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Computation, Volume 11, Issue 3 (March 2023) – 26 articles

Cover Story (view full-size image): Advanced modeling and analysis techniques are fundamental for the design and optimization of energy harvesting systems. This work demonstrates the benefits of using advanced techniques, often borrowed from other fields, to improve the performance of such systems. The review is focused on the modeling techniques that apply to the source/mechanical oscillator/transducer/electrical load chain, describing mechanical–electrical analogies to represent the collective behavior as the cascade of equivalent electrical two-ports, introducing matching networks enhancing the energy transfer to the load, and discussing the main numerical techniques in the frequency and time domains that can be used to analyze linear and nonlinear harvesters, both in the case of deterministic and stochastic excitations. View this paper
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18 pages, 4312 KiB  
Article
Artificial Neural Network Model for Membrane Desalination: A Predictive and Optimization Study
by MieowKee Chan, Amin Shams, ChanChin Wang, PeiYi Lee, Yousef Jahani and Seyyed Ahmad Mirbagheri
Computation 2023, 11(3), 68; https://doi.org/10.3390/computation11030068 - 22 Mar 2023
Cited by 1 | Viewed by 1912
Abstract
Desalination is a sustainable method to solve global water scarcity. A Response Surface Methodology (RSM) approach is widely applied to optimize the desalination performance, but further investigations with additional inputs are restricted. An Artificial neuron network (ANN) method is proposed to reconstruct the [...] Read more.
Desalination is a sustainable method to solve global water scarcity. A Response Surface Methodology (RSM) approach is widely applied to optimize the desalination performance, but further investigations with additional inputs are restricted. An Artificial neuron network (ANN) method is proposed to reconstruct the parameters and demonstrate multivariate analysis. Graphene oxide (GO) content, Polyhedral Oligomeric Silsesquioxane (POSS) content, operating pressure, and salinity were combined as input parameters for a four-dimensional regression analysis to predict the three responses: contact angle, salt rejection, and permeation flux. Average coefficient of determination (R2) values ranged between 0.918 and 0.959. A mathematical equation was derived to find global max and min values. Three objective functions and three-dimensional diagrams were applied to optimize effective cost conditions. It served as the database for the membranologists to decide the amount of GO to be used to fabricate membranes by considering the effects of operating conditions such as salinity and pressure to achieve the desired salt rejection, permeation flux, contact angle, and cost. The finding suggested that a membrane with 0.0063 wt% of GO, operated at 14.2 atm for a 5501 ppm salt solution, is the preferred optimal condition to achieve high salt rejection and permeation flux simultaneously. Full article
(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)
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21 pages, 7232 KiB  
Article
Passive Control of Boundary Layer on Wing: Numerical and Experimental Study of Two Configurations of Wing Surface Modification in Cruise and Landing Speed
by Dionysios G. Karkoulias, Panagiota-Vasiliki N. Bourdousi and Dionissios P. Margaris
Computation 2023, 11(3), 67; https://doi.org/10.3390/computation11030067 - 22 Mar 2023
Cited by 1 | Viewed by 1925
Abstract
Minimizing the carbon footprint of the aviation industry is of critical importance for the forthcoming years, allowing the mitigation of climate change through fossil fuel economy. Significant progress toward this goal can be achieved through the aerodynamic optimization of wing surfaces. In a [...] Read more.
Minimizing the carbon footprint of the aviation industry is of critical importance for the forthcoming years, allowing the mitigation of climate change through fossil fuel economy. Significant progress toward this goal can be achieved through the aerodynamic optimization of wing surfaces. In a previous study, a custom-designed wing equipped with an Eppler 420 airfoil, including an appendant custom-designed blended winglet, was developed and studied in flight conditions. The present paper researches potential improvements to the aerodynamic behavior of this wing by attempting to regenerate the boundary layer. The main goal was to achieve passive control of the boundary layer, which would be approached by means of two different configurations. In the first case, dimples were added at the points where the separation of the boundary layer was expected, for the majority of the wing surface; in the second case, bumps of the same diameter were added at the same points. Both wings were studied in two different Reynolds (Re) numbers and five angles of attack (AoA). The computational fluid dynamics (CFD) simulations were implemented using a pressure-based solver, the spatial discretization was conducted with a second-order upwind scheme, and the k-omega SST (k-ω SST) turbulence model was applied by utilizing the pseudo-transient method. The experimental procedure was conducted in an open-type subsonic flow wind tunnel, for Reynolds 86,000, with 3D-printed models of the wings having undergone suitable surface treatment. The numerical and experimental results converged, showing a degradation in the wing’s aerodynamic performance when bumps were implemented, as well as a slight improvement for the configuration with dimples. Full article
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17 pages, 10140 KiB  
Article
An Energy-Saving Road-Lighting Control System Based on Improved YOLOv5s
by Ren Tang, Chaoyang Zhang, Kai Tang, Xiaoyang He and Qipeng He
Computation 2023, 11(3), 66; https://doi.org/10.3390/computation11030066 - 21 Mar 2023
Cited by 1 | Viewed by 1691
Abstract
Road lighting is one of the largest consumers of electric energy in cities. Research into energy-saving street lighting is of great significance to city sustainable development and economies, especially given that many countries are now in a period of energy shortage. The control [...] Read more.
