Search results for: fuzzy genetic network programming

Authors: Gaye Ibrahima, Mendy Gervais, Seck Diaraf, Ouya Samuel

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In this paper, we consider the influence maximization in social networks. Here we give importance to initial diffuser called the seeds. The goal is to find efficiently a subset of k elements in the social network that will begin and maximize the information diffusion process. A new approach which treats the social network before to determine the seeds, is proposed. This treatment eliminates the information feedback toward a considered element as seed by extracting an acyclic spanning social network. At first, we propose two algorithm versions called SCG − algoritm (v1 and v2) (Spanning Connected Graphalgorithm). This algorithm takes as input data a connected social network directed or no. And finally, a generalization of the SCG − algoritm is proposed. It is called SG − algoritm (Spanning Graph-algorithm) and takes as input data any graph. These two algorithms are effective and have each one a polynomial complexity. To show the pertinence of our approach, two seeds set are determined and those given by our approach give a better results. The performances of this approach are very perceptible through the simulation carried out by the R software and the igraph package.

Keywords: acyclic spanning graph, centrality measures, information feedback, influence maximization, social network

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Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: Joy Ghosh, Debasmita Biswas

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The road network of any area is the most important indicator of regional planning. For proper utilization of urban road networks, the structural parameters such as connectivity and accessibility should be analyzed and evaluated. This paper aims to explain the application of GIS on urban road network connectivity and accessibility analysis with a case study of Chandannagar City. This paper has been made to analyze the road network connectivity through various connectivity measurements like the total number of nodes and links, Cyclomatic Number, Alpha Index, Beta Index, Gamma index, Eta index, Pi index, Theta Index, and Aggregated Transport Score, Road Density based on existing road network in Chandannagar city in India. Accessibility is measured through the shortest Path Matrix, associate Number, and Shimbel Index. Various urban services, such as schools, banks, Hospitals, petrol pumps, ATMs, police stations, theatres, parks, etc., are considered for the accessibility analysis for each ward. This paper also highlights the relationship between urban land use/ land cover (LULC) and urban road network and population density using various spatial and statistical measurements. The datasets were collected through a field survey of 33 wards of the Chandannagar Municipal Corporation area, and the secondary data were collected through an open street map and satellite image of LANDSAT8 OLI & TIRS from USGS. Chandannagar was actually once a French colony, and at that time, various sort of planning was applied, but now Chandannagar city continues to grow haphazardly because that city is facing some problems; the knowledge gained from this paper helps to create a more efficient and accessible road network. Therefore, it would be suggested that some wards need to improve their connectivity and accessibility for the future growth and development of Chandannagar.

Keywords: accessibility, connectivity, transport, road network

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Sheren H. Salah

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The inverted pendulum is a highly nonlinear and open-loop unstable system. An inverted pendulum (IP) is a pendulum which has its mass above its pivot point. It is often implemented with the pivot point mounted on a cart that can move horizontally and may be called a cart and pole. The characteristics of the inverted pendulum make identification and control more challenging. This paper presents the simulation study of several control strategies for an inverted pendulum system. The goal is to determine which control strategy delivers better performance with respect to pendulum’s angle. The inverted pendulum represents a challenging control problem, which continually moves toward an uncontrolled state. For controlling the inverted pendulum. The simulation study that sliding mode control (SMC) control produced better response compared to Genetic Algorithm Control (GAs) and proportional-integral-derivative(PID) control.

Keywords: Inverted Pendulum (IP) Proportional-Integral-Derivative (PID), Genetic Algorithm Control (GAs), Sliding Mode Control (SMC)

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Authors: V. S. S. Kumar, B. Vikram

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The development of effective decision support tools that adopted in the construction industry is vital in the world we live in today, since it can lead to substantial cost reduction and efficient resource consumption. Solving the time-cost trade off problems and its related variants is at the heart of scientific research for optimizing construction planning problems. In general, the classical optimization techniques have difficulties in dealing with TCT problems. One of the main reasons of their failure is that they can easily be entrapped in local minima. This paper presents an investigation on the application of meta-heuristic techniques to two particular variants of the time-cost trade of analysis, the time-cost trade off problem (TCT), and time-cost trade off optimization problem (TCO). In first problem, the total project cost should be minimized, and in the second problem, the total project cost and total project duration should be minimized simultaneously. Finally it is expected that, the optimization models developed in this paper will contribute significantly for efficient planning and management of construction project.

