by Nishant Chauhan and Byung-Jae Choi

Computation 2023, 11(9), 180; https://doi.org/10.3390/computation11090180 - 08 Sep 2023

Viewed by 1207

Abstract

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition in children and is characterized by challenges in maintaining attention, hyperactivity, and impulsive behaviors. Despite ongoing research, we still do not fully understand what causes ADHD. Electroencephalography (EEG) has emerged as a valuable [...] Read more.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition in children and is characterized by challenges in maintaining attention, hyperactivity, and impulsive behaviors. Despite ongoing research, we still do not fully understand what causes ADHD. Electroencephalography (EEG) has emerged as a valuable tool for investigating ADHD-related neural patterns due to its high temporal resolution and non-invasiveness. This study aims to contribute to diagnostic accuracy by leveraging EEG data to classify children with ADHD and healthy controls. We used a dataset containing EEG recordings from 60 children with ADHD and 60 healthy controls. The EEG data were captured during cognitive tasks and comprised signals from 19 channels across the scalp. Our primary objective was to develop a machine learning model capable of distinguishing ADHD subjects from controls using EEG data as discriminatory features. We employed several well-known classifiers, including a support vector machine, random forest, decision tree, AdaBoost, Naive Bayes, and linear discriminant analysis, to discern distinctive EEG patterns. To further enhance classification accuracy, we explored the impact of regional data on the classification outcomes. We arranged the EEG data according to the brain regions from which they were derived (namely frontal, temporal, central, parietal, and occipital) and examined their collective effects on the accuracy of our classifications. Notably, we considered combinations of three regions at a time and found that certain combinations led to enhanced accuracy. Our findings underscore the potential of EEG-based classification in distinguishing children with ADHD from healthy controls. The Naive Bayes classifier yielded the highest accuracy (84%) when applied to specific region combinations. Moreover, we evaluated the classification performance based on hemisphere-specific EEG data and found promising results, particularly when using the right hemisphere region channels. Full article

(This article belongs to the Special Issue Computational Medical Image Analysis)

► Show Figures

Figure 1

24 pages, 1255 KiB

Open AccessArticle

The Efficacy of Multi-Period Long-Term Power Transmission Network Expansion Model with Penetration of Renewable Sources

by Gideon Ude Nnachi, Yskandar Hamam and Coneth Graham Richards

Computation 2023, 11(9), 179; https://doi.org/10.3390/computation11090179 - 07 Sep 2023

Viewed by 917

Abstract

The electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialisation, population growth, urbanisation and, of course, the evolution of modern technologies in this 4th industrial revolution era. Such a rapid increase in energy demand introduces a huge [...] Read more.

The electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialisation, population growth, urbanisation and, of course, the evolution of modern technologies in this 4th industrial revolution era. Such a rapid increase in energy demand introduces a huge challenge into the power system, which has paved way for network operators to seek alternative energy resources other than the conventional fossil fuel system. Hence, the penetration of renewable energy into the electricity supply mix has evolved rapidly in the past three decades. However, the grid system has to be well planned ahead to accommodate such an increase in energy demand in the long run. Transmission Network Expansion Planning (TNEP) is a well ordered and profitable expansion of power facilities that meets the expected electric energy demand with an allowable degree of reliability. This paper proposes a DC TNEP model that minimises the capital costs of additional transmission lines, network reinforcements, generator operation costs and the costs of renewable energy penetration, while satisfying the increase in demand. The problem is formulated as a mixed integer linear programming (MILP) problem. The developed model was tested in several IEEE test systems in multi-period scenarios. We also carried out a detailed derivation of the new non-negative variables in terms of the power flow magnitudes, the bus voltage phase angles and the lines’ phase angles for proper mixed integer variable decomposition techniques. Moreover, we intend to provide additional recommendations in terms of in which particular year (within a 20 year planning period) can the network operators install new line(s), new corridor(s) and/or additional generation capacity to the respective existing power networks. This is achieved by running incremental period simulations from the base year through the planning horizon. The results show the efficacy of the developed model in solving the TNEP problem with a reduced and acceptable computation time, even for large power grid system. Full article

