Search results for: Synthetic gene network

Authors: Naghmeh Moradpoor Sheykhkanloo

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Thousands of organisations store important and confidential information related to them, their customers, and their business partners in databases all across the world. The stored data ranges from less sensitive (e.g. first name, last name, date of birth) to more sensitive data (e.g. password, pin code, and credit card information). Losing data, disclosing confidential information or even changing the value of data are the severe damages that Structured Query Language injection (SQLi) attack can cause on a given database. It is a code injection technique where malicious SQL statements are inserted into a given SQL database by simply using a web browser. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLi attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLi attack categories, and a NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLi attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: Neural Networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection.

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Authors: Yuhong Lu

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Graves’ Disease (GD), an autoimmune health condition caused by the over reactiveness of the thyroid, affects about 1 in 200 people worldwide. GD is not caused by one specific single nucleotide polymorphism (SNP) or gene mutation, but rather determined by multiple factors, each differing from each other. Malfunction of the genes in Human Leukocyte Antigen (HLA) family tend to play a major role in autoimmune diseases, but other genes, such as LOC101929163, have functions that still remain ambiguous. Currently, little studies were done to study GD, resulting in inconclusive results. This study serves not only to introduce background knowledge about GD, but also to organize and pinpoint the major SNPs and genes that are potentially related to the occurrence of GD in humans. Collected from multiple sources from genome-wide association studies (GWAS) Central, the potential SNPs related to the causes of GD are included in this study. This study has located the genes that are related to those SNPs and closely examines a selected sample. Using the data from this study, scientists will then be able to focus on the most expressed genes in GD patients and develop a treatment for GD.

Keywords: CTLA4, Graves’ Disease, HLA, single nucleotide polymorphism, SNP.

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Authors: Muqdad Al-Juboori, Bithin Datta

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Hydraulic structures such as gravity dams are classified as essential structures, and have the vital role in providing strong and safe water resource management. Three major aspects must be considered to achieve an effective design of such a structure: 1) The building cost, 2) safety, and 3) accurate analysis of seepage characteristics. Due to the complexity and non-linearity relationships of the seepage process, many approximation theories have been developed; however, the application of these theories results in noticeable errors. The analytical solution, which includes the difficult conformal mapping procedure, could be applied for a simple and symmetrical problem only. Therefore, the objectives of this paper are to: 1) develop a surrogate model based on numerical simulated data using SEEPW software to approximately simulate seepage process related to a hydraulic structure, 2) develop and solve a linked simulation-optimization model based on the developed surrogate model to describe the seepage occurring under a concrete gravity dam, in order to obtain optimum and safe design at minimum cost. The result shows that the linked simulation-optimization model provides an efficient and optimum design of concrete gravity dams.

Keywords: Artificial neural network, concrete gravity dam, genetic algorithm, seepage analysis.

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Authors: Y. Araki, M. Kim, S. Okayama, K. Ikago, K. Uetani

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We study dynamic instability in high-rise steel moment resisting frames (SMRFs) subjected to synthetic long-period ground motions caused by hypothetical huge subduction earthquakes. Since long duration as well as long dominant periods is a characteristic of long-period ground motions, interstory drifts may enter the negative postyield stiffness range many times when high-rise buildings are subjected to long-period ground motions. Through the case studies of 9 high-rise SMRFs designed in accordance with the Japanese design practice in 1980s, we demonstrate that drifting, or accumulation of interstory drifts in one direction, occurs at the lower stories of the SMRFs, if their natural periods are close to the dominant periods of the long-period ground motions. The drifting led to residual interstory drift ratio over 0.01, or to collapse if the design base shear was small.

