Computation

19 pages, 1354 KiB

Open AccessArticle

Assessing the Impact of Psyllid Pesticide Resistance on the Spread of Citrus Huanglongbing and Its Ecological Paradox

by Runyun Gan, Youquan Luo and Shujing Gao

Computation 2024, 12(12), 242; https://doi.org/10.3390/computation12120242 - 5 Dec 2024

Excessive use of pesticides can lead to pesticide resistance in citrus psyllids, and studies have shown that this resistance is related to population genetics. This article proposes a dynamic model of Huanglongbing (HLB) that integrates the population genetics of the citrus psyllid vector [...] Read more.

Excessive use of pesticides can lead to pesticide resistance in citrus psyllids, and studies have shown that this resistance is related to population genetics. This article proposes a dynamic model of Huanglongbing (HLB) that integrates the population genetics of the citrus psyllid vector and considers the fitness cost associated with pesticide resistance to study how pesticide use affects the development of pesticide resistance at the population level. The basic reproduction number is introduced as a metric to assess whether HLB can be effectively controlled. Additionally, this article explores the impact of different parameters on the spread of HLB. Numerical simulations illustrate that the basic reproduction number decreases as the fitness cost of resistance increases, while an increase in the resistance index leads to an increase in the basic reproduction number. However, when the fitness cost is sufficiently high, a larger resistance index may result in a basic reproduction number less than 1, leading to the extinction of Asian citrus psyllid (ACP), thus causing a paradox effect. Full article

(This article belongs to the Section Computational Biology)

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32 pages, 12061 KiB

Open AccessArticle

Design of Trabecular Bone Mimicking Voronoi Lattice-Based Scaffolds and CFD Modelling of Non-Newtonian Power Law Blood Flow Behaviour

by Haja-Sherief N. Musthafa and Jason Walker

Computation 2024, 12(12), 241; https://doi.org/10.3390/computation12120241 - 5 Dec 2024

Abstract

Designing scaffolds similar to the structure of trabecular bone requires specialised algorithms. Existing scaffold designs for bone tissue engineering have repeated patterns that do not replicate the random stochastic porous structure of the internal architecture of bones. In this research, the Voronoi tessellation [...] Read more.

Designing scaffolds similar to the structure of trabecular bone requires specialised algorithms. Existing scaffold designs for bone tissue engineering have repeated patterns that do not replicate the random stochastic porous structure of the internal architecture of bones. In this research, the Voronoi tessellation method is applied to create random porous biomimetic structures. A volume mesh created from the shape of a Zygoma fracture acts as a boundary for the generation of random seed points by point spacing to create Voronoi cells and Voronoi diagrams. The Voronoi lattices were obtained by adding strut thickness to the Voronoi diagrams. Gradient Voronoi scaffolds of pore sizes (19.8 µm to 923 µm) similar to the structure of the trabecular bone were designed. A Finite Element Method-based computational fluid dynamics (CFD) simulation was performed on all designed Voronoi scaffolds to predict the pressure drops and permeability of non-Newtonian blood flow behaviour using the power law material model. The predicted permeability (0.33 × 10⁻⁹ m² to 2.17 × 10⁻⁹ m²) values of the Voronoi scaffolds from the CFD simulation are comparable with the permeability of scaffolds and bone specimens from other research works. Full article

(This article belongs to the Section Computational Engineering)

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10 pages, 551 KiB

Open AccessArticle

Theil’s Index of Inequality: Computation of Value-Validity Correction

by Tarald O. Kvålseth

Computation 2024, 12(12), 240; https://doi.org/10.3390/computation12120240 - 5 Dec 2024

Abstract

The Theil index is one of the most popular indices of economic inequality, one reason for which is no doubt due to its convenient additive decomposition property. One of its weaknesses, however, is its lack of any intuitively meaningful interpretations. Another, and more [...] Read more.