(This article belongs to the Topic Modern Power Systems and Units)

► Show Figures

Figure 1

18 pages, 10547 KiB

Open AccessArticle

An Image Encryption Scheme Combining 2D Cascaded Logistic Map and Permutation-Substitution Operations

by De Rosal Ignatius Moses Setiadi and Nova Rijati

Computation 2023, 11(9), 178; https://doi.org/10.3390/computation11090178 - 05 Sep 2023

Cited by 8 | Viewed by 1396

Abstract

Confusion, diffusion, and encryption keys affect the quality of image encryption. This research proposes combining bit- and pixel-level permutation and substitution methods based on three advanced chaotic logistic map methods. The three chaotic methods are the 2D Logistic-adjusted-Sine map (2D-LASM), the 2D Logistic-sine-coupling [...] Read more.

Confusion, diffusion, and encryption keys affect the quality of image encryption. This research proposes combining bit- and pixel-level permutation and substitution methods based on three advanced chaotic logistic map methods. The three chaotic methods are the 2D Logistic-adjusted-Sine map (2D-LASM), the 2D Logistic-sine-coupling map (2D-LSCM), and the 2D Logistic ICMIC cascade map (2D-LICM). The encryption method’s design consists of six stages of encryption, involving permutation operations based on chaotic order, substitution based on modulus and bitXOR, and hash functions. Hash functions are employed to enhance key space and key sensitivity quality. Several testing tools are utilized to assess encryption performance, including histogram and chi-square analysis, information entropy, correlation of adjacent pixels, differential analysis, key sensitivity and key space analysis, data loss and noise attacks, NIST randomness tests, and TestU01. Compared to using a single 2D logistic map, the amalgamation of bit-level and pixel-level encryption and the utilization of three 2D cascade logistic maps has improved encryption security performance. This method successfully passes the NIST, TestU01, and chi-square tests. Furthermore, it outperforms the previous method regarding correlation, information entropy, NPCR, and UACI tests. Full article

(This article belongs to the Section Computational Engineering)

► Show Figures

Figure 1

16 pages, 6102 KiB

Open AccessArticle

The Use of IoT for Determination of Time and Frequency Vibration Characteristics of Industrial Equipment for Condition-Based Maintenance

by Ihor Turkin, Viacheslav Leznovskyi, Andrii Zelenkov, Agil Nabizade, Lina Volobuieva and Viktoriia Turkina

Computation 2023, 11(9), 177; https://doi.org/10.3390/computation11090177 - 05 Sep 2023

Viewed by 1125

Abstract

The subject of study in this article is a method for industrial equipment vibration diagnostics that uses discrete Fourier transform and Allan variance to increase precision and accuracy of industrial equipment vibration diagnostics processes. We propose IoT-oriented solutions based on smart sensors. The [...] Read more.

The subject of study in this article is a method for industrial equipment vibration diagnostics that uses discrete Fourier transform and Allan variance to increase precision and accuracy of industrial equipment vibration diagnostics processes. We propose IoT-oriented solutions based on smart sensors. The primary objectives include validating the practicality of employing platform-oriented technologies for vibro-diagnostics of industrial equipment, creating software and hardware solutions for the IoT platform, and assessing measurement accuracy and precision through the analysis of measurement results in both time and frequency domains. The IoT system architecture for industrial equipment vibration diagnostics consists of three levels. At the autonomous sensor level, vibration acceleration indicators are obtained and transmitted via a BLE digital wireless data transmission channel to the second level, the hub, which is based on a BeagleBone single-board microcomputer. The computing power of BeagleBone is sufficient to work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the state of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is used. The verification of the accuracy and precision of the vibration diagnostics system was carried out on the developed stand. A comparison of the expected and measured results in the frequency and time domains confirms the correct operation of the entire system. Full article

(This article belongs to the Special Issue Integrated Computer Technologies in Mechanical Engineering – Synergetic Engineering Ⅱ)

► Show Figures

Figure 1

17 pages, 24020 KiB

Open AccessArticle

Filter Pruning with Convolutional Approximation Small Model Framework

by Monthon Intraraprasit and Orachat Chitsobhuk

Computation 2023, 11(9), 176; https://doi.org/10.3390/computation11090176 - 05 Sep 2023

Viewed by 889

Abstract

Convolutional neural networks (CNNs) are extensively utilized in computer vision; however, they pose challenges in terms of computational time and storage requirements. To address this issue, one well-known approach is filter pruning. However, fine-tuning pruned models necessitates substantial computing power and a large [...] Read more.

