Search results for: multidimensional hierarchical graph neuron

Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

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Authors: Mark Bezner, Shani Deri, Tom Trigano, Kfir Ben-Harush

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Intermediate filament (IF) proteins-based fibers are among the toughest fibers in nature, as was shown by native hagfish slime threads and by synthetic fibers that are based on type V IF-proteins, the nuclear lamins. It is assumed that their mechanical performance stems from two major factors: (1) the transition from elastic -helices to stiff-sheets during tensile load; and (2) the specific organization of the coiled-coil proteins into a hierarchical network of nano-filaments. Here, we investigated the interrelationship between these two factors by using wet-spun fibers based on C. elegans (Ce) lamin. We found that Ce-lamin fibers, whether assembled in aqueous or alcoholic solutions, had the same nonlinear mechanical behavior, with the elastic region ending at ~5%. The pattern of the transition was, however, different: the ratio between -helices and -sheets/random coils was relatively constant until a 20% strain for fibers assembled in an aqueous solution, whereas for fibers assembled in 70% ethanol, the transition ended at a 6% strain. This structural phenomenon in alcoholic solution probably occurred through the transition between compacted and extended conformation of the random coil, and not between -helix and -sheets, as cycle analyses had suggested. The different transition pattern can also be explained by the different higher order organization of Ce-lamins in aqueous or alcoholic solutions, as demonstrated by introducing a point mutation in conserved residue in Ce-lamin gene that alter the structure of the Ce-lamins’ nano-fibrils. In addition, biomimicking the layered structure of silk and hair fibers by coating the Ce-lamin fiber with a hydrophobic layer enhanced fiber toughness and lead to a reversible transition between -helix and the extended conformation. This work suggests that different hierarchical structures, which are formed by specific assembly conditions, lead to diverse secondary structure transitions patterns, which in turn affect the fibers’ mechanical properties.

Keywords: protein-based fibers, intermediate filaments (IF) assembly, toughness, structure-property relationships

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Authors: Dongqin Cheng

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The n-dimensional balanced hypercube BHn (n ≥ 1) has been proved to be a bipartite graph. Let P be a set of edges whose induced subgraph consists of pairwise vertex-disjoint paths. For any two vertices u, v from different partite sets of V (BHn). In this paper, we prove that if |P| ≤ 2n − 2 and the subgraph induced by P has neither u nor v as internal vertices, or both of u and v as end-vertices, then BHn contains a Hamiltonian path joining u and v passing through P. As a corollary, if |P| ≤ 2n−1, then the BHn contains a Hamiltonian cycle passing through P.

Keywords: interconnection network, balanced hypercube, Hamiltonian cycle, prescribed edges

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Authors: M. Chukhryaeva, R. Skhalyakho, J. Kagazegeva, E. Pocheshkhova, L. Yepiskopossyan, O. Balanovsky, E. Balanovska

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The Middle East is crossroad of different populations at different times. The Kurds are of particular interest in this region. Historical sources suggested that the origin of the Kurds is associated with Medes. Therefore, it was especially interesting to compare gene pool of Kurds with other supposed descendants of Medes-Tats. Yezidis are ethno confessional group of Kurds. Yezidism as a confessional teaching was formed in the XI-XIII centuries in Iraq. Yezidism has caused reproductively isolation of Yezidis from neighboring populations for centuries. Also, isolation helps to retain Yezidian caste system. It is unknown how the history of Yezidis affected its genу pool because it has never been the object of researching. We have examined the Y-chromosome variation in Yezidis and Kurdish males to understand their gene pool. We collected DNA samples from 90 Yezidi males and 24 Kurdish males together with their pedigrees. We performed Y-STR analysis of 17 loci in the samples collected (Yfiler system from Applied Biosystems) and analysis of 42 Y-SNPs by real-time PCR. We compared our data with published data from other Kurdish groups and from European, Caucasian, and West Asian populations. We found that gene pool of Yezidis contains haplogroups common in the Middle East (J-M172(xM67,M12)- 24%, E-M35(xM78)- 9%) and in South Western Asia (R-M124- 8%) and variant with wide distribution area - R-M198(xM458- 9%). The gene pool of Kurdish has higher genetic diversity than Yezidis. Their dominants haplogroups are R-M198- 20,3 %, E-M35- 9%, J-M172- 9%. Multidimensional scaling also shows that the Kurds and Yezidis are part of the same frontier Asian cluster, which, in addition, included Armenians, Iranians, Turks, and Greeks. At the same time, the peoples of the Caucasus and Europe form isolated clusters that do not overlap with the Asian clusters. It is noteworthy that Kurds from our study gravitate towards Tats, which indicates that most likely these two populations are descendants of ancient Medes population. Multidimensional scaling also reveals similarity between gene pool of Yezidis, Kurds with Armenians and Iranians. The analysis of Yezidis pedigrees and their STR variability did not reveal a reliable connection between genetic diversity and caste system. This indicates that the Yezidis caste system is a social division and not a biological one. Thus, we showed that, despite many years of isolation, the gene pool of Yezidis retained a common layer with the gene pool of Kurds, these populations have common spectrum of haplogroups, but Yezidis have lower genetic diversity than Kurds. This study received primary support from the RSF grant No. 16-36-00122 to MC and grant No. 16-06-00364 to EP.

