Computation

20 pages, 3382 KiB

Open AccessArticle

Optimization and Prediction of the Mechanical Properties of Concrete with Crumb Rubber and Stainless-Steel Fibers Under Varying Temperatures

by Ayman El-Zohairy and Osman Hamdy

Computation 2025, 13(1), 14; https://doi.org/10.3390/computation13010014 - 9 Jan 2025

Abstract

This research develops an equation to describe the relationship between stress (σ) and strain (ε) in concrete under different conditions. It includes important parameters from earlier studies to improve predictions of stress–strain behavior, especially for concrete with crumb rubber and stainless-steel fibers at [...] Read more.

This research develops an equation to describe the relationship between stress (σ) and strain (ε) in concrete under different conditions. It includes important parameters from earlier studies to improve predictions of stress–strain behavior, especially for concrete with crumb rubber and stainless-steel fibers at various temperatures. The initial phase assessed three existing stress–strain formulas as a basis for optimization. Using the Genetic Algorithm (GA) and the Whale Optimization Algorithm (WOA), a new equation was created to simulate the stress–strain relationship while considering temperature changes and material additions. Results showed that Formula (1), optimized with the WOA, performed much better than other polynomial and exponential formulas, proving the WOA’s effectiveness over the traditional GA. A comparison of the mechanical properties from experiments and those predicted by the new formula showed a high level of accuracy. Key properties like the maximum stress, strain at maximum stress, modulus of elasticity, and toughness were well captured. The findings highlight how temperature and material composition significantly affect concrete’s mechanical behavior. Overall, this research offers important insights into the factors influencing concrete performance, providing a solid framework for future studies and practical applications in engineering and construction. The proposed formula is a reliable tool for predicting concrete’s mechanical properties under various conditions, which aids in better modeling and optimization in concrete design. Full article

(This article belongs to the Special Issue Applications of Intelligent Computing and Modeling in Construction Engineering)

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13 pages, 2194 KiB

Open AccessArticle

Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks

by Van Truong Vo, Samad Noeiaghdam, Denis Sidorov, Aliona Dreglea and Liguo Wang

Computation 2025, 13(1), 13; https://doi.org/10.3390/computation13010013 - 8 Jan 2025

Abstract

Nonlinear differential equations and systems play a crucial role in modeling systems where time-dependent factors exhibit nonlinear characteristics. Due to their nonlinear nature, solving such systems often presents significant difficulties and challenges. In this study, we propose a method utilizing Physics-Informed Neural Networks [...] Read more.

Nonlinear differential equations and systems play a crucial role in modeling systems where time-dependent factors exhibit nonlinear characteristics. Due to their nonlinear nature, solving such systems often presents significant difficulties and challenges. In this study, we propose a method utilizing Physics-Informed Neural Networks (PINNs) to solve the nonlinear energy supply–demand (ESD) system. We design a neural network with four outputs, where each output approximates a function that corresponds to one of the unknown functions in the nonlinear system of differential equations describing the four-dimensional ESD problem. The neural network model is then trained, and the parameters are identified and optimized to achieve a more accurate solution. The solutions obtained from the neural network for this problem are equivalent when we compare and evaluate them against the Runge–Kutta numerical method of order 5(4) (RK45). However, the method utilizing neural networks is considered a modern and promising approach, as it effectively exploits the superior computational power of advanced computer systems, especially in solving complex problems. Another advantage is that the neural network model, after being trained, can solve the nonlinear system of differential equations across a continuous domain. In other words, neural networks are not only trained to approximate the solution functions for the nonlinear ESD system but can also represent the complex dynamic relationships between the system’s components. However, this approach requires significant time and computational power due to the need for model training. Furthermore, as this method is evaluated based on experimental results, ensuring the stability and convergence speed of the model poses a significant challenge. The key factors influencing this include the manner in which the neural network architecture is designed, such as the selection of hyperparameters and appropriate optimization functions. This is a critical and highly complex task, requiring experimentation and fine-tuning, which demand substantial expertise and time. Full article

(This article belongs to the Section Computational Engineering)

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24 pages, 7924 KiB

Open AccessArticle

Finite Element Analysis of Occupant Risk in Vehicular Impacts into Cluster Mailboxes

by Emre Palta, Lukasz Pachocki, Dawid Bruski, Qian Wang, Christopher Jaus and Howie Fang

Computation 2025, 13(1), 12; https://doi.org/10.3390/computation13010012 - 8 Jan 2025

Abstract

The deployment of cluster mailboxes (CMs) in the U.S. has raised safety concerns for passengers in potential vehicular crashes involving CMs. This study investigated the crashworthiness of two types of CMs through nonlinear finite element simulations. Two configurations of CM arrangements were considered: [...] Read more.

