The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.
This book contains a selection of papers accepted for the presentation and discussion at the 2018 International Conference on Digital Science (DSIC’18). This Conference had the support of the Institute of Certified Specialists, Russia, AISTI (Iberian Association for Information Systems and Technologies), and Springer. It will take place at Convention Centre, Budva, Montenegro, October 19–21, 2018. DSIC’18 is an international forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences, and concerns in the several perspectives of Digital Science. The main idea of this Conference is that the world of science is unified and united allowing all scientists/practitioners to be able to think, analyze, and generalize their thoughts. DSIC aims efficiently to disseminate original research results in natural, social, art, and humanities sciences. An important characteristic feature of the Conference should be the short publication time and worldwide distribution. This Conference enables fast dissemination, so conference participants can publish their papers in print and electronic format, which is then made available worldwide and accessible by numerous researchers. The Scientific Committee of DSIC’18 was composed of a multidisciplinary group of 26 experts. One hundred and seven invited reviewers who are intimately concerned with Digital Science have had the responsibility for evaluating, in a “double-blind review” process, the papers received for each of the main themes proposed for the Conference: Digital Art and Humanities; Digital Economics; Digital Education; Digital Engineering; Digital Environmental Sciences; Digital Finance, Business and Banking; Digital Media; Digital Medicine, Pharma and Public Health; Digital Public Administration; Digital Technology and Applied Sciences. DSIC’18 received 88 contributions from 16 countries around the world. The papers accepted for the presentation and discussion at the Conference are published by Springer (this book) and will be submitted for indexing by ISI, SCOPUS, among others.
Previously, the mathematical models (CoMPaS and CoM-III) of primary tumor (PT) growth and secondary distant metastases (sdMTS) growth of breast cancer (BC) considering TNM classification have been presented (Tyuryumina E., Neznanov A.; 2017, 2018). Goal: To detect the earliest diagnostics period of visible sdMTS via CoMPaS and CoM-III.
Increasing evidence suggests that neuronal communication is a defining property of functionally specialized brain networks and that it is implemented through synchronization between population activities of distinct brain areas. The detection of long-range coupling in electroencephalography (EEG) and magnetoencephalography (MEG) data using conventional metrics (such as coherence or phase-locking value) is by definition contaminated by spatial leakage. Methods such as imaginary coherence, phase-lag index or orthogonalized amplitude correlations tackle spatial leakage by ignoring zero-phase interactions. Although useful, these metrics will by construction lead to false negatives in cases where true zero-phase coupling exists in the data and will underestimate interactions with phase lags in the vicinity of zero. Yet, empirically observed neuronal synchrony in invasive recordings indicates that it is not uncommon to find zero or close-to-zero phase lag between the activity profiles of coupled neuronal assemblies. Here, we introduce a novel method that allows us to mitigate the undesired spatial leakage effects and detect zero and near zero phase interactions. To this end, we propose a projection operation that operates on sensor-space cross-spectrum and suppresses the spatial leakage contribution but retains the true zero-phase interaction component. We then solve the network estimation task as a source estimation problem defined in the product space of interacting source topographies. We show how this framework provides reliable interaction detection for all phase-lag values and we thus refer to the method as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS). Realistic simulations demonstrate that PSIICOS has better detector characteristics than existing interaction metrics. Finally, we illustrate the performance of PSIICOS by applying it to real MEG dataset recorded during a standard mental rotation task. Taken together, using analytical derivations, data simulations and real brain data, this study presents a novel source-space MEG/EEG connectivity method that overcomes previous limitations and for the first time allows for the estimation of true zero-phase coupling via non-invasive electrophysiological recordings.
