Search results for: gene co-expression network

Authors: Alireza Pour Ebrahimi, Vahid Abasi

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Recently by the extension of internet usage, Research on the intrusion detection system takes a significant importance. Many of improvement systems prevent internal and external network attacks by providing security through firewalls and antivirus. In recently years, intrusion detection systems gradually turn from host-based systems and depend on O.S to the distributed systems which are running on multiple O.S. In this work, by considering the diversity of computer networks whit respect to structure, architecture, resource, services, users and also security goals requirement a fully distributed collaborative intrusion detection system based on peer to peer architecture is suggested. in this platform each partner device (matched device) considered as a peer-to-peer network. All transmitted information to network are visible only for device that use security scanning of a source. Experimental results show that the distributed architecture is significantly upgradeable in respect to centralized approach.

Keywords: network, intrusion detection system, peer to peer, internal and external network

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Authors: Omojokun G. Aju, Adedayo O. Sule

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ZigBee wireless sensor and control network is one of the most popularly deployed wireless technologies in recent years. This is because ZigBee is an open standard lightweight, low-cost, low-speed, low-power protocol that allows true operability between systems. It is built on existing IEEE 802.15.4 protocol and therefore combines the IEEE 802.15.4 features and newly added features to meet required functionalities thereby finding applications in wide variety of wireless networked systems. ZigBee‘s current focus is on embedded applications of general-purpose, inexpensive, self-organising networks which requires low to medium data rates, high number of nodes and very low power consumption such as home/industrial automation, embedded sensing, medical data collection, smart lighting, safety and security sensor networks, and monitoring systems. Although the ZigBee design specification includes security features to protect data communication confidentiality and integrity, however, when simplicity and low-cost are the goals, security is normally traded-off. A lot of researches have been carried out on ZigBee technology in which emphasis has mainly been placed on ZigBee network performance characteristics such as energy efficiency, throughput, robustness, packet delay and delivery ratio in different scenarios and applications. This paper investigate and analyse the data accuracy, network implementation difficulties and security challenges of ZigBee network applications in star-based and mesh-based topologies with emphases on its home monitoring application using the ZigBee ProBee ZE-10 development boards for the network setup. The paper also expose some factors that need to be considered when designing ZigBee network applications and suggest ways in which ZigBee network can be designed to provide more resilient to network attacks.

Keywords: home monitoring, IEEE 802.14.5, topology, wireless security, wireless sensor network (WSN), ZigBee

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Authors: Ci Lin, Tet Yeap, Iluju Kiringa

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This paper introduces the subspace rotation algorithm (SRA) to train the Restricted Hopfield Network (RHN) as an auto-associative memory. Subspace rotation algorithm is a gradient-free subspace tracking approach based on the singular value decomposition (SVD). In comparison with Backpropagation Through Time (BPTT) on training RHN, it is observed that SRA could always converge to the optimal solution and BPTT could not achieve the same performance when the model becomes complex, and the number of patterns is large. The AUTS case study showed that the RHN model trained by SRA could achieve a better structure of attraction basin with larger radius(in general) than the Hopfield Network(HNN) model trained by Hebbian learning rule. Through learning 10000 patterns from MNIST dataset with RHN models with different number of hidden nodes, it is observed that an several components could be adjusted to achieve a balance between recovery accuracy and noise resistance.

Keywords: hopfield neural network, restricted hopfield network, subspace rotation algorithm, hebbian learning rule

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Authors: Zhizhong Yuan, Lili Wang, Chenya Zheng, Wuqi Yang

