Computerized Analysis System Research Articles

The Correlation Between Static Fatigue Testing and the Quantitative Myasthenia Gravis Score and Activities of Daily Living Profile

Fatigue is a common symptom in myasthenia gravis (MG), but both objective and subjective measures of fatigue are poorly studied in the disease. We conducted a pilot study of static fatigue testing (SFT) in a group of MG patients, using an isometric quantified muscle analysis computer system. Results from sustained isometric contraction of 5 muscle groups in 77 patients were correlated to the Quantitative MG Score (QMG) and the Activities of Daily Living Profile (MG-ADL), two commonly used outcome measures. Pearson correlation coefficients for the SFT were highest (0.33) for hand grip for both the QMG and MG-ADL. Correlations were quite poor for the proximal muscle groups and ankle dorsiflexion. More work is needed to develop objective and subjective measures of fatigue in MG.

Development and simulation of the quantum microarchitecture operation using an optimization algorithm for a quantum calculation process

The theory of quantum computing is being actively developed. Despite the fact that quantum computing device has some peculiarities, any task designed for a classical computer can be reproduced on a quantum system. The aim of the research is to develop a methodology for constructing models of quantum systems simulators using hardware simulators. The subject of this research is methods for constructing quantum computing devices and systems. In the course of the research, the system analysis methods and computer modeling, methods of object-oriented design were used. Modern high-level languages were used to solve the problems of constructing a modular quantum system with an open architecture. The work of a quantum microarchitecture using an algorithm for optimizing a quantum computational process has been developed and modeled. A universal methodology for modeling algorithms of a quantum nature using the hardware core and requirements for the mutual operation of software and hardware components for the efficient operation of a quantum system is derived.

Development and experimental verification of a new computationally efficient parallel narrowband active noise control system

The filtered-x least mean square based narrowband active noise control (FX-NANC) system with a parallel structure is typically used to cancel low-frequency multi-tonal noise. The computational efficiency and system performance may, however, be greatly degraded due to an increase in the number of controlled frequencies or the length of estimated secondary path. To alleviate this problem, a cost-saving FE-NANC system has been recently proposed, but the system suffers from slow convergence. This paper presents a local secondary path estimation based filtered-error least mean square (LFELMS) algorithm and applies it to a narrowband active noise control (NANC) system. By filtering a single error signal with a set of low-order local secondary path (LSP) models to obtain filtered signals for weight updating, the proposed LFELMS based NANC (LFE-NANC) system requires significantly less computational cost compared to the FX-NANC system and has considerably faster convergence speed than the FE-NANC system. Two new LSP modeling methods are also proposed. In addition, a computational complexity analysis of some typical NANC systems and the proposed LFE-NANC system is provided. Numerical simulations are performed to investigate the accuracy of the LSP modeling methods and compare the performance of the proposed system with other NANC systems. Finally, real-time experiments are conducted to confirm the effectiveness of the proposed system in single-tone, multi-tone and non-stationary situations.

Analysis of Particles' Size and Degree of Distribution of a Wooden Filler in Wood-Polymer Composites.

In wood–polymer composites (WPCs), regardless of the origin of the filler and its dimensions, their significant role in changing the properties of the WPCs’ material was found. Given the above, it is of particular importance to determine the size of the wood filler particles after their production. In addition, it is also important to determine the degree of distribution of the filler in the polymer matrix. The methodology for determining particle size and distribution is complex, even when using image analysis computer systems. This article presents the application and implementation of the multi-stage procedure for determining the size of wood particles and the degree of their distribution in the WPCs by means of image analysis using a numerical calculation program. The procedure, co-authored by the researchers at the Koszalin University of Technology and School of Mechanical and Materials Engineering, is published in the Industrial Crops and Products 2016 Comparing the results obtained for the PP/Lignocel 3-4 and PP/Lignocel C120 composites produced under highly different conditions in the target zone, it was found that the degree of the component distribution in the polymer matrix was significantly influenced by the width of the target gap. In both cases, the best homogeneity of the material and a good distribution of the filler in the polymer matrix was achieved within the parameters that have a mild effect on the material and allow it to stay longer in the plasticizing system, i.e., Ws = 1.0–3.0 mm with simultaneous impact medium to high speed in the range n = 26–40 rpm.