Keywords: long-period ground motion, P-Delta effect, high-rise steel moment resisting frame (SMRF), subduction earthquake

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Authors: Charbel Geryes Aoun, Loic Lagadec

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A Sensor Network (SN) is considered as an operation of two phases: (1) the observation/measuring, which means the accumulation of the gathered data at each sensor node; (2) transferring the collected data to some processing center (e.g. Fusion Servers) within the SN. Therefore, an underwater sensor network can be defined as a sensor network deployed underwater that monitors underwater activity. The deployed sensors, such as hydrophones, are responsible for registering underwater activity and transferring it to more advanced components. The process of data exchange between the aforementioned components perfectly defines the Marine Observatory (MO) concept which provides information on ocean state, phenomena and processes. The first step towards the implementation of this concept is defining the environmental constraints and the required tools and components (Marine Cables, Smart Sensors, Data Fusion Server, etc). The logical and physical components that are used in these observatories perform some critical functions such as the localization of underwater moving objects. These functions can be orchestrated with other services (e.g. military or civilian reaction). In this paper, we present an extension to our MO meta-model that is used to generate a design tool (ArchiMO). We propose constraints to be taken into consideration at design time. We illustrate our proposal with an example from the MO domain. Additionally, we generate the corresponding simulation code using our self-developed domain-specific model compiler. On the one hand, this illustrates our approach in relying on Enterprise Architecture (EA) framework that respects: multiple-views, perspectives of stakeholders, and domain specificity. On the other hand, it helps reducing both complexity and time spent in design activity, while preventing from design modeling errors during porting this activity in the MO domain. As conclusion, this work aims to demonstrate that we can improve the design activity of complex system based on the use of MDE technologies and a domain-specific modeling language with the associated tooling. The major improvement is to provide an early validation step via models and simulation approach to consolidate the system design.

Keywords: Smart sensors, data fusion, distributed fusion architecture, sensor networks, domain specific modeling language, enterprise architecture, underwater moving object, localization, marine observatory, NS-3, IMS.

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Authors: Faisal Amin, Abdul Mueed, Xu Jiadong

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This paper describes the development of a fully automated measurement software for antenna radiation pattern measurements in a Compact Antenna Test Range (CATR). The CATR has a frequency range from 2-40 GHz and the measurement hardware includes a Network Analyzer for transmitting and Receiving the microwave signal and a Positioner controller to control the motion of the Styrofoam column. The measurement process includes Calibration of CATR with a Standard Gain Horn (SGH) antenna followed by Gain versus angle measurement of the Antenna under test (AUT). The software is designed to control a variety of microwave transmitter / receiver and two axis Positioner controllers through the standard General Purpose interface bus (GPIB) interface. Addition of new Network Analyzers is supported through a slight modification of hardware control module. Time-domain gating is implemented to remove the unwanted signals and get the isolated response of AUT. The gated response of the AUT is compared with the calibration data in the frequency domain to obtain the desired results. The data acquisition and processing is implemented in Agilent VEE and Matlab. A variety of experimental measurements with SGH antennas were performed to validate the accuracy of software. A comparison of results with existing commercial softwares is presented and the measured results are found to be within .2 dBm.

Keywords: Antenna measurement, calibration, time-domain gating, VNA, Positioner controller

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Authors: Justinian Anatory, Ekael Stephen Manase

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In recent years, the adoption of mobile phones has been exceptionally rapid in many parts of the world, and Tanzania is not exceptional. We are witnessing a number of new mobile network operators being licensed from time to time by Tanzania Communications Regulatory Authority (TCRA). This makes competition in the telecommunications market very stiff. All mobile phone companies are struggling to earn more new customers into their networks. This trend courses a stiff competition. The various measures are being taken by different companies including, lowering tariff, and introducing free short messages within and out of their networks, and free calls during off-peak periods. This paper is aimed at investigating the influence of tariffs on students’ mobile customers in selecting their mobile network operators. About seventy seven students from high learning institutions in Dodoma Municipality, Tanzania, participated in responding to the prepared questionnaires. The sought information was aimed at determining if tariffs influenced students into selection of their current mobile operators. The results indicate that tariffs were the major driving factor in selection of mobile operators. However, female mobile customers were found to be more easily attracted into subscribing to a mobile operator due to low tariffs, a bigger number of free short messages or discounted call charges than their fellow male customers.

