Search results for: graph colouring

Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

Procedia PDF Downloads 33

Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

Procedia PDF Downloads 37

Authors: Souad Slimani, Sylvain Gravier, Simon Schmidt

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Recently, several vertex identifying notions were introduced (identifying coloring, lid-coloring,...); these notions were inspired by identifying codes. All of them, as well as original identifying code, is based on separating two vertices according to some conditions on their closed neighborhood. Therefore, twins can not be identified. So most of known results focus on twin-free graph. Here, we show how twins can modify optimal value of vertex-identifying parameters for identifying coloring and locally identifying coloring.

Keywords: identifying coloring, locally identifying coloring, twins, separating

Procedia PDF Downloads 108

Authors: Yuxi Liu, Boris Belousov, Mehrzad Esmaeili Charkhab, Oliver Tessmann

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This paper presents a computational solution for designing reconfigurable interlocking structures that are fully assembled with SL Blocks. Formed by S-shaped and L-shaped tetracubes, SL Block is a specific type of interlocking puzzle. Analogous to molecular self-assembly, the aggregation of SL blocks will build a reversible hierarchical and discrete system where a single module can be numerously replicated to compose semi-interlocking components that further align, wrap, and braid around each other to form complex high-order aggregations. These aggregations can be disassembled and reassembled, responding dynamically to design inputs and changes with a unique capacity for reconfiguration. To use these aggregations as architectural structures, we developed computational tools that automate the configuration of SL blocks based on architectural design objectives. There are three critical phases in our work. First, we revisit the hierarchy of the SL block system and devise a top-down-type design strategy. From this, we propose two key questions: 1) How to translate 3D polyominoes into SL block assembly? 2) How to decompose the desired voxelized shapes into a set of 3D polyominoes with interlocking joints? These two questions can be considered the Hamiltonian path problem and the 3D polyomino tiling problem. Then, we derive our solution to each of them based on two methods. The first method is to construct the optimal closed path from an undirected graph built from the voxelized shape and translate the node sequence of the resulting path into the assembly sequence of SL blocks. The second approach describes interlocking relationships of 3D polyominoes as a joint connection graph. Lastly, we formulate the desired shapes and leverage our methods to achieve their reconfiguration within different levels. We show that our computational strategy will facilitate the efficient design of hierarchical interlocking structures with a self-replicating geometric module.

Keywords: computational design, SL-blocks, 3D polyomino puzzle, combinatorial problem

Procedia PDF Downloads 94

Authors: Amirhossein Dodankeh, Hadi Dabiryan, Saeed Hamze

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Using fabrics to reinforce composites considerably leads to improved mechanical properties, including resistance to the impact load and the energy absorption of composites. Warp-knitted spacer fabrics (WKSF) are fabrics consisting of two layers of warp-knitted fabric connected by pile yarns. These connections create a space between the layers filled by pile yarns and give the fabric a three-dimensional shape. Today because of the unique properties of spacer fabrics, they are widely used in the transportation, construction, and sports industries. Polyurethane (PU) foams are commonly used as energy absorbers, but WKSF has much better properties in moisture transfer, compressive properties, and lower heat resistance than PU foam. It seems that the use of warp-knitted spacer fabric reinforced PU foam (WKSFRF) can lead to the production and use of composite, which has better properties in terms of energy absorption from the foam, its mold formation is enhanced, and its mechanical properties have been improved. In this paper, the energy absorption efficiency (EAE) of WKSFRF under low-velocity impact is investigated experimentally. The contribution of the effect of each of the structural parameters of the WKSF on the absorption of impact energy has also been investigated. For this purpose, WKSF with different structures such as two different thicknesses, small and large mesh sizes, and position of the meshes facing each other and not facing each other were produced. Then 6 types of composite samples with different structural parameters were fabricated. The physical properties of samples like weight per unit area and fiber volume fraction of composite were measured for 3 samples of any type of composites. Low-velocity impact with an initial energy of 5 J was carried out on 3 samples of any type of composite. The output of the low-velocity impact test is acceleration-time (A-T) graph with a lot deviation point, in order to achieve the appropriate results, these points were removed using the FILTFILT function of MATLAB R2018a. Using Newtonian laws of physics force-displacement (F-D) graph was drawn from an A-T graph. We know that the amount of energy absorbed is equal to the area under the F-D curve. Determination shows the maximum energy absorption is 2.858 J which is related to the samples reinforced with fabric with large mesh, high thickness, and not facing of the meshes relative to each other. An index called energy absorption efficiency was defined, which means absorption energy of any kind of our composite divided by its fiber volume fraction. With using this index, the best EAE between the samples is 21.6 that occurs in the sample with large mesh, high thickness, and meshes facing each other. Also, the EAE of this sample is 15.6% better than the average EAE of other composite samples. Generally, the energy absorption on average has been increased 21.2% by increasing the thickness, 9.5% by increasing the size of the meshes from small to big, and 47.3% by changing the position of the meshes from facing to non-facing.

