Search results for: multidimensional hierarchical graph neuron

Authors: Sun Zhe, Ruggero Micheletto

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The research of neural network is very important for the development of advanced next generation intelligent devices and the medical treatment. The most important part of the neural network research is the learning. The process of learning in our brain is essentially several adjustment processes of connection strength between neurons. It is very difficult to figure out how this mechanism works in the complex network and how the connection strength influences brain functions. For this reason, we made a model with only two coupled neurons and studied the influence of connection strength between them. To emulate the neuronal activity of realistic neurons, we prefer to use the Izhikevich neuron model. This model can simulate the neuron variables accurately and it’s simplicity is very suitable to implement on computers. In this research, the parameter ρ is used to estimate the correlation coefficient between spike train of two coupling neurons.We think the results is very important for figuring out the mechanism between synchronization of coupling neurons and synaptic plasticity. The result also presented the importance of the spike frequency adaptation in complex systems.

Keywords: neural networks, noise, stochastic processes, coupled neurons, correlation coefficient, synchronization, pacemaker, synaptic plasticity

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Authors: Verónica Díaz

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A Cartesian graph, as a mathematical object, becomes a tool for configuration of change. Its best comprehension is done through everyday life problem-solving associated with its representation. Despite this, the current educational framework favors general graphs, without consideration of their argumentation. Students are required to find the mathematical function without associating it to the development of graphical language. This research describes the use made by students of configurations made prior to Cartesian graphs with regards to an everyday life problem related to a time and distance variation phenomenon. The theoretical framework describes the function conditions of study and their modeling. This is a qualitative, descriptive study involving six undergraduate case studies that were carried out during the first term in 2016 at University of Los Lagos. The research problem concerned the graphic modeling of a real person’s movement phenomenon, and two levels of analysis were identified. The first level aims to identify local and global graph interpretations; a second level describes the iconicity and referentiality degree of an image. According to the results, students were able to draw no figures before the Cartesian graph, highlighting the need for students to represent the context and the movement of which causes the phenomenon change. From this, they managed Cartesian graphs representing changes in position, therefore, achieved an overall view of the graph. However, the local view only indicates specific events in the problem situation, using graphic and verbal expressions to represent movement. This view does not enable us to identify what happens on the graph when the movement characteristics change based on possible paths in the person’s walking speed.

Keywords: cartesian graphs, higher education, movement modeling, problem solving

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Authors: Ersin Sener, Ibrahim Demir, Hasan Aykut Karaboga, Kadir Kilinc

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Bayesian methods, which have very wide range of applications, are implemented to the data obtained from the production of F class fly ash-based geopolymer bricks’ experimental design. In this study, dependent variable is compressive strength, independent variables are treatment type (oven and steam), treatment time, molding time, temperature, water absorbtion ratio and density. The effect of independent variables on compressive strength is investigated. There is no difference among treatment types, but there is a correlation between independent variables. Therefore, hierarchical Bayesian path analysis is applied. In consequence of analysis we specified that treatment time, temperature and density effects on compressive strength is higher, molding time, and water absorbtion ratio is relatively low.

Keywords: experimental design, F class fly ash, geopolymer bricks, hierarchical Bayesian path analysis

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Authors: Patrick Guggenberger

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Extant literature has highlighted the importance of individual employees in the entrepreneurial process, as they are those that come up with novel ideas and promote their implementation throughout the organization. However, research on antecedents of individual entrepreneurial behavior (IEB) is very limited. The present study takes an initial step to investigate the interplay between transformational leader behaviors of direct supervisors and employees’ ability and willingness to act entrepreneurial and sheds light on the moderating role of an individual’s hierarchical level. A theoretically derived research model is empirically tested, drawing on survey data of 450 individuals working in medium- and large-sized corporations in two countries. Findings indicate that various transformational leader behaviors have a strong positive impact on IEB, while the ability of direct supervisors to influence their followers’ entrepreneurial behavior depends strongly on their own hierarchical level. The study reveals that transformational leadership has most impact at lower hierarchical levels, where employees’ motivation to act entrepreneurial is the lowest.