Keywords: fuzzy sets, uncertainty, optimization, time cost trade off problems

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Authors: N. A. Ismail, Abdul Arif, Sharul Hafiz, Lu S. J., Tham W. S., Wong S. K.

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This paper investigates the visulization of Malaysia universities websites. Twenty (20) public universities websites in Malaysia has been chosen as samples to explore and visualize the link relationship between their academic websites using social network analysis methods such as inlink, degree, weight, betweenness and modularity class. All of the connection and relation demonstrate the power to influence, comprehensive strength and also the variety of subject types that are present in universities. The experimental results also show that University Malaysia Sabah (UMS) is the biggest back links provider.

Keywords: academic websites, link analysis, social network analysis, experimental result

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Authors: Shaan Khosla, Jon Krohn

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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: Lan Nguyen, Kien Le Tan, Bao Nguyen Pham Gia

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Due to the high cost, specialized dynamic measurement devices for industrial lands are difficult for many colleges to equip for hands-on teaching. This study connects a dynamic measurement sensor and receiver utilizing an inexpensive Raspberry Pi 4 board, some 24-bit ADC circuits, a geophone vibration sensor, and embedded Python open-source programming. Gather and analyze signals for dynamic measuring, ground vibration monitoring, and structure vibration monitoring. The system may wirelessly communicate data to the computer and is set up as a communication node network, enabling real-time monitoring of background vibrations at various locations. The device can be utilized for a variety of dynamic measurement and monitoring tasks, including monitoring earthquake vibrations, ground vibrations from construction operations, traffic, and vibrations of building structures.

Keywords: sensors, FFT, signal processing, real-time data monitoring, ground propagation wave, python, raspberry Pi 4

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Authors: Margarita Mayoral-Villa, J. Klapp, L. Di G. Sigalotti, J. E. V. Guzmán

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Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency.

Keywords: multiphase diffusion, message passing neural network, well interconnection, interwell connectivity, graph neural network, capacitance-resistance models

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from McRae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-worls, resilience to damage

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Authors: Fariba Ebrahimi, Mehdi Ghorbani, Ali Salajegheh

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Comprehensively water management considers economic, environmental, technical and social and also sustainability of water resources for future generations. Grassland management implies cooperative approach and involves all stakeholders and also introduces issues to managers, decision and policy makers. Solving these issues needs integrated and system approach. According to the recognition of actors or key persons in necessary to apply cooperative management of Water. Therefore, based on stakeholder analysis and social network analysis can be used to demonstrate the most effective actors for environmental decisions. In this research, social powers according are specified to social network approach at Water utilizers’ level of Natural in Jirood catchment of Latian basin. In this paper, utilizers of water resources were recognized using field trips and then, trust and collaboration matrix produced using questionnaires. In the next step, degree centrality index were Examined. Finally, geometric position of each actor was illustrated in the network. The results of the research based on centrality index have a key role in recognition of cooperative management of Water in Jirood and also will help managers and planners of water in the case of recognition of social powers in order to organization and implementation of sustainable management of Water.

Keywords: social network analysis, water co-management, social power, centrality index, local stakeholders network, Jirood catchment

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Muhammet Dursun Kaya, Hasan Asil

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In recent years, different methods have been proposed to optimize question processing in database. Although different methods have been proposed to optimize the query, but the problem which exists here is that most of these methods destroy the query execution plan after executing the query. This research attempts to solve the above problem by using a combination of methods of communicating with the database (the present questions in the programming code and using store procedures) and making query processing adaptive in database, and proposing a new approach for optimization of query processing by introducing the idea of dynamic store procedures. This research creates dynamic store procedures in the database according to the proposed algorithm. This method has been tested on applied software and results shows a significant improvement in reducing the query processing time and also reducing the workload of DBMS. Other advantages of this algorithm include: making the programming environment a single environment, eliminating the parametric limitations of the stored procedures in the database, making the stored procedures in the database dynamic, etc.

Keywords: relational database, agent, query processing, adaptable, communication with the database

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Authors: Shidrokh Goudarzi, Wan Haslina Hassan

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Heterogeneous wireless networks are converging towards an all-IP network as part of the so-called next-generation network. In this paradigm, different access technologies need to be interconnected; thus, vertical handovers or vertical handoffs are necessary for seamless mobility. In this paper, we conduct a review of existing vertical handover decision-making mechanisms that aim to provide ubiquitous connectivity to mobile users. To offer a systematic comparison, we categorize these vertical handover measurement and decision structures based on their respective methodology and parameters. Subsequently, we analyze several vertical handover approaches in the literature and compare them according to their advantages and weaknesses. The paper compares the algorithms based on the network selection methods, complexity of the technologies used and efficiency in order to introduce our vertical handover decision framework. We find that vertical handovers on heterogeneous wireless networks suffer from the lack of a standard and efficient method to satisfy both user and network quality of service requirements at different levels including architectural, decision-making and protocols. Also, the consolidation of network terminal, cross-layer information, multi packet casting and intelligent network selection algorithm appears to be an optimum solution for achieving seamless service continuity in order to facilitate seamless connectivity.