Keywords: Consumer Buying, mobile operators, tariff.

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Authors: Feng Chia Hsieh, Sheng Kuo Hsieh, Tzyy Rong Jinn

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Characterization and evaluation of the activity of Vespa basalis DPP-IV, which expressed in Spodoptera frugiperda 21 cells. The expression of rDPP-IV was confirmed by SDS–PAGE, Western blot analyses, LC-MS/MS and measurement of its peptidase specificity. One-step purification by Ni-NTA affinity chromatography and the total amount of rDPP-IV recovered was approximately 6.4mg per liter from infected culture medium; an equivalent amount would be produced by 1x109 infected Sf21 insect cells. Through the affinity purification led to highly stable rDPP-IV enzyme was recovered and with significant peptidase activity. The rDPP-IV exhibited classical Michaelis–Menten kinetics, with kcat/Km in the range of 10-500 mM-1×S-1 for the five synthetic substrates and optimum substrate is Ala-Pro-pNA. As expected in inhibition assay, the enzymatic activity of rDPP-IV was significantly reduced by 80 or 60% in the presence of sitagliptin (a DPP-IV inhibitor) or PMSF (a serine protease inhibitor), but was not apparently affected by iodoacetamide (a cysteine protease inhibitor).

Keywords: Dipeptidyl-Peptidase IV, Phenylmethylsulfonyl fluoride; Serine protease, Sitagliptin, Vespa basalis

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Authors: M. F. Miah, H. Ali, E. Zannath, T. M. Shuvra, M. N. Naser, M. K. Ahmed

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In this study, breeding biology and induced breeding of freshwater mud eel, Monopterus cuchia was observed during the experimental period from February to June, 2013. Breeding biology of freshwater mud eel, Monopterus cuchia was considered in terms of gonadosomatic index, length-weight relationship of gonad, ova diameter and fecundity. The ova diameter was recorded from 0.3 mm to 4.30 mm and the individual fecundity was recorded from 155 to 1495 while relative fecundity was found from 2.64 to 12.45. The fecundity related to body weight and length of fish was also discussed. A peak of GSI was observed 2.14±0.2 in male and 5.1 ±1.09 in female. Induced breeding of freshwater mud eel, Monopterus cuchia was also practiced with different doses of different inducing agents like pituitary gland (PG), human chorionic gonadotropin (HCG), Gonadotropin releasing hormone (GnRH) and Ovuline-a synthetic hormone in different environmental conditions. However, it was observed that the artificial breeding of freshwater mud eel, Monopterus cuchia was not yet succeeded through inducing agents in captive conditions, rather the inducing agent showed negative impacts on fecundity and ovarian tissues. It was seen that mature eggs in the oviduct were reduced, absorbed and some eggs were found in spoiled condition.

Keywords: Breeding biology, induced breeding, Monopterus cuchia.

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Authors: Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah

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Fuel cells have become one of the major areas of research in the academia and the industry. The goal of most fish farmers is to maximize production and profits while holding labor and management efforts to the minimum. Risk of fish kills, disease outbreaks, poor water quality in most pond culture operations, aeration offers the most immediate and practical solution to water quality problems encountered at higher stocking and feeding rates. Many units of aeration system are electrical units so using a continuous, high reliability, affordable, and environmentally friendly power sources is necessary. Aeration of water by using PEM fuel cell power is not only a new application of the renewable energy, but also, it provides an affordable method to promote biodiversity in stagnant ponds and lakes. This paper presents a new design and control of PEM fuel cell powered a diffused air aeration system for a shrimp farm in Mersa Matruh in Egypt. Also Artificial intelligence (AI) techniques control is used to control the fuel cell output power by control input gases flow rate. Moreover the mathematical modeling and simulation of PEM fuel cell is introduced. A comparison study is applied between the performance of fuzzy logic control (FLC) and neural network control (NNC). The results show the effectiveness of NNC over FLC.

