Computation

10 pages, 847 KiB

Open AccessBrief Report

Minimizing Cohort Discrepancies: A Comparative Analysis of Data Normalization Approaches in Biomarker Research

by Alisa Tokareva, Natalia Starodubtseva, Vladimir Frankevich and Denis Silachev

Computation 2024, 12(7), 137; https://doi.org/10.3390/computation12070137 (registering DOI) - 5 Jul 2024

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Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood [...] Read more.

Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood spots in the context of the Rice–Vannucci model of hypoxic–ischemic encephalopathy (HIE) in rats. The quality of normalization was assessed through the performance of Orthogonal Partial Least Squares (OPLS) models built on the training datasets; the sensitivity and specificity of these models were calculated by application to validation datasets. PQN, MRN, and VSN demonstrated a higher diagnostic quality of OPLS models than the other methods studied. The OPLS model based on VSN demonstrated superior performance (86% sensitivity and 77% specificity). After VSN, the VIP-identified potential biomarkers notably diverged from those identified using other normalization methods. Glycine consistently emerged as the top marker in six out of seven models, aligning perfectly with our prior research findings. Likewise, alanine exhibited a similar pattern. Notably, VSN uniquely highlighted pathways related to the oxidation of brain fatty acids and purine metabolism. Our findings underscore the widespread utility of VSN in metabolomics, suggesting its potential for use in large-scale and cross-study investigations. Full article

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

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20 pages, 445 KiB

Open AccessArticle

Interpolation for Neural Network Operators Activated by Smooth Ramp Functions

by Fesal Baxhaku, Artan Berisha and Behar Baxhaku

Computation 2024, 12(7), 136; https://doi.org/10.3390/computation12070136 - 4 Jul 2024

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Abstract

In the present article, we extend the results of the neural network interpolation operators activated by smooth ramp functions proposed by Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016). We give different results from Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016) we discuss the [...] Read more.

In the present article, we extend the results of the neural network interpolation operators activated by smooth ramp functions proposed by Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016). We give different results from Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016) we discuss the high-order approximation result using the smoothness of

φ

and a related Voronovskaya-type asymptotic expansion for the error of approximation. In addition, we showcase the related fractional estimates result and the fractional Voronovskaya type asymptotic expansion. We investigate the approximation degree for the iterated and complex extensions of the aforementioned operators. Finally, we provide numerical examples and graphs to effectively illustrate and validate our results. Full article

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21 pages, 4376 KiB

Open AccessArticle

Novel Methods for Synthesizing Self-Checking Combinational Circuits by Means of Boolean Signal Correction and Polynomial Codes

by Dmitry V. Efanov, Ruslan B. Abdullaev, Dmitry G. Plotnikov, Marina V. Bolsunovskaya, Alexey S. Odoevsky and Georgy S. Vasilyanov

Computation 2024, 12(7), 135; https://doi.org/10.3390/computation12070135 - 1 Jul 2024

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Abstract

This paper proposes the use of a polynomial code for synthesizing self-checking digital devices. The code is chosen for its error detection characteristics in data symbols and is used for Boolean signals correction in embedded control circuits. In practice, it is possible to [...] Read more.

This paper proposes the use of a polynomial code for synthesizing self-checking digital devices. The code is chosen for its error detection characteristics in data symbols and is used for Boolean signals correction in embedded control circuits. In practice, it is possible to equip the device with the ability to detect faults. In contrast to the approaches found in the world literature to solve this problem, this proposal suggests identifying groups of structurally independent outputs to distinguish between convertible and non-convertible outputs of the diagnosed block in the embedded control circuit. The only outputs that can be converted are those that are used as checking symbols for the polynomial code in the embedded control circuit. The other functions remain unchanged. The polynomial codes are used to select them. The authors present algorithms for synthesizing fault detection devices using the proposed approach. Full article

(This article belongs to the Section Computational Engineering)

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19 pages, 8959 KiB

Open AccessArticle

Mathematical Modeling of the Drug Particles Deposition in the Human Respiratory System—Part 1: Development of Virtual Models of the Upper and Lower Respiratory Tract

by Natalia Menshutina, Elizaveta Mokhova and Andrey Abramov

Computation 2024, 12(7), 134; https://doi.org/10.3390/computation12070134 - 1 Jul 2024

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Abstract

In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototy** of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid, [...] Read more.