Keywords: composites, energy absorption efficiency, foam, geometrical parameters, low-velocity impact, warp-knitted spacer fabric

Procedia PDF Downloads 135

Authors: Fahimeh Palizban, Farshad Noravesh, Amir Hossein Saeidian, Mahya Mehrmohamadi

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In the recent decade, the emergence of liquid biopsy has significantly improved cancer monitoring and detection. Dying cells, including those originating from tumors, shed their DNA into the blood and contribute to a pool of circulating fragments called cell-free DNA. Accordingly, identifying the tissue origin of these DNA fragments from the plasma can result in more accurate and fast disease diagnosis and precise treatment protocols. Open chromatin regions are important epigenetic features of DNA that reflect cell types of origin. Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides insights into tissue-specific and disease-specific regulatory mechanisms. There have been several studies in the area of cancer liquid biopsy that integrate distinct genomic and epigenomic features for early cancer detection along with tissue of origin detection. However, multimodal analysis requires several types of experiments to cover the genomic and epigenomic aspects of a single sample, which will lead to a huge amount of cost and time. To overcome these limitations, the idea of predicting OCRs from WGS is of particular importance. In this regard, we proposed a computational approach to target the prediction of open chromatin regions as an important epigenetic feature from cell-free DNA whole genome sequence data. To fulfill this objective, local sequencing depth will be fed to our proposed algorithm and the prediction of the most probable open chromatin regions from whole genome sequencing data can be carried out. Our method integrates the signal processing method with sequencing depth data and includes count normalization, Discrete Fourie Transform conversion, graph construction, graph cut optimization by linear programming, and clustering. To validate the proposed method, we compared the output of the clustering (open chromatin region+, open chromatin region-) with previously validated open chromatin regions related to human blood samples of the ATAC-DB database. The percentage of overlap between predicted open chromatin regions and the experimentally validated regions obtained by ATAC-seq in ATAC-DB is greater than 67%, which indicates meaningful prediction. As it is evident, OCRs are mostly located in the transcription start sites (TSS) of the genes. In this regard, we compared the concordance between the predicted OCRs and the human genes TSS regions obtained from refTSS and it showed proper accordance around 52.04% and ~78% with all and the housekeeping genes, respectively. Accurately detecting open chromatin regions from plasma cell-free DNA-seq data is a very challenging computational problem due to the existence of several confounding factors, such as technical and biological variations. Although this approach is in its infancy, there has already been an attempt to apply it, which leads to a tool named OCRDetector with some restrictions like the need for highly depth cfDNA WGS data, prior information about OCRs distribution, and considering multiple features. However, we implemented a graph signal clustering based on a single depth feature in an unsupervised learning manner that resulted in faster performance and decent accuracy. Overall, we tried to investigate the epigenomic pattern of a cell-free DNA sample from a new computational perspective that can be used along with other tools to investigate genetic and epigenetic aspects of a single whole genome sequencing data for efficient liquid biopsy-related analysis.

