Search results for: fuzzy genetic network programming

Authors: Z. Kourdi, B. Bouazza, M. Khaouani, A. Guen-Bouazza, Z. Djennati, A. Boursali

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We present a simulation of a HEMT (high electron mobility transistor) structure with and without a field plate. We extract the device characteristics through the analysis of DC, AC and high frequency regimes, as shown in this paper. This work demonstrates the optimal device with a gate length of 15 nm, InAlN/GaN heterostructure and field plate structure, making it superior to modern HEMTs when compared with otherwise equivalent devices. This improves the ability to bear the burden of the current density passes in the channel. We have demonstrated an excellent current density, as high as 2.05 A/mm, a peak extrinsic transconductance of 590 mS/mm at VDS=2 V, and cutting frequency cutoffs of 638 GHz in the first HEMT and 463 GHz for Field plate HEMT., maximum frequency of 1.7 THz, maximum efficiency of 73%, maximum breakdown voltage of 400 V, DIBL=33.52 mV/V and an ON/OFF current density ratio higher than 1 x 1010. These values were determined through the simulation by deriving genetic and Monte Carlo algorithms that optimize the design and the future of this technology.

Keywords: HEMT, Silvaco, field plate, genetic algorithm, quantum

Procedia PDF Downloads 478

Authors: Parsa Sheikhzadeh

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Polar bodies are cells that form by oogenesis in meiosis which differentiate and develop from oocytes. Although in many animals, these cells often die following meiotic maturation of the oocyte. Oocyte activation is during mammalian fertilization, sperm is fused with the oocyte's membrane, triggering the resumption of meiosis from the metaphase II arrest, the extrusion of the second polar body, and the exocytosis of cortical granules. The origin recognition complex proteins 4 (ORC4) forms a cage around the set of chromosomes that will be extruded during polar body formation before it binds to the chromatin shortly before zygotic DNA replication. One unique feature of the female gamete is that the polar bodies can provide beneficial information about the genetic background of the oocyte without potentially destroying it. Testing at the polar body (PB) stage was the least accurate, mainly due to the high incidence of post-zygotic events. On the other hand, the results from PB1-MII oocyte pair validated that PB1 contains nearly the same methylome (average Pearson correlation is 0.92) with sibling MII oocyte. In this article, we comprehensively examine the role of polar bodies in female human gametes.

Keywords: polar bodies, ORC4, oocyte, genetic, methylome, gamete, female

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Authors: Amjad Khan

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The security of Vehicular ad hoc networking is of great importance as it involves serious life threats. Thus to provide secure communication amongst Vehicles on road, the conventional security system is not enough. It is necessary to prevent the network resources from wastage and give them protection against malicious nodes so that to ensure the data bandwidth availability to the legitimate nodes of the network. This work is related to provide a non conventional security system by introducing some constraints to minimize the DoS (Denial of services) especially data and bandwidth. The data packets received by a node in the network will pass through a number of tests and if any of the test fails, the node will drop those data packets and will not forward it anymore. Also if a node claims to be the nearest node for forwarding emergency messages then the sender can effectively identify the true or false status of the claim by using these constraints. Consequently the DoS(Denial of Services) attack is minimized by the instant availability of data without wasting the network resources.

Keywords: black hole attack, grey hole attack, intransient traffic tempering, networking

Procedia PDF Downloads 288

Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Minsu Kim, Hae-Chang Cho, Jae Hoon Chung, Inwook Heo, Kang Su Kim

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For safe design of the UHPC flexural members, accurate estimations of their shear strengths are very important. However, since the shear strengths are significantly affected by various factors such as tensile strength of concrete, shear span to depth ratio, volume ratio of steel fiber, and steel fiber factor, the accurate estimations of their shear strengths are very challenging. In this study, therefore, the Adaptive Neuro-Fuzzy System (ANFIS), which has been widely used to solve many complex problems in engineering fields, was introduced to estimate the shear strengths of UHPC flexural members. A total of 32 experimental results has been collected from previous studies for training of the ANFIS algorithm, and the well-trained ANFIS algorithm provided good estimations on the shear strengths of the UHPC test specimens. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2016R1A2B2010277).