The deployment of cluster mailboxes (CMs) in the U.S. has raised safety concerns for passengers in potential vehicular crashes involving CMs. This study investigated the crashworthiness of two types of CMs through nonlinear finite element simulations. Two configurations of CM arrangements were considered: a single- and a dual-unit setup. These CM designs were tested on flat-road conditions with and without a curb. A 2010 Toyota Yaris and a 2006 Ford F250, both in compliance with the Manual for Assessing Safety Hardware (MASH), were employed in the analysis. The simulations incorporated airbag models, seatbelt restraint systems, and a Hybrid III 50th percentile adult male dummy. The investigations focused on evaluating the safety of vehicle occupants in 32 impact scenarios and under MASH Test Level 1 conditions (with an impact speed of 50 km/h). The simulation results provided insights into occupant risk and determined the primary failure mode of the CMs. No components of the mailboxes were found intruding into the vehicle’s occupant compartment. For all considered cases, the safety factors remained within allowable limits, indicating only a marginal risk of potential injury to occupants posed by the considered CMs. Full article

(This article belongs to the Special Issue Advances in Crash Simulations: Modeling, Analysis, and Applications)

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28 pages, 7469 KiB

Open AccessArticle

Bifurcation Analysis of a Discrete Basener–Ross Population Model: Exploring Multiple Scenarios

by A. A. Elsadany, A. M. Yousef, S. A. Ghazwani and A. S. Zaki

Computation 2025, 13(1), 11; https://doi.org/10.3390/computation13010011 - 7 Jan 2025

Abstract

The Basener and Ross mathematical model is widely recognized for its ability to characterize the interaction between the population dynamics and resource utilization of Easter Island. In this study, we develop and investigate a discrete-time version of the Basener and Ross model. First, [...] Read more.

The Basener and Ross mathematical model is widely recognized for its ability to characterize the interaction between the population dynamics and resource utilization of Easter Island. In this study, we develop and investigate a discrete-time version of the Basener and Ross model. First, the existence and the stability of fixed points for the present model are investigated. Next, we investigated various bifurcation scenarios by establishing criteria for the occurrence of different types of codimension-one bifurcations, including flip and Neimark–Sacker bifurcations. These criteria are derived using the center manifold theorem and bifurcation theory. Furthermore, we demonstrated the existence of codimension-two bifurcations characterized by 1:2, 1:3, and 1:4 resonances, emphasizing the model’s complex dynamical structure. Numerical simulations are employed to validate and illustrate the theoretical predictions. Finally, through bifurcation diagrams, maximal Lyapunov exponents, and phase portraits, we uncover a diversity of dynamical characteristics, including limit cycles, periodic solutions, and chaotic attractors. Full article

(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)

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27 pages, 959 KiB

Open AccessReview

From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems

by Xin Gu, Muralee Krish, Shaleeza Sohail, Sweta Thakur, Fariza Sabrina and Zongwen Fan

Computation 2025, 13(1), 10; https://doi.org/10.3390/computation13010010 - 3 Jan 2025

Abstract

Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores various integer programming techniques and [...] Read more.

Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores various integer programming techniques and their effectiveness in optimising complex scheduling requirements in higher education institutions. We analysed 95 integer programming-based models developed for solving university timetabling problems, covering relevant research from 1990 to 2023. The goal is to provide insights into the evolution of these algorithms and their impact on improving university scheduling. We identify that the implementation rate of models using integer programming is 98%, which is much higher than 34% implementation rates using meta-heuristics algorithms from the existing review. The integer programming models are analysed by the problem types, solutions, tools, and datasets. For three types of timetabling problems including course timetabling, class timetabling, and exam timetabling, we dive deeper into the commercial solvers CPLEX (47), Gurobi (11), Lingo (5), Open Solver (4), C++ GLPK (4), AIMMS (2), GAMS (2), XPRESS (2), CELCAT (1), AMPL (1), and Google OR-Tools CP-SAT (1) and identify that CPLEX is the most frequently used integer programming solver. We explored the uses of machine learning algorithms and the hybrid solutions of combining the integer programming and machine learning algorithms in higher education timetabling solutions. We also identify areas for future work, which includes an emphasis on using integer programming algorithms in other industrial areas, and using machine learning models for university timetabling to allow data-driven solutions. Full article

(This article belongs to the Section Computational Social Science)

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18 pages, 2813 KiB

Open AccessArticle

Multimodal Data Fusion for Depression Detection Approach

by Mariia Nykoniuk, Oleh Basystiuk, Nataliya Shakhovska and Nataliia Melnykova

Computation 2025, 13(1), 9; https://doi.org/10.3390/computation13010009 - 2 Jan 2025

Abstract

Depression is one of the most common mental health disorders in the world, affecting millions of people. Early detection of depression is crucial for effective medical intervention. Multimodal networks can greatly assist in the detection of depression, especially in situations where in patients [...] Read more.

Depression is one of the most common mental health disorders in the world, affecting millions of people. Early detection of depression is crucial for effective medical intervention. Multimodal networks can greatly assist in the detection of depression, especially in situations where in patients are not always aware of or able to express their symptoms. By analyzing text and audio data, such networks are able to automatically identify patterns in speech and behavior that indicate a depressive state. In this study, we propose two multimodal information fusion networks: early and late fusion. These networks were developed using convolutional neural network (CNN) layers to learn local patterns, a bidirectional LSTM (Bi-LSTM) to process sequences, and a self-attention mechanism to improve focus on key parts of the data. The DAIC-WOZ and EDAIC-WOZ datasets were used for the experiments. The experiments compared the precision, recall, f1-score, and accuracy metrics for the cases of using early and late multimodal data fusion and found that the early information fusion multimodal network achieved higher classification accuracy results. On the test dataset, this network achieved an f1-score of 0.79 and an overall classification accuracy of 0.86, indicating its effectiveness in detecting depression. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health: 2nd Edition)

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24 pages, 7611 KiB

Open AccessArticle

Advancements in Predictive Analytics: Machine Learning Approaches to Estimating Length of Stay and Mortality in Sepsis

by Houssem Ben Khalfallah, Mariem Jelassi, Jacques Demongeot and Narjès Bellamine Ben Saoud

Computation 2025, 13(1), 8; https://doi.org/10.3390/computation13010008 - 1 Jan 2025

Abstract

Sepsis remains a major global health concern, causing high mortality rates, prolonged hospital stays, and substantial economic burdens. The accurate prediction of clinical outcomes, such as mortality and length of stay (LOS), is critical for optimizing hospital resource allocation and improving patient management. [...] Read more.

Sepsis remains a major global health concern, causing high mortality rates, prolonged hospital stays, and substantial economic burdens. The accurate prediction of clinical outcomes, such as mortality and length of stay (LOS), is critical for optimizing hospital resource allocation and improving patient management. The present study investigates the potential of machine learning (ML) models to predict these outcomes using a dataset of 1492 sepsis patients with clinical, physiological, and demographic features. After rigorous preprocessing to address missing data and ensure consistency, multiple classifiers, including Random Forest, Extra Trees, and Gradient Boosting, were trained and validated. The results demonstrate that Random Forest and Extra Trees achieve high accuracy for LOS prediction, while Gradient Boosting and Bernoulli Naïve Bayes effectively predict mortality. Feature importance analysis identified ICU stay duration (ICU_DAYS_OBS) as the most influential predictor for both outcomes, alongside vital signs, white blood cell counts, and lactic acid levels. These findings highlight the potential of ML-driven clinical decision support systems (CDSSs) to enhance early risk assessment, optimize ICU resource planning, and support timely interventions. Future research should refine predictive features, integrate advanced biomarkers, and validate models across larger and more diverse datasets to improve scalability and clinical impact. Full article

(This article belongs to the Special Issue Generative AI in Action: Trends, Applications, and Implications)