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How to encourage and support peer-to-peer (P2P) network lending, and effectively monitor the risk of P2P network lending, has become the focus of the Chinese government departments, industrialists, experts and scholars in recent years. The reason is that this convenient online micro-credit service brings a series of credit risks and other issues. Avoiding the risks brought by the P2P network lending model, it can better play a benign role and help China's small and medium-sized private enterprises with vigorous development to solve the capital needs; otherwise, it will bring confusion to the normal financial order. As a form of financial services, P2P network lending has injected new blood into China's non-government finance in the past ten years, and has found a way out for idle funds and made up for the shortage of traditional financial services in China. However, it lacks feasible measures in credit evaluation and government supervision. This paper collects a large amount of data about P2P network lending of China. The data collection comes from the official media of the Chinese government, the public achievements of existing researchers and the analysis and collation of correlation data by the authors. The research content of this paper includes literature review; the current situation of China's P2P network lending development; the risk analysis of P2P network lending in China; the risk prevention strategy of P2P network lending in China. The focus of this paper is to try to find a specific program to strengthen supervision and avoid risks from the perspective of government regulators, operators of P2P network lending platform, investors and users of funds. These main measures include: China needs to develop self-discipline organization of P2P network lending industry and formulate self-discipline norms as soon as possible; establish a regular information disclosure system of P2P network lending platform; establish censorship of credit rating of borrowers; rectify the P2P network lending platform in compliance through the implementation of bank deposition. The results and solutions will benefit all the P2P network lending platforms, creditors, debtors, bankers, independent auditors and government agencies of China and other countries.

Keywords: peer-to-peer(P2P), regulation, risk prevention, supervision

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Authors: Katsumi Hirata

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Certain systems can function well only if they recognize the sound environment as humans do. In this research, we focus on sound classification by adopting a convolutional neural network and aim to develop a method that automatically classifies various environmental sounds. Although the neural network is a powerful technique, the performance depends on the type of input data. Therefore, we propose an approach via a slice bispectrogram, which is a third-order spectrogram and is a slice version of the amplitude for the short-time bispectrum. This paper explains the slice bispectrogram and discusses the effectiveness of the derived method by evaluating the experimental results using the ESC‑50 sound dataset. As a result, the proposed scheme gives high accuracy and stability. Furthermore, some relationship between the accuracy and non-Gaussianity of sound signals was confirmed.

Keywords: environmental sound, bispectrum, spectrogram, slice bispectrogram, convolutional neural network

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Authors: Berredjem Hajira, Benlaifa Meriem, Becheker Imene, Bardi Rafika, Djebar Med Reda

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Recent acquired or reactivation T.gondii infection is a serious complication in HIV patients. Classical serological diagnosis relies on the detection of anti-Toxoplasma immunoglobulin ; however, serology may be unreliable in HIV immunodeficient patients who fail to produce significant titers of specific antibodies. PCR assays allow a rapid diagnosis of Toxoplasma infection. In this study, we compared the value of the PCR for diagnosing active toxoplasmosis in cerebrospinal fluid and blood samples from HIV patients. Anti-Toxoplasma antibodies IgG and IgM titers were determined by ELISA. In parallel, nested PCR targeting B1 gene and conventional PCR-ELISA targeting P30 gene were used to detect T. gondii DNA in 25 blood samples and 12 cerebrospinal fluid samples from patients in whom toxoplasmic encephalitis was confirmed by clinical investigations. A total of 15 negative controls were used. Serology did not contribute to confirm toxoplasmic infection, as IgG and IgM titers decreased early. Only 8 out 25 blood samples and 5 out 12 cerebrospinal fluid samples PCRs yielded a positive result. 5 patients with confirmed toxoplasmosis had positive PCR results in either blood or cerebrospinal fluid samples. However, conventional nested B1 PCR gave best results than the P30 gene one for the detection of T.gondii DNA in both samples. All samples from control patients were negative. This study demonstrates the unusefulness of the serological tests and the high sensitivity and specificity of PCR in the diagnosis of toxoplasmic encephalitis in HIV patients.

Keywords: cerebrospinal fluid, HIV, Toxoplasmosis, PCR

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Authors: Krisana Chueachainat

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The study is about the identity, typology and symbol using in communication through offline and online social network of each Thai football Premier League supporters. Also, study is about the factors that affected the sport to become the growing business and the communication factors that play the important role in the growth of the sport business. The Thai Premier League communicated with supporters in order to show the identity of each supporter and club in different ways. The expression of the identity was shown through online social network and offline told other people who they were. The study also about the factor that impacted the roles and communication factors that make football become the growing business. The factor that impact to the growth of football to the business, if clubs can action, the sport business would be to higher level and also push Thailand’s football to be effective and equal to other countries.