Keywords: gene pool, haplogroup, Kurds, SNP and STR markers, Yezidis

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Authors: M. Skibinska, A. Rajewska-Rager, M. Dmitrzak-Weglarz, N. Lepczynska, P. Sibilski, P. Kapelski, J. Pawlak, J. Twarowska-Hauser

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Introduction: Neurotrophic factors have been implicated in neuropsychiatric disorders. Brain-Derived Neurotrophic Factor (BDNF) influences neuron differentiation in development as well as synaptic plasticity and neuron survival in adulthood. BDNF is widely studied in mood disorders and has been proposed as a biomarker for depression. BDNF is synthesized as precursor protein – proBDNF. Both forms are biologically active and exert opposite effects on neurons. Aim: The aim of the study was to examine the serum levels of BDNF and proBDNF in unipolar and bipolar young patients below 24 years old during hypo/manic, depressive episodes and in remission compared to healthy control group. Methods: In a prospective 2 years follow-up study, we investigated alterations in levels of BDNF and proBDNF in 79 patients (23 males, mean age 19.08, SD 3.3 and 56 females, mean age 18.39, SD 3.28) diagnosed with mood disorders: unipolar and bipolar disorder compared with 35 healthy control subjects (7 males, mean age 20.43, SD 4.23 and 28 females, mean age 21.25, SD 2.11). Clinical characteristics including mood, comorbidity, family history, and treatment, were evaluated during control visits and clinical symptoms were rated using the Hamilton Depression Rating Scale and Young Mania Rating Scale. Serum BDNF and proBDNF concentrations were determined by Enzyme-Linked Immunosorbent Assays (ELISA) method. Serum BDNF and proBDNF levels were analysed with covariates: sex, age, age > 18 and < 18 years old, family history of affective disorders, drug-free vs. medicated status. Normality of the data was tested using Shapiro-Wilk test. Levene’s test was used to calculate homogeneity of variance. Non-parametric Tests: Mann-Whitney U test, Kruskal-Wallis ANOVA, Friedman’s ANOVA, Wilcoxon signed rank test, Spearman correlation coefficient were applied in analyses The statistical significance level was set at p < 0.05. Results: BDNF and proBDNF serum levels did not differ between patients at baseline and controls as well as comparing patients in acute episode of depression/hypo/mania at baseline and euthymia (at month 3 or 6). Comparing BDNF and proBDNF levels between patients in euthymia and control group no differences have been found. Increased BDNF level in women compared to men at baseline (p=0.01) have been observed. BDNF level at baseline was negatively correlated with depression and mania occurence at 24 month (p=0.04). BDNF level at 12 month was negatively correlated with depression and mania occurence at 12 month (p=0.01). Correlation of BDNF level with sex have been detected (p=0.01). proBDNF levels at month 3, 6 and 12 negatively correlated with disease status (p=0.02, p=0.008, p=0.009, respectively). No other correlations of BDNF and proBDNF levels with clinical and demographical variables have been detected. Discussion: Our results did not show any differences in BDNF and proBDNF levels between depression, mania, euthymia, and controls. Imbalance in BDNF/proBDNF signalling may be involved in pathogenesis of mood disorders. Further studies on larger groups are recommended. Grant was founded by National Science Center in Poland no 2011/03/D/NZ5/06146.

