Search results for: graph matching

Authors: Thanusha D. Abeywickrama, Inoka C. Perera, Genji Kurisu

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Tuberculosis, considered being as the ninth leading cause of death worldwide, cause from a single infectious agent M. tuberculosis and the drug resistance nature of this bacterium is a continuing threat to the world. Therefore TB preventing treatment is expanding, where this study designed to analyze the regulatory mechanism of GntR transcriptional regulator gene Rv0792c, which lie between several genes codes for some hypothetical proteins, a monooxygenase and an oxidoreductase. The gene encoding Rv0792c was cloned into pET28a and expressed protein was purified to near homogeneity by Nickel affinity chromatography. It was previously reported that the protein binds within the intergenic region (BS region) between Rv0792c gene and monooxygenase (Rv0793). This resulted in binding of three protein molecules with the BS region suggesting tight control of monooxygenase as well as its own gene. Since monooxygenase plays a key role in metabolism, this gene may have a global regulatory role. The natural ligand for this regulator is still under investigation. In relation to the Rv0792 protein structure, a Circular Dichroism (CD) spectrum was carried out to determine its secondary structure elements. Percentage-wise, 17.4% Helix, 21.8% Antiparallel, 5.1% Parallel, 12.3% turn and 43.5% other were revealed from CD spectrum data under room temperature. Differential Scanning Calorimetry (DSC) was conducted to assess the thermal stability of Rv0792, which the melting temperature of protein is 57.2 ± 0.6 °C. The graph of heat capacity (Cp) versus temperature for the best fit was obtained for non-two-state model, which concludes the folding of Rv0792 protein occurs through stable intermediates. Peak area (∆HCal ) and Peak shape (∆HVant ) was calculated from the graph and ∆HCal / ∆HVant was close to 0.5, suggesting dimeric nature of the protein.

Keywords: CD spectrum, DSC analysis, GntR transcriptional regulator, protein structure

Procedia PDF Downloads 223

Authors: M. Nekri, A. Khelladi

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The world wide web network is a network with a complex topology, the main properties of which are the distribution of degrees in power law, A low clustering coefficient and a weak average distance. Modeling the web as a graph allows locating the information in little time and consequently offering a help in the construction of the research engine. Here, we present a model based on the already existing probabilistic graphs with all the aforesaid characteristics. This work will consist in studying the web in order to know its structuring thus it will enable us to modelize it more easily and propose a possible algorithm for its exploration.

Keywords: clustering coefficient, preferential attachment, small world, web community

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Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

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The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

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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

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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

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Authors: Jae-Neung Lee, Keun-Chang Kwak

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This paper describes an analysis of Yacht Simulator international trends and also explains about Yacht. Examples of yacht Simulator using Yacht Simulator include image processing for totaling the total number of vehicles, edge/target detection, detection and evasion algorithm, image processing using SIFT (scale invariant features transform) matching, and application of median filter and thresholding.

Keywords: yacht simulator, simulator, trends analysis, SIFT

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Authors: Seyi Olalekan Olawuyi, Abbyssiania Mushunje

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The potential benefits of agricultural production to improve the welfare condition of smallholder farmers in developing countries is no more a news because it has been widely documented. Yet majority of these farming households suffer from shortfall in production output to meet both the consumption needs and market demand which adversely affects output market participation and by extension welfare condition. Therefore, this study investigated the impacts of output market participation on households’ welfare of cassava-based farmers in Oyo State, Nigeria. Multistage sampling technique was used to select 324 sample size used for this study. The findings from the data obtained and analyzed through composite score and crosstab analysis revealed that there is varying degree of output market participation among the farmers which also translate to the observed welfare profile differentials in the study area. The probit model analysis with respect to the selection equation identified gender of household head, household size, access to remittance, off-farm income and ownership of farmland as significant drivers of output market participation in the study area. Furthermore, the treatment effect model of the welfare equation and propensity score matching (PSM) technique were used as robust checks; and the findings attest to the fact that, complimentarily with other significant variables highlighted in this study, output market participation indeed has a significant impact on farming households’ welfare. As policy implication inferences, the study recommends female active inclusiveness and empowerment in farming activities, birth control strategies, secondary income smoothing activities and discouragement of land fragmentation habits, to boost productivity and output market participation, which by extension can significantly improve farming households’ welfare.