Keywords: ultra-high-performance concrete, ANFIS, shear strength, flexural member

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Authors: Ayesha Imtiaz, Darlina Md. Naim

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Family Nemipteridae is among the most abundantly distributed family in Malaysian fish markets due to its high contribution to landing sites of Malaysia. Using an advanced molecular approach that used two mitochondrial (Cytochrome oxidase c I and Cytochrome oxidase b) and one nuclear gene (Recombination activating gene, RAGI) to expose cryptic diversity and phylogenetic relationships among commercially important species of family Nemipteridae. Our research covered all genera (including 31 species out total 45 species) of family Nemipteridae, distributed in Malaysia. We also found certain type of geographical barriers in the South China sea that reduces dispersal and stops a few species to intermix. Northside of the South China Sea (near Vietnam) does not allow genetic diversity to mix with the Southern side of the South China sea (Sarawak) and reduces dispersal. Straits of Malacca reduce the intermixing genetic diversity of South China Sea and the Indian Ocean.

Keywords: Nemipteridae, RAG I, south east Asia, Malaysia

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Authors: Satya S. N. Narayana, P. Parthiban, T. Niranjan, N. Kannan

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Enterprises adopt methodologies to increase their business performance and to stay competent in the volatile global market. Lean manufacturing is one such manufacturing paradigm which focuses on reduction of cost by elimination of wastes or non-value added activities. With increased awareness about social responsibility and the necessary to meet the terms of the environmental policy, green manufacturing is becoming increasingly important for industries. Large plants have more resources, have started implementing lean and green practices and they are getting good results. Small and medium scale enterprises (SMEs) are facing problems in implementing lean and green concept. This paper aims to identify the key issues for implementation of lean and green concept in Indian SMEs. The key factors identified based on literature review and expert opinions are grouped into different levels by Modified Interpretive Structural Modeling (MISM) to explore the importance among the factors to implement lean and green manufacturing. Finally, Fuzzy Analytic Network Process (FANP) method has been used to determine the extent to which the main principles of lean and green manufacturing have been carried out in the six Indian medium scale manufacturing industries.

Keywords: lean manufacturing, green manufacturing, MISM, FANP

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Authors: Jennifer A. Kowalkowski

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Production agriculture (farming) is a physically, emotionally, and cognitively stressful occupation, where workers have little control over the stressors that impact both their work and their lives. In an occupation already rife with hazards, these occupational-related stressors have been shown to increase farm workers’ risks for illness, injury, disability, and death associated with their work. Despite efforts to mitigate the negative health effects from occupational-related stress (ORS) and to promote health and well-being (HWB) among farmers in the US, marked improvements have not been attained. Social support accessed through social networks has been shown to buffer against the negative health effects from stress, yet no studies have directly examined these relationships among farmers. The purpose of this study was to use social network analysis to explore the social networks of farm owner-operators and the social supports available to them for mitigating the negative health effects of ORS. A convenience sample of 71 farm owner-operators from a Midwestern County in the US completed and returned a mailed survey (55.5% response rate) that solicited information about their social networks related to ORS. Farmers reported an average of 2.4 individuals in their personal networks and higher levels of comfort discussing ORS with female network members. Farmers also identified few connections (3.4% density) and indicated low comfort with members of affiliation networks specific to ORS. Findings from this study highlighted that farmers accessed different social networks and resources for their personal HWB than for issues related to occupational(farm-related) health and safety. In addition, farmers’ social networks for personal HWB were smaller, with different relational characteristics than reported in studies of farmers’ social networks related to occupational health and safety. Collectively, these findings suggest that farmers conceptualize personal HWB differently than farm health and safety. Therefore, the same research approaches and targets that guide occupational health and safety research may not be appropriate for personal HWB for farmers. Interventions and programming targeting ORS and HWB have largely been offered through the same platforms or mechanisms as occupational health and safety programs. This may be attributed to the significant overlap between the farm as a family business and place of residence, or that ORS stems from farm-related issues. However, these assumptions translated to health research of farmers and farm families from the occupational health and safety literature have not been directly studied or challenged. Thismay explain why past interventions have not been effective at improving health outcomes for farmers and farm families. A close examination of findings from this study raises important questions for researchers who study agricultural health. Findings from this study have significant implications for future research agendas focused on addressing ORS, HWB, and health disparities for farmersand farm families.