Keywords: open chromatin regions, cancer, cell-free DNA, epigenomics, graph signal processing, correlation clustering

Procedia PDF Downloads 103

Authors: Shankaran Sitarama

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New Product Development (NPD) is invariably a team effort and involves effective teamwork. NPD team has members from different disciplines coming together and working through the different phases all the way from conceptual design phase till the production and product roll out. Creativity and Innovation are some of the key factors of successful NPD. Team members going through the different phases of NPD interact and work closely yet challenge each other during the design phases to brainstorm on ideas and later converge to work together. These two traits require the teams to have a divergent and a convergent thinking simultaneously. There needs to be a good balance. The team dynamics invariably result in conflicts among team members. While some amount of conflict (ideational conflict) is desirable in NPD teams to be creative as a group, relational conflicts (or discords among members) could be detrimental to teamwork. Team communication truly reflect these tensions and team dynamics. In this research, team communication (emails) between the members of the NPD teams is considered for analysis. The email communication is processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. The amount of communication (content and not frequency of communication) defines the interaction strength between the members. Social network adjacency matrix is thus obtained for the team. Standard social network analysis techniques based on the Adjacency Matrix (AM) and Dichotomized Adjacency Matrix (DAM) based on network density yield network graphs and network metrics like centrality. The social network graphs are then rendered for visual representation using a Metric Multi-Dimensional Scaling (MMDS) algorithm for node placements and arcs connecting the nodes (representing team members) are drawn. The distance of the nodes in the placement represents the tie-strength between the members. Stronger tie-strengths render nodes closer. Overall visual representation of the social network graph provides a clear picture of the team’s interactions. This research reveals four distinct patterns of team interaction that are clearly identifiable in the visual representation of the social network graph and have a clearly defined computational scheme. The four computational patterns of team interaction defined are Central Member Pattern (CMP), Subgroup and Aloof member Pattern (SAP), Isolate Member Pattern (IMP), and Pendant Member Pattern (PMP). Each of these patterns has a team dynamics implication in terms of the conflict level in the team. For instance, Isolate member pattern, clearly points to a near break-down in communication with the member and hence a possible high conflict level, whereas the subgroup or aloof member pattern points to a non-uniform information flow in the team and some moderate level of conflict. These pattern classifications of teams are then compared and correlated to the real level of conflict in the teams as indicated by the team members through an elaborate self-evaluation, team reflection, feedback form and results show a good correlation.

Keywords: team dynamics, team communication, team interactions, social network analysis, sna, new product development, latent semantic analysis, LSA, NPD teams

Procedia PDF Downloads 32

Authors: Abisuga Oluwayemisi Adebola, Kerri Akiwowo, Deon de Beer, Kobus Van Der Walt

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The convergence of additive manufacturing (AM) and traditional textile dyeing techniques has initiated innovative possibilities for improving the visual application and customization potential of 3D-printed polymer objects. Textile dyeing techniques have progressed to transform fabrics with vibrant colours and complex patterns over centuries. The layer-by-layer deposition characteristic of AM necessitates adaptations in dye application methods to ensure even colour penetration across complex surfaces. Compatibility between dye formulations and polymer matrices influences colour uptake and stability, demanding careful selection and testing of dyes for optimal results. This study investigates the development interaction between these areas, revealing the challenges and opportunities of applying textile dyeing methods to colour 3D-printed polymer materials. The method explores three innovative approaches to colour the 3D-printed polymer object: (a) Additive Manufacturing of a Prototype, (b) the traditional dyebath method, and (c) the contemporary digital sublimation technique. The results show that the layer lines inherent to AM interact with dyes differently and affect the visual outcome compared to traditional textile fibers. Skillful manipulation of textile dyeing methods and dye type used for this research reduced the appearance of these lines to achieve consistency and desirable colour outcomes. In conclusion, integrating textile dyeing techniques into colouring 3D-printed polymer materials connects historical craftsmanship with innovative manufacturing. Overcoming challenges of colour distribution, compatibility, and layer line management requires a holistic approach that blends the technical consistency of AM with the artistic sensitivity of textile dyeing. Hence, applying textile dyeing methods to 3D-printed polymers opens new dimensions of aesthetic and functional possibilities.

Keywords: polymer, 3D-printing, sublimation, textile, dyeing, additive manufacturing

Procedia PDF Downloads 31

Authors: Ebru Cubukcu, Gozde Eksioglu Cetintahra, Burcin Hepguzel Hatip, Mert Cubukcu