Keywords: Cassava market participation, households' welfare, propensity score matching, treatment effect model

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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

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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

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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

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Authors: T. Bryan , V. Kepuska, I. Kostnaic

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A method of data compression and denoising is introduced that is based on atomic decomposition of audio data using “basis vectors” that are learned from the audio data itself. The basis vectors are shown to have higher data compression and better signal-to-noise enhancement than the Gabor and gammatone “seed atoms” that were used to generate them. The basis vectors are the input weights of a Sparse AutoEncoder (SAE) that is trained using “envelope samples” of windowed segments of the audio data. The envelope samples are extracted from the audio data by performing atomic decomposition with Gabor or gammatone seed atoms. This process identifies segments of audio data that are locally coherent with the seed atoms. Envelope samples are extracted by identifying locally coherent audio data segments with Gabor or gammatone seed atoms, found by matching pursuit. The envelope samples are formed by taking the kronecker products of the atomic envelopes with the locally coherent data segments. Oracle signal-to-noise ratio (SNR) verses data compression curves are generated for the seed atoms as well as the basis vectors learned from Gabor and gammatone seed atoms. SNR data compression curves are generated for speech signals as well as early American music recordings. The basis vectors are shown to have higher denoising capability for data compression rates ranging from 90% to 99.84% for speech as well as music. Envelope samples are displayed as images by folding the time series into column vectors. This display method is used to compare of the output of the SAE with the envelope samples that produced them. The basis vectors are also displayed as images. Sparsity is shown to play an important role in producing the highest denoising basis vectors.

Keywords: sparse dictionary learning, autoencoder, sparse autoencoder, basis vectors, atomic decomposition, envelope sampling, envelope samples, Gabor, gammatone, matching pursuit

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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

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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

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Authors: Sami Aboujafar

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Oil recovery in petroleum reservoirs is greatly affected by fluid-rock and fluid-fluid interactions. These interactions directly control rock wettability, capillary pressure and relative permeability curves. Laboratory core-floods and centrifuge experiments were conducted on sandstone and carbonate cores to study the effect of low and high brine salinity and viscosity and oil viscosity on residual saturations and relative permeability. Drainage and imbibition relative permeability in two phase system were measured, refined lab oils with different viscosities, heavy and light, and several brine salinities were used. Sensitivity analysis with different values for the salinity and viscosity of the fluids,, oil and water, were done to investigate the effect of these properties on water/oil relative permeability, residual oil saturation and oil recovery. Experiments were conducted on core material from viscous/heavy and light oil fields. History matching core flood simulator was used to study how the relative permeability curves and end point saturations were affected by different fluid properties using several correlations. Results were compared with field data and literature data. The results indicate that there is a correlation between the oil viscosity and/or brine salinity and residual oil saturation and water relative permeability end point. Increasing oil viscosity reduces the Krw@Sor and increases Sor. The remaining oil saturation from laboratory measurements might be too high due to experimental procedures, capillary end effect and early termination of the experiment, especially when using heavy/viscous oil. Similarly the Krw@Sor may be too low. The effect of wettability on the observed results is also discussed. A consistent relationship has been drawn between the fluid parameters, water/oil relative permeability and residual saturations, and a descriptor may be derived to define different flow behaviors. The results of this work will have application to producing fields and the methodologies developed could have wider application to sandstone and carbonate reservoirs worldwide.

Keywords: history matching core flood simulator, oil recovery, relative permeability, residual saturations

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Authors: T. Benyetho, L. El Abdellaoui, J. Terhzaz, H. Bennis, N. Ababssi, A. Tajmouati, A. Tribak, M. Latrach

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This work presents a new planar multiband antenna based on fractal geometry. This structure is optimized and validated into simulation by using CST-MW Studio. To feed this antenna we have used a CPW line which makes it easy to be incorporated with integrated circuits. The simulation results presents a good matching input impedance and radiation pattern in the GSM band at 900 MHz and ISM band at 2.4 GHz. The final structure is a dual band fractal antenna with 70 x 70 mm² as a total area by using an FR4 substrate.

Keywords: Antenna, CPW, fractal, GSM, multiband

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Authors: Mark L. Caay, Rodolfo E. Maza

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In this paper, the authors define two new variants of domination in graphs: the marriage and the divorce domination. A subset S ⊆ V (G) is said to be a marriage dominating set of G if for every e ∈ E(G), there exists a u ∈ V (G) such that u is one of the end vertex of e. A marriage dominating set S ⊆ V (G) is said to be a divorce dominating set of G if G\S is a disconnected graph. In this study, the authors present conditions of graphs for which the marriage and the divorce domination will take place and for which the two sets will coincide. Furthermore, the author gives the necessary and sufficient conditions for marriage domination to avoid divorce.

Keywords: domination, decomposition, marriage domination, divorce domination, marriage theorem

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Authors: Ibrahim M. S. Shnawa, Naeem R. R. Algebory

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The procedure for the assessment of the urinary mucosal cryoglobulin (UMCG) is being reviewed, testified and evaluated. The major features of UMCG are rather similar to that of serum cryoglobulin. Such evident similarities are forming the reality for the existence of the UMCG. There were seven characterizing criteria useable for the identification for UMCG. Upon matching them to the Irish criteria for serum cryoglobulin, some modifications are being proposed to the 16th standards that has been formulated and built as an Irish criterion. The existence of UMCG is being reported for the first time in human chronic infectious bacterial disease.