Keywords: bipolar disorder, Brain-Derived Neurotrophic Factor (BDNF), proBDNF, unipolar depression

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Authors: Haffaf Hafid

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Hypergraphs, after being used to model the structural organization of System of Sytems (SoS) at macroscopic level, has recent trends towards generalizing this powerful representation at different stages of complex system modelling. In this paper, we first describe different applications of hypergraph theory, and step by step, introduce multilevel modeling of SoS by means of integrating Constraint Programming Langages (CSP) dealing with engineering system reconfiguration strategy. As an application, we give an A.C.T Terminal controlled by a set of Intelligent Automated Vehicle.

Keywords: hypergraph model, structural analysis, bipartite graph, monitoring, system of systems, reconfiguration analysis, hypernetwork

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Authors: Xi Yuan, Xuebin Zhu, Bojun Li

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This work describes the experiment to produce high-intensity pulsed neutron beams on XingGuangIII laser facility. The high-intensity laser is utilized to drive protons and deuterons, which hit a thick solid target to produce neutrons. The pulse duration of the laser used in the experiment is about 0.8 ps, and the laser energy is around 100 J. Protons and deuterons are accelerated from a 10-μm-thick deuterated polyethylene (CD₂) foil and diagnosed by a Thomson parabola ion-spectrometer. The energy spectrum of neutrons generated via ⁷Li(d,n) and ⁷Li(p,n) reaction when proton and deuteron beams hit a 5-mm-thick LiF target is measured by a scintillator-based time-of-flight spectrometer. Results from the neuron measurements show that the maximum neutron energy is about 12.5 MeV and the neutron yield is up to 2×10⁹/pulse. The high-intensity pulsed neutron beams demonstrated in this work can provide a valuable neutron source for material research, fast neutron induced fission research, and so on.

Keywords: picosecond laser driven, fast neutron, time-of-flight spectrometry, XinggungIII

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: biometric characters, facial recognition, neural network, OpenCV

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Authors: Amlan Datta

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Dengue is an arboviral, vector borne infection, quite prevalent in tropical countries such as India. Approximately, 1 to 25% of cases may give rise to neurological complication, such as, seizure, delirium, Guillian-Barre syndrome (GBS), multiple cranial nerve palsies, intracranial thrombosis, stroke-like presentations, to name a few. Dengue fever, as an antecedent to GBS is uncommon, especially in adults.Here, we report a case about a middle aged lady who presented with an acute onset areflexic ascending type of polyradiculoneuropathy along with bilateral lower motor neuron type of facial nerve palsy, as well as abducens and motor component of trigeminal (V3) weakness. Her respiratory and neck muscles were spared. She had an established episode of dengue fever (NS1 and dengue IgM positive) 7 days prior to the weakness. Nerve conduction study revealed a demyelinating polyradiculopathy of both lower limbs and cerebrospinal fluid examination showed albuminocytological dissociation. She was treated with 5 days of intravenous immunoglobulin (IVIg), following which her limb weakness improved considerably. This case highlights GBS as a potential complication following dengue fever.

Keywords: areflexic, demyelinating, dengue, polyradiculoneuropathy

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Authors: Mohita Gangwar Sharma

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Different factors are compelling manufacturers to move towards the phenomenon of servitization i.e. when firms go beyond giving support to the customers in operating the equipment. The challenges that are being faced in this transition by the manufacturing firms from being a product provider to a product- service provider are multipronged. Product-Service Systems (PSS) lies in between the pure-product and pure-service continuum. Through this study, we wish to understand the dimensions of ‘PSS-quality’. We draw upon the quality literature for both the product and services and through an expert survey for a specific transportation sector using analytical hierarchical process (AHP) derive a conceptual model that can be used as a comprehensive measurement tool for PSS offerings.