Keywords: heterogeneous wireless networks, vertical handovers, vertical handover metric, decision-making algorithms

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Authors: Yair Bleiberg, Michael Werman

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Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional Encoding (PE), which has become quite popular. However, PE has drawbacks. It has high-frequency artifacts and adds another hyper hyperparameter, just like batch normalization and dropout do. We believe that under certain circumstances, PE is not necessary, and a smarter construction of the network architecture together with a smart training method is sufficient to achieve similar results. In this paper, we show that very simple MLPs can quite easily output a frequency when given input of the half-frequency and quarter-frequency. Using this, we design a network architecture in blocks, where the input to each block is the output of the two previous blocks along with the original input. We call this a Fibonacci Network. By training each block on the corresponding frequencies of the signal, we show that Fibonacci Networks can reconstruct arbitrarily high frequencies.

Keywords: neural networks, positional encoding, high frequency intepolation, fully connected

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

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Authors: Shatha A. Yousif, Hatem Jasim, Ali R. Abas, Dheya P. Yousef

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To study the effect of the cross direction in bead wheat, three hybrid combinations (Babyle 113 , Iratome), (Sawa , Tamose2) and (Al Hashymya Al Iraq) were tested for plant height, number of tillers/m, number of grains per spike, weight of grains per spike, 1000-grain weight and grain yield. The results revealed that the direction of the cross had significant effect the number of grain/spike, tillers/m and grain yields. Grain yield was positively and significantly correlated with 1000-grain weight, number of grains per spike and tillers. Depend on the result of heritability and genetic advance it was suggested that 1000-grain weight number of grains per spike and tillers should be given emphasis for future wheat yield improvement programs.

Keywords: correlation, genetic advance, heritability, wheat, yield traits

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Authors: Said Rabah Azzam, Andrew Graham

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Fragmentation of packets caused by encryption applied on the network layer of the IOS model in Internet Protocol version 4 (IPv4) networks as well as the possibility of using fragmentation and Access Control Lists (ACLs) as a method of restricting network access to certain hosts or areas of a network.Using default settings, fragmentation is expected to occur and each fragment to be reassembled at the other end. If this does not occur then a high number of ICMP messages should be generated back towards the source host indicating that the packet is too large and that it needs to be made smaller. This result is also expected when the MTU is changed for certain links between devices.When using ACLs and packet fragments to restrict access to hosts or network segments it is possible that ACLs cannot be set up in this way. If ACLs cannot be setup to allow only fragments then it is a limitation of the hardware’s firmware holding back this particular method. If the ACL on the restricted switch can be set up in such a way to allow only fragments then a connection that forces packets to fragment should be allowed to pass through the ACL. This should then make a network connection to the destination machine allowing data to be sent to and from the destination machine. ICMP messages from the restricted access switch and host should also be blocked from being sent back across the link which will be shown in an SSH session into the switch.

Keywords: fragmentation, encryption, security, switch

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Authors: Yi Yang

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In the era of 'Internet plus', the rapid development of webcast has posed new challenges to the intellectual property law. Meanwhile, traditional copyright protection has also exposed the existing theoretical imbalance in webcast. Through the analysis of the outstanding problems in the copyright protection of the network live broadcast, this paper points out that the main causes of the problems are the unclear nature of the copyright of the network live broadcast, the copyright protection system of the game network live broadcast has not yet been constructed, and the copyright infringement of the pan entertainment live broadcast is mostly, and so on. Based on the current practice, this paper puts forward the specific thinking of the protection path of online live broadcast copyright. First of all, to provide a reasonable judicial solution for a large number of online live copyright cases, we need to integrate the right scope and regulatory behavior of broadcasting right and information network communication right. Secondly, in order to protect the rights of network anchors, the webcast should be regarded as works. Thirdly, in order to protect the copyright of webcast and prevent the infringement of copyright by webcast, the webcast platform will be used as an intermediary to provide solutions for solving the judicial dilemma. In the era of 'Internet plus', it is a theoretical attempt to explore the protection and method of copyright protection on webcast, which has positive guiding significance for judicial practice.