Keywords: agricultural health, occupational-related stress, social networks, well-being

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Authors: Siti Indati Mustapa, Hussain Ali Bekhet

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Transportation sector represents more than 40% of total energy consumption in Malaysia. This sector is a major user of fossils based fuels, and it is increasingly being highlighted as the sector which contributes least to CO2 emission reduction targets. Considering this fact, this paper attempts to investigate the problem of reducing CO2 emission using linear programming approach. An optimization model which is used to investigate the optimal level of CO2 emission reduction in the road transport sector is presented. In this paper, scenarios have been used to demonstrate the emission reduction model: (1) utilising alternative fuel scenario, (2) improving fuel efficiency scenario, (3) removing fuel subsidy scenario, (4) reducing demand travel, (5) optimal scenario. This study finds that fuel balancing can contribute to the reduction of the amount of CO2 emission by up to 3%. Beyond 3% emission reductions, more stringent measures that include fuel switching, fuel efficiency improvement, demand travel reduction and combination of mitigation measures have to be employed. The model revealed that the CO2 emission reduction in the road transportation can be reduced by 38.3% in the optimal scenario.

Keywords: CO2 emission, fuel consumption, optimization, linear programming, transportation sector, Malaysia

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Authors: Michael N. O'Sullivan, Con Sheahan

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Researchers have spent decades developing tools and techniques to aid teams in the new product development (NPD) process. Despite this, it is evident that there is a huge gap between their academic prevalence and their industry adoption. For the fuzzy front-end, in particular, there is a wide range of tools to choose from, including the Kano Model, the House of Quality, and many others. In fact, there are so many tools that it can often be difficult for teams to know which ones to use and how they interact with one another. Moreover, while the benefits of using these tools are obvious to industrialists, they are rarely used as they carry a learning curve that is too steep and they become too complex to manage over time. In essence, it is commonly believed that they are simply not worth the effort required to learn and use them. This research explores a streamlined process for the fuzzy front-end, assembling the most effective tools and making them accessible to everyone. The process was developed iteratively over the course of 3 years, following over 80 final year NPD teams from engineering, design, technology, and construction as they carried a product from concept through to production specification. Questionnaires, focus groups, and observations were used to understand the usability issues with the tools involved, and a human-centred design approach was adopted to produce a solution to these issues. The solution takes the form of physical toolkit, similar to a board game, which allows the team to play through an example of a new product development in order to understand the process and the tools, before using it for their own product development efforts. A complimentary website is used to enhance the physical toolkit, and it provides more examples of the tools being used, as well as deeper discussions on each of the topics, allowing teams to adapt the process to their skills, preferences and product type. Teams found the solution very useful and intuitive and experienced significantly less confusion and mistakes with the process than teams who did not use it. Those with a design background found it especially useful for the engineering principles like Quality Function Deployment, while those with an engineering or technology background found it especially useful for design and customer requirements acquisition principles, like Voice of the Customer. Products developed using the toolkit are added to the website as more examples of how it can be used, creating a loop which helps future teams understand how the toolkit can be adapted to their project, whether it be a small consumer product or a large B2B service. The toolkit unlocks the potential of these beneficial tools to those in industry, both for large, experienced teams and for inexperienced start-ups. It allows users to assess the market potential of their product concept faster and more effectively, arriving at the product design stage with technical requirements prioritized according to their customers’ needs and wants.

Keywords: new product development, fuzzy front-end, usability, Kano model, quality function deployment, voice of customer

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Authors: Anchana Sooksomchitra

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This study aims to explore and compare the current condition of community radio stations in Phutthamonthon district, Nakhon Pathom province, Thailand, as well as the challenges they are facing. Qualitative research tools including in-depth interviews; documentary analysis; focus group interviews; and observation, are used to examine the content, programming, and management structure of three community radio stations currently in operation within the district. Research findings indicate that the management and operational approaches adopted by the two non-profit stations included in the study, Salaya Pattana and Voice of Dhamma, are more structured and effective than that of the for-profit Tune Radio. Salaya Pattana – backed by the Faculty of Engineering, Mahidol University, and the charity-funded Voice of Dhamma, are comparatively free from political and commercial influence, and able to provide more relevant and consistent community-oriented content to meet the real demand of the audience. Tune Radio, on the other hand, has to rely solely on financial support from political factions and business groups, which heavily influence its content.