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Obesity and overweight cause serious health problems. Public and private organizations aim to encourage walking in various ways in order to cope with the problem of obesity and overweight. This study aims to understand how the spatial characteristics of urban street pattern, connectivity and complexity influence young people’s choice to walk or not. 185 public university students in Izmir, the third largest city in Turkey, participated in the study. Each participant had worn an accelerometer and a global positioning (GPS) device for a week. The accelerometer device records data on the intensity of the participant’s activity at a specified time interval, and the GPS device on the activities’ locations. Combining the two datasets, activity maps are derived. These maps are then used to differentiate the participants’ walk trips and motor vehicle trips. Given that, the frequency of walk and motor vehicle trips are calculated at the street segment level, and the street segments are then categorized into two as ‘preferred by pedestrians’ and ‘preferred by motor vehicles’. Graph Theory-based accessibility indices are calculated to quantify the spatial characteristics of the streets in the sample. Six different indices are used: (I) edge density, (II) edge sinuosity, (III) eta index, (IV) node density, (V) order of a node, and (VI) beta index. T-tests show that the index values for the ‘preferred by pedestrians’ and ‘preferred by motor vehicles’ are significantly different. The findings indicate that the spatial characteristics of the street network have a measurable effect on young people’s choice to walk or not. Policy implications are discussed. This study is funded by the Scientific and Technological Research Council of Turkey, Project No: 116K358.

Keywords: graph theory, walkability, accessibility, street network

Procedia PDF Downloads 181

Authors: Gururaj Bhajantri

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Financial sector is the backbone of any economy and it plays a crucial role in the mobilisation and allocation of resources. One of the main objectives of financial sector is inclusive growth. The constituents of the financial sector are banks, and financial Institutions, which mobilise the resources from the surplus sector and channelize the same to the different needful sectors in the economy. Micro Small and the Medium Enterprises sector in India cover a wide range of economic activities. These enterprises are divided on the basis of investment on equipment. The micro enterprises are divided into manufacturing and services sector. Micro Service enterprises have investment limit up to ten lakhs on equipment. Hairdresser is one who not only cuts and shaves but also provides different types of hair cut, hairstyles, trimming, hair-dye, massage, manicure, pedicure, nail services, colouring, facial, makeup application, waxing, tanning and other beauty treatments etc., hairdressing salons provide these services with the help of equipment. They need investment on equipment not more than ten lakhs. Hence, they can be considered as Micro service enterprises. Hairdressing salons require more than Rs 2.50,000 to start a moderate salon. Moreover, hairdressers are unable to access the organised finance. Still these individuals access finance from money lenders with high rate of interest to lead life. The socio economic conditions of hairdressers are not known properly. Hence, the present study brings a light on the role of financial institutions in promoting hairdressing salons. The study also focuses the socio-economic background of individuals in hairdressings salons, problems faced by them. The present study is based on primary and secondary data. Primary data collected among hairdressing salons in Davangere city. Samples selected with the help of simple random sampling techniques. Collected data analysed and interpreted with the help of simple statistical tools.

Keywords: micro service enterprises, financial institutions, hairdressing salons, financial sector

Procedia PDF Downloads 171

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

Procedia PDF Downloads 74

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

Procedia PDF Downloads 167

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

Procedia PDF Downloads 454

Authors: Priya Tomar, Pallavi Mittal

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As industrialization is inevitable and progress with rapid acceleration, the need for innovative ways to get rid of waste has increased. Recent advancement in bioresource technology paves novel ideas for recycling of factory waste that has been polluting the agro-industry, soil and water bodies. Paper industries in India are in a considerable number, where molasses and impure alcohol are still being used as raw materials for manufacturing of paper. Paper mills based on nonconventional agro residues are being encouraged due to increased demand of paper and acute shortage of forest-based raw materials. The colouring body present in the wastewater from pulp and paper mill is organic in nature and is comprised of wood extractives, tannin, resins, synthetic dyes, lignin and its degradation products formed by the action of chlorine on lignin which imparts an offensive colour to the water. These mills use different chemical process for paper manufacturing due to which lignified chemicals are released into the environment. Therefore, the chemical oxygen demand (COD) of the emanating stream is quite high. This paper presents some new techniques that were developed for the efficiency of bioremediation on paper industry. A short introduction to paper industry and a variety of presently available methods of bioremediation on paper industry and different strategies are also discussed here. For solving the above problem, two bacterial strains (Pseudomonas aeruginosa and Bacillus subtilis) and their consortia (Pseudomonas aeruginosa and Bacillus subtilis) were utilized for the pulp and paper mill effluent. Pseudomonas aeruginosa and Bacillus subtilis named as T–1, T–2, T–3, T–4, T–5, T–6, for the decolourisation of paper industry effluent. The results indicated that a maximum colour reduction is (60.5%) achieved by Pseudomonas aeruginosa and COD reduction is (88.8%) achieved by Bacillus subtilis, maximum pH changes is (4.23) achieved by Pseudomonas aeruginosa, TSS reduction is (2.09 %) achieved by Bacillus subtilis, and TDS reduction is (0.95 %) achieved by Bacillus subtilis. When the wastewater was supplemented with carbon (glucose) and nitrogen (yeast extract) source and data revealed the efficiency of Bacillus subtilis, having more with glucose than Pseudomonas aeruginosa.