METHOD FOR DIAGNOSING DATA ERRORS OF A COMPUTER SYSTEM FUNCTIONING IN THE SYSTEM OF RESIDUAL CLASSES

The subject of the article is the development of a method for diagnosing data errors of a computer system functioning in the residue number system (RNS). This method is based on the tabular principle of data diagnostics operation implementation. The purpose of the article is to reduce the time of performing the operation of diagnosing the data of a computer system that functions in the RNS in the dynamics of the computational process. Tasks: to analyze the possible application of RNS as a number system in computer data processing systems; to investigate existing methods of data diagnostics and identify possible shortcomings in data diagnostics in the dynamics of the computational process with the introduction of minimal information redundancy; to develop a method for diagnosing the data presented in the RNS, which is based on the tabular principle of the implementation of the diagnostic operation. Research methods: methods of analysis and synthesis of computer systems, number theory, coding theory in RNS. The following results were obtained. The work shows that the data diagnostics is carried out after the control operation has been performed. To carry out the operation of diagnosing data in the RNS, when one-time errors occur, methods for determining an alternative set (AS) of numbers are used. In this case, the main disadvantage of the considered methods is the considerable time required to determine the AS. To reduce the time for determining the AC numbers in the work, the method for diagnosing data in the RNS has been improved. Improvement of the method involves the compilation of a table (the first stage) of correspondence to each correct number of a possible set of incorrect numbers when one-time errors occur in the data. Based on the analysis of the contents of the table of the first stage, a table (of the second stage) is drawn up of the correspondence of each incorrect number to the possible values of the correct numbers. Conclusions. The use of the improved method increases the efficiency of diagnostics of the data presented in the RNS by reducing the time for determining the AC numbers. This is achieved by quickly fetching pre-calculated AC table values.

The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis

Human activities, such as energy consumption and economic development, will significantly affect the natural environment, while changes in the natural environment will also affect the sustainability of human society. Studying the energy consumption changes of human society and forecasting medium and long-term electricity demand will help realize the sustainable development of energy in future society. However, current medium- and long-term electricity consumption forecasts have insufficient data samples and the inability to consider policy impacts. Here, we develop an Economy and Policy Incorporated Computing System (EPICS), which can use artificial intelligence technology to extract the summaries of energy policy texts automatically and calculate the importance index of energy policy. It can also process economic data of different lengths to expand samples of medium- and long-term electricity consumption forecasting effectively. A forecasting method that considers policy factors and mixed-frequency economic data is introduced to estimate future social energy and power consumption. This method has shown good forecasting ability in 27 months. The effect of EPICS can be demonstrated by predicting the medium- and long-term electricity demand.

Bayesian Unification of Sound Source Localization and Separation with Permutation Resolution

Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these components. This paper presents a unified Bayesian framework to solve these problems simultaneously where localization and separation are regarded as a clustering problem. Experimental results confirm that our method outperforms state-of-the-art methods in terms of the separation quality with various setups including practical reverberant environments.

A selective ensemble model for cognitive cybersecurity analysis

Dynamic data-driven vulnerability assessments face massive heterogeneous data contained in, and produced by SOCs (Security Operations Centres). Manual vulnerability assessment practices result in inaccurate data and induce complex analytical reasoning. Contemporary security repositories’ diversity, incompleteness and redundancy contribute to such security concerns. These issues are typical characteristics of public and manufacturer vulnerability reports, which exacerbate direct analysis to root out security deficiencies. Recent advances in machine learning techniques promise novel approaches to overcome these notorious diversity and incompleteness issues across massively increasing vulnerability reports corpora. Yet, these techniques themselves exhibit varying degrees of performance as a result of their diverse methods. We propose a cognitive cybersecurity approach that empowers human cognitive capital along two dimensions. We first resolve conflicting vulnerability reports and preprocess embedded security indicators into reliable data sets. Then, we use these data sets as a base for our proposed ensemble meta-classifier methods that fuse machine learning techniques to improve the predictive accuracy over individual machine learning algorithms. The application and implication of this methodology in the context of vulnerability analysis of computer systems are yet to unfold the full extent of its potential. The proposed cognitive security methodology in this paper is shown to improve performances when addressing the above-mentioned incompleteness and diversity issues across cybersecurity alert repositories. The experimental analysis conducted on actual cybersecurity data sources reveals interesting tradeoffs of our proposed selective ensemble methodology, to infer patterns of computer system vulnerabilities.