Keywords: PEM fuel cell, Diffused aeration system, Artificialintelligence (AI) techniques, neural network control, fuzzy logiccontrol

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Authors: Wann-Ming Wey

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The link between urban planning and design principles and the built environment of an urban renewal area is of interest to the field of urban studies. During the past decade, there has also been increasing interest in urban planning and design; this interest is motivated by the possibility that design policies associated with the built environment can be used to control, manage, and shape individual activity and behavior. However, direct assessments and design techniques of the links between how urban planning design policies influence individuals are still rare in the field. Recent research efforts in urban design have focused on the idea that land use and design policies can be used to increase the quality of design projects for an urban renewal area-s built environment. The development of appropriate design techniques for the built environment is an essential element of this research. Quality function deployment (QFD) is a powerful tool for improving alternative urban design and quality for urban renewal areas, and for procuring a citizen-driven quality system. In this research, we propose an integrated framework based on QFD and an Analytic Network Process (ANP) approach to determine the Alternative Technical Requirements (ATRs) to be considered in designing an urban renewal planning and design alternative. We also identify the research designs and methodologies that can be used to evaluate the performance of urban built environment projects. An application in an urban renewal built environment planning and design project evaluation is presented to illustrate the proposed framework.

Keywords: Analytic Network Process, Built Environment, Quality Function Deployment, Urban Design, Urban Renewal.

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Lèvy flight, situation awareness, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other.

As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO.

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Authors: Aisha Hassan Abdalla Hashim, Fauzana Ridzuan, Nazreen Rusli

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Mobile IPv6 (MIPv6) describes how mobile node can change its point of attachment from one access router to another. As a demand for wireless mobile devices increases, many enhancements for macro-mobility (inter-domain) protocols have been proposed, designed and implemented in Mobile IPv6. Hierarchical Mobile IPv6 (HMIPv6) is one of them that is designed to reduce the amount of signaling required and to improve handover speed for mobile connections. This is achieved by introducing a new network entity called Mobility Anchor Point (MAP). This report presents a comparative study of the Hierarchical Mobility IPv6 and Mobile IPv6 protocols and we have narrowed down the scope to micro-mobility (intra-domain). The architecture and operation of each protocol is studied and they are evaluated based on the Quality of Service (QoS) parameter; handover latency. The simulation was carried out by using the Network Simulator-2. The outcome from this simulation has been discussed. From the results, it shows that, HMIPv6 performs best under intra-domain mobility compared to MIPv6. The MIPv6 suffers large handover latency. As enhancement we proposed to HMIPv6 to locate the MAP to be in the middle of the domain with respect to all Access Routers. That gives approximately same distance between MAP and Mobile Node (MN) regardless of the new location of MN, and possible shorter distance. This will reduce the delay since the distance is shorter. As a future work performance analysis is to be carried for the proposed HMIPv6 and compared to HMIPv6.

Keywords: Intra-domain mobility, HMIPv6, Handover Latency, proposed HMIPv6.

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Authors: S. Fallah, M. Seifi, M. Firoozrai, T. Godarzi, M. Jafarzadeh, L. H. Ghohari

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The ε4 allele of the ε2, ε3 and ε4 protein isoform polymorphism in the gene encoding apolipoprotein E (Apo E) has previously been associated with increased cardiac artery disease (CAD); therefore to investigate the significance of this polymorphism in pathogenesis of CAD in Iranian patients with stenosis and control subjects. To investigate the association between  Apo E polymorphism and coronary artery disease we performed a comparative case control study of the frequency of Apo E  polymorphism in One hundred CAD patients with stenosis who underwent coronary angiography (>50% stenosis) and 100 control subjects (<10% stenosis). The Apo E alleles and genotypes were determined by polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP). We observed an association between the Apo E polymorphism and CAD in this study. These data suggest that the Apo ε4 and ε2 alleles increase the risk for CAD in Iranian population (χ2 =4.26, p= 0.05, OR=2 and χ2 =0.38, p=0.53, OR=1.2). These results suggest that ε4 and ε2 alleles are risk factors for stenosis.