Keywords: servitisation, quality, product-service system, AHP

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Authors: Phenphak Horadee, Rodchares Hanrinth, Saithip Suttiruksa

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Inappropriate use of antibiotics has happened in several hospitals, Thailand. Drug use evaluation (DUE) is one strategy to overcome this difficulty. However, most community hospitals still encounter incomplete evaluation resulting overuse of antibiotics with high cost. Consequently, drug-resistant bacteria have been rising due to inappropriate antibiotic use. The aim of this study was to involve stakeholders in conceptualizing, developing, and prioritizing a feasible intervention strategy to promote and support appropriate antibiotic prescribing in a community hospital, Thailand. Study antibiotics included four antibiotics such as Meropenem, Piperacillin/tazobactam, Amoxicillin/clavulanic acid, and Vancomycin. The study was conducted for the 1-year period between March 1, 2018, and March 31, 2019, in a community hospital in the northeastern part of Thailand. Concept mapping was used in a purposive sample, including doctors (one was an administrator), pharmacists, and nurses who involving drug use evaluation of antibiotics. In-depth interviews for each participant and survey research were conducted to seek the problems for inappropriate use of antibiotics based on drug use evaluation system. Seventy-seven percent of DUE reported appropriate antibiotic prescribing, which still did not reach the goal of 80 percent appropriateness. Meropenem led other antibiotics for inappropriate prescribing. The causes of the unsuccessful DUE program were classified into three themes such as personnel, lack of public relation and communication, and unsupported policy and impractical regulations. During the first meeting, stakeholders (n = 21) expressed the generation of interventions. During the second meeting, participants who were almost the same group of people in the first meeting (n = 21) were requested to independently rate the feasibility and importance of each idea and to categorize them into relevant clusters to facilitate multidimensional scaling and hierarchical cluster analysis. The outputs of analysis included the idealist, cluster list, point map, point rating map, cluster map, and cluster rating map. All of these were distributed to participants (n = 21) during the third meeting to reach consensus on an intervention model. The final proposed intervention strategy included 29 feasible and crucial interventions in seven clusters: development of information technology system, establishing policy and taking it into the action plan, proactive public relations of the policy, action plan and workflow, in cooperation of multidisciplinary teams in drug use evaluation, work review and evaluation with performance reporting, promoting and developing professional and clinical skill for staff with training programs, and developing practical drug use evaluation guideline for antibiotics. These interventions are relevant and fit to several intervention strategies for antibiotic stewardship program in many international organizations such as participation of the multidisciplinary team, developing information technology to support antibiotic smart use, and communication. These interventions were prioritized for implementation over a 1-year period. Once the possibility of each activity or plan is set up, the proposed program could be applied and integrated into hospital policy after evaluating plans. Effectiveness of each intervention could be promoted to other community hospitals to promote and support antibiotic smart use.

Keywords: antibiotic, concept mapping, drug use evaluation, multidisciplinary teams

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Authors: Igwenagu Chinelo Mercy

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This study is on the concept of Neurostatistics in relation to neuroscience. Neuroscience also known as neurobiology is the scientific study of the nervous system. In the study of neuroscience, it has been noted that brain function and its relations to the process of acquiring knowledge and behaviour can be better explained by the use of various interrelated methods. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. On the other hand, Neurostatistics based on this study is viewed as a statistical concept that uses similar techniques of neuron mechanisms to solve some problems especially in the field of life science. This study is imperative in this era of Artificial intelligence/Machine leaning in the sense that clear understanding of the technique and its proper application could assist in solving some medical disorder that are mainly associated with the nervous system. This will also help in layman’s understanding of the technique of the nervous system in order to overcome some of the health challenges associated with it. For this concept to be well understood, an illustrative example using a brain associated disorder was used for demonstration. Structural equation modelling was adopted in the analysis. The results clearly show the link between the techniques of statistical model and nervous system. Hence, based on this study, the appropriateness of Neurostatistics application in relation to neuroscience could be based on the understanding of the behavioural pattern of both concepts.

Keywords: brain, neurons, neuroscience, neurostatistics, structural equation modeling

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of the present work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. Based on feature selection in different phases, in this research, we design a neural network system that has optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each ROI, 6 distinct set of texture features are extracted such as first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. We show that with the injection of liquid and the analysis of more phases the high relevant features in each region changed. Our results show that for detecting HCC tumor phase3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between these two classes according to our method, relates to first order histogram parameters with the accuracy of 85% in phase 1, 95% phase 2, and 95% in phase 3.