Substation Load Management for Electric Power Distribution's Sustainability

In Nigeria, perhaps due to lack of conscious and planned preventive maintenance culture coupled with inadequate measuring equipment, power system equipment such as transformers are not adequately monitored for preventive maintenance. As a result, most are left to work until a breakdown occurs. Poor loading and uneven distributions are also common phenomena in many of these distribution transformers. Hence, there is a frequent power outage and supply interruption. This paper reports daytime monitoring of voltage, current and apparent power from three transformer substations at Federal Polytechnic Ilaro, Nigeria to ascertain loading and transformer conditions. The work involved data acquisition using Lutron DW 609 power harmonic analyzer of the parameters of interest (voltage, current and apparent power) for 2-4 hours using 5 min logging interval. The data were assessed using the computer system for analysis. The range of loading conditions encountered was: Substation 1: 75.00−80.60 kVA which was 15−16.14% of transformer rating; Substation 2: 73.70−90.30 kVA representing 14.74−18.08% of the transformer rating; Substation 3: 19.04−19.28 kVA representing 3.81−3.85% of the rated transformer capacity. Load factors of 0.97, 0.87, and 0.99 were obtained for the three substations respectively indicating that energy usage was relatively constant during the period under consideration. An ANOVA test at a 5% significant level on each transformer phase current reading yielded F-Values of 4475.38, 859.92 and 239117.68 respectively for the substation transformers indicating a huge level of imbalance in the transformer loading. Load balancing is recommended for a sustainable electric power distribution in the locality.

THE DATA DIAGNOSTIC METHOD OF IN THE SYSTEM OF RESIDUE CLASSES

The subject of the article is the development of a method for diagnosing data that are presented in the system of residual classes (SRC). The purpose of the article is to develop a method for fast diagnostics of data in the SRC when entering the minimum information redundancy. Tasks: to analyze and identify possible shortcomings of existing methods for diagnosing data in the SRC, to explore possible ways to eliminate the identified shortcomings, to develop a method for prompt diagnosis of data in SRC. Research methods: methods of analysis and synthesis of computer systems, number theory, coding theory in SRC. The following results were obtained. It is shown that the main disadvantage of the existing methods is the significant time of data diagnostics when it is necessary to introduce significant information redundancy into the non-positional code structure (NCS). The method considered in the article makes it possible to increase the efficiency of the diagnostic procedure when introducing minimal information redundancy into the NCS. The data diagnostics time, in comparison with the known methods, is reduced primarily due to the elimination of the procedure for converting numbers from the NCS to the positional code, as well as the elimination of the positional operation of comparing numbers. Secondly, the data diagnostics time is reduced by reducing the number of SRC bases in which errors can occur. Third, the data diagnostics time is reduced due to the presentation of the set of values of the alternative set of numbers in a tabular form and the possibility of sampling them in one machine cycle. The amount of additionally introduced information redundancy is reduced due to the effective use of the internal information redundancy that exists in the SRC. An example of using the proposed method for diagnosing data in SRC is given. Conclusions. Thus, the proposed method makes it possible to reduce the time for diagnosing data errors that are presented in the SRC, which increases the efficiency of diagnostics with the introduction of minimal information redundancy.

Perspectives for systems biology in the management of tuberculosis.

