Software Platform For Modeling Research Articles

PyMANGA: A modular, open and extendable software platform for modeling of forest and vegetation dynamics

Agent-based vegetation models are a widely used tool in ecology, for example, to understand and predict the response of vegetation to environmental change. Models are based on well-established descriptions of processes such as vegetation establishment, growth and mortality. However, they are often developed from scratch, which can be inefficient. Here we present pyMANGA, a free and open-source platform for plant growth modelers. pyMANGA's modular design allows for the combination of different concepts and theories of how plants establish, grow or compete in response to above- and below-ground resource availability. New or alternative modules describing, e.g., competition or facilitation, can be easily added. The interchangeability of modules supports the systematic testing of different hypotheses, e.g., on dominant processes in soil-plant feedback loops. Here we further present the thorough benchmarking strategy to maintain the platform and how pyMANGA can be used to compare models with different levels of abstraction and complexity.

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study.

Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based modeling workflow involves a series of interconnected components and various algorithms, but this makes it challenging, tedious, and labor intensive for radiologists and researchers to build customized models for specific clinical applications if they lack expertise in machine learning methods. We developed a user-friendly artificial intelligence-assisted diagnosis modeling software (AIMS) platform, which supplies standardized machine learning-based modeling workflows for computer-assisted diagnosis and prognosis systems with medical images. In contrast to other existing software platforms, AIMS contains both radiomics and CNN-based deep learning workflows, making it an all-in-one software platform for machine learning-based medical image analysis. The modular design of AIMS allows users to build machine learning models easily, test models comprehensively, and fairly compare the performance of different models in a specific application. The graphical user interface (GUI) enables users to process large numbers of medical images without programming or script addition. Furthermore, AIMS also provides a flexible image processing toolkit (e.g., semiautomatic segmentation, registration, morphological operations) to rapidly create lesion labels for multiphase analysis, multiregion analysis of an individual tumor (e.g., tumor mass and peritumor), and multimodality analysis. The functionality and efficiency of AIMS were demonstrated in 3 independent experiments in radiation oncology, where multiphase, multiregion, and multimodality analyses were performed, respectively. For clear cell renal cell carcinoma (ccRCC) Fuhrman grading with multiphase analysis (sample size =187), the area under the curve (AUC) value of the AIMS was 0.776; for ccRCC Fuhrman grading with multiregion analysis (sample size =177), the AUC value of the AIMS was 0.848; for prostate cancer Gleason grading with multimodality analysis (sample size =206), the AUC value of the AIMS was 0.980. AIMS provides a user-friendly infrastructure for radiologists and researchers, lowering the barrier to building customized machine learning-based computer-assisted diagnosis models for medical image analysis.

Development and testing of coarse-grained models for ultrasonic simulations of cast austenitic stainless steel

Ultrasonic inspection of cast austenitic stainless steel (CASS) in the nuclear industry is particularly challenging because of sound field scatter and attenuation caused by the coarse-grained microstructure. Modeling and simulation are important tools in ultrasonic testing, as they can be used to help address key aspects of inspections, such as developing new probe designs, predicting inspection reliability, and testing phased-array focal laws. However, developing a useful and reliable CASS model is challenging due to the many grain interfaces and crystalline orientations that must be captured. We demonstrate a method of creating a realistic CASS model that is usable in CIVA, a commercially available modeling and simulation software platform. Using polished and chemically etched sections to highlight the grain structure and orientation, we generate models of a coarse-grained equiaxed specimen and a columnar specimen. We also test an alternative method of generating a coarse-grained model using Voronoi regions. We qualitatively compare sound field scatter and quantitatively compare sound field attenuation and beam partitioning in simulated sound fields to those of laboratory-measured sound fields. Results show that the Voronoi models perform as well as or better than the models based on actual grain morphology. We also show that model-to-model randomness in Voronoi grain structure can impact the magnitude of a simulated echo response by a factor of two or more. Although CASS models are potentially a good depiction of reality for a given scenario, they should not be considered representative since CASS morphology can change significantly from specimen to specimen or within the same specimen.

EChem: A Notebook Exploration of Quantum Chemistry

The eChem project features an e-book published as a web page (10.30746/978-91-988114-0-7), collecting a repository of Jupyter notebooks developed for the dual purpose of explaining and exploring the theory underlying computational chemistry in a highly interactive manner as well as providing a tutorial-based presentation of the complex workflows needed to simulate embedded molecular systems of real biochemical and/or technical interest. For students ranging from beginners to advanced users, the eChem book is well suited for self-directed learning, but workshops led by experienced instructors and targeting student bodies with specific needs and interests can readily be formed from its components. This has been done by using eChem as the base for a workshop directed toward graduate students learning the theory and practices of quantum chemistry, resulting in very positive assessment of the interactive nature of this framework. The members of the eChem team are engaged in both education and research, and as a mirroring activity, we develop the open-source software upon which this e-book is predominantly based. The overarching vision and goal of our work is to provide a science- and education-enabling software platform for quantum molecular modeling on contemporary and future high-performance computing systems, and to document the resulting development and workflows in the eChem book.

Dynamic Reallocation Model of Multiple Unmanned Aerial Vehicle Tasks in Emergent Adjustment Scenarios

Due to their strong risk tolerance, low manufacturing cost and good maneuverability, unmanned aerial vehicles (UAVs) have been widely used in various fields. Among related challenges, coordinated task assignment is a key scientific issue for autonomous control of UAVs. In this paper, based on the idea of fuzzy C-means (FCM) clustering and the ant colony optimization (ACO) algorithm, a cooperative multiple task reallocation problem with target precedence constraints for heterogeneous UAVs is proposed. The contributions of this research are the performance evaluation of the original algorithms in a dynamic context, consideration of changes in some attributes of the environment, and the extension of these algorithms to properly address more realistic dynamic emergent adjustment scenarios. According to the corresponding task reallocation strategy, the scenarios are divided into three categories: the complete redistribution strategy can effectively cope with scenarios where tasks have changed significantly, the partial adjustment strategy can induce partial responses to the changes of individual tasks, and group redistribution can effectively solve the problem of task target threat rating changes. The simulation results show that the dynamic reallocation model of multi-UAV tasks in dynamic emergent adjustment scenarios can achieve better performance to complete the corresponding tasks based on the proposed scheme. In addition, we deployed the developed graphical modeling and analysis software (GMAS) platform to implement the dynamic reallocation model of multi-UAV tasks in dynamic emergent scenarios, and the validity and reliability of the proposed task reallocation model were verified.

Software Platform for Supercomputer Modeling of Aerothermodynamics Problems

Numerical solution of aerothermodynamics problems requires a lot of processor time, and for this reason, the numerical codes for such simulations must be efficiently parallelized. This paper presents a software platform based on a technological approach that greatly simplifies the parallelization of problems with unstructured grids. The paper formulates the principle of a unified mathematical address space of the problem for all used cluster nodes. The formalization of the presented technological approach and the implementation of its software basis in the form of data structures, exchange routines and work rules form a software platform on the basis of which parallel computational programs can be built. This approach is implemented and tested for the problem of modeling the characteristics of aircrafts, but it can be applied to other problems using unstructured grids with one-dimensional cell numbering. In this case, the physical and mathematical specifics of the problem are not important. Parallelization of the code with the new approach is carried out with minimal effort, without changing the main parts of the program. As a result, a single computational code can be created for all regimes — sequential, multi-threaded, and cluster. The performance results obtained with the new code confirm the good scalability of the parallelization method.

A Simulation-based Contingency Planning Tool for Offshore Oil Spill Response

Session: (PS-03) Response The improvement of offshore oil spill responses efficiency to minimize economic and environmental impacts has become a major need worldwide. This paper presents an Oil Spill Simulation and Response Selection tool (OSSRS) for international applications that is designed to support spill response contingency planning by industry and government. This proposed tool combines the advantages of currently efficient oil spill tools (i.e., Response Options Calculator (ROC) and Incident Command System (ICS)) and upgrades the capabilities of spill simulation and response selection by integrating a new Agent-Based Modeling (ABM) software platform, scenarios of icy marine environment and a new optimization module developed by Northern Region Persistent Organic Pollution Control Laboratory (NRPOP Lab). The agent-based offshore oil spill simulation module has been developed to simulate the over-time changes of oil spill behaviors and characteristics as well as the actions of and interactions among individual devices and/or response centre. The simulation tool can consider an offshore spill with multiple slicks and multiple means of response techniques (e.g., booming, skimmers, dispersant, in-situ burning). The effects of oil weathering and characteristics include slick thickness, viscosity, remaining oil volume, affected area, temperature, ice cover, evaporation, dispersion, and emulsification, etc. The optimization module can provide contingency plans of response selections and resource allocation with a minimal demand of response sources. Response time, response cost, and environmental sensitivity and impacts are considered as three criteria for planning and decision making. A hypothetic case in the North Atlantic is applied to examine the efficiency of the proposed tool under an icy marine environment. Based on the result, OSSRS has a comparable capacity. The system has considerable scalability. The response capabilities and simulation modules can be adjusted according to needs. Requirements for levels of response competency may change over time or from different stakeholders. The proposed tool is a powerful and useful tool to help decision makers for oil spill responses by providing the optimal contingency plans under different requirements of criteria.

Research on 3D virtual simulation of geology based on GIS

A simplified method based on GIS combined with virtual reality modeling language (VRML) to achieve real time and fast 3D geological modeling is proposed. By calling the borehole rock and soil information stored in the GIS database, it is automatically created using spatial interpolation technology. The spatial interpolation technology is used to automatically create the side map of the model, and automatically output this information as a VRML script according to the standard format. The created model can be displayed independently without relying on the modeling software platform and can realize functions such as free rotation, flying, and zooming. Successfully developed a 3D geological modeling system based on the introduced design ideas, and used the system to achieve 3D geological modeling of a certain bridge site. This method has the characteristics of simple operation, fast modeling speed, less manual intervention, and visual interactive operation and can be used as an auxiliary decision-making method for engineering surveys.

Machine learning based prognostic model and mobile application software platform for predicting infection susceptibility of COVID-19 using healthcare data

IntroductionFrom public health perspectives of COVID-19 pandemic, accurate estimates of infection severity of individuals are extremely valuable for the informed decision-making and targeted response to an emerging pandemic. This paper presents machine learning based prognostic model for providing early warning to the individuals for COVID-19 infection using the healthcare dataset. In the present work, a prognostic model using random forest classifier and support vector regression is developed for predicting the infection susceptibility probability (ISP) score of COVID-19, and it is applied on an open healthcare dataset containing 27 field values. The typical fields of the healthcare dataset include basic personal details such as age, gender, number of children in the household, and marital status along with medical data like coma score, pulmonary score, blood glucose level, HDL cholesterol, etc. An effective preprocessing method is carried out for handling the numerical and categorical values (non-numerical) and missing data in the healthcare dataset. The correlation between the variables in the healthcare data is analyzed using the correlation coefficient, and heat map with a color code is used to identify the influencing factors on the infection susceptibility probability (ISP) score of COVID-19. Based on the accuracy, precision, sensitivity, and F-scores, it is noted that the random forest classifier provides an improved classification performance as compared to support vector regression for the given healthcare dataset. Android-based mobile application software platform is developed using the proposed prognostic approach for enabling the healthy individuals to predict the susceptibility infection score of COVID-19 to take the precautionary measures. Based on the results of the proposed method, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread.MethodsIn the present work, random forest classifier and support vector regression techniques are applied to a medical healthcare dataset containing 27 variables for predicting the susceptibility score of an individual towards COVID-19 infection, and the accuracy of prediction is compared. An effective preprocessing is carried for handling the missing data in the healthcare dataset. Correlation analysis using heat map is carried on the healthcare data for analyzing the influencing factors of infection susceptibility probability (ISP) score of COVID-19. A confusion matrix is calculated for understanding the performance of classification based on the number of true-positives, true-negatives, false-positives, and false-negatives. These values further used to calculate the accuracy, precision, sensitivity, and F-scores.ResultsFrom the classification results, it is noted that the random forest classifier provides a classification accuracy of 99.7%, precision of 99.8%, sensitivity of 98.8%, and F-score of 99.29% for the given medical dataset.ConclusionProposed machine learning approach can help the individuals to take additional precautions for protecting people from the COVID-19 infection, and clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread.

Surface runoff model in Gera Software: parallel implementation and surface-subsurface coupling

In this article we present a mathematical model used for surface runoff simulation in GeRa software. The model is based on diffusive wave approximation for the shallow water equations with Manning formula for flow velocity estimation. It is implemented using INMOST software platform for parallel mathematical modeling. Parallel efficiency of the model implementation is adressed for some widely used verification benchmarks. We also present surface-subsurface coupling approach used in GeRa software and discuss practical aspects of the nonlinear solver.

Mobile software platform for radio information systems statistical modeling

For the static research purposes in the field of radio signals reception under freely fluctuating interference conditions, specialized software for the Android platform based on the Kotlin language has been developed using. The program design allowing to work with both the information recorded by the radio receiver analog system and the signals obtained using the simulation model. CDMA system operation under Gaussian white noise and chaotic pulse interference conditions has been simulated. The dependences of the bit error probability on additive radio channel effects of a different nature were obtained. The work originality is an attempt to implement a software-defined binary signal processing system on an accessible platform.

Developing an “intelligent” high-fidelity GIS-based travel demand model framework for improved network-wide traffic estimation

The four-step travel demand model (FSTDM) is based on coarse rural traffic analysis zones (TAZs) which tends to exaggerate the intrazonal trips resulting in biased and unbalanced trip distribution over the roadway network with high estimation errors. These limitations have necessitated developing a geographic information systems (GIS)-based high-fidelity travel demand model framework (HFTDMF) capable of achieving network-wide traffic volume estimation with improved model accuracy. This requires using an all functional class roadway network and enhancing the census-based coarse TAZ structure with finer-grained spatial resolution TAZs by integrating the travel demand modeling software platform, remotely-sensed images, parcel-based digital property maps, the AZTool aggregation algorithm, and areal interpolation technique. Preliminary results from the Greater Fredericton Area (GFA) showed that increasing the GFA spatial resolution from the coarsest TAZ structure at census tract (CT) level (27 CT TAZs) to the finest TAZ structure at 4252 “fine” TAZs resulted in an improvement to modeling accuracy of R2, by 0.4092 (from 0.2490 to 0.6582) and an improvement in traffic assignment coverage by 46 percentage points (from 29% to 75%).

SYNTHESIS OF THE ORGANIZATION IMAGE ON THE BASIS OF COMBINED APPROACHES AND MODELS

Purpose. Consider the possibility of using the concepts and technologies of complex modeling to describe complex objects and synthesize their appearance.Materials and methods. The combined approach and models used for the synthesis of the image of the organization, which is considered as a situational center, are proposed. At the same time, the technical theories, organizational cybernetics and autopoiesis were chosen as the basic theories underlying the developed methodology. Results. A method of synthesizing the appearance of a situational center was proposed and substantiated, which allowed, firstly, to ensure continuous and constructive interaction between conceptual model developers and software platform architects, as well as between specialists from different subject areas, developers of typical design solutions and users of situational centers, and – second, to form a universal organization pattern, taking into account the invariant, resource and structural characteristics of the synthesized organization. Conclusion. It is proposed to use in the synthesis of the image of the organization three well-known classes of conceptual models: constructs, patterns and typical design solutions in the “technological inheritance” mode. Modes of use of conceptual models are synchronized with the appropriate tools and developers.

Development of HUMGEOSTAT: A New Geological Tool for Geostatistical Analysis of Mineral Deposit: A Case Study at Sin Quyen Mine (Northern Vietnam)

This paper presents the results of the design and implementation of a new geological tool, named as HUMGEOSTAT, for advanced geostatistical analysis of mineral deposit, a case study at the Sin Quyen, one of the important mines for the Vietnam mining industry in Vietnam. The tool is developed based on the Stanford Geostatistical Modeling Software (SgeMS) platform, which is an open-source computer package for solving problems involving spatially related variables. The tool is capable of supporting users in reducing uncertainty and improving the quality of mining exploration tasks. The tool has all the functions needed to solve a complete geostatistical issues i.e. Variogram models (such as Spherical, Exponential, Gausian, Linear and Pure nugget effect); and four commonly used Kriging methods (Ordinary Kriging, Simple Kriging, Indicator Kriging, Co-Kriging). The tool was applied to test various types of input data and particularly for complex copper ore bodies of Sin Quyen mine. The results of analysis of orebody spatial structure has found that Cu content has non-uniform change, regional anisotropy in north-south (0±22.50) direction, and anisotropic index is around 1.3. The mineral reserves are estimated according to sub-blocks and points in the orebody-space. The results indicate that the Ordinary Kriging (OK) has reliable results, variance estimation is small; the predicted result gained from OK have strong correlation (K=0.905) with ore samples. HUMGEOSTAT was applied at the Sin Quyen mine and various coal mines, the results were adapted with reality of mines.

Pulse Physiology Engine: an Open-Source Software Platform for Computational Modeling of Human Medical Simulation

The Pulse Physiology Platform is an open-source software application designed to enable accurate and consistent, real-time physiologic simulations for improved medical training and clinical decision-making tools. The platform includes a physiology engine comprised of well-validated lumped-parameter models, differential equations representing feedback mechanisms, and a pharmacokinetic/pharmacodynamic model. The platform also includes a common data model for standard model and data definitions and a common software interface for engine control and robust physics-based circuit and transport solvers. The Pulse Platform has been incorporated into a number of commercial, research, and academic tools for medical simulation. Significance: The Pulse Platform is an innovative, well-validated, open-source tool for medical modeling and simulation in the training and clinical decision-making field.

UNIVERSAL SOFTWARE PLATFORM FOR SIMULATION MODELING OF WARFARE

Replacing part of full-scale tests of weapons and military equipment with imitation experiments is highly relevant, since it allows saving substantial material and technical resources, reducing development time, and in some cases carrying out virtual tests, the full‑scale analogue of which cannot be done. In the Almaz-Antey Corporation, a simulation system is built, consisting of a simulation platform and particular simulation models that represent the forces and means of aerospace defense and aerospace attack. The paper presents the basic principles and results of the creation of a universal software platform for simulation modeling of combat operations based on the HLA IEEE‑1516 technology. These principles and approaches partly follow from the IEEE-1516 standard itself, while others are original technical solutions. The platform is based on the principles of openness, scalability, distributed computing. The composition and features of particular models are not considered in the work.

«Кибер ГРП» – программная платформа для моделирования, оптимизации и контроля операций гидроразрыва пласта

«Кибер ГРП» – программная платформа для моделирования, оптимизации и контроля операций гидроразрыва пласта

Modeling Software Platform of Multi-Organization Core Based on Voxels Interpolation Method

Modeling Software Platform of Multi-Organization Core Based on Voxels Interpolation Method

Application of the Eclipse EMF technology for multifaceted organization modelling

Eduard A. Babkin - Professor, Department of Information Systems and Technology, National Research University Higher School of EconomicsAddress: 25/12, Bolshaya Pecherskaya Street, Nizhny Novgorod, 603155, Russian FederationE-mail: eababkin@hse.ruOksana V. Radzinskaya - Lecturer, Department of Information Systems and Technology, National Research University Higher School of EconomicsAddress: 25/12, Bolshaya Pecherskaya Street, Nizhny Novgorod, 603155, Russian FederationE-mail: oradzinskaya@hse.ru This research paper explores the most developed methodologies used for multifaceted modeling of organizational structures. It is asserted that the existing methodologies (DEMO - Design and Engineering Methodology for Organizations, BORM - Business Object Relation Modeling and OntoUML) provide tools for analysis of the organization and its business processes through different ways. They lead to different results and make the process of organization modeling complicated. There is no software to support work with these methodologies together. The purpose of this research is to create a unified meta-model (within the Eclipse EMF technology) for a new methodology based on the existing ones and to analyze the completeness of these methodologies for describing enterprise architecture. It will serve as a basis for a new open software platform for multifaceted modeling of the organization. In this research, we have compared the above-mentioned methodologies and concluded that despite the fact that these methodologies provide an analysis of different aspects of organizational structure, they have a common basic set of concepts. This study demonstrates the implementation of the Ecore model that is built on the basis of the selected group of common elements of these methodologies. We have also found that the combination of the considered methodologies contains all the concepts inherent in the systematic approach to the modeling of organizational structure. Furthermore, we have demonstrated the aspects of the organization that can be modeled by the methodologies considered. Using the Zachman framework, it was shown that: the DEMO, BORM and OntoUML methodologies allow us to describe in detail the business processes that take place at different levels of the organization, from the ontological to the datalogical, and therefore provide comprehensive information for the multifaceted modeling of an organization. However, none of the existing methodologies takes the time component and goal-setting into consideration.

A Computational Framework for Realistic Retina Modeling.

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.