Journal of Computational Science最新文献

The development of statistically and biologically competent Community Detection Algorithm (CDA) is essential for extracting hidden information from massive biological datasets. This study introduces a novel community index as well as a CDA based on the newly introduced community index. To validate the effectiveness and robustness of the communities identified by the proposed CDA, we compare with six sets of communities identified by well-known CDAs, namely, FastGreedy, infomap, labelProp, leadingEigen, louvain, and walktrap. It is observed that the proposed algorithm outperforms its competing algorithms in terms of several prominent statistical and biological measures. We implement the hardware coding with Verilog, which surprisingly reduces the computation time by 20% compared to R programming while extracting the communities. Next, the communities identified by the proposed algorithm are used for topological and biological analysis with reference to the elite genes, obtained from Genecards, to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC). Finally, we discover that the genes F2RL3, CALM1, LPAR1, ARPC2, and CLDN7 carry significantly high topological and biological relevance of previously established ESCC elite genes. Further the established wet lab results also substantiate our claims. Hence, we affirm the aforesaid genes, as ESCC potential biomarkers.

The accurate detection of outliers in gene expression datasets plays a crucial role in the unraveling of intricate biological processes. This research introduces "SymNOM-GED," an innovative algorithm for outlier mining in gene expression datasets, with a focus on Esophageal Squamous Cell Carcinoma (ESCC). SymNOM-GED leverages symmetric neighbor to effectively identify outliers by considering local and global gene expression patterns. Extensive experiments demonstrate that SymNOM-GED outperforms existing algorithms in terms of accuracy, robustness, and scalability. The algorithm's performance is validated using clustering coefficient, graph density, and modularity, confirming its superiority. SymNOM-GED's precise and reliable outlier detection capabilities contribute significantly to bioinformatics research, offering insights into gene expression patterns in diverse biological contexts.

Natural disasters, such as tsunamis and earthquakes, may affect gravely on human lives, infrastructure, and economy. Negative effects of these situations can be minimized with the help of technology. In this paper we propose ITS4Tsunamis, an Intelligent Transportation System (ITS) that combines different technologies to help emergency management agencies to provide safe routes for their emergency vehicles. In the case of a tsunami emergency, these agencies must consider different aspects when moving with their vehicles, such as the road state and the vehicle features. Technologies such as Complex Event Processing (CEP) can be used to gather and process the information provided by a collection of sensors and assess the corresponding emergency level. In addition, we introduce uncertainty as a key element when determining the road-status, since this factor is uncertain by itself, and is based on the flood level and the number and size of obstacles in the roads. Fuzzy Logic (FL) is then used to deal with uncertainty and help authorities to evaluate the road accessibility. Safe routes are obtained using Colored Petri Nets (CPNs), a graphical formalism that allows us to analyze concurrent systems. This approach has been applied to Cádiz, a city in the southwest of Spain, which is close to an active tectonic rift. This work is an extended version of the conference paper by Díaz et al. (2023) [1].

The construction of implicit RKN is investigated in this paper. We finally obtain four four-stages implicit integrators by considering the symmetric, symplectic, and trigonometric fitting conditions. For the new obtained methods, we analyze their global convergence and stability property. And we carry out numerical experiments on some commonly considered problems in the literature. In view of the numerical experiments, we observe that the new methods outperform several efficient RKN methods in terms of accuracy and efficiency.

Simulation is a useful and effective way to analyze and study complex, real-world systems, allowing researchers, practitioners, and decision makers to make sense of the inner working of a system involving many factors, often resulting in some sort of emergent behavior. The number of parameter value combinations grows exponentially and it quickly becomes infeasible to test them all or even to explore a suitable subset. How does one then efficiently identify the parameter value combinations that matter for a particular simulation study, and determine their impact on the result? In addition, is it possible to train a machine learning model to predict the outcome of an agent-based model (ABM) with a systematically chosen small subset of parameter value combinations, such that the result could be predicted without running the ABM? We use covering arrays to create $t$-way ($t$ = 2, 3, etc.) combinations of parameter values to significantly reduce an ABM’s parameter value exploration space, which is supported by our prior work. In our ICCS 2023 paper (Olsen et al., 2023) we built on that work by applying it to Wilensky’s Heatbugs model and training a random forest machine learning model to predict simulation results by using the covering arrays to select our training and test data. Our results show that a 2-way covering array provides sufficient training data to train our random forest to predict three different simulation outcomes. Our process of using covering arrays to decrease parameter space to then predict ABM results using machine learning is successful. In this paper that extends the ICCS 2023 paper (Olsen et al., 2023), we analyze the role of parameter combinations and parameter values in determining model output via combination frequency difference (CFD) analysis and Shapley values. CFD has not previously been applied to agent-based models; we provide a process for using this approach and compare and contrast with Shapley values and random forest feature importance.