Standardised management of tuberculosis may soon be replaced by individualised, precision medicine-guided therapies informed with knowledge provided by the field of systems biology. Systems biology is a rapidly expanding field of computational and mathematical analysis and modelling of complex biological systems that can provide insights into mechanisms underlying tuberculosis, identify novel biomarkers, and help to optimise prevention, diagnosis and treatment of disease. These advances are critically important in the context of the evolving epidemic of drug-resistant tuberculosis. Here, we review the available evidence on the role of systems biology approaches - human and mycobacterial genomics and transcriptomics, proteomics, lipidomics/metabolomics, immunophenotyping, systems pharmacology and gut microbiomes - in the management of tuberculosis including prediction of risk for disease progression, severity of mycobacterial virulence and drug resistance, adverse events, comorbidities, response to therapy and treatment outcomes. Application of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach demonstrated that at present most of the studies provide "very low" certainty of evidence for answering clinically relevant questions. Further studies in large prospective cohorts of patients, including randomised clinical trials, are necessary to assess the applicability of the findings in tuberculosis prevention and more efficient clinical management of patients.

Implications of Noise on Neural Correlates of Consciousness: A Computational Analysis of Stochastic Systems of Mutually Connected Processes

Random fluctuations in neuronal processes may contribute to variability in perception and increase the information capacity of neuronal networks. Various sources of random processes have been characterized in the nervous system on different levels. However, in the context of neural correlates of consciousness, the robustness of mechanisms of conscious perception against inherent noise in neural dynamical systems is poorly understood. In this paper, a stochastic model is developed to study the implications of noise on dynamical systems that mimic neural correlates of consciousness. We computed power spectral densities and spectral entropy values for dynamical systems that contain a number of mutually connected processes. Interestingly, we found that spectral entropy decreases linearly as the number of processes within the system doubles. Further, power spectral density frequencies shift to higher values as system size increases, revealing an increasing impact of negative feedback loops and regulations on the dynamics of larger systems. Overall, our stochastic modeling and analysis results reveal that large dynamical systems of mutually connected and negatively regulated processes are more robust against inherent noise than small systems.

Mapping C-H⋅⋅⋅M Interactions in Confined Spaces: (α-ICyDMe )Au, Ag, Cu Complexes Reveal "Contra-electrostatic H Bonds" Masquerading as Anagostic Interactions*.

What happens when a C-H bond is forced to interact with unpaired pairs of electrons at a positively charged metal? Such interactions can be considered as "contra-electrostatic" H-bonds, which combine the familiar orbital interaction pattern characteristic for the covalent contribution to the conventional H-bonding with an unusual contra-electrostatic component. While electrostatics is strongly stabilizing component in the conventional C-H⋅⋅⋅X bonds where X is an electronegative main group element, it is destabilizing in the C-H⋅⋅⋅M contacts when M is Au(I), Ag(I), or Cu(I) of NHC-M-Cl systems. Such remarkable C-H⋅⋅⋅M interaction became experimentally accessible within (α-ICyDMe )MCl, NHC-Metal complexes embedded into cyclodextrins. Computational analysis of the model systems suggests that the overall interaction energies are relatively insensitive to moderate variations in the directionality of interaction between a C-H bond and the metal center, indicating stereoelectronic promiscuity of fully filled set of d-orbitals. A combination of experimental and computational data demonstrates that metal encapsulation inside the cyclodextrin cavity forces the C-H bond to point toward the metal, and reveals a still attractive "contra-electrostatic" H-bonding interaction.