Convolutional neural networks (CNNs) are extensively utilized in computer vision; however, they pose challenges in terms of computational time and storage requirements. To address this issue, one well-known approach is filter pruning. However, fine-tuning pruned models necessitates substantial computing power and a large retraining dataset. To restore model performance after pruning each layer, we propose the Convolutional Approximation Small Model (CASM) framework. CASM involves training a compact model with the remaining kernels and optimizing their weights to restore feature maps that resemble the original kernels. This method requires less complexity and fewer training samples compared to basic fine-tuning. We evaluate the performance of CASM on the CIFAR-10 and ImageNet datasets using VGG-16 and ResNet-50 models. The experimental results demonstrate that CASM surpasses the basic fine-tuning framework in terms of time acceleration (3.3× faster), requiring a smaller dataset for performance recovery after pruning, and achieving enhanced accuracy. Full article

► Show Figures

Figure 1

32 pages, 5498 KiB

Open AccessArticle

Knowledge Graph Engineering Based on Semantic Annotation of Tables

by Nikita Dorodnykh and Aleksandr Yurin

Computation 2023, 11(9), 175; https://doi.org/10.3390/computation11090175 - 05 Sep 2023

Cited by 1 | Viewed by 1918

Abstract

A table is a convenient way to store, structure, and present data. Tables are an attractive knowledge source in various applications, including knowledge graph engineering. However, a lack of understanding of the semantic structure and meaning of their content may reduce the effectiveness [...] Read more.

A table is a convenient way to store, structure, and present data. Tables are an attractive knowledge source in various applications, including knowledge graph engineering. However, a lack of understanding of the semantic structure and meaning of their content may reduce the effectiveness of this process. Hence, the restoration of tabular semantics and the development of knowledge graphs based on semantically annotated tabular data are highly relevant tasks that have attracted a lot of attention in recent years. We propose a hybrid approach using heuristics and machine learning methods for the semantic annotation of relational tabular data and knowledge graph populations with specific entities extracted from the annotated tables. This paper discusses the main stages of the approach, its implementation, and performance testing. We also consider three case studies for the development of domain-specific knowledge graphs in the fields of industrial safety inspection, labor market analysis, and university activities. The evaluation results revealed that the application of our approach can be considered the initial stage for the rapid filling of domain-specific knowledge graphs based on tabular data. Full article

(This article belongs to the Special Issue Advanced Information, Computation, and Control Systems for Distributed Environments II)

► Show Figures

Figure 1

27 pages, 10506 KiB

Open AccessArticle

AFB-GPSR: Adaptive Beaconing Strategy Based on Fuzzy Logic Scheme for Geographical Routing in a Mobile Ad Hoc Network (MANET)

by Raneen I. Al-Essa and Ghaida A. Al-Suhail

Computation 2023, 11(9), 174; https://doi.org/10.3390/computation11090174 - 04 Sep 2023

Cited by 4 | Viewed by 1611

Abstract

In mobile ad hoc networks (MANETs), geographical routing provides a robust and scalable solution for the randomly distributed and unrestricted movement of nodes. Each node broadcasts beacon packets periodically to exchange its position with neighboring nodes. However, reliable beacons can negatively affect routing [...] Read more.