Keywords: online social network, offline social network, Thai football, supporters

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Authors: Tuğçe Yücel

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A Wireless Sensor Network(WSN) is a collection of sensor nodes which are deployed randomly in an area for surveillance. Efficient utilization of limited battery energy of sensors for increased network lifetime as well as data security are major design objectives for WSN. Moreover secure transmission of data sensed to a base station for further processing. Producing multiple copies of data packets and sending them on different paths is one of the strategies for this purpose, which leads to redundant energy consumption and hence reduced network lifetime. In this work we develop a restricted multi-copy multipath strategy where data move through ‘frequently’ or ‘heavily’ used sensors is copied by the sensor incident to such central nodes and sent on node-disjoint paths. We develop a mixed integer programing(MIP) model and heuristic approach present some preleminary test results.

Keywords: MIP, sensor, telecommunications, WSN

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Authors: Eric Chabrol, Sidonia B.G. Eckle, Renate de Boer, James McCluskey, Jamie Rossjohn, Mirjam H.M. Heemskerk, Stephanie Gras

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αβ and γδ T-cells are disparate T-cell lineages that, via their use of either αβ or γδ T-cell antigen receptors (TCRs) respectively, can respond to distinct antigens. Here we characterise a new population of human T-cells, term δβ T-cells, that express TCRs comprising a TCR-δ variable gene fused to a Joining-α/Constant-α domain, paired with an array of TCR-β chains. We characterised the cellular, functional, biophysical and structural characteristic feature of this new T-cells population that reveal some new insight into TCR diversity. We provide molecular bases of how δβ T-cells can recognise viral peptide presented by Human Leukocyte Antigen (HLA) molecule. Our findings highlight how components from αβ and γδTCR gene loci can recombine to confer antigen specificity thus expanding our understanding of T-cell biology and TCR diversity.

Keywords: new delta-beta TCR, HLA, viral peptide, structural immunology

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Authors: Guangzhuang Yu, Chun-Chu Liu

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With the advent of the Internet era in recent years, social music education has gradually shifted from the original entity education mode to the mode of entity plus network teaching. No matter for school music education, professional music education or social music education, the teaching quality is the most important evaluation index. Regarding the research on teaching quality evaluation, scholars at home and abroad have contributed a lot of research results on the basis of multiple methods and evaluation subjects. However, to our best knowledge the complete evaluation model for the virtual teaching interaction mode of the emerging network music education Application (APP) has not been established. This research firstly found out the basic dimensions that accord with the teaching quality required by the three parties, constructing the quality evaluation index system; and then, on the basis of expounding the connotation of each index, it determined the weight of each index by using method of fuzzy analytic hierarchy process, providing ideas and methods for scientific, objective and comprehensive evaluation of the teaching quality of network education APP.

Keywords: network music education APP, teaching quality evaluation, index and connotation

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Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez

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Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

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Authors: P. Prasanna Murali Krishna, M. V. Subramanyam, K. Satya Prasad

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File sharing in networks are generally achieved using Peer-to-Peer (P2P) applications. Structured P2P approaches are widely used in adhoc networks due to its distributed and scalability features. Efficient mechanisms are required to handle the huge amount of data distributed to all peers. The intrinsic characteristics of P2P system makes for easier content distribution when compared to client-server architecture. All the nodes in a P2P network act as both client and server, thus, distributing data takes lesser time when compared to the client-server method. CHORD protocol is a resource routing based where nodes and data items are structured into a 1- dimensional ring. The structured lookup algorithm of Chord is advantageous for distributed P2P networking applications. Though, structured approach improves lookup performance in a high bandwidth wired network it could contribute to unnecessary overhead in overlay networks leading to degradation of network performance. In this paper, the performance of existing CHORD protocol on Wireless Mesh Network (WMN) when nodes are static and dynamic is investigated.