Keywords: corporate entrepreneurship, hierarchy, individual entrepreneurial behavior, transformational leadership

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Authors: Wenliang Song, Yu Zhang, Hua Jin, Il Kim

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The self-assembly of the polypeptide (PP) into well-defined structures at different length scales is both biomimetic relevant and fundamentally interesting. Although there are various reports of nanostructures fabricated by the self-assembly of various PPs, directed self-assembly of PP into three-dimensional (3D) hierarchical structure has proven to be difficult, despite their importance for biological applications. Herein, an efficient method has been developed through living polymerization of phenylalanine N-Carboxy anhydride (NCA) towards the linear and cyclic polyphenylalanine, and the new invented swelling methodology can form diverse hierarchical polypeptide crystals. The solvent-dependent self-assembly behaviors of these homopolymers were characterized by high-resolution imaging tools such as atomic force microscopy, transmission electron microscopy, scanning electron microscope. The linear and cyclic polypeptide formed 3D nano hierarchical shapes, such as a sphere, cubic, stratiform and hexagonal star in different solvents. Notably, a crystalline packing model was proposed to explain the formation of 3D nanostructures based on the various diffraction patterns, looking forward to give an insight for their dissimilar shape inflection during the self-assembly process.

Keywords: self-assembly, polypeptide, bio-polymer, crystalline polymer

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Authors: Mehmet Karaata

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Given two distinct vertices (nodes) source s and target t of a graph G = (V, E), the two node-disjoint paths problem is to identify two node-disjoint paths between s ∈ V and t ∈ V . Two paths are node-disjoint if they have no common intermediate vertices. In this paper, we present an algorithm with O(m)-time complexity for finding two node-disjoint paths between s and t in arbitrary graphs where m is the number of edges. The proposed algorithm has a wide range of applications in ensuring reliability and security of sensor, mobile and fixed communication networks.

Keywords: disjoint paths, distributed systems, fault-tolerance, network routing, security

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Authors: Luis Andrey Fajardo Fajardo

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We are immersed in complex systems. The human brain, the galaxies, the snowflakes are examples of complex systems. An area of interest in Complex systems is the chaos theory. This revolutionary field of science presents different ways of study than determinism and reductionism. Here is where in junction with the Nonlinear DSP, chaos theory offer valuable techniques that establish a link between time series and complex theory in terms of complex networks, so that, the study of signals can be explored from the graph theory. Recently, some people had purposed a method to transform time series in graphs, but no one had developed a suitable implementation in Python with signals extracted from Chaotic Systems or Complex systems. That’s why the implementation in Python of an existing method to transform one dimensional chaotic signals from time domain to graph domain and some measures that may reveal information not extracted in the time domain is proposed.

Keywords: Python, complex systems, graph theory, dynamical systems

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Authors: N. Nivedita, S. Durbha

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Critical Infrastructure (CI) consists of services and technological networks such as healthcare, transport, water supply, electricity supply, information technology etc. These systems are necessary for the well-being and to maintain effective functioning of society. Critical Infrastructures can be represented as nodes in a network where they are connected through a set of links depicting the logical relationship among them; these nodes are interdependent on each other and interact with each at other at various levels, such that the state of each infrastructure influences or is correlated to the state of another. Disruption in the service of one infrastructure nodes of the network during a disaster would lead to cascading and escalating disruptions across other infrastructures nodes in the network. The operation of Healthcare Infrastructure is one such Critical Infrastructure that depends upon a complex interdependent network of other Critical Infrastructure, and during disasters it is very vital for the Healthcare Infrastructure to be protected, accessible and prepared for a mass casualty. To reduce the consequences of a disaster on the Critical Infrastructure and to ensure a resilient Critical Health Infrastructure network, knowledge, understanding, modeling, and analyzing the inter-dependencies between the infrastructures is required. The paper would present inter-dependencies related to Healthcare Critical Infrastructure based on Hierarchical Coloured Petri Nets modeling approach, given a flood scenario as the disaster which would disrupt the infrastructure nodes. The model properties are being analyzed for the various state changes which occur when there is a disruption or damage to any of the Critical Infrastructure. The failure probabilities for the failure risk of interconnected systems are calculated by deriving a reachability graph, which is later mapped to a Markov chain. By analytically solving and analyzing the Markov chain, the overall vulnerability of the Healthcare CI HCPN model is demonstrated. The entire model would be integrated with Geographic information-based decision support system to visualize the dynamic behavior of the interdependency of the Healthcare and related CI network in a geographically based environment.