Keywords: Arterial blood vessels, atherosclerosis, cholesterol.

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Authors: H. Sadi-Guettaf, F. Hadj Bekkouche

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Dexamethasone (Dex) is a synthetic glucocorticoid that is used in therapy. However prolonged treatments with high doses are often required. This causes side effects that interfere with the activity of several endocrine systems, including the gonadotropic axis. The aim of our study is to determine the effect of Dex on testicular function in prepubertal Wistar rats. Newborn Wistar rats are submitted to intraperitoneal injection of Dex (1μg of Dex dissolved in NaCl 0.9% / 5g bw) for 20 days and then sacrificed at the age of 40days. A control group received NaCl 0.9%. The rat is weighed daily. The plasmatic levels of testosterone, LH and FSH were measured by radioimmunoassay. A histomorphometric study was performed on sections of testis. Treated groups showed a significant decrease in body weight (p < 0.05), testis weight (p < 0.05) and plasma levels of testosterone (p < 0.05), of LH (P < .05) and FSH (p> 0.05). There is a reduction of seminiferous tubules average diameter and also of the seminiferous epithelium thickness with an increasing of lumen tubular. The diameter of the Leydig cells and Sertoli cell nucleus is also significantly reduced. Spermatogenesis is blocked at the stage round spermatid unlike witnesses or elongated spermatid stage is found. These results suggest that Dex administered during neonatal life influences testicular activity in the long term.

Keywords: Dexamethasone, FSH, LH, rat, testis, testosterone.

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Authors: Yogita, Durga Toshniwal

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Outlier detection in streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose an unsupervised outlier detection scheme for streaming data. This scheme is based on clustering as clustering is an unsupervised data mining task and it does not require labeled data, both density based and partitioning clustering are combined for outlier detection. In this scheme partitioning clustering is also used to assign weights to attributes depending upon their respective relevance and weights are adaptive. Weighted attributes are helpful to reduce or remove the effect of noisy attributes. Keeping in view the challenges of streaming data, the proposed scheme is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approach (CORM) in terms of outlier detection rate, false alarm rate, and increasing percentages of outliers.

Keywords: Concept Evolution, Irrelevant Attributes, Streaming Data, Unsupervised Outlier Detection.

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Authors: Seema Chopra, R. Mitra, Vijay Kumar

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A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.

Keywords: Fuzzy control, neuro-fuzzy techniques, fuzzy subtractive clustering, extraction of rules, and optimization of membership functions.

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Authors: Li-Yeh Chuang, Yu-Jen Hou, Jr., Cheng-Hong Yang

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Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.

Keywords: Genetic Algorithm (GA), Genotype, Single nucleotide polymorphism (SNP), tag SNPs.

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Authors: Shih-Bin Wang, Ping Yuan, Syu-Fang Liu, Ming-Jun Kuo

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By the application of an improved back-propagation neural network (BPNN), a model of current densities for a solid oxide fuel cell (SOFC) with 10 layers is established in this study. To build the learning data of BPNN, Taguchi orthogonal array is applied to arrange the conditions of operating parameters, which totally 7 factors act as the inputs of BPNN. Also, the average current densities achieved by numerical method acts as the outputs of BPNN. Comparing with the direct solution, the learning errors for all learning data are smaller than 0.117%, and the predicting errors for 27 forecasting cases are less than 0.231%. The results show that the presented model effectively builds a mathematical algorithm to predict performance of a SOFC stack immediately in real time. Also, the calculating algorithms are applied to proceed with the optimization of the average current density for a SOFC stack. The operating performance window of a SOFC stack is found to be between 41137.11 and 53907.89. Furthermore, an inverse predicting model of operating parameters of a SOFC stack is developed here by the calculating algorithms of the improved BPNN, which is proved to effectively predict operating parameters to achieve a desired performance output of a SOFC stack.