Keywords: multi-phasic liver images, texture analysis, neural network, hidden layer

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Authors: E. M. Rifqi Wilda Pradana, Bagus Bayu Prabowo, Meida Riski Pujiyati, Efraim Maykhel Hagana Ginting, Virgiawan Arya Hangga Reksa

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The VLF (Very Low Frequency) method is an electromagnetic method that uses low frequencies between 10-30 KHz which results in a fairly deep penetration. In this study, the VLF method was used for zonation of disaster-prone areas by identifying geological structures in the form of faults. Data acquisition was carried out in Trimulyo Region, Jetis District, Bantul Regency, Special Region of Yogyakarta, Indonesia with 8 measurement paths. This study uses wave transmitters from Japan and Australia to obtain Tilt and Elipt values that can be used to create RAE (Rapat Arus Ekuivalen or Current Density) sections that can be used to identify areas that are easily crossed by electric current. This section will indicate the existence of a geological structure in the form of faults in the study area which is characterized by a high RAE value. In data processing of VLF method, it is obtained Tilt vs Elliptical graph and Moving Average (MA) Tilt vs Moving Average (MA) Elipt graph of each path that shows a fluctuating pattern and does not show any intersection at all. Data processing uses Matlab software and obtained areas with low RAE values that are 0%-6% which shows medium with low conductivity and high resistivity and can be interpreted as sandstone, claystone, and tuff lithology which is part of the Semilir Formation. Whereas a high RAE value of 10% -16% which shows a medium with high conductivity and low resistivity can be interpreted as a fault zone filled with fluid. The existence of the fault zone is strengthened by the discovery of a normal fault on the surface with strike N550W and dip 630E at coordinates X= 433256 and Y= 9127722 so that the activities of residents in the zone such as housing, mining activities and other activities can be avoided to reduce the risk of natural disasters.

Keywords: current density, faults, very low frequency, zonation

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Authors: Hooshmand Kalayeh

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The human brain consists of reptilian, mammalian, and thinking brain. And mind consists of conscious, subconscious, and unconscious parallel neural-net programs. The primary objective of this paper is to propose a methodology for quantitative analysis of neural-nets associated with these mental activities in the neocortex. The secondary objective of this paper is to suggest a methodology for quantitative analysis of presence; the proposed methodologies can be used as a first-step to measure, monitor, and understand consciousness and presence. This methodology is based on Neural-Networks (NN), number of neuron in each NN associated with consciousness, subconsciouness, and unconsciousness, and number of neurons in neocortex. It is assumed that the number of neurons in each NN is correlated with the associated area and volume. Therefore, online and offline visualization techniques can be used to identify these neural-networks, and online and offline measurement methods can be used to measure areas and volumes associated with these NNs. So, instead of the number of neurons in each NN, the associated area or volume also can be used in the proposed methodology. This quantitative analysis and associated online and offline measurements and visualizations of different Neural-Networks enable us to rewire the connections in our brain for a more balanced living.

Keywords: brain, mind, consciousness, presence, sub-consciousness, unconsciousness, skills, concentrations, attention

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Authors: N. S. Vassilieva-Vashakmadze, R. A. Gakhokidze, I. M. Khachatryan

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In the first part of the paper it is shown that both the depolarization of the cytoplasmic membrane of rods observed in invertebrates and hyperpolarization characteristic of vertebrates on the light may activate the functioning of ion (Na+) channels of cytoplasmic membrane of rods and thus provide the emergence of nerve impulse and its transfer to the neighboring neuron etc. In the second part, using the quantum mechanical program for modeling of the molecular processes, we got a clear picture demonstrating the effect of charged phosphate groups on the protein components of α-helical subunits of the visual rhodopsin receptor. The analysis shows that the phosphorylation of terminal amino acid of seventh α-helical subunits of the visual rhodopsin causes a redistribution of electron density on the atoms, i.e. polarization of subunits, also the changing the configuration of the nuclear subsystem, which corresponds to the deformation process in the molecule. Based on the use of models it can be concluded that this system has an internal relationship between polarization and deformation processes that indicates on the allosteric effect. The allosteric effect is based on quantum-mechanical principle of the self-consistency of the molecules.