Keywords: urinary, mucosal, cryoglubulin, standard immunofixation

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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

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Authors: Showkat Ahmad Bhat, M. S. Bhatt

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The study evaluates the ex-post impact of institutional credit on agricultural output. It first examines the key factors that influence the accessibility of institutional credit by farm households. For quantitative analysis both program participant and non-participant respondents were drawn and cross-sectional survey data were collected from 412 households in Pulwama District of Jammu & Kashmir (India). Propensity Score Matching Method was employed to analyze the impact of the institutional credit on agricultural output. Results show that institutional credit has a positive and significant impact on the agricultural output measured in terms of farm income and crop productivity. To estimate the accessibility of credit, an examination of both demand side and supply side factors were carried out. The demand for credit was measured with respect to respondents who applied for credit. Supply side credit allocation measured in terms of the proportion of ‘credit amount’ farmers obtained. Logit and Two-limit Tobit Regression Models were used to investigate the determinants that influence the accessibility of formal credit for Demand for and supply of credit respectively. The estimated results suggested that the demand for credit is positively and significantly affected by the factors such as: age of the household head, formal education, membership, cash crop grown, farm size and saving account. All the variables were found significantly increasing the household’s likelihood to demand for and supply of credit from banks. However, the impact of these factors varies considerably across the credit markets. Factors which were found negatively and significantly influencing the accessibility of credit were: ‘square of the age’, household assets and rate of interest. The credit constraints analysis suggested that square of the age; household assets and rate of interest were the three most important factors that increased the probability of being constrained. The study finally discusses these results in detail and draws some recommendations.

Keywords: institutional credit, agriculture, propensity score matching logit model, Tobit model

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

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

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

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Authors: Dongqin Cheng

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

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

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Authors: Haffaf Hafid

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

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

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Authors: A. El Hamdouni, J. Zbitou, A. Tajmouati, L. El Abdellaoui, A. Errkik, A. Tribak, M. Latrach

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This paper presents a novel fractal antenna structure proposed for UWB (Ultra – Wideband) applications. The frequency band 3.1-10.6 GHz released by FCC (Federal Communication Commission) as the commercial operation of UWB has been chosen as frequency range for this antenna based on coplanar waveguide (CPW) feed and circular shapes fulfilled according to fractal geometry. The proposed antenna is validated and designed by using an FR4 substrate with overall area of 34 x 43 mm2. The simulated results performed by CST-Microwave Studio and compared by ADS (Advanced Design System) show good matching input impedance with return loss less than -10 dB between 2.9 GHz and 11 GHz.

Keywords: Fractal antenna, Fractal Geometry, CPW Feed, UWB, FCC

Procedia PDF Downloads 388

Authors: Yuji Kageyama, Shin Nagata, Tatsuya Takino, Izuru Nomura, Hiroyuki Kamata

Abstract:

In previous study, technique to estimate a self-location by using a lunar image is proposed. We consider the improvement of the conventional method in consideration of FPGA implementation in this paper. Specifically, we introduce Artificial Bee Colony algorithm for reduction of search time. In addition, we use fixed point arithmetic to enable high-speed operation on FPGA.

Keywords: SLIM, Artificial Bee Colony Algorithm, location estimate, evolutional triangle similarity

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

Abstract:

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

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

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

Abstract:

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

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

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Authors: Jianrong Wu, Xuan Fu, Akihiro Higashi, Zhiming Tan

Abstract:

CMOS image sensors with a rolling shutter are used broadly in the digital cameras embedded in mobile devices. The rolling shutter suffers the flicker artifacts from the fluorescent lamp, and it could be observed easily. In this paper, the characteristics of illumination flicker in motion case were analyzed, and two efficient detection methods based on matching fragment selection were proposed. According to the experimental results, our methods could achieve as high as 100% accuracy in static scene, and at least 97% in motion scene.

Keywords: illumination flicker, embedded camera, rolling shutter, detection

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Authors: Kourosh Modarresi

Abstract:

The Netflix problem has brought the topic of “Recommendation Systems” into the mainstream of computer science, mathematics, and statistics. Though much progress has been made, the available algorithms do not obtain satisfactory results. The success of these algorithms is rarely above 5%. This work is based on the belief that the main challenge is to come up with “scalable personalization” models. This paper uses an adaptive regularization of inverse singular value decomposition (SVD) that applies adaptive penalization on the singular vectors. The results show far better matching for recommender systems when compared to the ones from the state of the art models in the industry.

Keywords: convex optimization, LASSO, regression, recommender systems, singular value decomposition, low rank approximation

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Authors: Giorgi Methodishvili, Iuliia Methodishvili

Abstract:

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: Thomas Bryan, Veton Kepuska, Ivica Kostanic

Abstract:

Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

Procedia PDF Downloads 290