Keywords: critical infrastructure interdependency, hierarchical coloured petrinet, healthcare critical infrastructure, Petri Nets, Markov chain

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Authors: Seung-Jun Yu, Young-Min Ko, Hyoung-Kyu Song

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Recently developed cooperative diversity scheme enables a terminal to get transmit diversity through the support of other terminals. However, most of the introduced cooperative schemes have a common fault of decreased transmission rate because the destination should receive the decodable compositions of symbols from the source and the relay. In order to achieve high data rate, we propose a cooperative scheme that employs hierarchical modulation. This scheme is free from the rate loss and allows seamless cooperative communication.

Keywords: cooperative communication, hierarchical modulation, high data rate, transmission scheme

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Authors: Nadeem Akhtar, Hira Javed

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An important aspect of research is literature survey. Availability of a large amount of literature across different domains triggers the need for optimized systems which provide relevant literature to researchers. We propose a search system based on keywords for text documents. This experimental approach provides a hierarchical structure to the document corpus. The documents are labelled with keywords using KEA (Keyword Extraction Algorithm) and are automatically organized in a lattice structure using Formal Concept Analysis (FCA). This groups the semantically related documents together. The hierarchical structure, based on keywords gives out only those documents which precisely contain them. This approach open doors for multi-domain research. The documents across multiple domains which are indexed by similar keywords are grouped together. A hierarchical relationship between keywords is obtained. To signify the effectiveness of the approach, we have carried out the experiment and evaluation on Semeval-2010 Dataset. Results depict that the presented method is considerably successful in indexing of scientific papers.

Keywords: formal concept analysis, keyword extraction algorithm, scientific documents, lattice

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Authors: Tomáš Bublík, Miroslav Virius

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This paper presents general results on the Java source code snippet detection problem. We propose the tool which uses graph and sub graph isomorphism detection. A number of solutions for all of these tasks have been proposed in the literature. However, although that all these solutions are really fast, they compare just the constant static trees. Our solution offers to enter an input sample dynamically with the Scripthon language while preserving an acceptable speed. We used several optimizations to achieve very low number of comparisons during the matching algorithm.

Keywords: AST, Java, tree matching, scripthon source code recognition

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Authors: S. Sadallah

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In this paper, dynamic programming is used to determine the optimal management of financial resources in company. Solution of the problem by consider into simpler substructures is constructed. The optimal management of internal capital of company are simulated. The tools applied in this development are based on graph theory. The software of given problems is built by 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

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Authors: Ahmed Aboforn

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Butene-1 product is consider effectively raw material in Polyethylene production, however Oxygenates impurities existing will be effected ethylene/butene-1 copolymers synthesized through titanium-magnesium-supported Ziegler-Natta catalysts. Laterally, Petrochemical industries are challenge against poor quality of Butene-1 and other C4 mix – feedstock that reflected on business impact and production losing. In addition, propylene product suffering from contamination by oxygenates components and causing for lose production and plant upset of Polypropylene process plants. However, Multidimensional gas chromatography (MDGC) innovative analytical methodology is a chromatography technique used to separate complex samples, as mixing different functional group as Hydrocarbon and oxygenates compounds and have similar retention factors, by running the eluent through two or more columns instead of the customary single column. This analytical study striving to enhance the quality of Oxygenates analytical method, as monitoring the concentration of oxygenates with accurate and precise analytical method by utilizing multidimensional GC supported by Backflush technique and Flame Ionization Detector, which have high performance separation of hydrocarbon and Oxygenates; also improving the minimum detection limits (MDL) to detect the concentration <1.0 ppm. However different types of oxygenates as (Alcohols, Aldehyde, Ketones, Ester and Ether) may be determined in other Hydrocarbon streams asC3, C4-mix, until C12 mixture, supported by liquid injection auto-sampler.

Keywords: analytical chemistry, gas chromatography, petrochemicals, oxygenates

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Authors: Karunambigai Mevani Govindasamy, Sathishkumar Ayyappan

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In this paper, we formulate the algorithm to find out the dominating function parameters of Intuitionistic Fuzzy Graphs(IFG). The methodology we adopted here is converting any physical problem into an IFG, and that has been transformed into Intuitionistic Fuzzy Matrix. Using Linear Program Solver software (LiPS), we found the defined parameters for the given IFG. We obtained these parameters for a path and cycle IFG. This study can be extended to other varieties of IFG. In particular, we obtain the definition of edge dominating function, minimal edge dominating function, fractional edge domination number (γ_if^') and upper fractional edge domination number (Γ_if^') of an intuitionistic fuzzy graph. Also, we formulated an algorithm which is appropriate to work on LiPS to find fractional edge domination number and upper fractional edge domination number of an IFG.