The Theil index is one of the most popular indices of economic inequality, one reason for which is no doubt due to its convenient additive decomposition property. One of its weaknesses, however, is its lack of any intuitively meaningful interpretations. Another, and more serious, limitation of Theil’s index, as argued in this paper, is its lack of the value-validity property. That is, this index does not meet a particular condition based on metric distances between income-share distributions required in order for the range of potential index values to provide true, realistic, and valid representations of the economic inequality characteristic. After outlining the value-validity condition, this paper derives a simple transformation of Theil’s index that meets this condition to a high degree of approximation. Randomly generated income-share distributions are used to demonstrate and verify the validity of the corrected index. The new index formulation, which is simply a power function of Theil’s index, can then be used to make appropriate and reliable representations of absolute and relative difference comparisons of economic inequalities. Full article

(This article belongs to the Section Computational Social Science)

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23 pages, 4695 KiB

Open AccessArticle

Dynamic Modeling of Bacterial Cellulose Production Using Combined Substrate- and Biomass-Dependent Kinetics

by Alejandro Rincón, Fredy E. Hoyos and John E. Candelo-Becerra

Computation 2024, 12(12), 239; https://doi.org/10.3390/computation12120239 - 3 Dec 2024

Abstract

In this work, kinetic models are assessed to describe bacterial cellulose (BC) production, substrate consumption, and biomass growth by K. xylinus in a batch-stirred tank bioreactor, under 700 rpm and 500 rpm agitation rates. The kinetic models commonly used for Acetobacter or Gluconacetobacter [...] Read more.

In this work, kinetic models are assessed to describe bacterial cellulose (BC) production, substrate consumption, and biomass growth by K. xylinus in a batch-stirred tank bioreactor, under 700 rpm and 500 rpm agitation rates. The kinetic models commonly used for Acetobacter or Gluconacetobacter were fitted to published data and compared using the Akaike Information Criterion (AIC). A stepwise fitting procedure was proposed for model selection to reduce computation effort, including a first calibration in which only the biomass and substrate were simulated, a selection of the three most effective models in terms of AIC, and a calibration of the three selected models with the simulation of biomass, substrate, and product. Also, an uncoupled product equation involving a modified Monod substrate function is proposed for a 500 rpm agitation rate, leading to an improved prediction of BC productivity. The M2c and M1c models were the most efficient for biomass growth and substrate consumption for the combined AIC, under 700 rpm and 500 rpm agitation rates, respectively. The average coefficients of determination for biomass, substrate, and product predictions were 0.981, 0.994, and 0.946 for the 700 rpm agitation rate, and 0.984, 0.991, and 0.847 for the 500 rpm agitation rate. It is shown that the prediction of BC productivity is improved through the proposed substrate function, whereas the computation effort is reduced through the proposed model fitting procedure. Full article

(This article belongs to the Section Computational Biology)

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10 pages, 9289 KiB

Open AccessPerspective

Quantum Mechanics/Molecular Mechanics Simulations for Chiral-Selective Aminoacylation: Unraveling the Nature of Life

by Tadashi Ando and Koji Tamura

Computation 2024, 12(12), 238; https://doi.org/10.3390/computation12120238 - 2 Dec 2024

Abstract

Biological phenomena are chemical reactions, which are inherently non-stopping or “flowing” in nature. Molecular dynamics (MD) is used to analyze the dynamics and energetics of interacting atoms, but it cannot handle chemical reactions involving bond formation and breaking. Quantum mechanics/molecular mechanics (QM/MM) umbrella [...] Read more.