Keywords: a SOFC stack, BPNN, inverse predicting model of operating parameters, optimization of the average current density

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Authors: Abdenour Bounsiar, Edith Grall, Pierre Beauseroy

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In this paper we are interested in classification problems with a performance constraint on error probability. In such problems if the constraint cannot be satisfied, then a rejection option is introduced. For binary labelled classification, a number of SVM based methods with rejection option have been proposed over the past few years. All of these methods use two thresholds on the SVM output. However, in previous works, we have shown on synthetic data that using thresholds on the output of the optimal SVM may lead to poor results for classification tasks with performance constraint. In this paper a new method for supervised classification with rejection option is proposed. It consists in two different classifiers jointly optimized to minimize the rejection probability subject to a given constraint on error rate. This method uses a new kernel based linear learning machine that we have recently presented. This learning machine is characterized by its simplicity and high training speed which makes the simultaneous optimization of the two classifiers computationally reasonable. The proposed classification method with rejection option is compared to a SVM based rejection method proposed in recent literature. Experiments show the superiority of the proposed method.

Keywords: rejection, Chow's rule, error-reject tradeoff, SupportVector Machine.

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Authors: M. H. Ahmad, A. A. A. Jamil, H. Ahmad, M. A. M. Piah, A. Darus, Y. Z. Arief, N. Bashir

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The use of synthetic retardants in polymeric insulated cables is not uncommon in the high voltage engineering to study electrical treeing phenomenon. However few studies on organic materials for the same investigation have been carried. .This paper describes the study on the effects of Oil Palm Empty Fruit Bunch (OPEFB) microfiller on the tree initiation and propagation in silicone rubber with different weight percentages (wt %) of filler to insulation bulk material. The weight percentages used were 0 wt % and 1 wt % respectively. It was found that the OPEFB retards the propagation of the electrical treeing development. For tree inception study, the addition of 1(wt %) OPEFB has increase the tree inception voltage of silicone rubber. So, OPEFB is a potential retardant to the initiation and growth of electrical treeing occurring in polymeric materials for high voltage application. However more studies on the effects of physical and electrical properties of OPEFB as a tree retardant material are required.

Keywords: Oil palm empty fruit bunch, electrical tree, siliconerubber, fillers.

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Authors: Carmen Grigoraş, Victor Grigoraş, Daniela Boişteanu

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The standard investigational method for obstructive sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG), which consists of a simultaneous, usually overnight recording of multiple electro-physiological signals related to sleep and wakefulness. This is an expensive, encumbering and not a readily repeated protocol, and therefore there is need for simpler and easily implemented screening and detection techniques. Identification of apnea/hypopnea events in the screening recordings is the key factor for the diagnosis of OSAS. The analysis of a solely single-lead electrocardiographic (ECG) signal for OSAS diagnosis, which may be done with portable devices, at patient-s home, is the challenge of the last years. A novel artificial neural network (ANN) based approach for feature extraction and automatic identification of respiratory events in ECG signals is presented in this paper. A nonlinear principal component analysis (NLPCA) method was considered for feature extraction and support vector machine for classification/recognition. An alternative representation of the respiratory events by means of Kohonen type neural network is discussed. Our prospective study was based on OSAS patients of the Clinical Hospital of Pneumology from Iaşi, Romania, males and females, as well as on non-OSAS investigated human subjects. Our computed analysis includes a learning phase based on cross signal PSG annotation.

Keywords: Artificial neural networks, feature extraction, obstructive sleep apnea syndrome, pattern recognition, signalprocessing.