Keywords: wireless mesh network (WMN), structured P2P networks, peer to peer resource sharing, CHORD Protocol, DHT

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Authors: Amtul Jamil Sami

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Apis mellifera is an insect of immense economic importance lives on rich carbohydrate diet including cellulose, nectar, honey and pollen. The carbohydrate metabolism in A mellifera has not been understood fully, as there are no data available, on the functional expression of cellulase gene. The cellulose hydrolyzing enzyme is required for the digestion of pollen cellulose wall, to release the important nutrients (amino acids, minerals, vitamins etc.) from the pollen. A dissection of Apis genome had revealed that there is one gene present for the expression of endo-beta-1,4-glucanase, for cellulose hydrolysis. In the presented work, functional expression of endo-beta-1,4 glucanase gene is reported. Total soluble proteins of the honey bee were isolated and were tested cellulose hydrolyzing enzyme activity, using carboxy-methyl cellulose, as a substrate. A mellifera proteins were able to hydrolyze carboxy-methyl cellulose, confirming its endo- type mode of action. Endo beta-1,4 glucanase enzyme was only present in the gut tissues, no activity was detected in the salivary glands. The pH optima of the enzyme were in the acidic pH range of 4-5-5-0, indicating its metabolic role in the acidic stomach of A mellifera. The reported enzyme is unique, as endo-beta- 1,4 glucanase was able to generate non reducing sugar, as an end product. The results presented, are supportive to the information that the honey bee is capable of producing its novel endo-beta-1,4 glucanase. Further it could be helpful, in understanding, the carbohydrate metabolism in A mellifera.

Keywords: honey bees, Endo-beta 1, 4- glucanase, Apis mellifera, functional expression

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

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Authors: Sheng Fu, Yinbo Gao, Hao Lin

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In the current mine air compressors monitoring system, there are some difficulties in the installation and maintenance because of the wired connection. To solve the problem, this paper introduces a new air compressors monitoring system based on ZigBee in which the monitoring parameters are transmitted wirelessly. The collecting devices are designed to form a cluster network to collect vibration, temperature, and pressure of air cylinders and other parameters. All these devices are battery-powered. Besides, the monitoring software in PC is developed using MFC. Experiments show that the designed wireless sensor network works well in the site environmental condition and the system is very convenient to be installed since the wireless connection. This monitoring system will have a wide application prospect in the upgrade of the old monitoring system of the air compressors.

Keywords: condition monitoring, wireless sensor network, air compressor, zigbee, data collecting

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Authors: Ali Mohammad Mobaser Azad, Alireza Akhlaghinia

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Blockchain is changing our world and launching a new generation of decentralized networks. Meanwhile, Blockchain-based networks like Ethereum have been created and they will facilitate these processes using tools like smart contracts. The Ethereum has fundamental structures, each of which affects the activity of the nodes. Our purpose in this paper is to review similar research and examine various components to demonstrate the performance of the Ethereum network and to do this, and we used the data published by the Ethereum Foundation in different time spots to examine the number of changes that determine the status of network performance. This will help other researchers understand better Ethereum in different situations.

Keywords: blockchain, ethereum, smart contract, decentralization consensus algorithm

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Authors: Rozeena Baig, R. Rehana Rehman, Rifat Ahmed