Keywords: bioremediation, paper and pulp mill effluent, treated effluent, lignin

Procedia PDF Downloads 224

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

Procedia PDF Downloads 132

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

Procedia PDF Downloads 485

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

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Authors: B. Selma

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The aim of this study is to test the feasibility of SIPINA method to predict the harmfulness parameters controlling the seismic response. The approach developed takes into consideration both the focal depth and the peak ground acceleration. The parameter to determine is displacement. The data used for the learning of this method and analysis nonlinear seismic are described and applied to a class of models damaged to some typical structures of the existing urban infrastructure of Jassy, Romania. The results obtained indicate an influence of the focal depth and the peak ground acceleration on the displacement.

Keywords: SIPINA algorithm, seism, focal depth, peak ground acceleration, displacement

Procedia PDF Downloads 271

Authors: Syed Irfan Raza Naqvi, Zheng Jiangbin, Ahmad Moshin, Pervez Akhter

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Currently, the IoT system depends on a centralized client-servant architecture that causes various scalability and privacy vulnerabilities. Distributed ledger technology (DLT) introduces a set of opportunities for the IoT, which leads to practical ideas for existing components at all levels of existing architectures. Blockchain Technology (BCT) appears to be one approach to solving several IoT problems, like Bitcoin (BTC) and Ethereum, which offer multiple possibilities. Besides, IoTs are resource-constrained devices with insufficient capacity and computational overhead to process blockchain consensus mechanisms; the traditional BCT existing challenge for IoTs is poor scalability, energy efficiency, and transaction fees. IOTA is a distributed ledger based on Direct Acyclic Graph (DAG) that ensures M2M micro-transactions are free of charge. IOTA has the potential to address existing IoT-related difficulties such as infrastructure scalability, privacy and access control mechanisms. We proposed an architecture, SLDBI: A Scalable, lightweight DAG-based Blockchain Design for Intelligent IoT Systems, which adapts the DAG base Tangle and implements a lightweight message data model to address the IoT limitations. It enables the smooth integration of new IoT devices into a variety of apps. SLDBI enables comprehensive access control, energy efficiency, and scalability in IoT ecosystems by utilizing the Masked Authentication Message (MAM) protocol and the IOTA Smart Contract Protocol (ISCP). Furthermore, we suggest proof-of-work (PoW) computation on the full node in an energy-efficient way. Experiments have been carried out to show the capability of a tangle to achieve better scalability while maintaining energy efficiency. The findings show user access control management at granularity levels and ensure scale up to massive networks with thousands of IoT nodes, such as Smart Connected Buildings (SCBDs).

Keywords: blockchain, IOT, direct acyclic graphy, scalability, access control, architecture, smart contract, smart connected buildings

Procedia PDF Downloads 78

Authors: Nayaka S.R., Putta Swamy, Purushothama S.

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Let G=(V, E) be a graph. A dominating set S in G is a pendant dominating set if < S > contains a pendant vertex. A pendant dominating set of G which intersects every minimum pendant dominating set in G is called a transversal pendant dominating set. The minimum cardinality of a transversal pendant dominating set is called the transversal pendant domination number of G, denoted by γ_tp(G). In this paper, we begin to study this parameter. We calculate γ_tp(G) for some families of graphs. Furthermore, some bounds and relations with other domination parameters are obtained for γ_tp(G).