In mobile ad hoc networks (MANETs), geographical routing provides a robust and scalable solution for the randomly distributed and unrestricted movement of nodes. Each node broadcasts beacon packets periodically to exchange its position with neighboring nodes. However, reliable beacons can negatively affect routing performance in dynamic environments, particularly when there is a sudden and rapid change in the nodes’ mobility. Therefore, this paper suggests an improved Greedy Perimeter Stateless Routing Protocol, namely AFB-GPSR, to reduce routing overhead and increase network reliability by maintaining correct route selection. To this end, an adaptive beaconing strategy based on a fuzzy logic scheme (AFB) is utilized to choose more optimal routes for data forwarding. Instead of constant periodic beaconing, the AFB strategy can dynamically adjust beacon interval time with the variation of three network parameters: node speed, one-hop neighbors’ density, and link quality of nodes. The routing evaluation of the proposed protocol is carried out using OMNeT++ simulation experiments. The results show that the AFB strategy within the GPSR protocol can effectively reduce the routing overhead and improve the packet-delivery ratio, throughput, average end-to-end delay, and normalized routing load as compared to traditional routing protocols (AODV and GPSR with fixed beaconing). An enhancement of the packet-delivery ratio of up to 14% is achieved, and the routing cost is reduced by 35%. Moreover, the AFB-GPSR protocol exhibits good performance versus the state-of-the-art protocols in MANET. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

► Show Figures

Graphical abstract

20 pages, 3839 KiB

Open AccessArticle

MPC Controllers in SIIR Epidemic Models

by Nikita Kosyanov, Elena Gubar and Vladislav Taynitskiy

Computation 2023, 11(9), 173; https://doi.org/10.3390/computation11090173 - 04 Sep 2023

Viewed by 989

Abstract

Infectious diseases are one of the most important problems of the modern world, for example, the periodic outbreaks of coronavirus infections caused by COVID-19, influenza, and many other respiratory diseases have significantly affected the economics of many countries. Hence, it is therefore important [...] Read more.

Infectious diseases are one of the most important problems of the modern world, for example, the periodic outbreaks of coronavirus infections caused by COVID-19, influenza, and many other respiratory diseases have significantly affected the economics of many countries. Hence, it is therefore important to minimize the economic damage, which includes both loss of work and treatment costs, quarantine costs, etc. Recent studies have presented many different models describing the dynamics of virus spread, which help to analyze the epidemic outbreaks. In the current work we focus on finding solutions that are robust to noise and take into account the dynamics of future changes in the process. We extend previous results by using a nonlinear model-predictive-control (MPC) controller to find effective controls. MPC is a computational mathematical method used in dynamically controlled systems with observations to find effective controls. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

► Show Figures

Figure 1

11 pages, 1999 KiB

Open AccessArticle

Solving the Problem of Elasticity for a Layer with N Cylindrical Embedded Supports

by Vitaly Miroshnikov, Oleksandr Savin, Vladimir Sobol and Vyacheslav Nikichanov

Computation 2023, 11(9), 172; https://doi.org/10.3390/computation11090172 - 03 Sep 2023

Viewed by 957

Abstract

The main goal of deformable solid mechanics is to determine the stress–strain state of parts, structural elements, and their connections. The most accurate results of calculations of this state allow us to optimize design objects. However, not all models can be solved using [...] Read more.

The main goal of deformable solid mechanics is to determine the stress–strain state of parts, structural elements, and their connections. The most accurate results of calculations of this state allow us to optimize design objects. However, not all models can be solved using exact methods. One such model is the problem of a layer with cylindrical embedded supports that are parallel to each other and the layer boundaries. In this work, the supports are represented by cylindrical cavities with zero displacements set on them. The layer is considered in Cartesian coordinates, and the cavities are in cylindrical coordinates. To solve the problem, the Lamé equation is used, where the basic solutions between different coordinate systems are linked using the generalized Fourier method. By satisfying the boundary conditions and linking different coordinate systems, a system of infinite linear algebraic equations is created. For numerical realization, the method of reduction is used to find the unknowns. The numerical analysis has shown that the boundary conditions are fulfilled with high accuracy, and the physical pattern of the stress distribution and the comparison with results of similar studies indicate the accuracy of the obtained results. The proposed method for calculating the stress–strain state can be applied to the calculation of structures whose model is a layer with cylindrical embedded supports. The numerical results of the work make it possible to predetermine the geometric parameters of the model to be designed. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Engineering)

► Show Figures

Figure 1

18 pages, 9991 KiB

Open AccessArticle

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

by Marina Rudenko, Anatoliy Kazak, Nikolay Oleinikov, Angela Mayorova, Anna Dorofeeva, Dmitry Nekhaychuk and Olga Shutova

Computation 2023, 11(9), 171; https://doi.org/10.3390/computation11090171 - 03 Sep 2023

Cited by 1 | Viewed by 1285

Abstract

Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases [...] Read more.

Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray rot, Black rot, White rot, and bacterial cancer of grapes. In addition, a set of healthy plants was included. In this paper, a new selective search algorithm for monitoring the state of plant development based on computer vision in viticulture, based on YOLOv5, was considered. The most difficult part of object detection is object localization. As a result, the fast and accurate detection of grape health status was realized. The test results showed that the accuracy was 97.5%, with a model size of 14.85 MB. An analysis of existing publications and patents found using the search “Computer vision in viticulture” showed that this technology is original and promising. The developed software package implements the best approaches to the control system in viticulture using computer vision technologies. A mobile application was developed for practical use by the farmer. The developed software and hardware complex can be installed in any vehicle. Such a mobile system will allow for real-time monitoring of the state of the vineyards and will display it on a map. The novelty of this study lies in the integration of software and hardware. Decision support system software can be adapted to solve other similar problems. The software product commercialization plan is focused on the automation and robotization of agriculture, and will form the basis for adding the next set of similar software. Full article

(This article belongs to the Special Issue Computation, AI and Simulation in Bioinformatics, Horticulture, Viticulture and Winemaking)

► Show Figures

Figure 1

15 pages, 611 KiB

Open AccessArticle

Predicting the Occurrence of Metabolic Syndrome Using Machine Learning Models

by Maria Trigka and Elias Dritsas

Computation 2023, 11(9), 170; https://doi.org/10.3390/computation11090170 - 03 Sep 2023

Cited by 3 | Viewed by 1494

Abstract

The term metabolic syndrome describes the clinical coexistence of pathological disorders that can lead to the development of cardiovascular disease and diabetes in the long term, which is why it is now considered an initial stage of the above clinical entities. Metabolic syndrome [...] Read more.

The term metabolic syndrome describes the clinical coexistence of pathological disorders that can lead to the development of cardiovascular disease and diabetes in the long term, which is why it is now considered an initial stage of the above clinical entities. Metabolic syndrome (MetSyn) is closely associated with increased body weight, obesity, and a sedentary lifestyle. The necessity of prevention and early diagnosis is imperative. In this research article, we experiment with various supervised machine learning (ML) models to predict the risk of developing MetSyn. In addition, the predictive ability and accuracy of the models using the synthetic minority oversampling technique (SMOTE) are illustrated. The evaluation of the ML models highlights the superiority of the stacking ensemble algorithm compared to other algorithms, achieving an accuracy of 89.35%; precision, recall, and F1 score values of 0.898; and an area under the curve (AUC) value of 0.965 using the SMOTE with 10-fold cross-validation. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning in Data Science)

► Show Figures

Figure 1

23 pages, 5619 KiB

Open AccessArticle

Impact of Cross-Tie Material Nonlinearity on the Dynamic Behavior of Shallow Flexible Cable Networks

by Amir Younespour and Shaohong Cheng

Computation 2023, 11(9), 169; https://doi.org/10.3390/computation11090169 - 01 Sep 2023

Viewed by 767

Abstract

Cross-ties have proven their efficacy in mitigating vibrations in bridge stay cables. Several factors, such as cross-tie malfunctions due to slackening or snapping, as well as the utilization of high-energy dissipative materials, can introduce nonlinear restoring forces in the cross-ties. While previous studies [...] Read more.