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Authors: Kritsada Khongnomnan, Wittaya Poomipak, Yong Poovorawan, Sunchai Payungporn

Abstract:

In this study, three subtypes of influenza A viruses (pH1N1, H5N1 and H3N2) which naturally infected human were analyzed by bioinformatic approaches to find candidate human cellular miRNAs targeting viral genomes. There were 76 miRNAs targeting influenza A viruses. Among these candidates, 70 miRNAs were subtypes specifically targeting each subtype of influenza A virus including 21 miRNAs targeted subtype H1N1, 27 miRNAs targeted subtype H5N1 and 22 miRNAs targeted subtype H3N2. The remaining 6 miRNAs target on multiple subtypes of influenza A viruses. Uniquely, hsa-miR-3145 is the only one candidate miRNA targeting PB1 gene of all three subtypes. Obviously, most of the candidate miRNAs are targeting on polymerase complex genes (PB2, PB1 and PA) of influenza A viruses. This study predicted potential human miRNAs targeting on different subtypes of influenza A viruses which might be useful for inhibition of viral replication and for better understanding of the interaction between virus and host cell.

Keywords: Human miRNAs, Influenza A viruses, H1N1, H5N1, H3N2

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Authors: Zeinep A. Berkimbayeva, Almagul T. Mansharipova, Elmira M. Khussainova, Leyla B. Djansugurova

Abstract:

It is believed that DNA damaging toxic metabolites contributes to the development of different pathological conditions. To prevent harmful influence of toxic agents, cells developed number of protecting mechanisms, such as enzymatic reaction of detoxification of reactive metabolites and repair of DNA damage. The aim of the study was to examine the association between polymorphism of GSTT1/GSTM1 and XRCC1/3 genes and coronary artery disease (CAD) incidence. To examine a polymorphism of these genes in CAD susceptibility in patients and controls, PCR based genotyping assay was performed. For GST genes, frequency of GSTM1 null genotype among CAD affected group was significantly increased than in control group (P<0.001). Frequencies of the GSTT1 null and positive alleles are almost equal in both groups (P>0.1). We found that neither XRCC1 Arg399Gln nor XRCC3 Thr241Met were associated with CAD risk. Obtained data suggests that GSTM1 null genotype carriers are more susceptible to CAD development.

Keywords: Cardiovascular disease, DNA reparation, gene polymorphism, risk factors, xenobiotic detoxification.

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Authors: Indiramma M., K. R. Anandakumar

Abstract:

Trust management and Reputation models are becoming integral part of Internet based applications such as CSCW, E-commerce and Grid Computing. Also the trust dimension is a significant social structure and key to social relations within a collaborative community. Collaborative Decision Making (CDM) is a difficult task in the context of distributed environment (information across different geographical locations) and multidisciplinary decisions are involved such as Virtual Organization (VO). To aid team decision making in VO, Decision Support System and social network analysis approaches are integrated. In such situations social learning helps an organization in terms of relationship, team formation, partner selection etc. In this paper we focus on trust learning. Trust learning is an important activity in terms of information exchange, negotiation, collaboration and trust assessment for cooperation among virtual team members. In this paper we have proposed a reinforcement learning which enhances the trust decision making capability of interacting agents during collaboration in problem solving activity. Trust computational model with learning that we present is adapted for best alternate selection of new project in the organization. We verify our model in a multi-agent simulation where the agents in the community learn to identify trustworthy members, inconsistent behavior and conflicting behavior of agents.

Keywords: Collaborative Decision making, Trust, Multi Agent System (MAS), Bayesian Network, Reinforcement Learning.