Biological phenomena are chemical reactions, which are inherently non-stopping or “flowing” in nature. Molecular dynamics (MD) is used to analyze the dynamics and energetics of interacting atoms, but it cannot handle chemical reactions involving bond formation and breaking. Quantum mechanics/molecular mechanics (QM/MM) umbrella sampling MD simulations gives us a significant clue about transition states of chemical reactions and their energy levels, which are the pivotal points in understanding the nature of life. To demonstrate the importance of this method, we present here the results of our application of it to the elucidation of the mechanism of chiral-selective aminoacylation of an RNA minihelix considered to be a primitive form of tRNA. The QM/MM MD simulation, for the first time, elucidated the “flowing” atomistic mechanisms of the reaction and indicated that the L-Ala moiety stabilizes the transition state more than D-Ala, resulting in L-Ala preference in the aminoacylation reaction in the RNA. The QM/MM method not only provides important clues to the elucidation of the origin of homochirality of biological systems, but also is expected to become an important tool that will play a critical role in the analysis of biomolecular reactions, combined with the development of artificial intelligence. Full article

(This article belongs to the Section Computational Biology)

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Graphical abstract

22 pages, 1650 KiB

Open AccessArticle

Interpretable Conversation Routing via the Latent Embeddings Approach

by Daniil Maksymenko and Oleksii Turuta

Computation 2024, 12(12), 237; https://doi.org/10.3390/computation12120237 - 1 Dec 2024

Abstract

Large language models (LLMs) are quickly implemented to answer question and support systems to automate customer experience across all domains, including medical use cases. Models in such environments should solve multiple problems like general knowledge questions, queries to external sources, function calling and [...] Read more.

Large language models (LLMs) are quickly implemented to answer question and support systems to automate customer experience across all domains, including medical use cases. Models in such environments should solve multiple problems like general knowledge questions, queries to external sources, function calling and many others. Some cases might not even require a full-on text generation. They possibly need different prompts or even different models. All of it can be managed by a routing step. This paper focuses on interpretable few-shot approaches for conversation routing like latent embeddings retrieval. The work here presents a benchmark, a sorrow analysis, and a set of visualizations of the way latent embeddings routing works for long-context conversations in a multilingual, domain-specific environment. The results presented here show that the latent embeddings router is able to achieve performance on the same level as LLM-based routers with additional interpretability and higher level of control over model decision-making. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)

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18 pages, 1427 KiB

Open AccessArticle

Asymptotic and Probabilistic Perturbation Analysis of Controllable Subspaces

by Vera Angelova, Mihail Konstantinov and Petko Petkov

Computation 2024, 12(12), 236; https://doi.org/10.3390/computation12120236 - 28 Nov 2024

Abstract

In this paper, we consider the sensitivity of the controllable subspaces of single-input linear control systems to small perturbations of the system matrices. The analysis is based on the strict component-wise asymptotic bounds for the matrix of the orthogonal transformation to canonical form [...] Read more.

In this paper, we consider the sensitivity of the controllable subspaces of single-input linear control systems to small perturbations of the system matrices. The analysis is based on the strict component-wise asymptotic bounds for the matrix of the orthogonal transformation to canonical form derived by the method of the splitting operators. The asymptotic bounds are used to obtain probabilistic bounds on the angles between perturbed and unperturbed controllable subspaces implementing the Markoff inequality. It is demonstrated that the probability bounds allow us to obtain sensitivity estimates, which are much tighter than the usual deterministic bounds. The analysis is illustrated by a high-order example. Full article

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27 pages, 2988 KiB

Open AccessArticle

UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness

by Oleksandr Liubimov, Ihor Turkin, Valeriy Cheranovskiy and Lina Volobuieva

Computation 2024, 12(12), 235; https://doi.org/10.3390/computation12120235 - 27 Nov 2024

Abstract

The rising popularity of UAVs and other autonomous control systems coupled with real-time operating systems has increased the complexity of developing systems with the proper robustness, performance, and reactivity. The growing demand for more sophisticated computational tasks, proportionally larger payloads, battery limitations, and [...] Read more.