Cardiovascular disease associated with metabolic syndrome has a high prevalence, but the mechanistic basis of metabolic cardiomyopathy remains poorly understood. We characterised the cardiac transcriptome in a murine metabolic syndrome (MetS) model (LDLR−/−; ob/ob, DKO) relative to the healthy, control heart (C57BL/6, WT) and the transcriptional changes induced by ACE-inhibition in those hearts. RNA-Seq, differential gene expression and transcription factor analysis identified 288 genes differentially expressed between DKO and WT hearts implicating 72 pathways. Hallmarks of metabolic cardiomyopathy were increased activity in integrin-linked kinase signalling, Rho signalling, dendritic cell maturation, production of nitric oxide and reactive oxygen species in macrophages, atherosclerosis, LXR-RXR signalling, cardiac hypertrophy, and acute phase response pathways. ACE-inhibition had a limited effect on gene expression in WT (55 genes, 23 pathways), and a prominent effect in DKO hearts (1143 genes, 104 pathways). In DKO hearts, ACE-I appears to counteract some of the MetS-specific pathways, while also activating cardioprotective mechanisms. We conclude that MetS and control murine hearts have unique transcriptional profiles and exhibit a partially specific transcriptional response to ACE-inhibition.
Background & Objective: The presented design attempt of building an advanced prosthetic lower limb has a vast requirement in these days to connect paralytic patients and elderly people with the external environment for a better lifestyle. The Recordings of Electromyography (EMG) patterns and other data analysis of the muscle signals has lightened the path of technology to a wide range of studies and experiments in the field of prosthesis of certain moveable body parts using the acquired EMG signal of muscles of lower limb. An effort has been made to acquire the EMG signal and study the signals with the incorporation of sufficient technology to build an artificial lower limb with the help of Flex-sensor and ‘Arduino Uno’ programming. The main aspect about the hardware circuitry of this practical work is its ease of use and applicability. The Bio-electrodes are attached on the specified location to acquire the muscle signals and also Laboratory Virtual Instrument Engineering Workbench (LabVIEW) to acquire the signals. In addition, the movements of certain joints of the lower limb can be replicated controlling the movement of a servo-motor for the implementation of the design of the artificial lower limb. Conclusion: The different output parameter changes can be measured according to the relaxation and the contraction of the Flex-sensor attached to the knee, and the ankle joints. The Servo-motors can further be utilized in the construction of the artificial lower leg to be moved externally without any voluntary motion of the subject. The measurement of dispersion has been introduced in the mathematical analysis.
The ECG features analysis in a patient born in 1946 led to repeated statement of misdiagnosis, particularly of myocardial infarction. About 2,500 patients’s ECGshave been analyzed for the three-year period of the patient’s observation. A Fourier analysis of the spectra of the ECGs of the patient 2506 allowed to assume that there exists an additional leading center in his myocardium, with a variable start phase of the myocardium relative to the phase of the fundamental frequency of contractions of the myocardium. Experimental confirmation of this hypothesis is in particular; found in the patient jump-like transition from synchronous phase daily changes in the function dynamics of the P wave width and the PQ segment to the antiphase ones, which reflects the change in the conditions for triggering the process of myocardial contraction. As a result, the coupling of the two leading frequencies of the myocardium, with a variable phase shift of the triggering, can be expressed both in the extra systoles appearance as well as in unusual cardio cycles not coinciding in the form and phase of cardio cycles of a single ECG. Cluster analysis of the entire set of collected patient’s ECGs in the similarity of their forms revealed 18 separate clusters of similarity. While cluster analysis of the ECG samples in other 4857 patients in most cases led to the identification of only one cluster characteristic for a certain patient’s ECG form. Mathematical modeling of the proposed hypothesis about the presence of more than one leading center of myocardial triggering which resulted in getting model patterns of split ECGs, qualitatively corresponding to the split forms of ECGs observed in the studied patient, confirmed its validity.
The paper presents a possibility of estimating a human cardiac pacemaker using combined application of nonlinear integral transformation and fuzzy logic, which allows carrying out the analysis in the real-time mode. The system of fuzzy logical conclusion is proposed, membership functions and rules of fuzzy products are defined. It was shown that the ratio of the value of a truth degree of the winning rule condition to the value of a truth degree of any other rule condition is at least 3.
The paper presents the automated system intended to prevent industrial-caused diseases of workers, the basis of which is represented by algorithms of preventing several negative functional conditions (stress, monotony). The emergence of such state shall be determined based on an analysis of bioelectric signals, in particular, skin-galvanic reactions. Proceeding from the dynamics of the functional state, the automated system offers to perform an optimized set of measures to restore the health of the worker. Implementation of an automated system is presented in Visual Programming system LabVIEW.