Keywords: membrane potential, ion channels, visual rhodopsin, allosteric effect

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Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

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Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

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Authors: Andrea N. Alarcon, Julian D. Castro, Gloria C. Rojas, Paola A. Vaca, Santiago Ortiz, Gustavo Martinez, Pablo D. Lemoine

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Recently, electoral results across many countries have shown to be inconsistent with rational decision theory, which states that individuals make decisions based on maximizing benefits and reducing risks. An alternative explanation has emerged: Fear and rage-driven vote have been proved to be highly effective for political persuasion and mobilization. This phenomenon has been evident in the 2016 elections in the United States, 2006 elections in Mexico, 1998 elections in Venezuela, and 2004 elections in Bolivia. In Colombia, it has occurred recently in the 2016 plebiscite for peace and 2018 presidential elections. The aim of this study is to explain this phenomenon using cultural cognition theory, referring to the psychological predisposition individuals have to believe that its own and its peer´s behavior is correct and, therefore, beneficial to the entire society. Cultural cognition refers to the tendency of individuals to fit perceived risks, and factual beliefs into group shared values; the Cultural Cognition Worldview Scales (CCWS) measures cultural perceptions through two different dimensions: Individualism-communitarianism and hierarchy-egalitarianism. The former refers to attitudes towards social dominance based on conspicuous and static characteristics (sex, ethnicity or social class), while the latter refers to attitudes towards a social ordering in which it is expected from individuals to guarantee their own wellbeing without society´s or government´s intervention. A probabilistic national sample was obtained from different polls from the consulting and public opinion company Centro Nacional de Consultoría. Sociodemographic data was obtained along with CCWS scores, a subjective measure of left-right ideological placement and vote intention for 2019 Mayor´s elections were also included in the questionnaires. Finally, the question “In your opinion, what is the greatest risk Colombia is facing right now?” was included to identify perceived risk in the population. Preliminary results show that Colombians are highly distributed among hierarchical communitarians and egalitarian individualists (30.9% and 31.7%, respectively), and to a less extent among hierarchical individualists and egalitarian communitarians (19% and 18.4%, respectively). Males tended to be more hierarchical (p < .000) and communitarian (p=.009) than females. ANOVA´s revealed statistically significant differences between groups (quadrants) for the level of schooling, left-right ideological orientation, and stratum (p < .000 for all), and proportion differences revealed statistically significant differences for groups of age (p < .001). Differences and distributions for vote intention and perceived risks are still being processed and results are yet to be analyzed. Results show that Colombians are differentially distributed among quadrants in regard to sociodemographic data and left-right ideological orientation. These preliminary results indicate that this study may shed some light on why Colombians vote the way they do, and future qualitative data will show the fears emerging from the identified values in the CCWS and the relation this has with vote intention.

Keywords: communitarianism, cultural cognition, egalitarianism, hierarchy, individualism, perceived risks

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Authors: Akiode Ayobami, Akiode Akinsewa, Odeku Mojisola, Salako Busola, Odutolu Omobola, Nuhu Khadija

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Purpose: Evidence exists that family planning use can contribute to reduction in infant and maternal mortality in any country. Despite these benefits, contraceptive use in Nigeria still remains very low, only 10% among married women. Understanding factors that predict contraceptive use is very important in order to improve the situation. In this paper, we analysed data from the 2013 Nigerian Demographic and Health Survey (NDHS) to better understand predictors of contraceptive use in Nigeria. The use of logistics regression and other traditional models in this type of situation is not appropriate as they do not account for social structure influence brought about by the hierarchical nature of the data on response variable. We therefore used multilevel modelling to explore the determinants of contraceptive use in order to account for the significant variation in modern contraceptive use by socio-demographic, and other proximate variables across the different Nigerian states. Method: This data has a two-level hierarchical structure. We considered the data of 26, 403 married women of reproductive age at level 1 and nested them within the 36 states and the Federal Capital Territory, Abuja at level 2. We modelled use of modern contraceptive against demographic variables, being told about FP at health facility, heard of FP on TV, Magazine or radio, husband desire for more children nested within the state. Results: Our results showed that the independent variables in the model were significant predictors of modern contraceptive use. The estimated variance component for the null model, random intercept, and random slope models were significant (p=0.00), indicating that the variation in contraceptive use across the Nigerian states is significant, and needs to be accounted for in order to accurately determine the predictors of contraceptive use, hence the data is best fitted by the multilevel model. Only being told about family planning at the health facility and religion have a significant random effect, implying that their predictability of contraceptive use varies across the states. Conclusion and Recommendation: Results showed that providing FP information at the health facility and religion needs to be considered when programming to improve contraceptive use at the state levels.