The rising popularity of UAVs and other autonomous control systems coupled with real-time operating systems has increased the complexity of developing systems with the proper robustness, performance, and reactivity. The growing demand for more sophisticated computational tasks, proportionally larger payloads, battery limitations, and smaller take-off mass requires higher energy efficiency for all avionics and mission computers. This paper aims to develop a technique for experimentally studying the indicators of reactivity and energy consumption in a computing platform for unmanned aerial vehicles (UAVs). The paper provides an experimental assessment of the ‘Boryviter 0.1’ computing platform, which is implemented on the ATSAMV71 microprocessor and operates under the open-source FreeRTOS operating system. The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. This paper provides experimental results of measurements of the energy consumed by the microcontroller and estimates of the reduction in system energy consumption due to additional time costs for suspending and resuming the computer’s operation. The results show that the ‘Boryviter 0.1’ computing platform can be used as a UAV mission computer for typical flight control tasks requiring real-time computing under the influence of external factors. As a further work direction, we plan to investigate the proposed energy-saving algorithms within the planned NASA F’Prime software flight framework. Such an investigation, which should use the mission computer’s actual flight computation load, will help to qualify the obtained energy-saving methods and their implementation results. Full article

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

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23 pages, 5123 KiB

Open AccessArticle

Application of Smart Condensed H-Adsorption Nanocomposites in Batteries: Energy Storage Systems and DFT Computations

by Fatemeh Mollaamin and Majid Monajjemi

Computation 2024, 12(12), 234; https://doi.org/10.3390/computation12120234 - 27 Nov 2024

Abstract

A comprehensive investigation of hydrogen grabbing towards the formation of hetero-clusters of AlGaN–H, Si–AlGaN–H, Ge–AlGaN–H, Pd–AlGaN–H, and Pt–AlGaN–H was carried out using DFT computations at the CAM–B3LYP–D3/6-311+G (d,p) level of theory. The notable fragile signal intensity close to the parallel edge of the [...] Read more.

A comprehensive investigation of hydrogen grabbing towards the formation of hetero-clusters of AlGaN–H, Si–AlGaN–H, Ge–AlGaN–H, Pd–AlGaN–H, and Pt–AlGaN–H was carried out using DFT computations at the CAM–B3LYP–D3/6-311+G (d,p) level of theory. The notable fragile signal intensity close to the parallel edge of the nanocluster sample might be owing to silicon or germanium binding-induced non-spherical distribution of Si–AlGaN or Ge–AlGaN hetero-clusters. Based on TDOS, the excessive growth technique of doping silicon, germanium, palladium, or platinum is a potential approach to designing high-efficiency hybrid semipolar gallium nitride devices in a long-wavelength zone. Therefore, it can be considered that palladium or platinum atoms in the functionalized Pd–AlGaN or Pt–AlGaN might have more impressive sensitivity for accepting the electrons in the process of hydrogen adsorption. The advantages of platinum or palladium over aluminum gallium nitride include its higher electron and hole mobility, allowing platinum or palladium doping devices to operate at higher frequencies than silicon or germanium doping devices. In fact, it can be observed that doped hetero-clusters of Pd–AlGaN or Pt–AlGaN might ameliorate the capability of AlGaN in transistor cells for energy storage. Full article

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40 pages, 773 KiB

Open AccessArticle

Multiple Behavioral Conditions of the Forward Exchange Rates and Stock Market Return in the South Asian Stock Markets During COVID-19: A Novel MT-QARDL Approach

by Mosab I. Tabash, Adel Ahmed, Suzan Sameer Issa, Marwan Mansour, Manishkumar Varma and Mujeeb Saif Mohsen Al-Absy

Computation 2024, 12(12), 233; https://doi.org/10.3390/computation12120233 - 26 Nov 2024

Abstract

This study examines the short- and long-term effects of multiple quantiles of forward exchange rate premiums (FERPs) and COVID-19 cases on the quantiles of stock market returns (SMRs). We extend the Quantile Autoregressive Distributive Lag (QARDL) model, and the Multiple Threshold Non-linear Autoregressive [...] Read more.