The vertical structure of vegetation canopies creates micro-climates. However, the land components of most Earth System Models, including the Energy Exascale Earth System Model (E3SM), typically neglect vertical canopy structure by using a single layer big-leaf representation to simulate water, CO${}_2$, and energy exchanges between the land and the atmosphere. In this study, we developed a Multi-Layer Canopy Model for the E3SM Land Model to resolve the micro-climate created by vegetation canopies. The model developed in this study re-implements the CLM-ml_v1 to support heterogeneous computing architectures consisting of CPUs and GPUs and includes three additional optimization-based stomatal conductance models. The use of Portable, Extensible Toolkit for Scientific Computation provides a speedup of 25–50 times on a GPU relative to a CPU. The numerical implementation of the model was verified against CLM-ml_v1 for a month-long simulation using data from the Ameriflux US-University of Michigan Biological Station site. Model structural uncertainty was explored by performing control simulations for five stomatal conductance models that exclude and include the control of plant hydrodynamics (PHD) on photosynthesis. The bias in simulated sensible and latent heat fluxes was lower when PHD was accounted for in the model. Additionally, six idealized simulations were performed to study the impact of three environmental variables (i.e. air temperature, atmospheric CO${}_2$, and soil moisture) on canopy processes (i.e. net CO${}_2$ assimilation, leaf temperature, and leaf water potential). Increasing air temperature reduced net CO${}_2$ assimilation and increased air temperature. Net CO${}_2$ assimilation increased at higher atmospheric CO${}_2$, while decreasing soil moisture resulted in lower leaf water potential.

This study focuses on the task of developing automated models for complex aerobatic aircraft maneuvers. The approach employed here utilizes Behavioral Cloning, a technique in which human pilots supply a series of sample maneuvers. These maneuvers serve as training data for a Machine Learning algorithm, enabling the system to generate control models for each maneuver. The optimal instances for each maneuver were chosen based on a set of objective evaluation criteria. By utilizing these selected sets of examples, resilient models were developed, capable of reproducing the maneuvers performed by the human pilots who supplied the examples. In certain instances, these models even exhibited superior performance compared to the pilots themselves, a phenomenon referred to as the “clean-up effect”. We also explore the application of transfer learning to adapt the developed controllers to various airplane models, revealing compelling evidence that transfer learning is effective for refining them for targeted aircraft. A comprehensive set of intricate maneuvers was executed through a meta-controller capable of orchestrating the fundamental maneuvers acquired through imitation. This undertaking yielded promising outcomes, demonstrating the proficiency of several Machine Learning models in successfully executing highly intricate aircraft maneuvers. This paper is an extended version of the previously ICCS 2023 published conference paper [1] .

One of the critical aspects of structure-based drug design is to choose important druggable binding sites in the protein's crystallography structures. As experimental processes are costly and time-consuming, computational drug design using machine learning algorithms is recommended. Over recent years, deep learning methods have been utilized in a wide variety of research applications such as binding site prediction. In this study, a new combination of attention blocks in the 3D U-Net model based on semantic segmentation methods is used to improve localization of pocket prediction. The attention blocks are tuned to find which point and channel of features should be emphasized along spatial and channel axes. Our model's performance is evaluated through extensive experiments on several datasets from different sources, and the results are compared to the most recent deep learning-based models. The results indicate the proposed attention model (Att-UNet) can predict binding sites accurately, i.e. the overlap of the predicted pocket using the proposed method with the true binding site shows statistically significant improvement when compared to other state-of-the-art models. The attention blocks may help the model focus on the target structure by suppressing features in irrelevant regions.

In recent years, multi-objective evolutionary algorithm based on decomposition has gradually attracted people's interest. However, this algorithm has some problems. For example, the diversity of the algorithm is poor, and the convergence and diversity of the algorithm are unbalanced. In addition, users don't always care about the entire Pareto front. Sometimes they may only be interested in specific areas of entire Pareto front. Based on the above problems, this paper proposes a decomposition-based multi-objective evolutionary algorithm with dynamic weight vector (MOEA/D-DWV). Firstly, a weight vector generation model with uniform distribution or preference distribution is proposed. Users can decide which type of weight vector to generate according to their own wishes. Then, two combination evolution operators are proposed to better balance the convergence and diversity of the algorithm. Finally, a dynamic adjustment strategy of weight vector is proposed. This strategy can adjust the distribution of weight vector adaptively according to the distribution of solutions in the objective space, so that the population can be uniformly distributed in the objective space as much as possible. MOEA/D-DWV algorithm is compared with 9 advanced multi-objective evolutionary algorithms. The comparison results show that MOEA/D-DWV algorithm is more competitive.

Data availability

Data will be made available on request.

A spreading process can be observed when particular information, substances, or diseases spread through a population over time in social and biological systems. It is widely believed that contact interactions among individual entities play an essential role in the spreading process. Although contact interactions are often influenced by geometrical conditions, little attention has been paid to understand their effects, especially on contact duration among pedestrians. To examine how the pedestrian flow setups affect contact duration distribution, we have analyzed trajectories of pedestrians in contact interactions collected from pedestrian flow experiments of uni-, bi- and multi-directional setups. Based on turning angle entropy and efficiency, we have classified the type of motion observed in the contact interactions. We have found that the majority of contact interactions in the unidirectional flow setup can be categorized as confined motion, hinting at the possibility of long-lived contact duration. However, ballistic motion is more frequently observed in the other flow conditions, yielding frequent, brief contact interactions. Our results demonstrate that observing more confined motions is likely associated with the increase of parallel contact interactions regardless of pedestrian flow setups. This study highlights that the confined motions tend to yield longer contact duration, suggesting that the infectious disease transmission risk would be considerable even for low transmissibility. These results have important implications for crowd management in the context of minimizing spreading risk.

This work is an extended version of Kwak et al. (2023) presented at the 2023 International Conference on Computational Science (ICCS).