Keywords: multilevel modelling, family planning, predictors, Nigeria

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Authors: Abdul Jalil M. Khalaf, Esraa M. Kadhim

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The Hosoya polynomial of a graph G is a graphical invariant polynomial that its first derivative at x= 1 is equal to the Wiener index and second derivative at x=1 is equal to the Hyper-Wiener index. In this paper we study the Hosoya polynomial of zero-divisor graphs.

Keywords: Hosoya polynomial, wiener index, Hyper-Wiener index, zero-divisor graphs

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Authors: Hamid Alalwany, Nabeel A. Koshak, Mohammad K. Ibrahim

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Both knowledge economy and sustainable development are considered key dimensions in the policy action lines of many developed and developing countries. In this context, universities and other higher education institutes have a vital role in developing and sustaining wellbeing communities. In this paper, the authors’ aim is to address the links between the concepts of innovation and entrepreneurial capacity and knowledge economy, and to utilize the approach of intellectual capital development in building a sustainable knowledge economy. The paper will contribute to two discourses: (1) Developing a common understanding of the intersection aspects between the three concepts: Knowledge economy, Innovation and entrepreneurial system, and sustainable development; (2) Paving the road towards developing an integrated multidimensional framework for sustainable knowledge economy.

Keywords: innovation and entrepreneurial capacity, intellectual capital development, sustainable development, sustainable knowledge economy.

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Authors: Petri Solanti, Russell Klein

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Many designers are asking for an automated path from an abstract mathematical MATLAB model to a high-quality Register-Transfer Level (RTL) hardware description. Manual transformations of MATLAB or intermediate code are needed, when the design abstraction is changed. Design conversion is problematic as it is multidimensional and it requires many different design steps to translate the mathematical representation of the desired functionality to an efficient hardware description with the same behavior and configurability. Yet, a manual model conversion is not an insurmountable task. Using currently available design tools and an appropriate design methodology, converting a MATLAB model to efficient hardware is a reasonable effort. This paper describes a simple and flexible design methodology that was developed together with several design teams.

Keywords: design methodology, high-level synthesis, MATLAB, verification

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Authors: Fernanda Ludmilla Rossi Rocha, Thamiris Cavazzani Vegro, Silvia Helena Henriques Camelo, Carmen Silvia Gabriel, Andrea Bernardes

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Introduction: Organizations are cultural, symbolic and imaginary systems composed by values and norms. These values and norms represent the organizational culture, which determines the behavior of the workers, guides the work practices and impacts the quality of care and the safety culture of health services worldwide. Objective: To analyze the organizational culture of a public and a private hospital in Brazil. Method: Descriptive study with quantitative approach developed in a public and in a private hospital of Brazil. Sample was composed by 281 nursing workers, of which 73 nurses and 208 nursing auxiliaries and technicians. The data collection instrument comprised the Brazilian Instrument for Assessing Organizational Culture. Data were collected from March to December 2013. Results: At the public hospital, the results showed an average score of 2.85 for the values concerning cooperative professionalism (CP); 3.02 for values related to hierarchical rigidity and the centralization of power (HR); 2.23 for individualistic professionalism and competition at work (IP); 2.22 for values related to satisfaction, well-being and motivation of workers (SW); 3.47 for external integration (EI); 2.03 for rewarding and training practices (RT); 2.75 for practices related to the promotion of interpersonal relationships (IR) About the private hospital, the results showed an average score of 3.24 for the CP; 2.83 for HR; 2.69 for IP; 2.71 for SW; 3.73 for EI; 2.56 for RT; 2.83 for IR at the hospital. Discussion: The analysis of organizational values of the studied hospitals shows that workers find the existence of hierarchical rigidity and the centralization of power in the institutions; believed there was cooperation at workplace, though they perceived individualism and competition; believed that values associated with the workers’ well-being, satisfaction and motivation were seldom acknowledged by the hospital; believed in the adoption of strategic planning actions within the institution, but considered interpersonal relationship promotion, continuous education and the rewarding of workers to be little valued by the institution. Conclusion: This work context can lead to professional dissatisfaction, compromising the quality of care and contributing to the occurrence of occupational diseases.

