Phenomenological Model of Nonlinear Dynamics and Deterministic Chaotic Gas Migration in Bentonite: Experimental Evidence and Diagnostic Parameters

Understanding gas migration in compacted clay materials, e.g., bentonite and claystone, is important for the design and performance assessment of an engineered barrier system of a radioactive waste repository system, as well as many practical applications. Existing field and laboratory data on gas migration processes in low-permeability clay materials demonstrate the complexity of flow and transport processes, including various types of instabilities, caused by nonlinear dynamics of coupled processes of liquid–gas exchange, dilation, fracturing, fracture healing, etc., which cannot be described by classical models of fluid dynamics in porous media. We here show that the complexity of gas migration processes can be explained using a phenomenological concept of nonlinear dynamics and deterministic chaos theory. To do so, we analyzed gas pressure and gas influx (i.e., input) and outflux (i.e., output), recorded during the gas injection experiment in the compact Mx80-D bentonite sample, and calculated a set of the diagnostic parameters of nonlinear dynamics and chaos, such a global embedding dimension, a correlation dimension, an information dimension, and a spectrum of Lyapunov exponents, as well as plotted 2D and 3D pseudo-phase-space strange attractors, based on the univariate influx and outflux time series data. These results indicate the presence of phenomena of low-dimensional deterministic chaotic behavior of gas migration in bentonite. In particular, during the onset of gas influx in the bentonite core, before the breakthrough, the development of gas flow pathways is characterized by the process of chaotic gas diffusion. After the breakthrough, with inlet-to-outlet movement of gas, the prevailing process is chaotic advection. During the final phase of the experiment, with no influx to the sample, the relaxation pattern of gas outflux is resumed back to a process of chaotic diffusion. The types of data analysis and a proposed phenomenological model can be used to establish the basic principles of experimental data-gathering, modeling predictions, and a research design.

Design of a non-invasive sensing system for diagnosing gastric disorders

Gastric disorders are widely spread among the population of any age. At the moment, the diagnosis is made by using invasive systems that cause several side effects. The present manuscript proposes an innovative non-invasive sensing system for diagnosing gastric dysfunctions. The Electro-GastroGraphy (EGG) technique is used to record myoelectrical signals of stomach activities. Although EGG technique is well known for a long time, several issues concerning the signal processing and the definition of suitable diagnostic criteria are still unresolved. So, EGG is to this day a trial practice. The authors want to overcome the current limitations of the technique and improve its relevance. To this purpose, a smart EGG sensing system has been designed to non-invasively diagnose gastric disorders. In detail, the system records the gastric slow waves by means of skin surface electrodes placed in the epigastric area. Cutaneous myoelectrical signals are so acquired from the body surface in proximity of stomach. Electro-gastrographic record is then processed. According to the diagnostic model designed from the authors, the system estimates specific diagnostic parameters in time and frequency domains. It uses Discrete Wavelet Transform to obtain power spectral density diagrams. The frequency and power of the EGG waveform and the dominant frequency components are so analyzed. The defined diagnostic parameters are put in comparison with the reference values of a normal EGG in order to estimate the presence of gastric pathologies by the analysis of arrhythmias (<em>tachygastria</em>, <em>bradygastria</em> and irregular rhythm). The paper aims to describe the design of the system and of the arrhythmias detection algorithm. Prototype development and experimental data will be presented in future works. Preliminary results show an interesting relevance of the suggested technique so that it can be considered as a promising non-invasive tool for diagnosing gastrointestinal motility disorders.

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.

An expert neural system for diagnostics of motor-driven valves

Trouble-free operation of motor-driven valves (MDV) is one of the key factors behind the operating safety of NPPs. As critical components, MDVs are a part of a safety system and a safety-related system. This imposes the highest possible requirements on the MDV reliability. MDVs are the most numerous category of the NPP components. Depending on design, one power unit contains 1500 to 3000 motor-driven valves alone. It follows from an analysis of the NPP failures that many of these are caused by failed motor-driven valves of safety and safety-related systems. The paper presents a description of an automated system for diagnostics of shutoff and control MDVs used in the NPP pipelines. The developed diagnostic algorithms make it possible to take into account the variability of the MDV technical parameters, while taking into account, at the same time, rated restrictions on diagnostic parameters, if any.

Development of a Method for Evaluating the Technical Condition of a Car’s Hybrid Powertrain

The article presents the results of a study performed and substantiated based on the principles of a new method of diagnostics of technical conditions of a hybrid powertrain regardless of the structural diagram and design features of a hybrid vehicle. The presented new technology of the diagnostics of hybrid powertrains allows an objective complex assessment of their technical condition by diagnostic parameters in contrast to existing diagnostic methods. In the proposed method, a mechanism for the general standardization of diagnostic parameters has been developed as well as for determining the numerical values of the parameters of the powertrain. The control subset was used to control the learning error. As a result of debugging the system, the scatter of experimental and calculated points has decreased, which confirms the quality of debugging the tested fuzzy model. As a result of training the artificial neural network, the standard deviation of the error in the control sample was 0.012·Pk. A symmetry method of diagnostics of the technical state of a hybrid propulsion system was developed based on the concept of a neural network together with a neuro-fuzzy control with an adaptive criteria based on the method of training a neural network with reinforcement. The components of the vector functional include the criteria for control accuracy, the use of traction battery energy, and the degree of toxicity of exhaust gases. It is proposed to use the principle of symmetry of the guaranteed result and the linear inversion of the vector criterion into a supercriterion to determine the technical state of a hybrid powertrain on a set of Pareto-optimal controls under unequal conditions of optimality.