Keywords: fractional edge domination number, intuitionistic fuzzy cycle, intuitionistic fuzzy graph, intuitionistic fuzzy path

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Authors: Shao-Hsuan Chien, Ju-Hui Fu

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Parkinson’s disease (PD) is a degenerative disorder of the central nervous system that is characterized by progressive loss of dopaminergic neurons in the substantia nigra pars compacta and motor impairment. Aggregation of α-synuclein in neuronal cells plays a key role in this disease. At present, therapeutics for PD provides moderate symptomatic benefit but is not able to delay the development of this disease. Current efforts for the treatment of PD are to identify new drugs that show slow or arrest progressive course of PD by interfering with a disease-specific pathogenetic process in PD patients. Maackiain is a bioactive compound isolated from the roots of the Chinese herb Sophora flavescens. The purpose of the present study was to assess the potential for maackiain to ameliorate PD in Caenorhabditis elegans models. Our data reveal that maackiain prevents α-synuclein accumulation in the transgenic Caenorhabditis elegans model and also improves dopaminergic neuron degeneration, food-sensing behavior, and life-span in 6-hydroxydopamine-induced Caenorhabditis elegans model, thus indicating its potential as a candidate antiparkinsonian drug.

Keywords: maackiain, Parkinson’s disease, dopaminergic neurons, α-Synuclein

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Authors: Paolo Fantozzi, Luigi Laura, Umberto Nanni

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The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses.

Keywords: cooccurrence graph, entity relation graph, unstructured text, weighted distance

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Authors: Ahlem Abid, Farah Jemili

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Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

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Authors: Seyed Ahmad Mojallal

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Let G be a connected threshold graph of order n with m edges and trace T. In this talk we give a lower bound on Laplacian energy in terms of n, m, and T of G. From this we determine the threshold graphs with the first four minimal Laplacian energies. We also list the first 20 minimal Laplacian energies among threshold graphs. Let σ=σ(G) be the number of Laplacian eigenvalues greater than or equal to average degree of graph G. Using this concept, we obtain the threshold graphs with the largest and the second largest Laplacian energies.

Keywords: Laplacian eigenvalues, Laplacian energy, threshold graphs, extremal graphs

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Authors: Mohammad Aziz Shah Mohamed Arip, Aslina Ahmad, Fauziah Mohd Sa'ad, Samsiah Mohd Jais, Syed Sofian Syed Salim

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This experimental study evaluates the effect of using Cognitive-Behavioral Therapy (CBT) and Multidimensional Self-Concept Model (MSCM) in a drug prevention programme to increase resiliency and reduce aggression among at-risk youth in Malaysia. A number of 60 (N=60) university students who were at-risk of taking drugs were involved in this study. Participants were identified with self-rating scales, Adolescent Resilience Attitude Scale (ARAS) and Aggression Questionnaire. Based on the mean score of these instruments, the participants were divided into the treatment group, and the control group. Data were analyzed using t-test. The finding showed that the mean score of resiliency was increased in the treatment group compared to the control group. It also shows that the mean score of aggression was reduced in the treatment group compared to the control group. Drug Prevention Programme was found to help in enhancing resiliency and reducing aggression among participants in the treatment group compared to the controlled group. Implications were given regarding the preventive actions on drug abuse among youth in Malaysia.

Keywords: drug prevention programme, cognitive-behavioral therapy (CBT), multidimensional self concept model (MSCM), resiliency, aggression, at-risk youth

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Authors: Rafal Maciag

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Due to the development of the current civilization, one must create suitable models of its pervasive massive phenomena. Such a phenomenon is the digital transformation, which has a substantial number of disciplined, methodical interpretations forming the diversified reflection. This reflection could be understood pragmatically as the current temporal, a local differential state of knowledge. The model of the discursive space is proposed as a model for the analysis and description of this knowledge. Discursive space is understood as an autonomous multidimensional space where separate discourses traverse specific trajectories of what can be presented in multidimensional parallel coordinate system. Discursive space built on the world of facts preserves the complex character of that world. Digital transformation as a discursive space has a relativistic character that means that at the same time, it is created by the dynamic discourses and these discourses are molded by the shape of this space.