This study examines the short- and long-term effects of multiple quantiles of forward exchange rate premiums (FERPs) and COVID-19 cases on the quantiles of stock market returns (SMRs). We extend the Quantile Autoregressive Distributive Lag (QARDL) model, and the Multiple Threshold Non-linear Autoregressive Distributive Lag (NARDL) model propose a new Multiple Threshold Quantile Autoregressive Distributive Lag (MT-QARDL) approach. Unlike MT-NARDL, QARDL, and NARDL, the MT-QARDL model, which integrates the MT-NARDL model and the quantile regression methodology, captures both short- and long-term locational and sign-based asymmetries. For instance, at lower quantiles for Indian and Sri Lankan SMRs, bearish FERP exerts a positive influence, while bullish FERP has a negative effect during COVID-19. Conversely, bullish FERP negatively affects lower quantiles of SMRs of Bangladesh, India, and Sri Lanka, whereas bearish FERP either yields an opposite effect or remain statistically insignificant during COVID-19. The findings underscore long-term sign-based asymmetries due to the differential bearish and bullish FERP impact during COVID-19. However, in the long term, location-based asymmetries also existed as bullish FERP negative influence the SMRs of India, Bangladesh and Sri Lanka at higher quantiles but SMRs at lower quantiles insignificantly respond to the bullish FERP fluctuations during COVID-19. Full article

(This article belongs to the Special Issue Computational Approaches in Corporate Finance, Risk Management and Financial Markets)

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13 pages, 5738 KiB

Open AccessArticle

Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology

by Mariangel Rodríguez, Claudio Córdova, Isabel Benjumeda and Sebastián San Martín

Computation 2024, 12(12), 232; https://doi.org/10.3390/computation12120232 - 26 Nov 2024

Abstract

Cervical cancer (CC) remains a significant health issue, especially in low- and middle-income countries (LMICs). While Pap smears are the standard screening method, they have limitations, like low sensitivity and subjective interpretation. Liquid-based cytology (LBC) offers improvements but still relies on manual analysis. [...] Read more.

Cervical cancer (CC) remains a significant health issue, especially in low- and middle-income countries (LMICs). While Pap smears are the standard screening method, they have limitations, like low sensitivity and subjective interpretation. Liquid-based cytology (LBC) offers improvements but still relies on manual analysis. This study explored the potential of deep learning (DL) for automated cervical cell classification using both Pap smears and LBC samples. A novel image segmentation algorithm was employed to extract single-cell patches for training a ResNet-50 model. The model trained on LBC images achieved remarkably high sensitivity (0.981), specificity (0.979), and accuracy (0.980), outperforming previous CNN models. However, the Pap smear dataset model achieved significantly lower performance (0.688 sensitivity, 0.762 specificity, 0.8735 accuracy). This suggests that noisy and poor cell definition in Pap smears pose challenges for automated classification, whereas LBC provides better classifiable cells patches. These findings demonstrate the potential of AI-powered cervical cell classification for improving CC screening, particularly with LBC. The high accuracy and efficiency of DL models combined with effective segmentation can contribute to earlier detection and more timely intervention. Future research should focus on implementing explainable AI models to increase clinician trust and facilitate the adoption of AI-assisted CC screening in LMICs. Full article

(This article belongs to the Section Computational Engineering)

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16 pages, 1920 KiB

Open AccessArticle

Data Analysis and Prediction for Emergency Supplies Demand Through Improved Dynamics Model: A Reflection on the Post Epidemic Era

by Weiqing Zhuang, Qiong Wu and Morgan C. Wang

Computation 2024, 12(11), 231; https://doi.org/10.3390/computation12110231 - 19 Nov 2024

Abstract

Throughout history, humanity has grappled with infectious diseases that pose serious risks to health and life. The COVID-19 pandemic has profoundly impacted society, prompting significant reflection on preparedness and response strategies. In the future, humans may face unexpected disasters or crises, making it [...] Read more.