Reasoning of additional diagnostic parameters for electric insulation diagnostics by absorption methods

The aim of this work is to analyze the inadequacies of the diagnostic parameters, in particular the absorption and polarization coefficients, which are manifested in their integral character and dependence on the ratio of values of several elements of the equivalent scheme of insulation replacement. This article contains the results of theoretical investigation of the extremе nature of the absorption diagnostic parameters, which leads to unambiguity of the diagnostics procedure. The ways of partial adjustment of this unambiguity have been proposed. Methodology. To determine the extremality of the absorption coefficients depending on the absorption time, absorption capacity and resistance, as well as the leakage resistance, the usual method of investigating the functions was used, detailed calculations have been obtained by using MATLAB software. Results. Has been shown that the ambiguity of diagnostic results is caused by the contradiction of the integral character of the diagnostic parameters and the local character of the isolation failures, in particular breakdown, by ambiguous dependence of the diagnostic parameters on the values of the elements of the insulation replacement scheme and the extremal nature of the diagnostic parameters. Based on the general expression describing all currently used absorption coefficients, it is shown that they all have an extremum, the value of which depends on the parameters of the insulation substitution scheme and the time interval between the measurements of the absorption current. The dependence of the extreme value of the absorption and polarization coefficients on the parameters of the insulation substitution scheme has been established. Has been shown that to eliminate the ambiguity caused by the extremity of the absorption coefficients, it is necessary to introduce additional diagnostic parameters, such as the ratio of leakage resistance to absorption resistance, as well as the critical value of the absorption time constant. Originality. The detailed analysis of the reasons of ambiguity of electric insulation technical diagnostics by absorption methods has been carried out. A method for eliminating the ambiguity caused by the extremity of the absorption coefficients has been proposed. Practical significance. To eliminate the ambiguity caused by the extremity of the absorption coefficients, additional diagnostic parameters are proposed – the ratio of leakage resistance to the absorption resistance and critical values of the absorption time constant. The applying of these parameters with the absorption coefficients will more adequately assess the technical condition of insulation.

Entropic model of network dynamics of clocking network synchronization

The main task of the clocking network synchronization (CNS) network subsystem is the formation, transmission, distribution and delivery of synchronization signals to the telecommunication system (TCS) digital equipment for the purpose of its coordinated interaction. Indicators of the telecommunication services quality are inextricably linked with the indicators of the CNS network functioning quality, in this regard, the process of monitoring and managing the CNS network comes to the fore for the purpose of prompt detection of failures and their subsequent elimination. The article provides an overview of the main classes of CNS network equipment and their diagnostic parameters, and also indicates the significant influence of the CNS network functioning process on the entire TCS functioning. To assess the technical condition of the CNS network an approach using the entropy analysis of the diagnostic parameters of the CNS network elements is proposed. The entropy model of the network dynamics is obtained in CNS work, which can later be used to develop a methodology for monitoring the technical condition of the CNS network. Using this model, it is possible to estimate not only the differential entropy of each CNS network element, but also to estimate the differential entropy of the entire CNS network or a separate fragment of the CNS network. Differential entropy parameters reflect the technical state of the CNS network.

CHARACTERISATION OF HYPERTROPHIC CARDIOMYOPATHY BY CARDIAC MAGNETIC RESONANCE IMAGING

Background: There are many causes of left ventricular hypertrophy which can result in arrhythmias and sudden cardiac death. Hypertrophic cardiomyopathy (HCM) is one of the commonly encountered cause of sudden cardiac death in young adults. Aim: Identifying the role of Cardiac MRI in characterising the diagnostic parameters of HCM. Materials and methods: 125 patients with clinical suspicion or genetic evidence of HCM referred for cardiac MRI between June 2013 to June 2021 were included under the study. Image interpretations were done by fellowship qualified cardiac imaging radiologist. Categorical variables were expressed using frequency and percentage. Numerical variables were presented using mean and standard deviation. Results: Out of the total population, 119 patients (95 %) had LV wall thickness > 13 mm, 48 patients (38.4%) had Left ventricle outflow tract obstruction (LVOTO) and 32 patients (25.6 %) had mid cavity obstruction, 39 patients (37.9 %) had myocardial scar > 15 % and asymmetric septal hypertrophy was the most frequently encountered left ventricle morphology Conclusion: Cardiac MRI detected HCM has a statistically significant association with the final diagnosis (histopathological and genetic correlation). CMRI hence serves as a reliable tool in identifying and characterising the various diagnostic and non- diagnostic parameters of HCM.

Detection of Sepsis in Platelets Using MicroRNAs and Membrane Antigens

The present study proposes to legitimize in sepsis a characteristic found in platelets that suffer storage lesions in blood banks, which is the increased expression of miRNA miR-320a in relation to miR-127. Under physiologically normal conditions, an inverse relationship is observed. The aim of this study was to verify whether the analysis of miR-320a and miR-127 expression in platelets could detect a decrease in their viability and function due to the presence of pathogens in the blood of patients hospitalized in the Intensive Care Unit. We also investigated the expression of membrane antigens sensitive to platelet activation. Of the 200 patients analyzed, only those who developed sepsis (140) were found to have a higher relative quantity of miR-320a than that of miR-127. This characteristic and the increased expression of membrane antigens P2Y12, CD62P, CD41, and CD61 showed a significant association (p < 0.01) with all types of sepsis evaluated in this study. Additionally, 40% of patients hospitalized for sepsis had negative results for the first cultures. We conclude that analysis of miR-127 and miR-320a expression combined with membrane antigens evaluation, in association with the available clinical and diagnostic parameters, are important tools to detect the onset of sepsis.