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Background: Osteoporosis, characterized by low bone mineral density, poses a global health concern. Diagnosis increases the likelihood of developing osteoporosis, a multifactorial disorder marked by low bone mass, elevating the risk of fractures in the lumbar spine, femoral neck, hip, vertebras, and distal forearm, particularly in postmenopausal women due to bone loss influenced by various pathophysiological factors. Objectives: The aim is to investigate the association of serum cytokine, bone turnover marker, bone mineral density and TLR4 gene polymorphism in pre and post-menopausal women and to find if any of these can be the potential predictor of osteoporosis in postmenopausal women. Material and methods: The study participants consisted of Group A (n=91) healthy pre-menopausal women and Group B (n=102) healthy postmenopausal women having ≥ 5 years’ history of menopause. ELISA was performed for cytokine (TNFα) and bone turnover markers (carboxytelopeptides), respectively. Bone Mineral Density (BMD)was measured through a dual X-ray absorptiometry (DEXA) scan. Toll-like Receptors 4 (TLR4) gene polymorphisms (A896G; Asp299Gly) and (C1196T; Thr399Ile) were investigated by PCR and Sanger sequencing. Results: Statistical analysis reveals a positive correlation of age and BMI with T scores in the premenopausal group, whereas in post-menopausal group found a significant negative correlation between age and T-score at hip (r = - 0.352**), spine (r = - .306**), and femoral neck (r = - 0.344**) and a significant negative correlation of BMI with TNF-α (- 0.316**). No association and significant differences were observed for TLR4 genotype and allele frequencies among studied groups However, both SNPs exhibited significant association with each other. Conclusions: This study concludes that BMI, BMD and TNF-α are the potential predictors of osteoporosis in post-menopausal women. However, CTX and TLR4 gene polymorphism did not appear as potential predictors of bone loss in this study and apparently cannot help in predicting bone loss in post-menopausal women.

Keywords: osteoporosis, post-menopausal, pre-menopausal woemn, genetics mutaiont, TLR4 genepolymorphsum

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Authors: Hanlin Liu, Hua Li, Yintan AI

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Network performance prediction (NPP) is essential for the management and optimization of software-defined networking (SDN) and contributes to improving the quality of service (QoS) in SDN to meet the requirements of users. Although current deep learning-based methods can achieve high effectiveness, they still suffer from some problems, such as difficulty in capturing global information of the network, inefficiency in modeling end-to-end network performance, and inadequate graph feature extraction. To cope with these issues, our proposed Graphormer-based architecture for NPP leverages the powerful graph representation ability of Graphormer to effectively model the graph structure data, and a node-edge transformation algorithm is designed to transfer the feature extraction object from nodes to edges, thereby effectively extracting the end-to-end performance characteristics of the network. Moreover, routing oriented centrality measure coefficient for nodes and edges is proposed respectively to assess their importance and influence within the graph. Based on this coefficient, an enhanced feature extraction method and an advanced centrality encoding strategy are derived to fully extract the structural information of the graph. Experimental results on three public datasets demonstrate that the proposed GraphNPP architecture can achieve state-of-the-art results compared to current NPP methods.

Keywords: software-defined networking, network performance prediction, Graphormer, graph neural network

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Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from traveling vehicles, such as taxis through installed global positioning system (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

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Authors: Swati Srivastava, Mattia Lauriola, Yuval Gilad, Adi Kimchi, Yosef Yarden

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The glucocorticoid receptor (GR) has been proposed to play important, but incompletely understood roles in cancer. Glucocorticoids (GCs) are widely used as co-medication of various carcinomas, due to their ability to reduce the toxicity of chemotherapy. Furthermore, GR antagonism has proven to be a strategy to treat triple negative breast cancer and castration-resistant prostate cancer. These observations suggest differential GR involvement in cancer subtypes. The goal of our study has been to elaborate the current understanding of GR signaling in tumor progression and metastasis. Our study involves two cellular models, non-tumorigenic breast epithelial cells (MCF10A) and Ewing sarcoma cells (CHLA9). In our breast cell model, the results indicated that the GR agonist dexamethasone inhibits EGF-induced mammary cell migration, and this effect was blocked when cells were stimulated with a GR antagonist, namely RU486. Microarray analysis for gene expression revealed that the mechanism underlying inhibition involves dexamenthasone-mediated repression of well-known activators of EGFR signaling, alongside with enhancement of several EGFR’s negative feedback loops. Because GR mainly acts primarily through composite response elements (GREs), or via a tethering mechanism, our next aim has been to find the transcription factors (TFs) which can interact with GR in MCF10A cells.The TF-binding motif overrepresented at the promoter of dexamethasone-regulated genes was predicted by using bioinformatics. To validate the prediction, we performed high-throughput Protein Complementation Assays (PCA). For this, we utilized the Gaussia Luciferase PCA strategy, which enabled analysis of protein-protein interactions between GR and predicted TFs of mammary cells. A library comprising both nuclear receptors (estrogen receptor, mineralocorticoid receptor, GR) and TFs was fused to fragments of GLuc, namely GLuc(1)-X, X-GLuc(1), and X-GLuc(2), where GLuc(1) and GLuc(2) correspond to the N-terminal and C-terminal fragments of the luciferase gene.The resulting library was screened, in human embryonic kidney 293T (HEK293T) cells, for all possible interactions between nuclear receptors and TFs. By screening all of the combinations between TFs and nuclear receptors, we identified several positive interactions, which were strengthened in response to dexamethasone and abolished in response to RU486. Furthermore, the interactions between GR and the candidate TFs were validated by co-immunoprecipitation in MCF10A and in CHLA9 cells. Currently, the roles played by the uncovered interactions are being evaluated in various cellular processes, such as cellular proliferation, migration, and invasion. In conclusion, our assay provides an unbiased network analysis between nuclear receptors and other TFs, which can lead to important insights into transcriptional regulation by nuclear receptors in various diseases, in this case of cancer.