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Authors: N. B. Dhayanithi, T. T. Ajith Kumar, T. Balasubramanian

Abstract:

Present study summarizes the control of Vibrio alginolyticus infection in hatchery reared Clownfish, Amphiprion sebae with the extract of the mangrove plant, Avicennia marina. Fishes with visible symptoms of hemorrhagic spots were chosen and the genomic DNA of the causative bacterium was isolated and sequenced based on 16S rDNA gene. The in vitro assay revealed that a fraction of A. marina leaf extract elucidated with ethyl acetate: methanol (6:4) showed a high activity (28 mm) at 125 μg/ml concentrations. About 4 % of the fraction fed along with live V. alginolyticus was significantly decreased the cumulative mortality (P<0.05) in the experimental groups than the control group. The responsible fraction was investigated by gas chromatography - mass spectroscopy and found the presence of active compounds. This is the first research in India to control vibriosis infection in marine ornamental fish with mangrove leaf extract.

Keywords: Amphiprion seabe, Avicennia marina, Gas Chromatography - Mass Spectroscopy, Vibrio alginolyticus

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Authors: H. Sakamoto, Y. Nakagawara, S. Oguri

Abstract:

Drought stress is a critical environmental factor that adversely affects crop productivity and quality. Because of their immobile nature, plants have evolved mechanisms to sense and respond to drought stress. We identified a novel locus of Arabidopsis, designated DRA1 (drought responsive ankyrin1), whose disruption leads to increased drought-stress tolerance. DRA1 encodes a transmembrane protein with an ankyrin-repeat motif that has been implicated in diverse cellular processes such as signal transduction. RT-PCR analysis revealed that there were at least two splicing variants of DRA1 transcripts in wild-type plants. In response to drought stress, the levels of DRA1 transcripts retaining second and third introns were increased, whereas these introns were removed under unstressed conditions. These results suggest that DRA1 protein may negatively regulate plant drought tolerance and that the expression of DRA1is regulated in response to drought stress by alternative splicing.

Keywords: Alternative splicing, ankyrin repeat, Arabidopsis, drought tolerance.

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Authors: L. Wang, Y. Chen, T. Wu, S. Hu

Abstract:

In order to enhance the security of critical financial infrastructure, this study carries out a transformation of the architecture of a financial trading terminal to a zero trust architecture (ZTA), constructs an active defense system for the cybersecurity, improves the security level of trading services in the Internet environment, enhances the ability to prevent network attacks and unknown risks, and reduces the industry and security risks brought about by cybersecurity risks. This study introduces Software Defined Perimeter (SDP) technology of ZTA, adapts and applies it to a financial trading terminal to achieve security optimization and fine-grained business grading control. The upgraded architecture of the trading terminal moves security protection forward to the user access layer, replaces VPN to optimize remote access and significantly improves the security protection capability of Internet transactions. The study achieves: 1. deep integration with the access control architecture of the transaction system; 2. no impact on the performance of terminals and gateways, and no perception of application system upgrades; 3. customized checklist and policy configuration; 4. introduction of industry-leading security technology such as single-packet authorization (SPA) and secondary authentication. This study carries out a successful application of ZTA in the field of financial trading, and provides transformation ideas for other similar systems while improving the security level of financial transaction services in the Internet environment.

Keywords: Zero trust, trading terminal, architecture, network security, cybersecurity.

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Authors: H. Sakamoto, Y. Nakagawara, S. Oguri

Abstract:

Drought stress is a critical environmental factor that adversely affects crop productivity and quality. Because of their immobile nature, plants have evolved mechanisms to sense and respond to drought stress. We identified a novel locus of Arabidopsis, designated DRA1 (drought responsive ankyrin1), whose disruption leads to increased drought-stress tolerance. DRA1 encodes a transmembrane protein with an ankyrin-repeat motif that has been implicated in diverse cellular processes such as signal transduction. RT-PCR analysis revealed that there were at least two splicing variants of DRA1 transcripts in wild-type plants. In response to drought stress, the levels of DRA1 transcripts retaining second and third introns were increased, whereas these introns were removed under unstressed conditions. These results suggest that DRA1 protein may negatively regulate plant drought tolerance and that the expression of DRA1is regulated in response to drought stress by alternative splicing.

Keywords: Alternative splicing, ankyrin repeat, Arabidopsis, drought tolerance.

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