Modeling of thermodynamic processes using the properties of matter presented in the form of spreadsheets

New thermodynamic cycles are developed in which the working fluid used cannot be considered as an ideal gas. This applies to oxy-fuel combustion cycles. In these cycles, oxygen is separated from the air prior to combustion. The combustion chamber is supplied with fuel and pure oxygen. The required temperature at the outlet of the combustion chamber is achieved by supplying some other substances from which it is easy to separate the CO2 formed during the combustion of the fuel. Commonly, CO2, or H2O, or their mixture is used as such substances. Thus, there are no exotic substances in the composition of the working fluid, but such a range of parameters is chosen for such cycles that the working fluid at certain points of the cycle can be both gaseous and liquid, or in a supercritical state. To model thermodynamic processes in such cycles, it is unacceptable to use the polytropic equation of ideal gases. A technique for integrating differential equations describing the state of the working fluid is proposed. This technique is based on the presentation of the thermodynamic properties of pure substances that make up the working fluid in the form of spreadsheets. The proposed technique is implemented in a software-computing module.

A non-printed integrated-circuit textile for wireless theranostics

While the printed circuit board (PCB) has been widely considered as the building block of integrated electronics, the world is switching to pursue new ways of merging integrated electronic circuits with textiles to create flexible and wearable devices. Herein, as an alternative for PCB, we described a non-printed integrated-circuit textile (NIT) for biomedical and theranostic application via a weaving method. All the devices are built as fibers or interlaced nodes and woven into a deformable textile integrated circuit. Built on an electrochemical gating principle, the fiber-woven-type transistors exhibit superior bending or stretching robustness, and were woven as a textile logical computing module to distinguish different emergencies. A fiber-type sweat sensor was woven with strain and light sensors fibers for simultaneously monitoring body health and the environment. With a photo-rechargeable energy textile based on a detailed power consumption analysis, the woven circuit textile is completely self-powered and capable of both wireless biomedical monitoring and early warning. The NIT could be used as a 24/7 private AI “nurse” for routine healthcare, diabetes monitoring, or emergencies such as hypoglycemia, metabolic alkalosis, and even COVID-19 patient care, a potential future on-body AI hardware and possibly a forerunner to fabric-like computers.

Implementation of the visual data processing algorithms for onboard computing units

According to the short analysis of modern experience of hardware and software for autonomous mobile robots a role of computer vision systems in the structure of those robots is considered. A number of configurations of onboard computers and implementation of algorithms for visual data capturing and processing are described. In original configuration space the «algorithms-hardware» plane is considered. For software designing the realtime vision system framework is used. Experiments with the computing module based on the Intel/Altera Cyclone IV FPGA (implementation of the histogram computation algorithm and the Canny's algorithm), with the computing module based on the Xilinx FPGA (implementation of a sparse and dense optical flow algorithms) are described. Also implementation of algorithm of graph segmentation of grayscale images is considered and analyzed. Results of the first experiments are presented.

Effective Distribution of Tasks in Multiprocessor and Multi-Computers Distributed Homogeneous Systems

Nowadays, a promising is the direction associated with the use of a large number of processors to solve the resource-intensive tasks. The enormous potential of multiprocessor and multicomputer systems can be fully revealed only when we apply effective methods for organizing the distribution of tasks between processors or computers. However, the problem of efficient distribution of tasks between processors and computers in similar computing systems remains relevant. Two key factors are critical and have an impact on system performance. This is load uniformity and interprocessor or intercomputer interactions. These conflicting factors must be taken into account simultaneously in the distribution of tasks in multiprocessor computing systems. A uniform loading plays a key role in achieving high parallel efficiency, especially in systems with a large number of processors or computers. Efficiency means not only the ability to obtain the result of computations in a finite number of iterations with the necessary accuracy, but also to obtain the result in the shortest possible time. The number of tasks intended for execution on each processor or each computer should be determined so that the execution time is minimal. This study offers a technique that takes into account the workload of computers and intercomputer interactions, and allows one to minimize the execution time of tasks. The technique proposed by the authors allows the comparison of different architectures of computers and computing modules. In this case, a parameter is used that characterizes the behavior of various models with a fixed number of computers, as well as a parameter that is necessary to compare the effectiveness of each computer architecture or computing module when a different number of computers are used. The number of computers can be variable at a fixed workload. The mathematical implementation of this method is based on the problem solution of the mathematical optimization or feasibility.