Throughout history, humanity has grappled with infectious diseases that pose serious risks to health and life. The COVID-19 pandemic has profoundly impacted society, prompting significant reflection on preparedness and response strategies. In the future, humans may face unexpected disasters or crises, making it essential to learn from the COVID-19 experience, especially in ensuring adequate emergency supplies and mobilizing resources effectively in times of need. Efficient emergency medical management is crucial during sudden outbreaks, and the preparation and allocation of medical supplies are vital to safeguarding lives, health, and safety. However, the unpredictable nature of epidemics, coupled with population dynamics, means that infection rates and supply needs within affected areas are uncertain. By studying the factors and mechanisms influencing emergency supply demand during such events, materials can be distributed more efficiently to minimize harm. This study enhances the existing dynamics model of infectious disease outbreaks by establishing a demand forecasting model for emergency supplies, using Hubei Province in China as a case example. This model predicts the demand for items such as masks, respirators, and food in affected regions. Experimental results confirm the model’s effectiveness and reliability, providing support for the development of comprehensive emergency material management systems. Ultimately, this study offers a framework for emergency supply distribution and a valuable guideline for relief efforts. Full article

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18 pages, 1030 KiB

Open AccessReview

Biomechanics of Parkinson’s Disease with Systems Based on Expert Knowledge and Machine Learning: A Scoping Review

by Luis Pastor Sánchez-Fernández

Computation 2024, 12(11), 230; https://doi.org/10.3390/computation12110230 - 17 Nov 2024

Abstract

Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, [...] Read more.

Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, motor clinical assessment depends on visual observations, which are mostly qualitative, with subtle differences not recognized. Many works have examined evaluations and analyses of these biomechanical alterations. However, there are no reviews on this topic. This paper presents a scoping review of computer models based on expert knowledge and machine learning (ML). The eligibility criteria and sources of evidence are represented by papers in journals indexed in the Journal Citation Report (JCR), and this paper analyzes the data, methods, results, and application opportunities in clinical environments or as support for new research. Finally, we analyze the results’ explainability and the acceptance of such systems as tools to help physicians, both now and in future contributions. Many researchers have addressed PD biomechanics by using explainable artificial intelligence or combining several analysis models to provide explainable and transparent results, considering possible biases and precision and creating trust and security when using the models. Full article

(This article belongs to the Special Issue Application of Biomechanical Modeling and Simulation)

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21 pages, 3239 KiB

Open AccessArticle

Diatomic: An Open-Source Excel Application to Calculate Thermodynamic Properties for Diatomic Molecules

by André Melo

Computation 2024, 12(11), 229; https://doi.org/10.3390/computation12110229 - 15 Nov 2024

Abstract

In this paper, I present Diatomic, an open-source Excel application that calculates molar thermodynamic properties for diatomic ideal gases. This application is very easy to use and requires only a limited number of molecular constants, which are freely available online. Despite its simplicity, [...] Read more.

In this paper, I present Diatomic, an open-source Excel application that calculates molar thermodynamic properties for diatomic ideal gases. This application is very easy to use and requires only a limited number of molecular constants, which are freely available online. Despite its simplicity, Diatomic provides methodologies and results that are usually unavailable in general quantum chemistry packages. This application uses the general formalism of statistical mechanics, enabling two models to describe the rotational structure and two models to describe the vibrational structure. In this work, Diatomic was used to calculate standard molar thermodynamic properties for a set of fifteen diatomic ideal gases. A special emphasis was placed on the analysis of four properties (standard molar enthalpy of formation, molar heat capacity at constant pressure, average molar thermal enthalpy, and standard molar entropy), which were compared with experimental values. A molecular interpretation for the molar heat capacity at constant pressure, as an interesting pedagogical application of Diatomic, was also explored in this paper. Full article