Keywords: epidermal growth factor, glucocorticoid receptor, protein complementation assay, transcription factor

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Authors: Adel Bougamouza, Boualam Remini, Abd El Hadi Ammari, Feteh Sakhraoui

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The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision.

Keywords: artificial neural network, Oued Abiod watershed, feedforward network, generalized regression network, radial basis network, sediment concentration

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez, Mehdi Mrad

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We investigate the large scale of networks in the context of network survivability under attack. We use appropriate techniques to evaluate and the attacker-based- and the defender-based-network survivability. The attacker is unaware of the operated links by the defender. Each attacked link has some pre-specified probability to be disconnected. The defender choice is so that to maximize the chance of successfully sending the flow to the destination node. The attacker however will select the cut-set with the highest chance to be disabled in order to partition the network. Moreover, we extend the problem to the case of selecting the best p paths to operate by the defender and the best k cut-sets to target by the attacker, for arbitrary integers p,k > 1. We investigate some variations of the problem and suggest polynomial-time solutions.

Keywords: defense/attack strategies, large scale, networks, partitioning a network

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Authors: Vishal H. Shukla, Sanjay B. Deshmukh

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Traffic engineering, at its center, is the ability of moving traffic approximately so that traffic from a congested link is moved onto the unused capacity on another link. Traffic Engineering ensures the best possible use of the resources. Now to support traffic engineering in the today’s network, Multiprotocol Label Switching (MPLS) is being used which is very helpful for reliable packets delivery in an ongoing internet services. Here a topology is been implemented on GNS3 to focus on the analysis of the communication take place from one site to other through the ISP. The comparison is made between the IP network & MPLS network based on Bandwidth & Jitter which are one of the performance parameters using JPERF simulator.

Keywords: GNS3, JPERF, MPLS, traffic engineering, VMware

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Authors: Afaneen Anwer, Samara M. Kamil

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Load Forecast required prediction accuracy based on optimal operation and maintenance. A good accuracy is the basis of economic dispatch, unit commitment, and system reliability. A good load forecasting system fulfilled fast speed, automatic bad data detection, and ability to access the system automatically to get the needed data. In this paper, the formulation of the load forecasting is discussed and the solution is obtained by using artificial neural network method. A MATLAB environment has been used to solve the load forecasting schedule of Iraqi super grid network considering the daily load for three years. The obtained results showed a good accuracy in predicting the forecasted load.