MATHEMATICAL MODELS PRODUCTIVITY OF CLUSTER SYSTEM BASED ON RASPBERRY PI 3B+

Context. High-performance computing systems are needed to solve many scientific problems and to work with complex applied problems. Previously, real parallel data processing was supported only by supercomputers, which are very limited and difficult to access. Currently, one way to solve this problem is to build small, cheap clusters based on single-board computers Raspberry Pi.
 Objective. The goal of the work is the creation of a complex criterion for the efficiency of the cluster system, which could properly characterize the operation of such a system and find the dependences of the performance of the cluster system based on Raspberry Pi 3B+ on the number of boards in it with different cooling systems.
 Method. It is offered to apply in the analysis of small cluster computer systems the complex criterion of efficiency of work of cluster system which will consider the general productivity of cluster computer system, productivity of one computing element in cluster computer system, electricity consumption by cluster system, electricity consumption per one computing element, the cost of calculating 1 Gflops cluster computer system, the total cost of the cluster computer system.
 Results. The developed complex criterion of cluster system efficiency was used to create an experimental cluster system based on single-board computers Raspberry Pi 3B+. Mathematical models of the dependence of the performance of a small cluster system based on single-board computers Raspberry Pi 3B+ depending on the number of boards in it with different cooling systems have also been developed.
 Conclusions. The conducted experiments confirmed the expediency of using the developed complex criterion of efficiency of the cluster system and allow to recommend it for use in practice when creating small cluster systems. Prospects for further research are to determine the weights of the constituent elements of the complex criterion of efficiency of the cluster system, as well as in the experimental study of the proposed weights.

Abstract IA-06: Artificial intelligence in cancer imaging

Abstract Medical imaging in oncology has traditionally been restricted to the diagnosis and staging of cancer. But technological advances in Artificial Intelligence (AI), deep learning in particular, are moving imaging modalities into the heart of patient care. Imaging can address a critical barrier in precision medicine as solid tumors can be spatial and temporal heterogeneous, and the standard approach to tumor sampling, often invasive needle biopsy, is unable to fully capture the spatial state of the tumor. Radiomics refers to the automatic quantification of this radiographic phenotype. Radiomic methods heavily rely on AI technologies to quantify phenotypic characteristics that can be used to develop non-invasive biomarkers. In this talk, Dr. Aerts will discuss recent developments from his group and collaborators performing research at the intersection of radiology, bioinformatics, and data science. Also, he will discuss recent work of building a computational image analysis system to extract a rich radiomics set and use these features to build radiomic signatures. The presentation will conclude with a discussion of future work on building integrative systems incorporating both molecular and phenotypic data to improve cancer therapies. Citation Format: Hugo Aerts. Artificial intelligence in cancer imaging [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-06.

SYNTHESIS OF STRUCTURE OF THE ADDER BY MODULE

The subject of the article is the study of the structure of low-bit binary adders for an arbitrary modulus of the residual class system (RCS). The purpose of this article is to develop an algorithm for synthesizing the structure of the adder of two residuals of numbers by an arbitrary value of the RCS module, by organizing inter-bit connections between the binary digits of the adder, the combination of which determines the structure of the adder modulo. Tasks: to investigate the possibility of performing the operation of addition of two residuals in RCS based on positional binary adders; to analyze the influence of additionally introduced interdigit connections into the positional binary adder on the value of the contents of this adder; to develop an algorithm for the synthesis of an adder by an arbitrary RCS module. Research methods: methods of analysis and synthesis of computer systems, number theory, coding theory in RCS. The following results were obtained. It is shown in the work that the introduction of additional interdigit connections in a positional binary adder allows changing the contents of this adder. The rules for the introduction of these additional links are formulated, on the basis of which an algorithm for the synthesis of an adder by an arbitrary RCS modulus is obtained. Specific examples of the synthesis of structures of binary adders for various values of the RCS modules are given. Conclusions. Thus, the paper proposes an algorithm for the synthesis of an adder by an arbitrary RCS module, which is based on the use of positional binary adders, by introducing additional inter-bit connections. The application of the considered algorithm expands the functionality of positional binary adders

Reliability analysis of station autonomous computer system based on fuzzy dynamic fault tree and Markov model

AbstractStation autonomous computer system is the core of the centralized traffic control (CTC) system. The working mode is dual computer hot standby, and its reliability is very important. Due to the dynamic and fuzzy issue with the reliability analysis of station autonomous computer system, a reliability analysis method is formulated based on fuzzy dynamic fault tree (DFT) and Markov model. First, the structure and working principle of the dual computer hot standby autonomous computer system and the double 2‐vote‐2 autonomous computer system are analyzed, and two autonomous computer systems based on DFT are formulated. Then, the failure rates and the fault detection probabilities are considered as fuzzy variables and represented as triangular fuzzy variables. The fuzzy state transition matrix of the system is obtained by conversion of DFT to the Markov model. Finally, the fuzzy reliability of the system is calculated by Laplace transform. Simulation results show that the reliability of the dual computer hot standby autonomous computer system is higher than that of the double 2‐vote‐2 autonomous computer system. Besides, the reliability of the hardware system is not only related to the structure of the system, but also affected by the uncertain unit failure rate, fault detection probabilities, and so on.