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20 pages, 3392 KiB

Open AccessArticle

Impedance Controller Analysis for a Two-Degrees-Of-Freedom Ankle Rehabilitation Machine with Serious Game Interactions

by Oscar I. Cirilo-Piñon, Agustín Barrera-Sánchez, Cesar H. Guzmán-Valdivia, Manuel Adam-Medina, Rafael Campos-Amezcua, Andrés Blanco-Ortega and Arturo Martínez-Mata

Computation 2025, 13(1), 7; https://doi.org/10.3390/computation13010007 - 31 Dec 2024

Abstract

An ankle sprain can be caused by daily activities such as running, walking, or playing sports. In many cases, the patient’s ankle suffers severe or permanent damage that requires rehabilitation to return to its initial state. Thanks to technological advances, robotics has allowed [...] Read more.

An ankle sprain can be caused by daily activities such as running, walking, or playing sports. In many cases, the patient’s ankle suffers severe or permanent damage that requires rehabilitation to return to its initial state. Thanks to technological advances, robotics has allowed for the development of machines that generate precise, efficient, and safe movements. In addition, these machines are manipulated by a specific control depending on the rehabilitation objective. Impedance control is used in ankle rehabilitation machines for active–resistive-type rehabilitation, where the patient participates by exerting a force on the machine repeatedly. Serious games are an example of an activity where the patient can interact with a video game while rehabilitating. Currently, most machines involving impedance control and targeted at serious gaming applications are mechanically composed of one degree of freedom, so the addition of another degree is a novelty. This paper presents simulation results comparing different impedance controls reported in the literature to determine the best option for applying a 2-DOF ankle rehabilitation machine using serious games. The results obtained are presented by comparing them according to the force applied to the rehabilitation machine (emulating the behavior of a patient). From the impedance controllers analyzed for horizontal (abduction/adduction) and vertical (dorsiflexion/plantarflexion) movements in the rehabilitation machine, it was determined that the PD control, which considers some mechanical parameters, presents a better performance. With this controller, fast and smooth angular movements are generated, while the consumption of kinetic energy is kept in a low range, proportional to the applied forces, compared to the other impedance controls analyzed. Full article

(This article belongs to the Special Issue Kinematics, Dynamics and Control for Rehabilitation Robotics and Prostheses)

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14 pages, 5255 KiB

Open AccessArticle

A Framework of the Meshless Method for Topology Optimization Using the Smooth-Edged Material Distribution for Optimizing Topology Method

by Jingbo Huang, Kai Long, Yutang Chen, Rongrong Geng, Ayesha Saeed, Hui Zhang and Tao Tao

Computation 2025, 13(1), 6; https://doi.org/10.3390/computation13010006 - 29 Dec 2024

Abstract

Density variables based on nodal or Gaussian points are naturally incorporated in meshless topology optimization approaches, pursuing distinct topological layouts with solid and void solutions. However, engineering applications have been hampered by the fact that the authentic structure boundary cannot be identified without [...] Read more.

Density variables based on nodal or Gaussian points are naturally incorporated in meshless topology optimization approaches, pursuing distinct topological layouts with solid and void solutions. However, engineering applications have been hampered by the fact that the authentic structure boundary cannot be identified without manual intervention. To alleviate this issue, the Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) method is developed within the context of meshless approximation. In meshless analysis, the non-overlap cell variables instead of nodal or Gaussian-based variables are adopted to characterize the existence or absence of sub-regions. This work proposes a non-penalized SEMDOT where an interpolation-based heuristic sensitivity expression is utilized. The 2D and 3D compliance minimization problems serve to validate the efficiency and applicability of the proposed non-penalized SEMDOT approach based on the framework of the meshless method. The numerical results demonstrated that the proposed approach is capable of generating final designs with continuous and smooth edges or surfaces. Full article

(This article belongs to the Special Issue Advanced Topology Optimization: Methods and Applications)

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23 pages, 7572 KiB

Open AccessArticle

Arabic Temporal Common Sense Understanding

by Reem Alqifari, Hend Al-Khalifa and Simon O’Keefe

Computation 2025, 13(1), 5; https://doi.org/10.3390/computation13010005 - 28 Dec 2024

Abstract

Natural language understanding (NLU) includes temporal text understanding, which can be complex and encompasses temporal common sense understanding. There are many challenges in comprehending common sense within a text. Currently, there is a limited number of datasets containing temporal common sense in English [...] Read more.