Scheduling UAV’s on Autonomous Charging Station

The paper considers the concept of a charging station for an Unmanned Aerial Vehicles (UAV, drone) fleet. The special feature of the station is its autonomy understood as independence from a constant energy source and an external module for managing its operation. It is assumed that the station gives the possibility to charge batteries of many drones simultaneously. However, the maximum number of simultaneously charged drones is limited by a temporary total charging current (i.e. there is a power limit). The paper proposes a mathematical model of charging a single drone battery. The problem of finding a schedule of charging tasks is formulated, in which the minimum time of the charging process for all drones is assumed as the optimization criterion. Searching for a solution to this problem is performed by an autonomous charging station with an appropriate computing module equipped with a Variable Speed Processor (VSP). To that end an appropriate algorithm is activated (i.e. a computational job), the execution of which consumes a certain amount of limited energy available to the charging station. In the paper we consider energy-aware execution of an implementation of an evolutionary algorithm (EA) as a computational job. The possibility of saving energy by controlling the CPU frequency of a VSP is analyzed. A characteristic feature of the processor is the non-linear relationship between the processing rate and electric power usage. According to this relationship, it turns out that slower execution of the computational job saves electrical energy consumed by the processor.

Implementation of MQTT protocol based network architecture for smart factory

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

TSN: Performance Creative Choreography Based on Twin Sensor Network

The purpose of this paper is to improve the efficiency of performance creative choreography (PCC). Our research work shows that we can realize the model integration and data optimization for PCC in complex environments based on the combined architecture of sensor network (SN) and machine-learning algorithm (MLA). In order to explain the process and content of this research better, this paper designs a specific problem description framework for PCC, which mainly includes the following content: (1) a twin sensor network (TSN) architecture based on digital twin information interaction is proposed, which defines and describes the acquisition method, classification (creative data, rehearsal data, and live data), and temporal and spatial features of performance data. (2) Proposed a mobile computing method based on director semantic annotation (DSA) as the core computing module of TSN. (3) A spatial dynamic line (SDL) model and a creative activation mechanism (CAM) based on DSA are proposed to realize fast and efficient PCC of dance with the TSN architecture. Experimental results show that the TSN architecture proposed in this article is reasonable and effective. The SDL model achieved significantly better performance with little time increase and improved the computability and aesthetics of PCC. New research ideas are proposed to solve the computational problem of PCC in complex environments.

BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

Blood transcriptional module repertoire analysis and visualization using R

Transcriptional modules have been widely used for the analysis, visualization and interpretation of transcriptome data. We have previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. The third and latest version that we have recently made available comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. We developed R scripts for performing module repertoire analyses and custom fingerprint visualization. These are made available here along with detailed descriptions. An illustrative public transcriptome dataset and corresponding intermediate output files are also included as supplementary material. Briefly, the steps involved in module repertoire analysis and visualization include: First, the annotation of the gene expression data matrix with module membership information. Second, running of statistical tests to determine for each module the proportion of its constitutive genes which are differentially expressed. Third, the results are expressed “at the module level” as percent of genes increased or decreased and plotted in a fingerprint grid format. A parallel workflow has been developed for computing module repertoire changes for individual samples rather than groups of samples. Such results are plotted in a heatmap format. The use case that is presented illustrates the steps involved in the generation of blood transcriptome repertoire fingerprints of septic patients at both group and individual levels.

Assessment of computing in the mathematics curriculum using Numbas

This case study discusses the use of the Numbas e-assessment system to assess computing skills across several modules in a mathematics undergraduate degree programme. The modules include basic computing, quantitative analysis of data, and numerical methods. Several approaches are discussed which fit with the teaching of SPSS, R and MATLAB, including randomised data files and questions which can replicate, and therefore mark, calculations made with R data frames and numerical algorithms, such as root finding and curve fitting. In each case, Numbas offers the opportunity to automatically mark and offer immediate feedback to the student. The application of questions inside a computing module is discussed, with a positive response from students to both practice material and hybrid tests, which include some automatic marking alongside submission of the students’ code for manual review. There is clear rationale for using an e-assessment system which is already familiar to students, with features such as adaptive marking and the scaffolding of questions, however limitations to the use of Numbas for this purpose are also discussed.