Keywords: 'Internet Plus' era, webcast, copyright, protection dilemma

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Authors: Faheema Khan

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In order to investigate the influence of genetic background on salt tolerance in Soybean (Glycine max) ten soybean genotypes released/notified in India were selected. (Pusa-20, Pusa-40, Pusa-37, Pusa-16, Pusa-24, Pusa-22, BRAGG, PK-416, PK-1042, and DS-9712). The 10-day-old seedlings were subjected to 0, 25, 50, 75, 100, 125, and 150 mM NaCl for 15 days. Plant growth, leaf osmotic adjustment, and RAPD analysis were studied. In comparison to control plants, the plant growth in all genotypes was decreased by salt stress, respectively. Salt stress decreased leaf osmotic potential in all genotypes however the maximum reduction was observed in genotype Pusa-24 followed by PK-416 and Pusa-20. The difference in osmotic adjustment between all the genotypes was correlated with the concentrations of ion examined such as Na+ and the leaf proline concentration. These results suggest that the genotypic variation for salt tolerance can be partially accounted for by plant physiological measures. The genetic polymorphisms between soybean genotypes differing in response to salt stress were characterized using 25 RAPD primers. These primers generated a total of 1640 amplification products, among which 1615 were found to be polymorphic. A very high degree of polymorphism (98.30%) was observed. UPGMA cluster analysis of genetic similarity indices grouped all the genotypes into two major clusters. Intra-clustering within the two clusters precisely grouped the 10 genotypes in sub-cluster as expected from their physiological findings. Our results show that RAPD technique is a sensitive, precise and efficient tool for genomic analysis in soybean genotypes.

Keywords: glycine max, NaCl, RAPD, proteomics

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Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Afsaneh Mohammadnejad, Marianne Nygaard, Jan Baumbach, Shuxia Li, Weilong Li, Jesper Lund, Jacob v. B. Hjelmborg, Lene Christensen, Qihua Tan

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Cognitive impairment in the elderly is a key issue affecting the quality of life. Despite a strong genetic background in cognition, only a limited number of single nucleotide polymorphisms (SNPs) have been found. These explain a small proportion of the genetic component of cognitive function, thus leaving a large proportion unaccounted for. We hypothesize that one reason for this missing heritability is the misspecified modeling in data analysis concerning phenotype distribution as well as the relationship between SNP dosage and the phenotype of interest. In an attempt to overcome these issues, we introduced a model-free method based on the generalized correlation coefficient (GCC) in a genome-wide association study (GWAS) of cognitive function in twin samples and compared its performance with two popular linear regression models. The GCC-based GWAS identified two genome-wide significant (P-value < 5e-8) SNPs; rs2904650 near ZDHHC2 on chromosome 8 and rs111256489 near CD6 on chromosome 11. The kinship model also detected two genome-wide significant SNPs, rs112169253 on chromosome 4 and rs17417920 on chromosome 7, whereas no genome-wide significant SNPs were found by the linear mixed model (LME). Compared to the linear models, more meaningful biological pathways like GABA receptor activation, ion channel transport, neuroactive ligand-receptor interaction, and the renin-angiotensin system were found to be enriched by SNPs from GCC. The GCC model outperformed the linear regression models by identifying more genome-wide significant genetic variants and more meaningful biological pathways related to cognitive function. Moreover, GCC-based GWAS was robust in handling genetically related twin samples, which is an important feature in handling genetic confounding in association studies.

Keywords: cognition, generalized correlation coefficient, GWAS, twins

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Authors: Feng Ji Mervin Goh, Edmund Chee Jian Pua, Stuart Alexander Cook