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24 pages, 19107 KiB

Open AccessArticle

Enhanced Wavelet Scattering Network for Image Inpainting Detection

by Adrian-Alin Barglazan and Remus Brad

Computation 2024, 12(11), 228; https://doi.org/10.3390/computation12110228 - 13 Nov 2024

Abstract

The rapid advancement of image inpainting tools, especially those aimed at removing artifacts, has made digital image manipulation alarmingly accessible. This paper proposes several innovative ideas for detecting inpainting forgeries based on a low-level noise analysis by combining Dual-Tree Complex Wavelet Transform (DT-CWT) [...] Read more.

The rapid advancement of image inpainting tools, especially those aimed at removing artifacts, has made digital image manipulation alarmingly accessible. This paper proposes several innovative ideas for detecting inpainting forgeries based on a low-level noise analysis by combining Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization, and lastly by employing an innovative combination of texture segmentation with noise variance estimations. The DT-CWT offers significant advantages due to its shift-invariance, enhancing its robustness against subtle manipulations during the inpainting process. Furthermore, its directional selectivity allows for the detection of subtle artifacts introduced by inpainting within specific frequency bands and orientations. Various neural network architectures were evaluated and proposed. Lastly, we propose a fusion detection module that combines texture analysis with noise variance estimation to give the forged area. Also, to address the limitations of existing inpainting datasets, particularly their lack of clear separation between inpainted regions and removed objects—which can inadvertently favor detection—we introduced a new dataset named the Real Inpainting Detection Dataset. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives. Full article

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8 pages, 1126 KiB

Open AccessBrief Report

Preliminary Study of Airfoil Design Synthesis Using a Conditional Diffusion Model and Smoothing Method

by Kazuo Yonekura, Yuta Oshima and Masaatsu Aichi

Computation 2024, 12(11), 227; https://doi.org/10.3390/computation12110227 - 13 Nov 2024

Abstract

Generative models such as generative adversarial networks and variational autoencoders are widely used for design synthesis. A diffusion model is another generative model that outperforms GANs and VAEs in image processing. It has also been applied in design synthesis, but was limited to [...] Read more.

Generative models such as generative adversarial networks and variational autoencoders are widely used for design synthesis. A diffusion model is another generative model that outperforms GANs and VAEs in image processing. It has also been applied in design synthesis, but was limited to only shape generation. It is important in design synthesis to generate shapes that satisfy the required performance. For such aims, a conditional diffusion model has to be used, but has not been studied. In this study, we applied a conditional diffusion model to the design synthesis and showed that the output of this diffusion model contains noisy data caused by Gaussian noise. We show that we can conduct flow analysis on the generated data by using smoothing filters. Full article

(This article belongs to the Section Computational Engineering)

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31 pages, 4874 KiB

Open AccessArticle

Power Quality Analysis of a Microgrid-Based on Renewable Energy Sources: A Simulation-Based Approach

by Emmanuel Hernández-Mayoral, Christian R. Jiménez-Román, Jesús A. Enriquez-Santiago, Andrés López-López, Roberto A. González-Domínguez, Javier A. Ramírez-Torres, Juan D. Rodríguez-Romero and O. A. Jaramillo

Computation 2024, 12(11), 226; https://doi.org/10.3390/computation12110226 - 12 Nov 2024

Abstract

At present, microgrids (μGs) are a focal point in both academia and industry due to their capability to sustain operations that are stable, resilient, reliable, and of high power quality. Power converters (PCs), a vital component in μGs, enable the decentralization of power [...] Read more.