Natural language understanding (NLU) includes temporal text understanding, which can be complex and encompasses temporal common sense understanding. There are many challenges in comprehending common sense within a text. Currently, there is a limited number of datasets containing temporal common sense in English and there is an absence of such datasets specifically for the Arabic language. In this study, an Arabic dataset was constructed based on an available English dataset. This dataset is considered a valuable resource for the Arabic community. Consequently, different multilingual pre-trained language models (PLMs) were applied to both the English and new Arabic datasets. Based on this, the effectiveness of these models in Arabic and English is compared and discussed. After analyzing the errors, a new categorization of errors was proposed. Finally, the ability of the PLMs to understand the input text and predict temporal features was evaluated. Through this detailed categorization of errors and classification of temporal elements, this study establishes a comprehensive framework aimed at clarifying the specific challenges encountered by PLMs in temporal common sense understanding (TCU). This methodology underscores the urgent need for further research on PLMs’ capabilities for TCU tasks. Full article

(This article belongs to the Special Issue Recent Advances on Computational Linguistics and Natural Language Processing)

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24 pages, 3377 KiB

Open AccessArticle

A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks

by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna and Roshan Kumar

Computation 2025, 13(1), 4; https://doi.org/10.3390/computation13010004 - 27 Dec 2024

Abstract

Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. Traditional models often underestimate yields because of limitations associated with single data sources and simplistic model architectures. These prevent complex, multifaceted factors influencing crop growth [...] Read more.

Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. Traditional models often underestimate yields because of limitations associated with single data sources and simplistic model architectures. These prevent complex, multifaceted factors influencing crop growth and yield from being captured. In this line, this work fuses multi-source data—satellite imagery, weather data, and soil properties—through the approach of multi-modal fusion using Convolutional Neural Networks and Recurrent Neural Networks. While satellite imagery provides information on spatial data regarding crop health, weather data provides temporal insights, and the soil properties provide important fertility information. Fusing these heterogeneous data sources embeds an overall understanding of yield-determining factors in the model, decreasing the RMSE by 15% and improving R² by 20% over single-source models. We further push the frontier of feature engineering by using Temporal Convolutional Networks (TCNs) and Graph Convolutional Networks (GCNs) to capture time series trends, geographic and topological information, and pest/disease incidence. TCNs can capture long-range temporal dependencies well, while the GCN model has complex spatial relationships and enhanced the features for making yield predictions. This increases the prediction accuracy by 10% and boosts the F1 score for low-yield area identification by 5%. Additionally, we introduce other improved model architectures: a custom UNet with attention mechanisms, Heterogeneous Graph Neural Networks (HGNNs), and Variational Auto-encoders. The attention mechanism enables more effective spatial feature encoding by focusing on critical image regions, while the HGNN captures interaction patterns that are complex between diverse data types. Finally, VAEs can generate robust feature representation. Such state-of-the-art architectures could then achieve an MAE improvement of 12%, while R² for yield prediction improves by 25%. In this paper, the state of the art in yield prediction has been advanced due to the employment of multi-source data fusion, sophisticated feature engineering, and advanced neural network architectures. This provides a more accurate and reliable soybean yield forecast. Thus, the fusion of Convolutional Neural Networks with Recurrent Neural Networks and Graph Networks enhances the efficiency of the detection process. Full article

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24 pages, 1540 KiB

Open AccessArticle

Stock Price Prediction in the Financial Market Using Machine Learning Models

by Diogo M. Teixeira and Ramiro S. Barbosa

Computation 2025, 13(1), 3; https://doi.org/10.3390/computation13010003 - 26 Dec 2024

Abstract

This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global [...] Read more.

This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices are analyzed to be used as inputs for machine learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and XGBoost. The results show that while each model possesses distinct characteristics, selecting the most efficient approach heavily depends on the specific data and forecasting objectives. The complexity of advanced models such as XGBoost and GRU is reflected in their overall performance, suggesting that they can be particularly effective at capturing patterns and making accurate predictions in more complex time series, such as stock prices. Full article

(This article belongs to the Section Computational Social Science)

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14 pages, 4749 KiB

Open AccessArticle

On Adaptive Fractional Dynamic Sliding Mode Control of Suspension System

by Ali Karami-Mollaee and Oscar Barambones

Computation 2025, 13(1), 2; https://doi.org/10.3390/computation13010002 - 25 Dec 2024

Abstract

This paper introduces a novel adaptive control method for suspension vehicle systems in response to road disturbances. The considered model is based on an active symmetry quarter car (SQC) fractional order suspension system (FOSS). The word symmetry in SQC refers to the symmetry [...] Read more.

This paper introduces a novel adaptive control method for suspension vehicle systems in response to road disturbances. The considered model is based on an active symmetry quarter car (SQC) fractional order suspension system (FOSS). The word symmetry in SQC refers to the symmetry of the suspension system in the front tires or the rear tires of the car. The active suspension controller is generally driven by an external force like a hydraulic or pneumatic actuator. The external force of the actuator is determined using fractional dynamic sliding mode control (FDSMC) to counteract road disturbances and eliminate the chattering caused by sliding mode control (SMC). In FDSMC, a fractional integral acts as a low-pass filter before the system actuator to remove high-frequency chattering, necessitating an additional state for FDSMC implementation assuming all FOSS state variables are available but the parameters are unknown and uncertain. Hence, an adaptive procedure is proposed to estimate these parameters. To enhance closed-loop system performance, an adaptive proportional-integral (PI) procedure is also employed, resulting in the FDSMC-PI approach. A comparison is made between two SQC suspension system models, the fractional order suspension system (FOSS) and the integer order suspension system (IOSS). The IOSS controller is based on dynamic sliding mode control (DSMC) and a PI procedure (DSMC-PI). The results show that FDSMC outperforms DSMC. Full article

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11 pages, 16045 KiB

Open AccessArticle

Study of Ventilation Strategies in a Passenger Aircraft Cabin Using Numerical Simulation

by S. M. Abdul Khader, John Valerian Corda, Kevin Amith Mathias, Gowrava Shenoy, Kamarul Arifin bin Ahmad, Augustine V. Barboza, Sevagur Ganesh Kamath and Mohammad Zuber

Computation 2025, 13(1), 1; https://doi.org/10.3390/computation13010001 - 24 Dec 2024

Abstract

Aircraft cabins have high occupant densities and may introduce the risk of COVID-19 contamination. In this study, a segment of a Boeing 767 aircraft cabin with a mixing type of air distribution system was investigated for COVID-19 deposition. A section of a Boeing [...] Read more.

Aircraft cabins have high occupant densities and may introduce the risk of COVID-19 contamination. In this study, a segment of a Boeing 767 aircraft cabin with a mixing type of air distribution system was investigated for COVID-19 deposition. A section of a Boeing 737-300 cabin, featuring four rows with 28 box-shaped mannequins, was used for simulation. Conditioned air entered through ceiling inlets and exited near the floor, simulating a mixed air distribution system. Cough droplets were modeled using the Discrete Phase Model from two locations: the centre seat in the second row and the window seat in the fourth row. These droplets had a mean diameter of 90 µm, an exhalation velocity of 11.5 m/s and a flow rate of 8.5 L/s. A high-quality polyhedral mesh of about 7.5 million elements was created, with a skewness of 0.65 and an orthogonality of 0.3. The SIMPLE algorithm and a second-order upwind finite volume method were used to model airflow and droplet dynamics. It was found that the ceiling accounted for the maximum concentration followed by the seats. The concentration of deposits was almost 50% more when the source was at window as compared to the centre seat. The Covid particles resided for longer duration when the source was at the centre of the cabin than when it was located near the widow. Full article

(This article belongs to the Special Issue Advances in Computational Methods for Fluid Flow)

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15 pages, 12297 KiB

Open AccessArticle

Enhancing Accessibility: Automated Tactile Graphics Generation for Individuals with Visual Impairments

by Yehor Dzhurynskyi, Volodymyr Mayik and Lyudmyla Mayik

Computation 2024, 12(12), 251; https://doi.org/10.3390/computation12120251 - 23 Dec 2024

Abstract

This study addresses the accessibility challenges faced by individuals with visual impairments due to limited access to graphic information, which significantly impacts their educational and social integration. Traditional methods for producing tactile graphics are labor-intensive and require specialized expertise, limiting their availability. Recent [...] Read more.

This study addresses the accessibility challenges faced by individuals with visual impairments due to limited access to graphic information, which significantly impacts their educational and social integration. Traditional methods for producing tactile graphics are labor-intensive and require specialized expertise, limiting their availability. Recent advancements in generative models, such as GANs, diffusion models, and VAEs, offer potential solutions to automate the creation of tactile images. In this work, we propose a novel generative model conditioned on text prompts, integrating a Bidirectional and Auto-Regressive Transformer (BART) and Vector Quantized Variational Auto-Encoder (VQ-VAE). This model transforms textual descriptions into tactile graphics, addressing key requirements for legibility and accessibility. The model’s performance was evaluated using cross-entropy, perplexity, mean square error, and CLIP Score metrics, demonstrating its ability to generate high-quality, customizable tactile images. Testing with educational and rehabilitation institutions confirmed the practicality and efficiency of the system, which significantly reduces production time and requires minimal operator expertise. The proposed approach enhances the production of inclusive educational materials, enabling improved access to quality education and fostering greater independence for individuals with visual impairments. Future research will focus on expanding the training dataset and refining the model for complex scenarios. Full article

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

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19 pages, 314 KiB

Open AccessArticle

Development of Blockchain Technology in Financial Accounting

by Olha Prokopenko, Artem Koldovskiy, Marina Khalilova, Aigul Orazbayeva and José Machado

Computation 2024, 12(12), 250; https://doi.org/10.3390/computation12120250 - 23 Dec 2024

Abstract

This study investigates the transformative potential of blockchain technology in financial accounting by examining its applications, challenges, and implications. The study begins with a review of blockchain’s origins and its ability to address inefficiencies, fraud risks, and transparency limitations in traditional accounting. A [...] Read more.

This study investigates the transformative potential of blockchain technology in financial accounting by examining its applications, challenges, and implications. The study begins with a review of blockchain’s origins and its ability to address inefficiencies, fraud risks, and transparency limitations in traditional accounting. A mixed-methods approach was employed, combining qualitative thematic analysis and quantitative statistical techniques. The qualitative analysis involved thematic coding of data from case studies and organizational reports, while the quantitative analysis assessed financial data using descriptive and inferential statistical methods. Eight organizations from diverse industries—including banking, retail, and technology—were purposively sampled to capture varied experiences and applications of blockchain technology. Key findings reveal blockchain’s ability to enhance transparency, efficiency, and security in financial transactions, offering significant advantages for financial reporting and auditing. However, challenges such as regulatory uncertainties, scalability concerns, and technical complexities remain barriers to its widespread adoption. This research provides actionable recommendations to overcome these challenges and maximize blockchain’s benefits in financial accounting. By integrating theoretical insights with empirical evidence, this study contributes to advancing the understanding of blockchain’s role in transforming financial practices, offering practical guidance for academia and industry practitioners alike. Full article

(This article belongs to the Section Computational Social Science)

18 pages, 930 KiB

Open AccessCase Report

Ontological Representation of the Structure and Vocabulary of Modern Greek on the Protégé Platform

by Nikoletta Samaridi, Evangelos Papakitsos and Nikitas Karanikolas

Computation 2024, 12(12), 249; https://doi.org/10.3390/computation12120249 - 23 Dec 2024

Abstract

One of the issues in Natural Language Processing (NLP) and Artificial Intelligence (AI) is language representation and modeling, aiming to manage its structure and find solutions to linguistic issues. With the pursuit of the most efficient capture of knowledge about the Modern Greek [...] Read more.