Keywords: complexity, digital transformation, discourse, discursive space, knowledge

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Authors: Mahsaossadat Pourrahmati Khelejan

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The purpose of this research was to identify and prioritize the sustainable development of sports venues using new and degradable energies with using the AHP Hierarchical Analysis approach. The research method is a descriptive strategy with regard to the direction of implementation and is a hierarchical research with a practical purpose. In this study, 30 experts (physical education faculty members, geography professors, accredited sports venues managers, and renewable energy engineers) were selected using purposeful sampling method as the research population. The research tool was a researcher-made questionnaire on the factors affecting the sustainable development of sports venues by using new technologies and degradable energy. Finally, the research questionnaire was designed with four components and 21 items. All steps were performed by using Expert Choice software. The importance of indicators that influence the sustainable development of sports venues is highlighted by the use of clean and degradable energy, for example: 1. Economic factor, weighing 0.420 2. Environmental index, weighing 0. 320 3. Physical index, weighing 0.148 4. Social index, weighing 0.122.

Keywords: Sports Venues, Sustainable Development, Degradable Energies, Prioritize

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Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

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Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

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Authors: Elif N. Coban

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With the political instability that has prevailed in Lebanon since October 2019, followed by a global pandemic and a deepening concurrent economic crisis after the Beirut Port explosion on August 4, 2020, Syrian refugees in Lebanon have struggled to survive what the World Bank has described as one of the worst economic crises in decades. This study aims to assess the vulnerability of Syrian refugee women. It will present a comparative analysis of refugee and Lebanese households using data from Lebanon’s Labour Force and Household Conditions Survey (LFHLCS) and from VASyr surveys, which are comprehensive annual surveys conducted jointly by the United Nations High Commissioner for Refugees (UNHCR), the United Nations Children’s Fund (UNICEF), and the United Nations World Food Programme (WFP). The study adopts an intersectionality-based method, which deals with gender and marginalized communities from many different perspectives, to put forward a gender-oriented approach. Examining the distribution of socioeconomic status among Syrian and Lebanese households might help to understand the disproportionate burdens borne by women. In this context, multidimensional poverty (MP) helps depict fragile communities’ socioeconomic status and allows a fuller grasp the multiple aspects of deprivation. Finally, this understanding may pave the way to more inclusive policy for decision-makers and practitioners working on refugee issues.

Keywords: multidimensional poverty, gender studies, intersectionality, Syrian refugees, Lebanon

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Authors: Qin Long, Li Xiaoge

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The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.

Keywords: distant named entity recognition, entity linking, knowledge graph, graph attention neural network

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Authors: J. C. Valenzuela-Tripodoro, P. Álvarez-Ruíz, M. A. Mateos-Camacho, M. Cera

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In 2004, it was introduced the concept of Roman domination in graphs. This concept was initially inspired and related to the defensive strategy of the Roman Empire. An undefended place is a city so that no legions are established on it, whereas a strong place is a city in which two legions are deployed. This situation may be modeled by labeling the vertices of a finite simple graph with labels {0, 1, 2}, satisfying the condition that any 0-vertex must be adjacent to, at least, a 2-vertex. Roman domination in graphs is a variant of classic domination. Clearly, the main aim is to obtain such labeling of the vertices of the graph with minimum cost, that is to say, having minimum weight (sum of all vertex labels). Formally, a function f: V (G) → {0, 1, 2} is a Roman dominating function (RDF) in the graph G = (V, E) if f(u) = 0 implies that f(v) = 2 for, at least, a vertex v which is adjacent to u. The weight of an RDF is the positive integer w(f)= ∑_(v∈V)▒〖f(v)〗. The Roman domination number, γ_R (G), is the minimum weight among all the Roman dominating functions? Obviously, the set of vertices with a positive label under an RDF f is a dominating set in the graph, and hence γ(G)≤γ_R (G). In this work, we start the study of a generalization of RDF in which we consider that any undefended place should be defended from a sudden attack by, at least, k legions. These legions can be deployed in the city or in any of its neighbours. A function f: V → {0, 1, . . . , k + 1} such that f(N[u]) ≥ k + |AN(u)| for all vertex u with f(u) < k, where AN(u) represents the set of active neighbours (i.e., with a positive label) of vertex u, is called a [k]-multiple Roman dominating functions and it is denoted by [k]-MRDF. The minimum weight of a [k]-MRDF in the graph G is the [k]-multiple Roman domination number ([k]-MRDN) of G, denoted by γ_[kR] (G). First, we prove that the [k]-multiple Roman domination decision problem is NP-complete even when restricted to bipartite and chordal graphs. A problem that had been resolved for other variants and wanted to be generalized. We know the difficulty of calculating the exact value of the [k]-MRD number, even for families of particular graphs. Here, we present several upper and lower bounds for the [k]-MRD number that permits us to estimate it with as much precision as possible. Finally, some graphs with the exact value of this parameter are characterized.