Road lighting is one of the largest consumers of electric energy in cities. Research into energy-saving street lighting is of great significance to city sustainable development and economies, especially given that many countries are now in a period of energy shortage. The control system is critical for energy-saving street lighting, due to its capability to directly change output power. Here, we propose a control system with high intelligence and efficiency, by incorporating improved YOLOv5s with terminal embedded devices and designing a new dimming method. The improved YOLOv5s has more balanced performance in both detection accuracy and detection speed compared to other state-of-the-art detection models, and achieved the highest cognition recall of 67.94%, precision of 81.28%, 74.53%AP50, and frames per second (FPS) of 59 in the DAIR-V2X dataset. The proposed method achieves highly complete and intelligent dimming control based on the prediction labels of the improved YOLOv5s, and a high energy-saving efficiency was achieved during a two week-long lighting experiment. Furthermore, this system can also contribute to the construction of the Internet of Things, smart cities, and urban security. The proposed control system here offered a novel, high-performance, adaptable, and economical solution to road lighting. Full article
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15 pages, 2455 KiB  
Article
EA2-IMDG: Efficient Approach of Using an In-Memory Data Grid to Improve the Performance of Replication and Scheduling in Grid Environment Systems
by Abdo H. Guroob
Computation 2023, 11(3), 65; https://doi.org/10.3390/computation11030065 - 20 Mar 2023
Cited by 3 | Viewed by 1346
Abstract
This paper proposes a novel approach, EA2-IMDG (Efficient Approach of Using an In-Memory Data Grid) to improve the performance of replication and scheduling in grid environment systems. Grid environments are widely used for distributed computing, but they are often faced with the challenge [...] Read more.
This paper proposes a novel approach, EA2-IMDG (Efficient Approach of Using an In-Memory Data Grid) to improve the performance of replication and scheduling in grid environment systems. Grid environments are widely used for distributed computing, but they are often faced with the challenge of high data access latency and poor scalability. By utilizing an in-memory data grid (IMDG), the aim is to significantly reduce the data access latency and improve the resource utilization of the system. The approach uses the IMDG to store data in RAM, instead of on disk, allowing for faster data retrieval and processing. The IMDG is used to distribute data across multiple nodes, which helps to reduce the risk of data bottlenecks and improve the scalability of the system. To evaluate the proposed approach, a series of experiments were conducted, and its performance was compared with two baseline approaches: a centralized database and a centralized file system. The results of the experiments show that the EA2-IMDG approach improves the performance of replication and scheduling tasks by up to 90% in terms of data access latency and 50% in terms of resource utilization, respectively. These results suggest that the EA2-IMDG approach is a promising solution for improving the performance of grid environment systems. Full article
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13 pages, 416 KiB  
Article
Modelling Qualitative Data from Repeated Surveys
by Marcella Corduas and Domenico Piccolo
Computation 2023, 11(3), 64; https://doi.org/10.3390/computation11030064 - 20 Mar 2023
Cited by 1 | Viewed by 1205
Abstract
This article presents an innovative dynamic model that describes the probability distributions of ordered categorical variables observed over time. For this purpose, we extend the definition of the mixture distribution obtained from the combination of a uniform and a shifted binomial distribution (CUB [...] Read more.
This article presents an innovative dynamic model that describes the probability distributions of ordered categorical variables observed over time. For this purpose, we extend the definition of the mixture distribution obtained from the combination of a uniform and a shifted binomial distribution (CUB model), introducing time-varying parameters. The model parameters identify the main components ruling the respondent evaluation process: the degree of attraction towards the object under assessment, the uncertainty related to the answer, and the weight of the refuge category that is selected when a respondent is unwilling to elaborate a thoughtful judgement. The method provides a tool to quantify the data from qualitative surveys. For illustrative purposes, the dynamic CUB model is applied to the consumers’ perceptions and expectations of inflation in Italy to investigate: (a) the effect of the COVID pandemic on inflation beliefs; (b) the impact of income level on respondents’ expectations. Full article
(This article belongs to the Special Issue Computational Issues in Insurance and Finance)
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43 pages, 10592 KiB  
Article
Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques
by Baidaa Mutasher Rashed and Nirvana Popescu
Computation 2023, 11(3), 63; https://doi.org/10.3390/computation11030063 - 20 Mar 2023
Cited by 6 | Viewed by 3069
Abstract
Today, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in this field is skyrocketing. Examining [...] Read more.
Today, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in this field is skyrocketing. Examining these data is crucial to find important underlying patterns. Classification is an effective method for identifying these patterns. This work proposes a deep investigation and analysis to evaluate and diagnose medical image data using various classification methods and to critically evaluate these methods’ effectiveness. The classification methods utilized include machine-learning (ML) algorithms like artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), logistic regression (LR), random subspace (RS), fuzzy logic and a convolution neural network (CNN) model of deep learning (DL). We applied these methods to two types of datasets: chest X-ray datasets to classify lung images into normal and abnormal, and melanoma skin cancer dermoscopy datasets to classify skin lesions into benign and malignant. This work aims to present a model that aids in investigating and assessing the effectiveness of ML approaches and DL using CNN in classifying the medical databases and comparing these methods to identify the most robust ones that produce the best performance in diagnosis. Our results have shown that the used classification algorithms have good results in terms of performance measures. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis)
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18 pages, 2814 KiB  
Article
Innovative Out-of-Stock Prediction System Based on Data History Knowledge Deep Learning Processing
by Concetta Giaconia and Aziz Chamas
Computation 2023, 11(3), 62; https://doi.org/10.3390/computation11030062 - 20 Mar 2023
Viewed by 2672
Abstract
Research and development efforts in the field of commercial applications have invested strategic interest in the design of intelligent systems that correctly handle out-of-stock events. An out-of-stock event refers to a scenario in which such customers do not have the availability of the [...] Read more.
Research and development efforts in the field of commercial applications have invested strategic interest in the design of intelligent systems that correctly handle out-of-stock events. An out-of-stock event refers to a scenario in which such customers do not have the availability of the products they want to buy. This scenario generates important economic damage to the producer and to the commercial store. Addressing the out-of-stock problem is currently of great interest in the commercial field as it would allow limiting the economic damages deriving from these events. Furthermore, in the era of online commerce (e-commerce), it would significantly limit out-of-stock events which show a considerable economic impact in the field. For these reasons, the authors proposed a solution based on deep learning for predicting the residual stock amount of a commercial product based on the intelligent analysis of specific visual–commercial data as well as seasonality. By means of a combined deep pipeline embedding convolutional architecture boosted with a self-attention mechanism and a downstream temporal convolutional network, the authors will be able to predict the remaining stock of a particular commodity. By integrating and interpreting climate/seasonal information, customers’ behavior data, and full history data on the dynamics of commercial sales, it will be possible to estimate the residual stock of a certain product and, therefore, define purchase orders efficiently. An accurate prediction of remaining stocks allows an efficient trade order policy which results in a significant reduction in out-of-stock events. The experimental results confirmed the effectiveness of the proposed approach with an accuracy (in the prediction of the remaining stock of such products) greater than 90%. Full article
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26 pages, 12902 KiB  
Article
Research on Risk Detection of Autonomous Vehicle Based on Rapidly-Exploring Random Tree
by Yincong Ma, Kit Guan Lim, Min Keng Tan, Helen Sin Ee Chuo, Ali Farzamnia and Kenneth Tze Kin Teo
Computation 2023, 11(3), 61; https://doi.org/10.3390/computation11030061 - 17 Mar 2023
Cited by 3 | Viewed by 1326
Abstract
There is no doubt that the autonomous vehicle is an important developing direction of the auto industry, and, thus, more and more scholars are paying attention to doing more research in this field. Since path planning plays a key role in the operation [...] Read more.
There is no doubt that the autonomous vehicle is an important developing direction of the auto industry, and, thus, more and more scholars are paying attention to doing more research in this field. Since path planning plays a key role in the operation of autonomous vehicles, scholars attach great importance to this field. Although it has been applied in many fields, there are still some problems, such as low efficiency of path planning and collision risk during driving. In order to solve these problems, an automotive vehicle-based rapid exploration random tree (AV-RRT)-based non-particle path planning method for autonomous vehicles is proposed. On the premise of ensuring safety and meeting the requirements of the vehicle’s kinematic constraints through the expansion of obstacles, the dynamic step size is used for random tree growth. A non-particle collision detection (NPCD) collision detection algorithm and path modification (PM) path modification strategy are proposed for the collision risk in the turning process, and geometric constraints are used to represent the possible security threats, so as to improve the efficiency and safety of vehicle global path driving and to provide reference for the research of driverless vehicles. Full article
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14 pages, 9442 KiB  
Article
Stress-Strained State of the Thrust Bearing Disc of Hydrogenerator-Motor
by Oleksii Tretiak, Dmitriy Kritskiy, Igor Kobzar, Mariia Arefieva, Volodymyr Selevko, Dmytro Brega, Kateryna Maiorova and Iryna Tretiak
Computation 2023, 11(3), 60; https://doi.org/10.3390/computation11030060 - 16 Mar 2023
Cited by 2 | Viewed by 1405
Abstract
In this article, the main causes of vibration in the thrust bearing of hydrogenerator motors rated 320 MW are considered. The main types of internal and surface defects that appear on the working surface of the thrust bearing disc during long-term operation are [...] Read more.
In this article, the main causes of vibration in the thrust bearing of hydrogenerator motors rated 320 MW are considered. The main types of internal and surface defects that appear on the working surface of the thrust bearing disc during long-term operation are considered. A method of three-dimensional modeling of such defects is presented, and an assessment of the stress-strain state of the heel disc is proposed, taking into account the main forces acting on the working surface using the finite element method. An analysis of the possible further operation of discs with similar defects, in accordance with the technical requirements, is carried out, and we consider ways to eliminate them. Full article
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17 pages, 2378 KiB  
Article
Enhanced Pre-Trained Xception Model Transfer Learned for Breast Cancer Detection
by Shubhangi A. Joshi, Anupkumar M. Bongale, P. Olof Olsson, Siddhaling Urolagin, Deepak Dharrao and Arunkumar Bongale
Computation 2023, 11(3), 59; https://doi.org/10.3390/computation11030059 - 13 Mar 2023
Cited by 10 | Viewed by 3630
Abstract
Early detection and timely breast cancer treatment improve survival rates and patients’ quality of life. Hence, many computer-assisted techniques based on artificial intelligence are being introduced into the traditional diagnostic workflow. This inclusion of automatic diagnostic systems speeds up diagnosis and helps medical [...] Read more.
Early detection and timely breast cancer treatment improve survival rates and patients’ quality of life. Hence, many computer-assisted techniques based on artificial intelligence are being introduced into the traditional diagnostic workflow. This inclusion of automatic diagnostic systems speeds up diagnosis and helps medical professionals by relieving their work pressure. This study proposes a breast cancer detection framework based on a deep convolutional neural network. To mine useful information about breast cancer through breast histopathology images of the 40× magnification factor that are publicly available, the BreakHis dataset and IDC(Invasive ductal carcinoma) dataset are used. Pre-trained convolutional neural network (CNN) models EfficientNetB0, ResNet50, and Xception are tested for this study. The top layers of these architectures are replaced by custom layers to make the whole architecture specific to the breast cancer detection task. It is seen that the customized Xception model outperformed other frameworks. It gave an accuracy of 93.33% for the 40× zoom images of the BreakHis dataset. The networks are trained using 70% data consisting of BreakHis 40× histopathological images as training data and validated on 30% of the total 40× images as unseen testing and validation data. The histopathology image set is augmented by performing various image transforms. Dropout and batch normalization are used as regularization techniques. Further, the proposed model with enhanced pre-trained Xception CNN is fine-tuned and tested on a part of the IDC dataset. For the IDC dataset training, validation, and testing percentages are kept as 60%, 20%, and 20%, respectively. It obtained an accuracy of 88.08% for the IDC dataset for recognizing invasive ductal carcinoma from H&E-stained histopathological tissue samples of breast tissues. Weights learned during training on the BreakHis dataset are kept the same while training the model on IDC dataset. Thus, this study enhances and customizes functionality of pre-trained model as per the task of classification on the BreakHis and IDC datasets. This study also tries to apply the transfer learning approach for the designed model to another similar classification task. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis)
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11 pages, 641 KiB  
Article
Research on Scientific Directions for Flying Cars at the Preliminary Design Stage
by Andrii Humennyi, Liliia Buival and Zeyan Zheng
Computation 2023, 11(3), 58; https://doi.org/10.3390/computation11030058 - 10 Mar 2023
Viewed by 3846
Abstract
This article was written to investigate the research on the scientific directions for flying cars at the preliminary design stage to provide a rationale for the choice of scientific research in the area of flying cars. At present, the population of the Earth [...] Read more.
This article was written to investigate the research on the scientific directions for flying cars at the preliminary design stage to provide a rationale for the choice of scientific research in the area of flying cars. At present, the population of the Earth is gradually increasing, and traffic congestion will become a common phenomenon in cities in the future. This work used the methods of theoretical and statistical analysis to form an overall picture of this area of research. We researched the statistical data analysis conducted by scientists who dealt with flying cars and the associated issues. We gave a rationale for the choice of the object of scientific research, which is flying cars. People can read this information to have a starting point in their understanding of flying car design. This analysis of famous scientific works provides possible scientific directions that the research can take with respect to designing a flying car that combines the advantages of an airplane and a car and can take off and land on a normal highway for a short distance, as well as help people reach their destination quickly and easily. Full article
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21 pages, 5549 KiB  
Article
Fractional-Step Method with Interpolation for Solving a System of First-Order 2D Hyperbolic Delay Differential Equations
by Karthick Sampath, Subburayan Veerasamy and Ravi P. Agarwal
Computation 2023, 11(3), 57; https://doi.org/10.3390/computation11030057 - 9 Mar 2023
Cited by 1 | Viewed by 1663
Abstract
In this article, we consider a delayed system of first-order hyperbolic differential equations. The presence of the delay term in first-order hyperbolic delay differential equations poses significant challenges in both analysis and numerical solutions. The delay term also makes it more difficult to [...] Read more.
In this article, we consider a delayed system of first-order hyperbolic differential equations. The presence of the delay term in first-order hyperbolic delay differential equations poses significant challenges in both analysis and numerical solutions. The delay term also makes it more difficult to use standard numerical methods for solving differential equations, as these methods often require that the differential equation be evaluated at the current time step. To overcome these challenges, specialized numerical methods and analytical techniques have been developed for solving first-order hyperbolic delay differential equations. We investigated and presented analytical results, such as the maximum principle and stability results. The propagation of discontinuities in the solution was also discussed, providing a framework for understanding its behavior. We presented a fractional-step method using a backward finite difference scheme and showed that the scheme is almost first-order convergent in space and time through the derivation of the error estimate. Additionally, we demonstrated an application of the proposed method to the problem of variable delay differential equations. We demonstrated the practical application of the proposed method to solving variable delay differential equations. The proposed algorithm is based on a numerical approximation method that utilizes a finite difference scheme to discretize the differential equation. We validated our theoretical results through numerical experiments. Full article
(This article belongs to the Topic Advances in Nonlinear Dynamics: Methods and Applications)
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23 pages, 5473 KiB  
Article
Feature Selection Using New Version of V-Shaped Transfer Function for Salp Swarm Algorithm in Sentiment Analysis
by Dinar Ajeng Kristiyanti, Imas Sukaesih Sitanggang, Annisa and Sri Nurdiati
Computation 2023, 11(3), 56; https://doi.org/10.3390/computation11030056 - 8 Mar 2023
Cited by 11 | Viewed by 2173
Abstract
(1) Background: Feature selection is the biggest challenge in feature-rich sentiment analysis to select the best (relevant) feature set, offer information about the relationships between features (informative), and be noise-free from high-dimensional datasets to improve classifier performance. This study aims to propose a [...] Read more.
(1) Background: Feature selection is the biggest challenge in feature-rich sentiment analysis to select the best (relevant) feature set, offer information about the relationships between features (informative), and be noise-free from high-dimensional datasets to improve classifier performance. This study aims to propose a binary version of a metaheuristic optimization algorithm based on Swarm Intelligence, namely the Salp Swarm Algorithm (SSA), as feature selection in sentiment analysis. (2) Methods: Significant feature subsets were selected using the SSA. Transfer functions with various types of the form S-TF, V-TF, X-TF, U-TF, Z-TF, and the new type V-TF with a simpler mathematical formula are used as a binary version approach to enable search agents to move in the search space. The stages of the study include data pre-processing, feature selection using SSA-TF and other conventional feature selection methods, modelling using K-Nearest Neighbor (KNN), Support Vector Machine, and Naïve Bayes, and model evaluation. (3) Results: The results showed an increase of 31.55% to the best accuracy of 80.95% for the KNN model using SSA-based New V-TF. (4) Conclusions: We have found that SSA-New V3-TF is a feature selection method with the highest accuracy and less runtime compared to other algorithms in sentiment analysis. Full article
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16 pages, 480 KiB  
Article
A Design Concept of an Intelligent Onboard Computer Network
by Alexander N. Pchelintsev, Andrey M. Solovyov, Mikhail E. Semenov, Nikolay I. Selvesyuk, Vladislav V. Kosyanchuck and Evgeniy Yu. Zybin
Computation 2023, 11(3), 55; https://doi.org/10.3390/computation11030055 - 8 Mar 2023
Viewed by 1463
Abstract
The article suggests design principles of an advanced onboard computer network with an intelligent control system. It describes the main advantages of designing an onboard computer network based on fibre optics, which allows the implementation of an integrated intellectual system performing intelligent inference [...] Read more.
The article suggests design principles of an advanced onboard computer network with an intelligent control system. It describes the main advantages of designing an onboard computer network based on fibre optics, which allows the implementation of an integrated intellectual system performing intelligent inference in emergency situations. The suggested principles significantly increase the reliability and fault tolerance of avionics suits, which, in turn, enhances flight safety. The suggested concept aims to solve a number of important problems including the design of a switchless computing environment, the development of the methods for dynamic reconfiguration of avionics suits with such an environment, and the implementation of a specialised multilevel intelligent avionics system within this environment. Full article
(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)
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34 pages, 3237 KiB  
Article
Discretization and Analysis of HIV-1 and HTLV-I Coinfection Model with Latent Reservoirs
by Ahmed M. Elaiw, Abdualaziz K. Aljahdali and Aatef D. Hobiny
Computation 2023, 11(3), 54; https://doi.org/10.3390/computation11030054 - 7 Mar 2023
Cited by 2 | Viewed by 2331
Abstract
This article formulates and analyzes a discrete-time Human immunodeficiency virus type 1 (HIV-1) and human T-lymphotropic virus type I (HTLV-I) coinfection model with latent reservoirs. We consider that the HTLV-I infect the CD4+T cells, while HIV-1 has two classes of [...] Read more.
This article formulates and analyzes a discrete-time Human immunodeficiency virus type 1 (HIV-1) and human T-lymphotropic virus type I (HTLV-I) coinfection model with latent reservoirs. We consider that the HTLV-I infect the CD4+T cells, while HIV-1 has two classes of target cells—CD4+T cells and macrophages. The discrete-time model is obtained by discretizing the original continuous-time by the non-standard finite difference (NSFD) approach. We establish that NSFD maintains the positivity and boundedness of the model’s solutions. We derived four threshold parameters that determine the existence and stability of the four equilibria of the model. The Lyapunov method is used to examine the global stability of all equilibria. The analytical findings are supported via numerical simulation. The impact of latent reservoirs on the HIV-1 and HTLV-I co-dynamics is discussed. We show that incorporating the latent reservoirs into the HIV-1 and HTLV-I coinfection model will reduce the basic HIV-1 single-infection and HTLV-I single-infection reproductive numbers. We establish that neglecting the latent reservoirs will lead to overestimation of the required HIV-1 antiviral drugs. Moreover, we show that lengthening of the latent phase can suppress the progression of viral coinfection. This may draw the attention of scientists and pharmaceutical companies to create new treatments that prolong the latency period. Full article
(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)
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17 pages, 3031 KiB  
Article
Mathematical Model and Numerical Method of Calculating the Dynamics of High-Temperature Drying of Milled Peat for the Production of Fuel Briquettes
by Natalia Sorokova, Miroslav Variny, Yevhen Pysmennyy and Yuliia Kol’chik
Computation 2023, 11(3), 53; https://doi.org/10.3390/computation11030053 - 6 Mar 2023
Viewed by 1303
Abstract
Milled peat must be dried for the production of peat fuel briquettes. The current trend in the creation of drying technologies is the intensification of the dehydration process while obtaining a high-quality final product. An increase in the temperature of the drying agent, [...] Read more.
Milled peat must be dried for the production of peat fuel briquettes. The current trend in the creation of drying technologies is the intensification of the dehydration process while obtaining a high-quality final product. An increase in the temperature of the drying agent, above 300 °C, significantly accelerates the reaching of the final moisture content of the peat. In the final stage, it is also accompanied by partial thermal decomposition of the solid phase. Its first stage, which is the decomposition of hemicellulose, contributes to a decrease in weight and an increase in the caloric content of the dry residue. The development of high-temperature drying modes consists of determining the temperature and velocity of the drying agent, wherein the duration of the material reaching the equilibrium moisture content will be minimal and the temperature of the material will not rise above the second-stage decomposition temperature of cellulose. This problem can be solved by the mathematical modeling of the dynamics of peat particles drying in the flow. The article presents a mathematical model of heat and mass transfer, phase transitions, and shrinkage during the dehydration of milled peat particles. The equations of the mathematical model were built based on the differential equation of mass transfer in open deformable systems, which, in the absence of deformations, turns into the known equation of state. A numerical method for implementing a mathematical model has been developed. The adequacy of the mathematical model is confirmed by comparing the results of numerical modeling with known experimental data. Full article
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23 pages, 4366 KiB  
Review
Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions
by Mohammad Mustafa Taye
Computation 2023, 11(3), 52; https://doi.org/10.3390/computation11030052 - 6 Mar 2023
Cited by 82 | Viewed by 26249
Abstract
Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided [...] Read more.
Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided through images. Convolutional neural networks are used to automatically learn a hierarchy of features that can then be utilized for classification, as opposed to manually creating features. In achieving this, a hierarchy of feature maps is constructed by iteratively convolving the input image with learned filters. Because of the hierarchical method, higher layers can learn more intricate features that are also distortion and translation invariant. The main goals of this study are to help academics understand where there are research gaps and to talk in-depth about CNN’s building blocks, their roles, and other vital issues. Full article
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13 pages, 1392 KiB  
Article
Artificial Intelligence Model Based on Grey Clustering to Access Quality of Industrial Hygiene: A Case Study in Peru
by Alexi Delgado, Ruth Condori, Miluska Hernández, Enrique Lee Huamani and Laberiano Andrade-Arenas
Computation 2023, 11(3), 51; https://doi.org/10.3390/computation11030051 - 3 Mar 2023
Cited by 1 | Viewed by 1515
Abstract
Industrial hygiene is a preventive technique that tries to avoid professional illnesses and damage to health caused by several possible toxic agents. The purpose of this study is to simultaneously analyze different risk factors (body vibration, lighting, heat stress and noise), to obtain [...] Read more.
Industrial hygiene is a preventive technique that tries to avoid professional illnesses and damage to health caused by several possible toxic agents. The purpose of this study is to simultaneously analyze different risk factors (body vibration, lighting, heat stress and noise), to obtain an overall risk assessment of these factors and to classify them on a scale of levels of Unacceptable, Not recommended or Acceptable. In this work, an artificial intelligence model based on the grey clustering method was applied to evaluate the quality of industrial hygiene. The grey clustering method was selected, as it enables the integration of objective factors related to hazards present in the workplace with subjective employee evaluations. A case study, in the three warehouses of a beer industry in Peru, was developed. The results obtained showed that the warehouses have an acceptable level of quality. These results could help industries to make decisions about conducting evaluations of the different occupational agents and determine whether the quality of hygiene represents a risk, as well as give certain recommendations with respect to the factors presented. Full article
(This article belongs to the Section Computational Engineering)
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18 pages, 487 KiB  
Article
An Algebraic-Based Primal–Dual Interior-Point Algorithm for Rotated Quadratic Cone Optimization
by Karima Tamsaouete and Baha Alzalg
Computation 2023, 11(3), 50; https://doi.org/10.3390/computation11030050 - 2 Mar 2023
Viewed by 1401
Abstract
In rotated quadratic cone programming problems, we minimize a linear objective function over the intersection of an affine linear manifold with the Cartesian product of rotated quadratic cones. In this paper, we introduce the rotated quadratic cone programming problems as a “self-made” class [...] Read more.