At present, microgrids (μGs) are a focal point in both academia and industry due to their capability to sustain operations that are stable, resilient, reliable, and of high power quality. Power converters (PCs), a vital component in μGs, enable the decentralization of power generation. However, this decentralization introduces challenges related to power quality. This paper introduces a μG model, based on the IEEE 14-bus distribution system, with the objective of investigating power quality when the μG is operating in conjunction with the conventional power grid. The μG model was developed using MATLAB-Simulink^®, a tool specialized for electrical engineering simulations. The results obtained undergo thorough analysis and are compared with the compatibility levels set by the IEEE-519 standard. This method enables a precise evaluation of the μGs’ capacity to maintain acceptable power quality levels while interconnected with the conventional power grid. In conclusion, this study contributes significantly to the field of μGs by providing a detailed and quantitative assessment of power quality. This will assist in the design and optimization of μGs for effective implementation in real-world electric power systems. Full article

(This article belongs to the Section Computational Engineering)

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16 pages, 4589 KiB

Open AccessArticle

Comparison of Preprocessing Method Impact on the Detection of Soldering Splashes Using Different YOLOv8 Versions

by Peter Klco, Dusan Koniar, Libor Hargas and Marek Paskala

Computation 2024, 12(11), 225; https://doi.org/10.3390/computation12110225 - 12 Nov 2024

Abstract

Quality inspection of electronic boards during the manufacturing process is a crucial step, especially in the case of specific and expensive power electronic modules. Soldering splash occurrence decreases the reliability and electric properties of final products. This paper aims to compare different YOLOv8 [...] Read more.

Quality inspection of electronic boards during the manufacturing process is a crucial step, especially in the case of specific and expensive power electronic modules. Soldering splash occurrence decreases the reliability and electric properties of final products. This paper aims to compare different YOLOv8 models (small, medium, and large) with the combination of basic image preprocessing techniques to achieve the best possible performance of the designed algorithm. As preprocessing methods, contrast-limited adaptive histogram equalization (CLAHE) and image color channel manipulation are used. The results show that a suitable combination of the YOLOv8 model and preprocessing methods leads to an increase in the recall parameter. In our inspection task, recall can be considered the most important metric. The results are supported by a standard two-way ANOVA test. Full article

(This article belongs to the Section Computational Engineering)

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24 pages, 6023 KiB

Open AccessArticle

Advanced Control Scheme Optimization for Stand-Alone Photovoltaic Water Pumping Systems

by Maissa Farhat and Oscar Barambones

Computation 2024, 12(11), 224; https://doi.org/10.3390/computation12110224 - 11 Nov 2024

Abstract

This study introduces a novel method for controlling an autonomous photovoltaic pumping system by integrating a Maximum Power Point Tracking (MPPT) control scheme with variable structure Sliding Mode Control (SMC) alongside Perturb and Observe (P&O) algorithms. The stability of the proposed SMC method [...] Read more.

This study introduces a novel method for controlling an autonomous photovoltaic pumping system by integrating a Maximum Power Point Tracking (MPPT) control scheme with variable structure Sliding Mode Control (SMC) alongside Perturb and Observe (P&O) algorithms. The stability of the proposed SMC method is rigorously analyzed using Lyapunov’s theory. Through simulation-based comparisons, the efficacy of the SMC controller is demonstrated against traditional P&O methods. Additionally, the SMC-based system is experimentally implemented in real time using dSPACE DSP1104, showcasing its robustness in the presence of internal and external disturbances. Robustness tests reveal that the SMC controller effectively tracks Maximum Power Points (MMPs) despite significant variations in load and solar irradiation, maintaining optimal performance even under challenging conditions. The results indicate that the SMC system can achieve up to a 70% increase in water flow rates compared with systems without MPPT controllers. Furthermore, SMC demonstrated high sensitivity to sudden changes in environmental conditions, ensuring efficient power extraction from the photovoltaic panels. This study highlights the advantages of integrating SMC into Photovoltaic Water Pumping Systems (PV-WPSs), providing enhanced control capabilities and optimizing system performance. The findings contribute to the development of sustainable water supply solutions, particularly in remote areas with limited access to the electrical grid. Full article

(This article belongs to the Section Computational Engineering)

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