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Dilated cardiomyopathy (DCM) is a common cause of heart failure. Genetic mutations account for 50% of DCM cases with TTN mutations being the most common, accounting for up to 25% of DCM cases. However, the genetic architecture underlying Asian DCM patients is unknown. We evaluated 68 patients (female= 17) with DCM who underwent follow-up at the National Heart Centre, Singapore from 2013 through 2014. Clinical data were obtained and analyzed retrospectively. Genomic DNA was subjected to next-generation targeted sequencing. Nextera Rapid Capture Enrichment was used to capture the exons of a panel of 169 cardiac genes. DNA libraries were sequenced as paired-end 150-bp reads on Illumina MiSeq. Raw sequence reads were processed and analysed using standard bioinformatics techniques. The average age of onset of DCM was 46.1±10.21 years old. The average left ventricular ejection fraction (LVEF), left ventricular diastolic internal diameter (LVIDd), left ventricular systolic internal diameter (LVIDs) were 26.1±11.2%, 6.20±0.83cm, and 5.23±0.92cm respectively. The frequencies of mutations in major DCM-associated genes were as follows TTN (5.88% vs published frequency of 20%), LMNA (4.41% vs 6%), MYH7 (5.88% vs 4%), MYH6 (5.88% vs 4%), and SCN5a (4.41% vs 3%). The average callability at 10 times coverage of each major gene were: TTN (99.7%), LMNA (87.1%), MYH7 (94.8%), MYH6 (95.5%), and SCN5a (94.3%). In conclusion, TTN mutations are not common in Singaporean DCM patients. The frequencies of other major DCM-associated genes are comparable to frequencies published in the current literature.

Keywords: heart failure, dilated cardiomyopathy, genetics, next-generation sequencing

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Authors: Mustafa Malki, Ewan R. Pearson

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Tools utilized by health care practitioners to flag potential adverse drug reactions secondary to drug-drug interactions ignore individual genetic variation, which has the potential to markedly alter the severity of these interactions. To our best knowledge, there have been limited published studies on the impact of genetic variation on drug-drug interactions. Therefore, our aim in this project is the discovery of previously unrecognized, clinically important drug-drug-gene interactions (DDGIs) within the list of most commonly used drug combinations in the UK. The UKBB database was utilized to identify the top most frequently prescribed drug combinations in the UK with at least one route of interaction (over than 200 combinations were identified). We have recognised 37 common and unique interacting genes considering all of our drug combinations. Out of around 600 potential genetic variants found in these 37 genes, 100 variants have met the selection criteria (common variant with minor allele frequency ≥ 5%, independence, and has passed HWE test). The association between these variants and the use of each of our top drug combinations has been tested with a case-control analysis under the log-additive model. As the data is cross-sectional, drug intolerance has been identified from the genotype distribution as presented by the lower percentage of patients carrying the risky allele and on the drug combination compared to those free of these risk factors and vice versa with drug tolerance. In GoDARTs database, the same list of common drug combinations identified by the UKBB was utilized here with the same list of candidate genetic variants but with the addition of 14 new SNPs so that we have a total of 114 variants which have met the selection criteria in GoDARTs. From the list of the top 200 drug combinations, we have selected 28 combinations where the two drugs in each combination are known to be used chronically. For each of our 28 combinations, three drug response phenotypes have been identified (drug stop/switch, dose decrease, or dose increase of any of the two drugs during their interaction). The association between each of the three phenotypes belonging to each of our 28 drug combinations has been tested against our 114 candidate genetic variants. The results show replication of four findings between both databases : (1) Omeprazole +Amitriptyline +rs2246709 (A > G) variant in CYP3A4 gene (p-values and ORs with the UKBB and GoDARTs respectively = 0.048,0.037,0.92,and 0.52 (dose increase phenotype)) (2) Simvastatin + Ranitidine + rs9332197 (T > C) variant in CYP2C9 gene (0.024,0.032,0.81, and 5.75 (drug stop/switch phenotype)) (3) Atorvastatin + Doxazosin + rs9282564 (T > C) variant in ABCB1 gene (0.0015,0.0095,1.58,and 3.14 (drug stop/switch phenotype)) (4) Simvastatin + Nifedipine + rs2257401 (C > G) variant in CYP3A7 gene (0.025,0.019,0.77,and 0.30 (drug stop/switch phenotype)). In addition, some other non-replicated, but interesting, significant findings were detected. Our work also provides a great source of information for researchers interested in DD, DG, or DDG interactions studies as it has highlighted the top common drug combinations in the UK with recognizing 114 significant genetic variants related to drugs' pharmacokinetic.

Keywords: adverse drug reactions, common drug combinations, drug-drug-gene interactions, pharmacogenomics

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Authors: Victor V. Ribeiro, Thais C. Lopes, Rafael A. A. Monteiro

Abstract:

The Albatroz, Baleia Jubarte, Coral Vivo, Golfinho Rotador and Tamar projects make up the Young Sea Network (YSN), part of the BIOMAR Network, which aims to integrate the environmental youths of the Brazilian coast. For this, three editions of the Young Sea Meeting (YSM) were performed. Seeking to stimulate belonging, self-knowledge, participation, autonomy and youth protagonism, the Albatroz Project hosted the III YSM, in Bertioga (SP), in April 2019 and aimed to collectively plan the meeting. Five pillars of Environmental Education were used: identity, community, dialogue, power to act and happiness, the OCA Method and the Young Educates Young; Young Chooses Young; and One Generation Learns from the Other principals. In December 2018, still in the II YSM, the participatory planning of the III YSM began. Two "representatives" of each group were voluntarily elected to facilitate joint decisions, propose, receive and communicate demands from their groups and coordinators. The Young Albatroz Collective (YAC) facilitated the organization process as a whole. The purpose of the meeting was collectively constructed, answering the following question: "What is the YSM for?". Only two of the five pairs of representatives responded. There was difficulty gathering the young people in each group, because it was the end of the year, with people traveling. Thus, due to the short planning time, the YAC built a pre-programming to be validated by the other groups, defining as the objective of the meeting the strengthening of youth protagonism within the YSN. In the planning process, the YAC held 20 meetings, with 60 hours of face-to-face work, in three months, and two technical visits to the headquarters of the III YSM. The participatory dynamics of consultation, when it occurred, required up to two weeks, evidencing the limits of participation. The project coordinations stated that they were not being included in the process by their young people. There is a need to work more to be able to aloud the participation, developing skills and understanding about its principles. This training must take place in an articulated way between the network, implying the important role of the five projects in jointly developing and implementing educator processes with this objective in a national dimension, but without forgetting the specificities of each young group. Finally, it is worth highlighting the great potential of the III YSM by stimulating the exercise of leading environmental youth in more than 50 young people from Brazilian coast, linked to the YSN, stimulating the learning and mobilization of young people in favor of coastal and marine conservation.

Keywords: Marine Conservation, Environmental Education, Youth, Participation, Planning

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Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

Abstract:

The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

Procedia PDF Downloads 302

Authors: Muhammad Faiz Liew Abdullah, Bhagwan Das, Nor Shahida, Abdul Fattah Chandio

Abstract:

This study presents the designed features of Graphical User Interface (GUI) for chromatic dispersion (CD) reduction using digital signal processing (DSP) techniques. GUI is specially designed for windows platform. The obtained simulation results from matlab are presented via this GUI. After importing results from matlab in GUI, It will present your work on any windows7 and onwards versions platforms without matlab software. First part of the GUI contains the research methodology block diagram and in the second part, output for each stage is shown in separate reserved area for the result display. Each stage of methodology has the captions to display the results. This GUI will be very helpful during presentations instead of making slides this GUI will present all your work easily in the absence of other software’s such as Matlab, Labview, MS PowerPoint. GUI is designed using C programming in MS Visio Studio.

Keywords: Matlab simulation results, C programming, MS VISIO studio, chromatic dispersion

Procedia PDF Downloads 444

Authors: Saeid Jalilzadeh

Abstract:

PID controllers are widely used to control the industrial plants because of their robustness and simple structures. Tuning of the controller's parameters to get a desired response is difficult and time consuming. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller, but many advanced tuning methods behave not so perfect as to be expected. Honey Bee algorithm (HBA) and genetic algorithm (GA) are extensively used for real parameter optimization in diverse fields of study. This paper describes an application of HBA and GA to the problem of designing a PID controller whose parameters comprise proportionality constant, integral constant and derivative constant. Presence of three parameters to optimize makes the task of designing a PID controller more challenging than conventional P, PI, and PD controllers design. The suitability of the proposed approach has been demonstrated through computer simulation using MATLAB/SIMULINK.

Keywords: controller, GA, optimization, PID, PSO

Procedia PDF Downloads 526

Authors: Moustapha Bande, Hakima Ould-Slimane, Hanifa Boucheneb

Abstract:

Software development for complex systems requires efficient and automatic tools that can be used to verify the satisfiability of some critical properties such as security ones. With the emergence of Aspect-Oriented Programming (AOP), considerable work has been done in order to better modularize the separation of concerns in the software design and implementation. The goal is to prevent the cross-cutting concerns to be scattered across the multiple modules of the program and tangled with other modules. One of the key challenges in the aspect-oriented programs is to be sure that all the pieces put together at the weaving time ensure the satisfiability of the overall system requirements. Our paper focuses on this problem and proposes a formal property verification approach for a given property from the woven program. The approach is based on the control flow graph (CFG) of the woven program, and the use of a satisfiability modulo theories (SMT) solver to check whether each property (represented par one aspect) is satisfied or not once the weaving is done.

Keywords: aspect-oriented programming, control flow graph, property verification, satisfiability modulo theories

Procedia PDF Downloads 159