In rotated quadratic cone programming problems, we minimize a linear objective function over the intersection of an affine linear manifold with the Cartesian product of rotated quadratic cones. In this paper, we introduce the rotated quadratic cone programming problems as a “self-made” class of optimization problems. Based on our own Euclidean Jordan algebra, we present a glimpse of the duality theory associated with these problems and develop a special-purpose primal–dual interior-point algorithm for solving them. The efficiency of the proposed algorithm is shown by providing some numerical examples. Full article
(This article belongs to the Section Computational Engineering)
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12 pages, 293 KiB  
Article
Unified Convergence Criteria of Derivative-Free Iterative Methods for Solving Nonlinear Equations
by Samundra Regmi, Ioannis K. Argyros, Stepan Shakhno and Halyna Yarmola
Computation 2023, 11(3), 49; https://doi.org/10.3390/computation11030049 - 1 Mar 2023
Viewed by 931
Abstract
A local and semi-local convergence is developed of a class of iterative methods without derivatives for solving nonlinear Banach space valued operator equations under the classical Lipschitz conditions for first-order divided differences. Special cases of this method are well-known iterative algorithms, in particular, [...] Read more.
A local and semi-local convergence is developed of a class of iterative methods without derivatives for solving nonlinear Banach space valued operator equations under the classical Lipschitz conditions for first-order divided differences. Special cases of this method are well-known iterative algorithms, in particular, the Secant, Kurchatov, and Steffensen methods as well as the Newton method. For the semi-local convergence analysis, we use a technique of recurrent functions and majorizing scalar sequences. First, the convergence of the scalar sequence is proved and its limit is determined. It is then shown that the sequence obtained by the proposed method is bounded by this scalar sequence. In the local convergence analysis, a computable radius of convergence is determined. Finally, the results of the numerical experiments are given that confirm obtained theoretical estimates. Full article
11 pages, 297 KiB  
Article
Problem Solving and Budget Allocation of SMEs: Application of NCA Approach
by Parisa Bouzari, Balázs Gyenge, Pejman Ebrahimi and Mária Fekete-Farkas
Computation 2023, 11(3), 48; https://doi.org/10.3390/computation11030048 - 28 Feb 2023
Cited by 1 | Viewed by 4262
Abstract
In order to achieve a specific result, a firm’s problem-solving activities can be thought of as a process that combines physical and cognitive actions. Its internal organization determines how information inputs are distributed among different task units and, as a result, how the [...] Read more.
In order to achieve a specific result, a firm’s problem-solving activities can be thought of as a process that combines physical and cognitive actions. Its internal organization determines how information inputs are distributed among different task units and, as a result, how the cognitive workload is distributed. We tested a case study related to Iranian small and medium enterprises (SMEs). We used NCA analysis as a creative and state-of-the-art method with the help of R software to evaluate data. According to the findings, six prerequisites must be met in order to achieve a 50% level of efficient performance: innovation at a minimum of 22.7%, CSR at a minimum of 30.4%, IT investment at a minimum of 56.7%, SMM at a minimum of 38.3%, product differentiation at a minimum of 11.7%, and CRM at a minimum of 38.3%. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems)
15 pages, 3187 KiB  
Article
Crack Detection in an Aluminium Oxide Grinding Wheel by Impact Hammer Tests
by Yubin Lee, David Turcic, Dan Danks and Chien Wern
Computation 2023, 11(3), 47; https://doi.org/10.3390/computation11030047 - 28 Feb 2023
Viewed by 1923
Abstract
Grinding is widely used as the last step of the manufacturing process when a good surface finish and precise dimensional tolerances are required. However, if the grinding wheels have cracks, they may lead to a hazardous working environment and produce poor tolerance in [...] Read more.
Grinding is widely used as the last step of the manufacturing process when a good surface finish and precise dimensional tolerances are required. However, if the grinding wheels have cracks, they may lead to a hazardous working environment and produce poor tolerance in machined products. Therefore, grinding wheels should be inspected for cracks before being mounted onto the machine. In this study, a novel method of finding possible internal cracks in the aluminium oxide grinding wheel will be explored by examining the natural frequency and displacement of wheels using an impact hammer testing method. Grinding wheels were cracked into two segments using a three-point bend fixture and then bonded intentionally to simulate cracks. The impact hammer test indicated that cracks in the grinding wheels caused a drop in natural vibration frequency and an increase in the maximum displacement of the accelerometer sensors. Full article
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11 pages, 2063 KiB  
Article
Numerical Study on Surface Reconstruction and Roughness of Magnetorheological Elastomers
by José Antonio Valencia, Johans Restrepo, Hernán David Salinas and Elisabeth Restrepo
Computation 2023, 11(3), 46; https://doi.org/10.3390/computation11030046 - 27 Feb 2023
Cited by 1 | Viewed by 1136
Abstract
A methodology is implemented to deform the surface of a magnetorheological elastomer (MRE) exposed to an external magnetic field by means of data matrix manipulation of the surface. The elastomer surface is created randomly using the Garcia and Stoll method to realize a [...] Read more.