Cross-ties have proven their efficacy in mitigating vibrations in bridge stay cables. Several factors, such as cross-tie malfunctions due to slackening or snapping, as well as the utilization of high-energy dissipative materials, can introduce nonlinear restoring forces in the cross-ties. While previous studies have investigated the influence of the former on cable network dynamics, the evaluation of the impact of nonlinear cross-tie materials remains unexplored. In this current research, an existing analytical model of a two-shallow-flexible-cable network has been extended to incorporate the cross-tie material nonlinearity in the formulation. The harmonic balance method (HBM) is employed to determine the equivalent linear stiffness of the cross-ties. The dynamic response of a cable network containing nonlinear cross-ties is approximated by comparing it to an equivalent linear system. Additionally, the study delves into the effects of the cable vibration amplitude, cross-tie material properties, installation location, and the length ratio between constituent cables on both the fundamental frequency of the cable network and the equivalent linear stiffness of the cross-ties. The findings reveal that the presence of cross-tie nonlinearity significantly influences the in-plane modal response of the cable network. Not only the frequencies of all the modes are reduced, but the formation of local modes is delayed to a high order. In contrast to an earlier finding based on a linear cross-tie assumption, with nonlinearity present, moving a cross-tie towards the mid-span of a cable would not enhance the in-plane stiffness of the network. Moreover, the impact of the length ratio on the network in-plane stiffness and frequency is contingent on its combined effect on the cross-tie axial stiffness and the lateral stiffness of neighboring cables. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Engineering)

► Show Figures

Figure 1

27 pages, 1255 KiB

Open AccessArticle

Adapting PINN Models of Physical Entities to Dynamical Data

by Dmitriy Tarkhov, Tatiana Lazovskaya and Valery Antonov

Computation 2023, 11(9), 168; https://doi.org/10.3390/computation11090168 - 01 Sep 2023

Viewed by 1151

Abstract

This article examines the possibilities of adapting approximate solutions of boundary value problems for differential equations using physics-informed neural networks (PINNs) to changes in data about the physical entity being modelled. Two types of models are considered: PINN and parametric PINN (PPINN). The [...] Read more.

This article examines the possibilities of adapting approximate solutions of boundary value problems for differential equations using physics-informed neural networks (PINNs) to changes in data about the physical entity being modelled. Two types of models are considered: PINN and parametric PINN (PPINN). The former is constructed for a fixed parameter of the problem, while the latter includes the parameter for the number of input variables. The models are tested on three problems. The first problem involves modelling the bending of a cantilever rod under varying loads. The second task is a non-stationary problem of a thermal explosion in the plane-parallel case. The initial model is constructed based on an ordinary differential equation, while the modelling object satisfies a partial differential equation. The third task is to solve a partial differential equation of mixed type depending on time. In all cases, the initial models are adapted to the corresponding pseudo-measurements generated based on changing equations. A series of experiments are carried out for each problem with different functions of a parameter that reflects the character of changes in the object. A comparative analysis of the quality of the PINN and PPINN models and their resistance to data changes has been conducted for the first time in this study. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Engineering)

► Show Figures

Figure 1

13 pages, 14534 KiB

Open AccessArticle

Numerical Computation of Hydrodynamic Characteristics of an Automated Hand-Washing System

by Thanh-Long Le, Thi-Hong-Nhi Vuong and Tran-Hanh Phung

Computation 2023, 11(9), 167; https://doi.org/10.3390/computation11090167 - 22 Aug 2023

Cited by 1 | Viewed by 939

Abstract

The aim of this study is to develop a physical model and investigate the bactericidal effect of an automated hand-washing system through numerical computation, which is essential in areas affected by COVID-19 to ensure safety and limit the spread of the pandemic. The [...] Read more.

The aim of this study is to develop a physical model and investigate the bactericidal effect of an automated hand-washing system through numerical computation, which is essential in areas affected by COVID-19 to ensure safety and limit the spread of the pandemic. The computational fluid dynamics approach is used to study the movement of the solution inside the hand-washing chamber. The finite element method with the k-

ε

model is applied to solve the incompressible Navier–Stokes equations. The numerical results provide insights into the solution’s hydrodynamic values, streamlines, and density in the two cases of with a hand and without a hand. The pressure and mean velocity of the fluid in the hand-washing chamber increases when the inlet flow rates increase. When the hand-washing chamber operates, it creates whirlpools around the hands, which remove bacteria. In addition, the liquid inlet flow affects the pressure in the hand-washing chamber. The ability to predict the hydraulic and cleaning performance efficiencies of the hand-washing chamber is crucial for evaluating its operability and improving its design in the future. Full article

(This article belongs to the Section Computational Engineering)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Computation, Volume 11, Issue 9 (September 2023) – 21 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI