A 3D Agent-Based Model of Lung Fibrosis

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).

Smart Medical Kit in Chronic Kidney Disease Management

Internet of things (IoT)-enabled devices perform remote monitoring of patients and keep them healthy. They also facilitate physicians to provide high-quality care to their patients with accurate data. Chronic disease involves a wide range of health issues like diabetics, asthma, heart disease, kidney disease, and other disorders. To avoid disease progression, the IoT-based smart medical kit helps in episodic patient monitoring, continuous patient monitoring in acute conditions, and patient alarm monitoring. The chapter focuses on the deployment of interconnected devices (sensors, actuators, monitors, detectors, and camera systems) to collect data from heterogeneous systems. The output is connected to a think speak dashboard for monitoring the variation over the period. The smart kit provides more accurate and reliable recommendations to assist patients in controlling their chronic disease and assists in remote monitoring of a patient's health conditions.

Device Hopping

Existing OS techniques for homogeneous many-core systems make it simple for single and multithreaded applications to migrate between cores. Heterogeneous systems do not benefit so fully from this flexibility, and applications that cannot migrate in mid-execution may lose potential performance. The situation is particularly challenging when a switch of language runtime would be desirable in conjunction with a migration. We present a case study in making heterogeneous CPU + GPU systems more flexible in this respect. Our technique for fine-grained application migration, allows switches between OpenMP, OpenCL, and CUDA execution, in conjunction with migrations from GPU to CPU, and CPU to GPU. To achieve this, we subdivide iteration spaces into slices, and consider migration on a slice-by-slice basis. We show that slice sizes can be learned offline by machine learning models. To further improve performance, memory transfers are made migration-aware. The complexity of the migration capability is hidden from programmers behind a high-level programming model. We present a detailed evaluation of our mid-kernel migration mechanism with the First Come, First Served scheduling policy. We compare our technique in a focused evaluation scenario against idealized kernel-by-kernel scheduling, which is typical for current systems, and makes perfect kernel to device scheduling decisions, but cannot migrate kernels mid-execution. Models show that up to 1.33× speedup can be achieved over these systems by adding fine-grained migration. Our experimental results with all nine applicable SHOC and Rodinia benchmarks achieve speedups of up to 1.30× (1.08× on average) over an implementation of a perfect but kernel-migration incapable scheduler when migrated to a faster device. Our mechanism and slice size choices introduce an average slowdown of only 2.44% if kernels never migrate. Lastly, our programming model reduces the code size by at least 88% if compared to manual implementations of migratable kernels.

A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV

To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability.

Approximation Algorithm for Scheduling a Chain of Tasks for Motion Estimation on Heterogeneous Systems MPSoC

Co-design embedded system are very important step in digital vehicle and airplane. The multicore and multiprocessor SoC (MPSoC) started a new computing era. It is becoming increasingly used because it can provide designers much more opportunities to meet specific performances. Designing embedded systems includes two main phases: (i) HW/SW Partitioning performed from high-level (eclipse C/C++ or python (machine learning and deep learning)) functional and architecture models (with virtual prototype and real prototype). And (ii) Software Design performed with significantly more detailed models with scheduling and partitioning tasks algorithm DAG Directed Acyclic Graph and GGEN Generation Graph Estimation Nodes (there are automatic DAG algorithm). Partitioning decisions are made according to performance assumptions that should be validated on the more refined software models for ME block and GGEN algorithm. In this paper, we focus to optimize a execution time and amelioration for quality of video with a scheduling and partitioning tasks in video codec. We show how they can be modeled the video sequence test with the size of video in height and width (three models of scheduling tasks in four processor). This modeling with DAG and GGEN are partitioning at different platform in OVP (partitioning, SW design). We can know the optimization of consumption energy and execution time in SoC and MPSoC platform.

MODERN APPROACHES TO TEACHING TELECOMMUNICATIONS DISCIPLINES

Background. The directions and methods of training telecommunications personnel are constantly evolving in accordance with the growing volume of information exchange in society. Telecommunications as a display of the means and methods of information transmission have come a long way from purely radio engineering systems to heterogeneous systems with a complex network infrastructure and intelligent methods of information processing. Accordingly, the approaches to the training of telecommunications personnel are also changing. If in the early 80s preference in training was given to radio technologies, now it is network and software technologies. Objective. The purpose of this work is to present the possibilities of increasing the efficiency of the educational process in standard and mixed modes, structuring subjects in accordance with the requirements of the modern labor market. Methods. The unpredictable deep essence and uncertainty of the information space of the professions for which higher education prepares students today leads to a change in the teaching paradigm. Methods and structuring of building the learning process with obtaining the maximum effect of the student's readiness for their practical activities are investigated. Results. Possible ways of implementation of new requirements for personnel training for the new paradigm of the unified industry "Information Technologies and Telecom" are presented; the main directions of the formation of the general structure of training of telecommunications personnel on the example of the educational program "Engineering and programming of infocommunications"; recommendations for the organization of the educational process in full-time and remote modes. It is shown that with the correct organization of the educational process, blended learning can improve the quality of learning, especially in the context of reducing the hours of "classroom lessons" by transferring part of the educational process to the online environment. Conclusions. University graduates constitute the potential foundation of today's specialist market. Nevertheless, the problem of their professional adaptation, at the moment, remains relevant. Therefore, the paper proposes to solve this problem using the presented multilateral approach.

Petri Net Toolbox for Multi-Robot Planning under Uncertainty

Currently, there is a lack of developer-friendly software tools to formally address multi-robot coordination problems and obtain robust, efficient, and predictable strategies. This paper introduces a software toolbox that encapsulates, in one single package, modeling, planning, and execution algorithms. It implements a state-of-the-art approach to representing multi-robot systems: generalized Petri nets with rewards (GSPNRs). GSPNRs enable capturing multiple robots, decision states, action execution states and respective outcomes, action duration uncertainty, and team-level objectives. We introduce a novel algorithm that simplifies the model design process as it generates a GSPNR from a topological map. We also introduce a novel execution algorithm that coordinates the multi-robot system according to a given policy. This is achieved without compromising the model compactness introduced by representing robots as indistinguishable tokens. We characterize the computational performance of the toolbox with a series of stress tests. These tests reveal a lightweight implementation that requires low CPU and memory usage. We showcase the toolbox functionalities by solving a multi-robot inspection application, where we extend GSPNRs to enable the representation of heterogeneous systems and system resources such as battery levels and counters.

RESEARCH OF GENERAL HYDROGEN TECHNOLOGY ACCORDING TO OPTIMAL PARAMETERS AS A COMPONENT OF COMPLEX DEVELOPMENT OF EFCE AND CFE-UA ASSOCIATIONS

The analysis of the prospects for the development of hydrogen energy in the EU and Ukraine is carried out. The possibilities of implementing projects and technologies for the production of green hydrogen for industrial use are considered. The conditions for the implementation of the project for the creation of a research and development center for hydrogen and hydrogen fuel cell technology are presented. A review of publications devoted to the process of obtaining hydrogen from water has been completed. The main factors influencing the course of reactions in the production of hydrogen from water using alloys are considered. Recommended alloys for producing hydrogen at autonomous facilities. The components of the research algorithm are given taking into account the system of process factors based on the analysis of literature data on the technology of hydrogen production by the electrolysis of water. The general principles of calculating gas generators have been established, which should be based on the basic principles of the thermodynamics of heterogeneous processes: classical thermodynamics of multiphase and heterogeneous systems.