International Journal of Modeling, Simulation, and Scientific Computing

The rise in urbanization has led to an increase in high-density communities. Our study drew inspiration from the fishbone structure and applied a bionic architectural layout to optimize the wind environment within the pedestrian areas of these communities. Additionally, our study delved into the intrinsic principles and simulation-based optimization methods for the wind environment in the pedestrian areas of the fishbone bionic architectural complex. Using Computational Fluid Dynamics (CFD) simulation technology, our study simulated the building models both before and after the community renovation. Furthermore, our study simulated nine architectural complex models proposed by our study, considering the architectural layout and facade height within the community. By verifying and comparing the simulation results, our study observed that the fishbone bionic architectural form significantly improved the wind environment within the community’s pedestrian areas. Regarding the facade height, our study discovered that taller architectural units should be positioned away from the prevailing wind direction to enhance the airflow within the architectural complex. This research carries substantial implications for the renovation and planning of residential communities, particularly in large urban settings with a substantial population.

Succinic acid, also known as dicarboxylic acid, is one of the biochemical products chemically produced from Escherichia coli (E. coli) metabolism. However, by using conventional methods succinic acid cannot be produced sufficiently and it is costly. Hence, there is a lot of ongoing research on E. coli by using in silico methods. Researchers build computational models of E. coli to analyze and modify their metabolic network. This paper proposes a hybrid of ant colony optimization–genetic algorithm–flux balance analysis (ACOGAFBA) in enhancing the succinic acid production of E. coli by identifying genes to be knocked out. Ant colony optimization (ACO) is a swarm intelligent optimization that is inspired based on the natural foraging behavior of ant colony. Local search technique like genetic algorithm (GA) is applied to solve optimization and search problem by approximation. Flux balance analysis (FBA) is used for fitness calculation after gene knockout. FBA identifies a point (fitness) in flux space by using quadratic programming, which is closest to the wild type point. ACOGAFBA produced three sets of gene knockout lists. The dataset $i$JR904 is used in this paper. The results show that ACOGAFBA can identify the set of knockout genes to improve succinic acid production in E. coli.

Non-premixed flames are encountered in different combustion facilities. Experimental and numerical studies of non-premixed flames have been made by many investigators, but only a few works reported simultaneous experimental and numerical studies on instantaneous flows and flame structures. In this paper, a review is given for studies on the instantaneous flow and flame structures of non-premixed flames using both particle image velocimetry (PIV) and large-eddy simulation (LES) by us. The LES statistical results are accessed by measurement results. The measured and simulated instantaneous results show the strong interaction between coherent structures and combustion. It is found that larger secondary-air flow leads to early formation of coherent structures and the transition from large vortexes to small vortices. The flame structures are significantly affected by the coherent structures. The spiral vortex structures formed by swirl lead to intensification of combustion

A reasonable electrochemical mechanism model is the basis for accurately describing the actual operation of hydrogen fuel cell (HFC). In this paper, a multidisciplinary modeling and simulation method is proposed. Based on the internal electrochemical reaction kinetics and electrochemical thermodynamics mechanism of the HFC, a voltage output model and a back pressure regulation model are constructed. By analyzing the relationship between the back pressure of the anode and cathode of the HFC and the effective partial pressure of the reactant gas, a voltage output model and back pressure adjustment model are integrated to form an HFC electrochemical mechanism model, which realizes the dynamic and accurate description of the HFC output performance. The final experimental results show that the electrochemical mechanism model of the HFC can achieve good results.

DEVS is a sound Modeling and Simulation (M&S) framework that describes a model in a modular and hierarchical way. It comes along with an abstract simulation algorithm which defines its operational semantics. Many variants of such an algorithm have been proposed by DEVS researchers. Yet, the proper interpretation and analysis of the computational complexity of such approaches have not been systematically addressed and defined. As systems become larger and more complex, the efficiency of the DEVS simulation algorithms in terms of time complexity measure becomes a major issue. Therefore, it is necessary to devise a method for computing this complexity. This paper proposes a generic method to address such an issue, taking advantage of the recursion embedded in the triggered-by-message principle of the DEVS simulation protocol. The applicability of the method is shown through the complexity analysis of various DEVS simulation algorithms.

Along with the rapid development of the Internet technology, social network sites (SNSs) are increasingly emerging. As various types of datasets are easily exposed to the network, privacy security in SNS becomes the focus of the study. Anonymization techniques, such as generalization and bucketization, and data perturbation techniques are usually employed in data publishing for privacy protection. However, in the meanwhile, the privacy protection methods reduce the utility of the data. Thus, the issue of privacy-utility trade-off becomes one very real problem. By considering the privacy-utility trade-off and combining the minimum spanning tree (MST) clustering technique and the sub-graph generalization technique, a privacy protection model is proposed for data publishing in weighted social networks. The MST-based clustering method is one representative and concise graph-based clustering algorithm. Meanwhile, as one MST of an edge-weighted graph is the minimum weight spanning tree, some important properties of the graph like the shortest path are preserved in the MST. Finally, on the basis of sub-graphs (clusters) divided by the MST-clustering, three sub-graph generalization approaches are designed in one reasonable way for data publishing to guarantee the privacy-utility trade-off of the proposed method. Experiments and result analysis indicate workability of the proposed method that the proposed model offers privacy protection for the data publishing by means of the sub-graph generalization approach while it can also improve the data utility especially for occasions like data mining, machine learning, and pattern recognition.

The flange pin of landing gears, which is made of high strength low alloy, is peened with ZirShot-ZC600 ceramic shots. The residual stresses generated in the peened flange pin, which will affect the fatigue performance of the pin, are investigated with finite element analysis (FEA). Three-dimensional FEA model is created to simulate the target surface and shot peening flow which is generated randomly using the MATLAB program. It is revealed that the residual stresses of the peened surface layer in longitudinal, circumferential and radial direction all vary in depth from the target surface and they are all compressive with the maximum compressive stresses occurring at subsurface. The effects of target surface curvature and shot peening angle on the residual stress profiles in the peened surface are also studied. The FEA simulation results show that the maximum residual stresses in flat surface are greater than that in the surface with curvature and with increasing peening angle, the location of the maximum residual stresses moves down in depth from the peened surface.

In this study, a predator–prey population model has been described with disease in the predator species. This is a three-dimensional study comprising of a prey and a predator taken in two different groups such as susceptible and infected predator species. Dynamical behavior of the spread of the disease having the potential to become epidemic has been discussed. Parametric conditions are determined for the control of disease outbreak. Some basic properties like boundedness, persistence of the system have been ensured. Minimal conditions are framed, in such way that the disease can be naturally controlled. Different qualitative behavior like stability, bifurcation and numerical simulations has been performed. Substantial numerical simulations have been carried out in order to validate the obtained theoretical results.

Spintronic systems demonstrate significant value due to variety of features that can be used in bio-inspired computing applications. Skyrmion is a magnetic nanostructure which can be used as an information carrier. In this paper, we simulate vortex-free Gaussian wave packet by incorporating spin–orbit coupling (SoC) into three-component Bose–Einstein condensates (BECs). We next observe that skyrmions can be naturally generated from vortex-free Gaussian wave packet, further these structurally asymmetric devices are constructed and initial device classifications are carried out. A spin device consisting of vertically-stacked multiple skyrmions is proposed termed as skyrmion spintronic synapse (SkSS) and skyrmion spintronic Neuron (SkSN). This paper depicts training the neural network with surrogate gradients with deep learning strategies using topologically nontrivial spin textures. The current popular Graphics Processing Unit (GPU)-based neural network computations are compared with the proposed method. This research paves a way for a neuromorphic computing systems on exotic topological solitons.

Social media initiatives educate the public about the tools and strategies necessary to defend themselves from emerging diseases. The effect of media advertisements in combating an infectious disease is explored in the present investigation. The numerical results show that the system destabilizes when the growth rate of media advertisements goes above a certain value. For lower ranges of the rate of distribution of awareness through social media platforms, our system showcases stable endemic state. But as the rate of disseminating awareness surpasses a critical value, the system undergoes a Hopf bifurcation and limit cycle oscillations appear. However, the persistent oscillations are killed out and the system returns to a stable endemic state as the rate of distribution of awareness exceeds another threshold value. Extremely large values of the awareness distribution through media advertisements eradicate the disease from the entire community. Model parameters with significant impacts on the disease prevalence and eradication are identified through sensitivity analysis. We extend our model to encapsulate the seasonal variation in the rate of media advertisements. Sufficient conditions have been derived for which the nonautonomous system exhibits globally attractive positive periodic solution. The nonautonomous system is shown to produce chaotic dynamics under the circumstances of the corresponding autonomous system that exhibits limit cycle oscillations. Overall, our findings deduce that media advertisements play a critical role in raising public awareness and ultimately resulting in the eradication of the disease from the society.

NP-complete and NP-hard problems, such as subgraph isomorphism, are well-known benchmarks to evaluate computation power of unconventional computing approaches. Optical computing, as one of the most important unconventional approaches, has determinant features such as massive parallelism that allows us to solve hard computational problems in an efficient time, while in the similar situations conventional computers require exponential time. In this paper, we solve subgraph isomorphism, as an NP-complete problem, using optical computing in a polynomial time after a polynomial preprocessing time. The proposed method has two main steps: first, possible solution space of the problem is generated using an optical manner and then all invalid solutions are eliminated from the space by several designed optical filters. The filters are made in the preprocessing phase in a polynomial time while the space of each filter is exponential. In this study, we have provided a solution to the YES/NO decision version of the problem.

In this paper, we propose an extended numerical algorithm for the numerical solution of the Benjamin–Bona–Mahony–Burgers equation. This algorithm involves the application of wavelet theory. First, we use the Quasilinearization technique of linearization and apply the 3-scale Haar wavelet approach for truncation error. This algorithm is constructed from two wavelet functions that make it robust and highly accurate. A multi-resolution is used to generate the Haar basis function. We consider three cases of a mathematical problem for the accuracy of the presented algorithm. The obtained results show good agreement with analytical solutions and have better accuracy.

Today clustering-based machine learning algorithms are the important field in data mining. Here, medical data clustering is one of the core applications of data mining to predict and identify the risk factor of the disease. At the same time, medical data clustering is a very important and challenging task due to its complexity and high frequency of data. In order to achieve proper data clustering, this paper proposed a hybrid data clustering algorithm by the combination of $K$-Means and Black Hole Entropic Fuzzy Clustering (BHEFC). $K$-Means is the first and one of the most popular and low-computation cost partitioned-based clustering algorithms. There are two modules in this hybrid clustering, first some number of iterations are executed by the first module of this hybrid clustering algorithm, which is $K$-Means clustering. After some number of iterations, the clustering solutions are shifted to the second module of this hybrid clustering algorithm, which is Entrophic Fuzzy Clustering. So, it can get the advantages of both algorithms. $K$-Means clustering algorithm can produce fast clustering solution due to its low-computation cost. But it can go for premature convergence. To overcome this problem, the second module used BHEFC, which can use large amount of high-frequency medical data. The experimental results are done with the medical practitioners to predict the risk factors of the heart disease patients and doctors can give the suggestions based on the risk factors. Finally, the efficiency of the proposed Hybrid $K$-Means and BHEFC is analyzed by three different performance measures.

Parkinson’s disease (PD) is a neurological disease that produces uncontrollable movements and a variety of other symptoms. It can be difficult to make an accurate PD diagnosis since the signs and symptoms, especially early on, might be mistaken for other medical diseases or physiological changes associated with normal aging. This research proposed novel technique in predicting PD based on dopamine transporter scan (DaTscan) images of brain using deep learning techniques. Here the aim is to collect the historical data and live DaTscan image of patients with symptoms of PD and predict disease. Initially input data have been pre-processed for image resize, noise removal and smoothening. Then the processed image has been selected based on their features using kernel-based deep convolution neural network (KDCNN). The selected deep features have been classified using reinforcement Q-learning-based neural networks (RQLNNs) to predict the presence of PD. Here experimental results show feature-selected and classified output of DaTscan brain image using the proposed model. For MRI image dataset, the proposed technique obtained accuracy of 97.5%, precision of 93%, recall of 82% and F-1 score of 87%. The proposed technique obtained accuracy of 98%, precision of 93%, recall of 80% and F-1 score of 88% for DaTscan dataset.

Picture highlights can be separated in different ways based on the shape highlights, shading highlights and surface highlights. There is a wide requirement for fragmenting objects in complex circumstances and recognizable proof of the articles. It has become mind-boggling because of the changeability of articles and the foundation. This paper targets planning a mixture approach called Enhanced Fractal Texture Analysis with Layout Descriptor by breaking down different element extraction and item acknowledgment methods. In this interaction, at first, the Adaptive Switching Median Filter pre-processes the picture. This is accomplished by eliminating commotion that is available in the picture without losing the fine characteristics. Other than the commotion decrease, safeguard edges are tended to a clamour-secured edge identifier. Afterward, in the morphological inclination method, which is the mix of shape and surface slope expulsion is applied for acquiring the characteristics of the picture. This methodology upholds further developing exactness expectations for the item. This planning strategy extricates shape included from the principal stage yield. Different subtleties like smallness, unpredictability and second invariants can be obtained in the methodology. The crossover approach diminishes the execution time when contrasted with the existing methods. This plan is powerful and creates better qualities as far as execution assessment. The term ‘coronary sickness’ suggests that square veins may provoke the conditions like cardiovascular disappointment, chest torture or stroke. The heart conditions will affect the heart’s muscle, valves, brain (cerebral aneurysm) or musicality inciting the heart pollution and sidestepping activity or coronary mediation which is utilized for settling these issues. In this examination work, a sensible cluster-based deep neural network (DNN) approach is proposed to recognize the angiographic coronary sickness (for instance to perceive the patients with half expansiveness abatement of a huge coronary vein). The educational assortment is collected using K-means clustering estimation and a while later the coronary sickness is expected using bunch based significant learning approach. The proposed method is differentiated and has various limits for classifier estimations like DNN, SVM-linear, SVM-polynomial, KNN, ELM, ELM-pack, and to show the system reasonability to the extent precision.

Breast cancer is one of the leading causes of untimely deaths among women in various countries across the world. This can be attributed to many factors including late detection which often increase its severity. Thus, detecting the disease early would help mitigate its mortality rate and other risks associated with it. This study developed a hybrid machine learning model for timely prediction of breast cancer to help combat the disease. The dataset from Kaggle was adopted to predict the breast tumor growth and sizes using random tree classification, logistic regression, XBoost tree and multilayer perceptron on the dataset. The implementation of these machine learning algorithms and visualization of the results was done using Python. The results achieved a high accuracy (99.65%) on training and testing datasets which is far better than traditional means. The predictive model has good potential to enhance early detection and diagnosis of breast cancer and improvement of treatment outcome. It could also assist patients to timely deal with their condition or life patterns to support their recovery or survival.

The thyroid gland generates hormones to influence human body metabolism. A proper analysis of thyroid glands functional data is needed for Thyroid Disease (TD) diagnosis. Detecting the TD early is a crucial issue. Disparate alternatives approach was generated for it over time, but inaccurate detection of TD remains. Thus, aimed at detecting TD, the work renders an effectual dual classification framework. The framework was split into seven phases. Initially, to convert the string data into a numerical value, data numeralization is performed. After that, preprocessing is executed, which processes the missing value by means of taking the average and eliminating the repeated data. Next, so as to scale the entire data into a single unit, normalization is carried out. And the essential features are extracted, and Modified Discrete Salp Swarm Algorithm (MDSSA) Features Selection (FS) technique is developed in order to get rid of relevant features. Currently, an Improved Sign Preserving Cluster Ensemble (ISPCE) is proposed to cluster the chosen features accurately. It clusters the TD utilizing base clusters and lessens the decision graph complications, time intricacy, etc. Finally, the clustered features are inputted to Improved Raven Roosting Optimization Algorithm with Gradient Cats Boost Artificial Neural Network (IRR-GCBANN) for classifying the TD as hypothyroidism, hyperthyroidism, or normal. Experimental outcomes exhibit that the proposed framework attains 96.94% accuracy for detecting the TD when weighted against the existent techniques.

Automatic speech emotion recognition (ASER) from source speech signals is quite a challenging task since the recognition accuracy is highly dependent on extracted features of speech that are utilized for the classification of speech emotion. In addition, pre-processing and classification phases also play a key role in improving the accuracy of ASER system. Therefore, this paper proposes a deep learning convolutional neural network (DLCNN)-based ASER model, hereafter denoted with ASERNet. In addition, the speech denoising is employed with spectral subtraction (SS) and the extraction of deep features is done using integration of linear predictive coding (LPC) with Mel-frequency Cepstrum coefficients (MFCCs). Finally, DLCNN is employed to classify the emotion of speech from extracted deep features using LPC-MFCC. The simulation results demonstrate the superior performance of the proposed ASERNet model in terms of quality metrics such as accuracy, precision, recall, and F1-score, respectively, compared to state-of-the-art ASER approaches.