One of the issues in Natural Language Processing (NLP) and Artificial Intelligence (AI) is language representation and modeling, aiming to manage its structure and find solutions to linguistic issues. With the pursuit of the most efficient capture of knowledge about the Modern Greek language and, given the scientifically certified usability of the ontological structuring of data in the field of the semantic web and cognitive computing, a new ontology of the Modern Greek language at the level of structure and vocabulary is presented in this paper, using the Protégé platform. With the specific logical and structured form of knowledge representation to express, this research processes and exploits in an easy and useful way the distributed semantics of linguistic information. Full article

(This article belongs to the Special Issue Recent Advances on Computational Linguistics and Natural Language Processing)

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21 pages, 7605 KiB

Open AccessArticle

Additive Manufacturing Gyroid Structures Used as Crash Energy Management

by Horacio Rostro-González, Guillermo Reyes-Pozo, Josep Maria Puigoriol-Forcada, Francisco-José López-Valdés, Sriharsha Srinivas Sundarram and Andres-Amador Garcia-Granada

Computation 2024, 12(12), 248; https://doi.org/10.3390/computation12120248 - 19 Dec 2024

Abstract

Gyroid-like structures are promising in terms of energy absorption levels. Due to additive manufacturing, they can now be manufactured and verified for different functions. In this article, it has been proven that a Gyroid manufactured by FDM using PLA with 0.2 relative density [...] Read more.

Gyroid-like structures are promising in terms of energy absorption levels. Due to additive manufacturing, they can now be manufactured and verified for different functions. In this article, it has been proven that a Gyroid manufactured by FDM using PLA with 0.2 relative density must be oriented so that compression takes place along the build direction to obtain higher levels of force and energy. The Gyroid can be scaled, allowing the use of a single compression curve with almost constant forces up to 50% compression. The model to predict properties as a function of relative density fits well with a power-law for n = 2.2. The ability of the Gyroid to absorb energy per kilogram is about seven times lower than that of a solid PLA cube, but it can be used to obtain desired levels of deceleration. It is possible to use a simple constant deceleration model to define the Gyroid size, mass, and velocity of the object to be impacted. The use of this approach allows the tailored combination of Gyroid sizes to meet multi-objective impact targets. The simulation of impacts with a finite element model of only 125 solid elements is possible with errors below 10%. By combining different Gyroid sizes, two different safety regulations can be met. Modeling the Gyroid by meshing the real geometry allows for the local maximum force magnified at high strain rates, but it is not able to correctly predict densification. Full article

(This article belongs to the Special Issue Advances in Crash Simulations: Modeling, Analysis, and Applications)

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21 pages, 2773 KiB

Open AccessArticle

Comparative Analysis of Energy Efficiency in Conventional, Modular, and 3D-Printing Construction Using Building Information Modeling and Multi-Criteria Decision-Making

by Abdullah Al Masri, Assed N. Haddad and Mohammad K. Najjar

Computation 2024, 12(12), 247; https://doi.org/10.3390/computation12120247 - 18 Dec 2024

Abstract

Energy efficiency has become a crucial focus with the growing attention on sustainable development and decreasing energy consumption in the built environment. Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This [...] Read more.

Energy efficiency has become a crucial focus with the growing attention on sustainable development and decreasing energy consumption in the built environment. Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This study aims to enhance the decision-making process by identifying optimal construction techniques, material selection, and ventilation window dimensions to promote sustainable energy use in buildings. A novel framework combining Building Information Modeling (BIM), computational analysis, and Multi-Criteria Decision-Making (MCDM) approaches is applied to assess the energy use intensity (EUI), annual electric energy consumption, and lifecycle energy cost across multiple sequences for each type of construction. Computational analysis in this research is combined in two main tools. Minitab is utilized for experimental design to determine the number and configurations of sequences analyzed. The Simple Additive Weighting (SAW) method, applied as an MCDM tool, is used to assess and rank the performance of sequences based on equally weighted criteria. Subsequently, 3D models of case study buildings are developed, and energy simulations are conducted using Autodesk Revit and Autodesk Green Building Studio, respectively, as BIM tools to compare the energy performance of various design alternatives. The results revealed that 3D printing surpassed other methods, where Sequence 7 achieved approximately 10.3% higher efficiency than modular methods and 40.5% better performance than conventional methods in the evaluated criteria. The findings underscore the higher energy efficiency of 3D printing, followed by modular construction as a competitive method, while conventional methods lagged significantly. Full article

(This article belongs to the Special Issue Applications of Intelligent Computing and Modeling in Construction Engineering)

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