In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototy** of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid, a mathematical model of airflow in channels of complex geometry (respiratory system) has been developed. Based on the data obtained, the results of computational experiments for a single-phase system are presented. Full article

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

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15 pages, 5474 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Models for Predicting Viscosity in Tri-n-Butyl Phosphate Mixtures Using Experimental Data

by Faranak Hatami and Mousa Moradi

Computation 2024, 12(7), 133; https://doi.org/10.3390/computation12070133 - 30 Jun 2024

Viewed by 268

Abstract

Tri-n-butyl phosphate (TBP) is essential in the chemical industry for dissolving and purifying various inorganic acids and metals, especially in hydrometallurgical processes. Recent advancements suggest that machine learning can significantly improve the prediction of TBP mixture viscosities, saving time and resources while minimizing [...] Read more.

Tri-n-butyl phosphate (TBP) is essential in the chemical industry for dissolving and purifying various inorganic acids and metals, especially in hydrometallurgical processes. Recent advancements suggest that machine learning can significantly improve the prediction of TBP mixture viscosities, saving time and resources while minimizing exposure to toxic solvents. This study evaluates the effectiveness of five machine learning algorithms for automating TBP mixture viscosity prediction. Using 511 measurements collected across different compositions and temperatures, the neural network (NN) model proved to be the most accurate, achieving a Mean Squared Error (MSE) of 0.157% and an adjusted R² (a measure of how well the model predicts the variability of the outcome) of 99.72%. The NN model was particularly effective in predicting the viscosity of TBP + ethylbenzene mixtures, with a minimal deviation margin of 0.049%. These results highlight the transformative potential of machine learning to enhance the efficiency and precision of hydrometallurgical processes involving TBP mixtures, while also reducing operational risks. Full article

(This article belongs to the Section Computational Engineering)

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22 pages, 3145 KiB

Open AccessArticle

Candlestick Pattern Recognition in Cryptocurrency Price Time-Series Data Using Rule-Based Data Analysis Methods

by Illia Uzun, Mykhaylo Lobachev, Vyacheslav Kharchenko, Thorsten Schöler and Ivan Lobachev

Computation 2024, 12(7), 132; https://doi.org/10.3390/computation12070132 - 29 Jun 2024

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Abstract

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of [...] Read more.

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market. Full article

22 pages, 925 KiB

Open AccessReview

Factors, Prediction, and Explainability of Vehicle Accident Risk Due to Driving Behavior through Machine Learning: A Systematic Literature Review, 2013–2023

by Javier Lacherre, José Luis Castillo-Sequera and David Mauricio

Computation 2024, 12(7), 131; https://doi.org/10.3390/computation12070131 - 28 Jun 2024

Viewed by 505

Abstract

Road accidents are on the rise worldwide, causing 1.35 million deaths per year, thus encouraging the search for solutions. The promising proposal of autonomous vehicles stands out in this regard, although fully automated driving is still far from being an achievable reality. Therefore, [...] Read more.

Road accidents are on the rise worldwide, causing 1.35 million deaths per year, thus encouraging the search for solutions. The promising proposal of autonomous vehicles stands out in this regard, although fully automated driving is still far from being an achievable reality. Therefore, efforts have focused on predicting and explaining the risk of accidents using real-time telematics data. This study aims to analyze the factors, machine learning algorithms, and explainability methods most used to assess the risk of vehicle accidents based on driving behavior. A systematic review of the literature produced between 2013 and July 2023 on factors, prediction algorithms, and explainability methods to predict the risk of traffic accidents was carried out. Factors were categorized into five domains, and the most commonly used predictive algorithms and explainability methods were determined. We selected 80 articles from journals indexed in the Web of Science and Scopus databases, identifying 115 factors within the domains of environment, traffic, vehicle, driver, and management, with speed and acceleration being the most extensively examined. Regarding machine learning advancements in accident risk prediction, we identified 22 base algorithms, with convolutional neural network and gradient boosting being the most commonly used. For explainability, we discovered six methods, with random forest being the predominant choice, particularly for feature importance analysis. This study categorizes the factors affecting road accident risk, presents key prediction algorithms, and outlines methods to explain the risk assessment based on driving behavior, taking vehicle weight into consideration. Full article

(This article belongs to the Section Computational Engineering)

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Computation, Volume 12, Issue 7 (July 2024) – 7 articles

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