Keywords: load forecasting, neural network, back-propagation algorithm, Iraqi power system

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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: Boukari Nassim

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This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Zeta Dooly, Aidan Duane

Abstract:

The collaborative European funded research and development landscape provides prime environmental conditions for multi-disciplinary teams to learn and enhance their knowledge beyond the capability of training and learning within their own organisation cocoons. Whilst the emergence of the academic entrepreneur has changed the focus of educational institutions to that of quasi-businesses, the training and professional development of lecturers and academic staff are often not formalised to the same level as industry. This research focuses on industry and academic collaborative research funded by the European Commission. The impact of research is scalable if an optimum research network is created and managed effectively. This paper investigates network embeddedness, the nature of relationships, links, and nodes within a research network, and the enhancement of the network’s knowledge. The contribution of this paper extends our understanding of establishing and maintaining effective collaborative research networks. The effects of network embeddedness are recognized in the literature as pertinent to innovation and the economy. Network theory literature claims that networks are essential to innovative clusters such as Silicon valley and innovation in high tech industries. This research provides evidence to support the impact collaborative research has on the disparate individuals toward their innovative contributions to their organisations and their own professional development. This study adopts a qualitative approach and uncovers some of the challenges of multi-disciplinary research through case study insights. The contribution of this paper recommends the establishment of scaffolding to accommodate cooperation in research networks, role appointment, and addressing contextual complexities early to avoid problem cultivation. Furthermore, it suggests recommendations in relation to network formation, intra-network challenges in relation to open data, competition, friendships, and competency enhancement. The network capability is enhanced by the adoption of the relevant theories; network theory, open innovation, and social exchange, with the understanding that the network structure has an impact on innovation and social exchange in research networks. The research concludes that there is an opportunity to deepen our understanding of the impact of network reuse and network hoping that provides scaffolding for the network members to enhance and build upon their knowledge using a progressive approach.

Keywords: research networks, competency building, network theory, case study

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Authors: Wanida Suwunniponth

Abstract:

The purpose of this research was to investigate the relationship between the factors of using online social network for tourism destination in case of Bangkok area in Thailand, by extending the use of technology acceptance model (TAM). This study employed by quantitative research and the target population were entrepreneurs and local people in Bangkok who use social network-Facebook concerning tourist destinations in Bangkok. Questionnaire was used to collect data from 300 purposive samples. The multiple regression analysis and path analysis were used to analyze data. The results revealed that most people who used Facebook for promoting tourism destinations in Bangkok perceived ease of use, perceived usefulness, perceived trust in using Facebook and influenced by social normative as well as having positive attitude towards using this application. Addition, the hypothesis results indicate that acceptance of online social network-Facebook was related to the positive attitude towards using of Facebook and related to their intention to use this application for tourism.

Keywords: Facebook, online social network, technology acceptance model, tourism destination

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Authors: Behzad Niknam, Kourosh Shahriar, Hassan Madani

Abstract:

This paper demonstrates the applicability of Hybrid Neural Networks that combine with back propagation networks (BPN) and Genetic Algorithms (GAs) for predicting the flame length of tunnel fire A hybrid neural network model has been developed to predict the flame length of tunnel fire based parameters such as Fire Heat Release rate, air velocity, tunnel width, height and cross section area. The network has been trained with experimental data obtained from experimental work. The hybrid neural network model learned the relationship for predicting the flame length in just 3000 training epochs. After successful learning, the model predicted the flame length.

Keywords: tunnel fire, flame length, ANN, genetic algorithm

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Authors: Fitri Rahmi Fadhilah, Edhyana Sahiratmadja, Ani Melani Maskoen, Ratu Safitri, Supartini Syarif, Herman Susanto

Abstract:

Cervical cancer is the second most common cancer in women after breast cancer. In Indonesia, the incidence of cervical cancer cases is estimated at 25-40 per 100,000 women per year. Human papillomavirus (HPV) infection is a major cause of cervical cancer, and HPV-16 is the most common genotype that infects the cervical tissue. The major late protein L1 may be associated with infectivity and pathogenicity and its variation can be used to classify HPV isolates. The aim of this study was to determine the phylogenetic tree of HPV 16 L1 gene from cervical cancer patient isolates in Bandung. After confirming HPV-16 by Linear Array Genotyping Test, L1 gene was amplified using specific primers and subject for sequencing. Phylogenetic analysis revealed that HPV 16 from Bandung was in the subgroup of Asia and East Asia, showing the close host-agent relationship among the Asian type.

Keywords: L1 HPV 16, cervical cancer, bandung, phylogenetic

Procedia PDF Downloads 493