Smart eNose Food Waste Management System

The modern age is an era of fast‐growing technology, all thanks to the Internet of Things. The IoT becomes a prime factor of human life. As in this running world, no one cares about the wastage of food. However, this causes environment pollution as well as loss of many lives. A lot of researchers help in this era by introducing some great and beneficial projects. Our work is introducing a new approach by utilizing some low‐cost sensors. In this work, Arduino UNO is used as a microcontroller. We use the eNose system that comprises MQ4 and MQ135 to detect gas emission from different food items, i.e., meat, rice, rice and meat, and bread. We collect our data from these food items. The MQ4 sensor detects the CH4 gas while the MQ135 sensor detects CO2 and NH3 in this system. We use a 5 kg strain gauge load cell sensor and HX711 A/D converter as a weight sensor to measure the weight of food being wasted. To ensure the accuracy and efficiency of our system, we first calibrate our sensors as per recommendations to run in the environment with the flow. We collect our data using cooked, uncooked, and rotten food items. To make this system a smart system, we use a machine learning algorithm to predict the food items on the basis of gas emission. The decision tree algorithm was used for training and testing purposes. We use 70 instances of each food item in the dataset. On the rule set, we implement this system working to measure the weight of food wastage and to predict the food item. The Arduino UNO board fetches the sensor data and sends it to the computer system for interpretation and analysis. Then, the machine learning algorithm works to predict the food item. At the end, we get our data of which food item is wasted in what amount in one day. We found 92.65% accuracy in our system. This system helps in reducing the amount of food wastage at home and restaurants as well by the daily report of food wastage in their computer system.

Validity of two-dimensional analysis using a tablet computer for estimation of foot arch height during walking.

[Purpose] To examine the validity of two-dimensional analysis using a tablet computer for the estimation of arch height during walking by comparing it with a motion capture system and static foot alignment screenings. [Participants and Methods] Fourteen healthy males and 15 healthy females participated in this study. The arch height of the right foot while walking was simultaneously measured using a tablet computer and motion capture system. Dynamic foot alignment, including arch height, at the mid-stance and pre-swing phases was calculated from the kinematic data measured using the tablet computer and motion analysis system. Static foot alignment was also assessed by screening tests including arch height index and foot posture index. [Results] Arch height measured using a tablet computer showed a significant high correlation with that measured using the motion capture system at the mid-stance and pre-swing phases. Arch height index showed a significant moderate correlation with arch height measured using the motion capture system at the mid-stance phase. Meanwhile, foot posture index showed no relationship with arch height measured by the motion capture system. [Conclusion] These results demonstrate the high validity of dynamic foot analysis using a tablet computer for the estimation of arch height during walking. Such gait analysis can be effective for assessing dynamic foot alignment in clinical practice.

A COMPUTER SYSTEM FOR ACQUISITION AND ANALYSIS OF MEASUREMENT DATA FOR A SKEW ROLLING MILL IN MANUFACTURING STEEL BALLS

The article presents the concept and capabilities of a computer system for analysing measurement data for a skew rolling mill used to produce steel balls. The computer system for data acquisition and analysis consists of cooperating systems designed to perform control and measurement tasks during the operation of a skew rolling mill. Their main task is to collect and record data related to the measured physical parameters of the batch rolling process. This system registers the current and analyses the radial forces and torque acting on the rolled element by the rolling tool. The process of data acquisition, analysis and archiving is carried out by means of an NI USB 6009 measuring card together with the attached systems of transducers and force and torque sensors and a computer with an installed application. The measurement application was developed in the LabVIEW environment. The application algorithm is based on the state machine architecture and enables the configuration of measurement elements and technical parameters, checking the functioning of the control and measurement system and the acquisition and archiving of measurement data.