Keywords: nursing management, organizational culture, quality of care, interpersonal relationships

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.

Keywords: pattern recognition, global terrorism database, Manhattan distance, k-means clustering, terrorism data analysis

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Authors: Faith Eweluegim Enahoro-Ofagbe, Alika Eke Joseph

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Information on genetic variation within a population is crucial for utilizing heterozygosity for breeding programs that aim to improve crop species. The study was conducted to ascertain the genotypic similarities among twelve sugarcane (Saccharum officinarum) varieties to group them for purposes of hybridizations for cane yield improvement. The experiment was conducted at the University of Benin, Faculty of Agriculture Teaching and Research Farm, Benin City. Twelve sugarcane varieties obtained from National Cereals Research Institute, Badeggi, Niger State, Nigeria, were planted in three replications in a randomized complete block design. Each variety was planted on a five-row plot of 5.0 m in length. Data were collected on 12 agronomic traits, including; the number of millable cane, cane girth, internode length, number of male and female flowers (fuss), days to flag leaf, days to flowering, brix%, cane yield, and others. There were significant differences, according to the findings among the twelve genotypes for the number of days to flag leaf, number of male and female flowers (fuss), and cane yield. The relationship between the twelve sugarcane varieties was expressed using hierarchical cluster analysis. The twelve genotypes were grouped into three major clusters based on hierarchical classification. Cluster I had five genotypes, cluster II had four, and cluster III had three. Cluster III was dominated by varieties characterized by higher cane yield, number of leaves, internode length, brix%, number of millable stalks, stalk/stool, cane girth, and cane length. Cluster II contained genotypes with early maturity characteristics, such as early flowering, early flag leaf development, growth rate, and the number of female and male flowers (fuss). The maximum inter-cluster distance between clusters III and I indicated higher genetic diversity between the two groups. Hybridization between the two groups could result in transgressive recombinants for agronomically important traits.

Keywords: sugarcane, Saccharum officinarum, genotype, cluster analysis, principal components analysis

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Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

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Authors: Sezen Gulec, Bilge Uzun

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Almost all of the world population marries at least once in their lifetime. Nevertheless, in reality, only half of all marriages last a lifetime. The most important factor in marriage to manage is the satisfaction that they obtain. It is reality that marital satisfaction does not only related to maintain the relationship but also related to the social and work relationships. In this respect, the purpose of the present research is to find the personal and relational factors predicted marital satisfaction. The sample including 378 (178 male and 200 females) married individuals were administered to marital life scale, multidimensional perfectionism scale, trait forgivingness scale, adjective based personality test and relationship happiness questionnaire. The findings revealed marital happiness, forgiveness and extravertedness and emotional inconsistency factors were found to be significant predictors of marital satisfaction.

Keywords: marital satisfaction, happiness, perfectionism, forgiveness, five factor personality

Procedia PDF Downloads 649

Authors: Issam Ben Jami

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Information retrieval in digital libraries is very important because most famous historical documents occupy a significant value. The word spotting in historical documents is a very difficult notion, because automatic recognition of such documents is naturally cursive, it represents a wide variability in the level scale and translation words in the same documents. We first present a system for the automatic recognition, based on the extraction of interest points words from the image model. The extraction phase of the key points is chosen from the representation of the image as a synthetic description of the shape recognition in a multidimensional space. As a result, we use advanced methods that can find and describe interesting points invariant to scale, rotation and lighting which are linked to local configurations of pixels. We test this approach on documents of the 15th century. Our experiments give important results.

Keywords: feature matching, historical documents, pattern recognition, word spotting

Procedia PDF Downloads 254

Authors: Giorgi Methodishvili, Iuliia Methodishvili

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In this paper product development algorithm is used to determine the optimal management of financial resources in company. Aspects of financial management considered include put initial investment, examine all possible ways to solve the problem and the optimal rotation length of profit. The software of given problems is based using greedy algorithm. The obtained model and program maintenance enable us to define the optimal version of management of proper financial flows by using visual diagram on each level of investment.

Keywords: management, software, optimal, greedy algorithm, graph-diagram

Procedia PDF Downloads 36