Keywords: multiple roman domination function, decision problem np-complete, bounds, exact values

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Authors: Fabiana Souza Orlandi, Izabel Cristina Chavez Gomes, Barbara Isabela De Paula Morais, Ana Carolina Ottaviani

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Chronic Kidney Disease (CKD) is considered a public health problem. Patients who present CKD in their more advanced stages usually present several biopsychosocial changes, which may include pain. Pain can be considered subjective and personal, and its perception is characterized as a multidimensional experience. The objective of this study was to evaluate the level and descriptors of pain of adults and elderly patients with chronic kidney disease, through the Multidimensional Pain Evaluation Scale (EMADOR). This is a descriptive cross-sectional study with a quantitative approach. The sample consisted of 100 subjects with CKD in hemodialysis treatment at a Renal Replacement Therapy Service in the interior of the state of São Paulo. Data were collected through an individual interview, using a Sociodemographic Characterization and Multidimensional Pain Evaluation Scale (EMADOR). All ethical precepts were respected. The majority of the respondents were men (61.0%), white (56.0%) and with a high school education (34.0%). Regarding the pain of the individuals, 89 patients reported pain, with Chronic Pain predominating (50.0%, n = 50), followed by Acute Pain (39.0%, n = 39). Of the subjects who presented acute pain most of the 89.0% described the pain felt as unbearable, and of those who presented chronic pain, 35.0% described the pain felt as painful, unbearable and uncomfortable. It was concluded that there was a significant presence of pain, being the chronic pain dominant in the studied population. Faced with such factors, the present study motivates researches in this population, in order to establish interventions with the objective of improving the quality of life of these individuals.

Keywords: pain, chronic kidney disease, dialysis, evaluation

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

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The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph

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Authors: Yan Li

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Purpose: There is a lack of suitable recommendation systems to assist engineering teaching. The existing traditional engineering pedagogies lack learning interests for postgraduate students. The knowledge graph-based recommender system aims to enhance postgraduate students’ programming skills, with a focus on programming courses. Design/methodology/approach: The case study will be used as a major research method, and the two case studies will be taken in both two teaching styles of the universities (Zhejiang University and the University of Nottingham Ningbo China), followed by the interviews. Quantitative and qualitative research methods will be combined in this study. Research limitations/implications: The case studies were only focused on two teaching styles universities, which is not comprehensive enough. The subject was limited to postgraduate students. Originality/value: The study collected and analyzed the data from two teaching styles of universities’ perspectives. It explored the challenges of Engineering education and tried to seek potential enhancement.

Keywords: knowledge graph and recommender system, engineering pedagogy, programming skills, postgraduate students

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Authors: Yan Yu, Wang Yu, Chen Xintong, Liu Yi, Zhang Yanzhong, Wang Yanji, Chen Xingyu, Zhang Miaocheng, Tong Yi

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Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to its high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO₂-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO₂-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems.

Keywords: leaky integrate-and-fire, memristor, spiking neural networks, spiking-time-dependent-plasticity

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Authors: Md. Minhazul Islam, Shah Ashisul Abed Nipun, Majharul Islam, Md. Abdur Rakib Rahat, Jonayet Miah, Salsavil Kayyum, Anwar Shadaab, Faiz Al Faisal

Abstract:

The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques.

Keywords: Machine Learning, K-means, Hierarchical Clustering, Myocardial Infarction, Heart Disease

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