Keywords: dominating set, pendant dominating set, pendant domination number, transversal pendant dominating set, transversal pendant domination number

Procedia PDF Downloads 144

Authors: Tribhuvan N. Soorya

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Charging and discharging of a capacitor through a resistor can be shown as exponential curve. Theoretically, it takes infinite time to fully charge or discharge a capacitor. The flow of charge is due to electrons having finite and fixed value of charge. If we carefully examine the charging and discharging process after several time constants, the points on q vs t graph become discrete and curve become discontinuous. Moreover for all practical purposes capacitor with charge (q0-e) can be taken as fully charged, as it introduces an error less than one part per million. Similar is the case for discharge of a capacitor, where the capacitor with the last electron (charge e) can be taken as fully discharged. With this, we can estimate the finite value of time for fully charging and discharging a capacitor.

Keywords: charging, discharging, RC Circuit, capacitor

Procedia PDF Downloads 405

Authors: Kritchakhris Na-Wattanaprasert

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The objective of this research is to design and develop a prototype of a key performance indicator system this is suitable for warehouse management in a case study and use requirement. In this study, we design a prototype of key performance indicator system (KPI) for warehouse case study of furniture business by methodology in step of identify scope of the research and study related papers, gather necessary data and users requirement, develop key performance indicator base on balance scorecard, design pro and database for key performance indicator, coding the program and set relationship of database and finally testing and debugging each module. This study use Balance Scorecard (BSC) for selecting and grouping key performance indicator. The system developed by using Microsoft SQL Server 2010 is used to create the system database. In regard to visual-programming language, Microsoft Visual C# 2010 is chosen as the graphic user interface development tool. This system consists of six main menus: menu login, menu main data, menu financial perspective, menu customer perspective, menu internal, and menu learning and growth perspective. Each menu consists of key performance indicator form. Each form contains a data import section, a data input section, a data searches – edit section, and a report section. The system generates outputs in 5 main reports, the KPI detail reports, KPI summary report, KPI graph report, benchmarking summary report and benchmarking graph report. The user will select the condition of the report and period time. As the system has been developed and tested, discovers that it is one of the ways to judging the extent to warehouse objectives had been achieved. Moreover, it encourages the warehouse functional proceed with more efficiency. In order to be useful propose for other industries, can adjust this system appropriately. To increase the usefulness of the key performance indicator system, the recommendations for further development are as follows: -The warehouse should review the target value and set the better suitable target periodically under the situation fluctuated in the future. -The warehouse should review the key performance indicators and set the better suitable key performance indicators periodically under the situation fluctuated in the future for increasing competitiveness and take advantage of new opportunities.

Keywords: key performance indicator, warehouse management, warehouse operation, logistics management

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Authors: S. Sumanth, Babu Rao Ponangi, K. N. Seetharamu

Abstract:

As the technology is emerging day by day, there is a need for some better methodology which will enhance the performance of radiator. Nanofluid is the one area which has promised the enhancement of the radiator performance. Currently, nanofluid has got a well effective solution for enhancing the performance of the automobile radiators. Suspending the nano sized particle in the base fluid, which has got better thermal conductivity value when compared to a base fluid, is preferably considered for nanofluid. In the current work, at first mathematical formulation has been carried out, which will govern the performance of the radiator. Current work is justified by plotting the graph for different parameters. Current work justifies the enhancement of radiator performance using nanofluid.

Keywords: nanofluid, radiator performance, graphene, gamma aluminium oxide (γ-Al2O3), titanium dioxide (TiO2)

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Authors: Shaan Khosla, Jon Krohn

Abstract:

In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

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Authors: Karina I. Hidas, Csaba Németh, Anna Visy, Judit Csonka, László Friedrich, Ildikó Cs. Nyulas-Zeke

Abstract:

Egg yolk is a popular ingredient in the food industry due to its gelling, emulsifying, colouring, and coagulating properties. Because of the heat sensitivity of proteins, egg yolk can only be heat treated at low temperatures, so its shelf life, even with the addition of a preservative, is only a few weeks. Freezing can increase the shelf life of liquid egg yolk up to 1 year, but it undergoes gelling below -6 ° C, which is an irreversible phenomenon. The degree of gelation depends on the time and temperature of freezing and is influenced by the process of thawing. Therefore, in our experiment, we examined egg yolks thawed in different ways. In this study, unpasteurized, industrially broken, separated, and homogenized liquid egg yolk was used. Freshly produced samples were frozen in plastic containers at -18°C in a laboratory freezer. Frozen storage was performed for 90 days. Samples were analysed at day zero (unfrozen) and after frozen storage for 1, 7, 14, 30, 60 and 90 days. Samples were thawed in two ways (at 5°C for 24 hours and 30°C for 3 hours) before testing. Calorimetric properties were examined by differential scanning calorimetry, where heat flow curves were recorded. Denaturation enthalpy values were calculated by fitting a linear baseline, and denaturation temperature values were evaluated. Besides, dry matter content of samples was measured by the oven method with drying at 105°C to constant weight. For statistical analysis two-way ANOVA (α = 0.05) was employed, where thawing mode and freezing time were the fixed factors. Denaturation enthalpy values decreased from 1.1 to 0.47 at the end of the storage experiment, which represents a reduction of about 60%. The effect of freezing time was significant on these values, already the enthalpy of samples stored frozen for 1 day was significantly reduced. However, the mode of thawing did not significantly affect the denaturation enthalpy of the samples, and no interaction was seen between the two factors. The denaturation temperature and dry matter content did not change significantly either during the freezing period or during the defrosting mode. Results of our study show that slow freezing and frozen storage at -18°C greatly reduces the amount of protein that can be denatured in egg yolk, indicating that the proteins have been subjected to aggregation, denaturation or other protein conversions regardless of how they were thawed.

Keywords: denaturation enthalpy, differential scanning calorimetry, liquid egg yolk, slow freezing

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Authors: Seyyedeh Faezeh Hassani Ziabari, Sadegh Eskandari, Maziar Salahi

Abstract:

Infinite Feature Selection (IFS) algorithm is an efficient feature selection algorithm that selects a subset of features of all sizes (including infinity). In this paper, we present an improved version of it, called clustering IFS (CIFS), by clustering the dataset in advance. To do so, first, we apply the K-means algorithm to cluster the dataset, then we apply IFS. In the CIFS method, the spatial and temporal complexities are reduced compared to the IFS method. Experimental results on 6 datasets show the superiority of CIFS compared to IFS in terms of accuracy, running time, and memory consumption.

Keywords: feature selection, infinite feature selection, clustering, graph

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Authors: Ivan Belik

Abstract:

In terms of social networks split graphs correspond to the variety of interpersonal and intergroup relations. In this paper we analyse the interaction between the cliques (socially strong and trusty groups) and the independent sets (fragmented and non-connected groups of people) as the basic components of any split graph. Based on the Semi-Lagrangean relaxation for the k-cardinality assignment problem we show the way of how to minimize the socially risky interactions between the cliques and the independent sets within the social network.

Keywords: cliques, independent sets, k-cardinality assignment, social networks, split graphs

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Authors: Mohamed Adel Abdallah

Abstract:

A high voltage magnetic pulse is designed by applying an electrical pulse to the coil. Capacitor banks are developed to generate a pulse current. Switching circuit consisting of DPDT switches, thyristor, and triggering circuit is built and tested. The coil current is measured using a Hall-effect current sensor. The magnetic pulse created is measured and tabulated in the graph. Simulation using FEMM is done to compare the results obtained between experiment and simulation. This technology can be applied to area such as medical equipment, measuring instrument, and military equipment.

Keywords: high voltage, magnetic pulse, capacitor discharge, coil

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Authors: Tet H. Yeap

Abstract:

An analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by directional weighted paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. By introducing hidden nodes, the proposed network can be trained to store patterns and has increased memory capacity. Training to be an associative memory, simulation results show that the associative memory performs better than a classical Hopfield network by being able to perform better memory recall when the input is noisy.

Keywords: restricted Hopfield network, Lyapunov function, simultaneous perturbation stochastic approximation

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Authors: Myung-Gon Yoon, Jung-Ho Moon, Tae Kwon Ha

Abstract:

This paper considers a robust stabilization problem of a single agent in a multi-agent consensus system composed of identical agents, when the network topology of the system is completely unknown. It is shown that the transfer function of an agent in a consensus system can be described as a multiplicative perturbation of the isolated agent transfer function in frequency domain. Applying known robust stabilization results, we present sufficient conditions for a robust stabilization of an agent against unknown network topology.

Keywords: single agent control, multi-agent system, transfer function, graph angle

Procedia PDF Downloads 416