A methodology is implemented to deform the surface of a magnetorheological elastomer (MRE) exposed to an external magnetic field by means of data matrix manipulation of the surface. The elastomer surface is created randomly using the Garcia and Stoll method to realize a nonuniform morphology similar to that found in real MREs. Deformations are induced by means of the translations of the magnetic particles inside the elastomer, under the influence of a uniform magnetic field, generating changes in the surface roughness. Our model computes these deformations using a three-dimensional Gaussian function bounded at 2 standard deviations from its mean value, taking as the standard deviation value the radius of the particle that causes the deformation. To find the regions deformed by the particles, we created a methodology based on the consultation, creation and modification of a system of matrices that control each point of the random surface created. This methodology allows us to work with external files of initial and subsequent positions of each particle inside the elastomer, and allows us to manipulate and analyze the results in a smoother and faster way. Results were found to be satisfactory and consistent when calculating the percentage of surface deformation of real systems. Full article
(This article belongs to the Topic Advances in Computational Materials Sciences)
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28 pages, 2214 KiB  
Review
A Circuit Theory Perspective on the Modeling and Analysis of Vibration Energy Harvesting Systems: A Review
by Michele Bonnin, Kailing Song, Fabio L. Traversa and Fabrizio Bonani
Computation 2023, 11(3), 45; https://doi.org/10.3390/computation11030045 - 25 Feb 2023
Viewed by 1674
Abstract
This paper reviews advanced modeling and analysis techniques useful in the description, design, and optimization of mechanical energy harvesting systems based on the collection of energy from vibration sources. The added value of the present contribution is to demonstrate the benefits of the [...] Read more.
This paper reviews advanced modeling and analysis techniques useful in the description, design, and optimization of mechanical energy harvesting systems based on the collection of energy from vibration sources. The added value of the present contribution is to demonstrate the benefits of the exploitation of advanced techniques, most often inherited from other fields of physics and engineering, to improve the performance of such systems. The review is focused on the modeling techniques that apply to the entire energy source/mechanical oscillator/transducer/electrical load chain, describing mechanical–electrical analogies to represent the collective behavior as the cascade of equivalent electrical two-ports, introducing matching networks enhancing the energy transfer to the load, and discussing the main numerical techniques in the frequency and time domains that can be used to analyze linear and nonlinear harvesters, both in the case of deterministic and stochastic excitations. Full article
(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)
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28 pages, 486 KiB  
Article
On Fitting the Lomax Distribution: A Comparison between Minimum Distance Estimators and Other Estimation Techniques
by Thobeka Nombebe, James Allison, Leonard Santana and Jaco Visagie
Computation 2023, 11(3), 44; https://doi.org/10.3390/computation11030044 - 23 Feb 2023
Viewed by 1613
Abstract
In this paper, we investigate the performance of a variety of frequentist estimation techniques for the scale and shape parameters of the Lomax distribution. These methods include traditional methods such as the maximum likelihood estimator and the method of moments estimator. A version [...] Read more.
In this paper, we investigate the performance of a variety of frequentist estimation techniques for the scale and shape parameters of the Lomax distribution. These methods include traditional methods such as the maximum likelihood estimator and the method of moments estimator. A version of the maximum likelihood estimator adjusted for bias is included as well. Furthermore, an alternative moment-based estimation technique, the L-moment estimator, is included, along with three different minimum distance estimators. The finite sample performances of each of these estimators are compared in an extensive Monte Carlo study. We find that no single estimator outperforms its competitors uniformly. We recommend one of the minimum distance estimators for use with smaller samples, while a bias-reduced version of maximum likelihood estimation is recommended for use with larger samples. In addition, the desirable asymptotic properties of traditional maximum likelihood estimators make them appealing for larger samples. We include a practical application demonstrating the use of the described techniques on observed data. Full article
(This article belongs to the Section Computational Engineering)
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17 pages, 353 KiB  
Article
Solutions of the Yang–Baxter Equation and Automaticity Related to Kronecker Modules
by Agustín Moreno Cañadas, Pedro Fernando Fernández Espinosa and Adolfo Ballester-Bolinches
Computation 2023, 11(3), 43; https://doi.org/10.3390/computation11030043 - 21 Feb 2023
Viewed by 1123
Abstract
The Kronecker algebra K is the path algebra induced by the quiver with two parallel arrows, one source and one sink (i.e., a quiver with two vertices and two arrows going in the same direction). Modules over K are said to be Kronecker [...] Read more.
The Kronecker algebra K is the path algebra induced by the quiver with two parallel arrows, one source and one sink (i.e., a quiver with two vertices and two arrows going in the same direction). Modules over K are said to be Kronecker modules. The classification of these modules can be obtained by solving a well-known tame matrix problem. Such a classification deals with solving systems of differential equations of the form Ax=Bx, where A and B are m×n, F-matrices with F an algebraically closed field. On the other hand, researching the Yang–Baxter equation (YBE) is a topic of great interest in several science fields. It has allowed advances in physics, knot theory, quantum computing, cryptography, quantum groups, non-associative algebras, Hopf algebras, etc. It is worth noting that giving a complete classification of the YBE solutions is still an open problem. This paper proves that some indecomposable modules over K called pre-injective Kronecker modules give rise to some algebraic structures called skew braces which allow the solutions of the YBE. Since preprojective Kronecker modules categorize some integer sequences via some appropriated snake graphs, we prove that such modules are automatic and that they induce the automatic sequences of continued fractions. Full article
(This article belongs to the Special Issue Graph Theory and Its Applications in Computing)
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