Keywords: radio broadcasting, programming, management, community radio, Thailand

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Authors: Hossam Abdelrahman, Sama Rostom, Reem Yassein, Yara Mohamed, Salma Salah, Nour Awny

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In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. The approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In this study, A model is proposed based on the in-painting of medical images with Stable Diffusion Inpainting method. Reconstructing missing and damaged sections of an image is known as image inpainting is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

Keywords: generative adversarial network, large mask inpainting, stable diffusion inpainting, plastic surgery

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Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun

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Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.

Keywords: complex network, social network, community detection, network hierarchy

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Sirilak Areerachakul, Nat Ployong, Supayothin Na Songkla

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This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: artificial neural network, classification, students, e-learning

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Authors: Ashkan Zakaryazad, Ekrem Duman

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A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

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Authors: Shuo Liu

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In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text.

Keywords: Chinese natural language processing, text classification, abstract extraction, graph attention network

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Authors: Shin Je Lee, Go Bong Choi, Jeong Cheol Seo, Jong Min Lee, Gibaek Lee

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Water pipe network is installed underground and once equipped; it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using Matlab. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance.

Keywords: fault detection, water pipeline model, fast Fourier transform, discrete wavelet transform

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Authors: Beatriz Martinez-Pastor, Maria Nogal, Alan O'Connor

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During the last years, the number of extreme weather events has increased. A variety of extreme weather events, including river floods, rain-induced landslides, droughts, winter storms, wildfire, and hurricanes, have threatened and damaged many different regions worldwide. These events have a devastating impact on critical infrastructure systems resulting in high social, economical and environmental costs. These events have a huge impact in transport systems. Since, transport networks are completely exposed to every kind of climatological perturbations, and its performance is closely related with these events. When a traffic network is affected by a climatological hazard, the quality of its service is threatened, and the level of the traffic conditions usually decreases. With the aim of understanding this process, the concept of resilience has become most popular in the area of transport. Transport resilience analyses the behavior of a traffic network when a perturbation takes place. This holistic concept studies the complete process, from the beginning of the perturbation until the total recovery of the system, when the perturbation has finished. Many concepts are included in the definition of resilience, such as vulnerability, redundancy, adaptability, and safety. Once the resilience of a transport network can be evaluated, in this case, the methodology used is a dynamic equilibrium-restricted assignment model that allows the quantification of the concept, the next step is its improvement. Through the improvement of this concept, it will be possible to create transport networks that are able to withstand and have a better performance under the presence of climatological hazards. Analyzing the impact of a perturbation in a traffic network, it is observed that the response of the different links, which are part of the network, can be completely different from one to another. Consequently and due to this effect, many questions arise, as what makes a link more critical before an extreme weather event? or how is it possible to identify these critical links? With this aim, and knowing that most of the times the owners or managers of the transport systems have limited resources, the identification of the critical links of a transport network before extreme weather events, becomes a crucial objective. For that reason, using the available resources in the areas that will generate a higher improvement of the resilience, will contribute to the global development of the network. Therefore, this paper wants to analyze what kind of characteristic makes a link a critical one when an extreme weather event damages a transport network and finally identify them.

Keywords: critical links, extreme weather events, hazard, resilience, transport network

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Authors: Jinan Fiaidhi, Sabah Mohammed

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Cross-domain learning in the medical field is a research challenge as many conditions, like in oncology imaging, use different imaging modalities. Moreover, in most of the medical learning applications, the sample training size is relatively small. Although few-shot learning (FSL) through the use of a Siamese neural network was able to be trained on a small sample with remarkable accuracy, FSL fails to be effective for use in multiple domains as their convolution weights are set for task-specific applications. In this paper, we are addressing this problem by enabling FSL to possess the ability to shift across domains by designing a two-layer FSL network that can learn individually from each domain and produce a shared features map with extra modulation to be used at the second layer that can recognize important targets from mix domains. Our initial experimentations based on mixed medical datasets like the Medical-MNIST reveal promising results. We aim to continue this research to perform full-scale analytics for testing our cross-domain FSL learning.

Keywords: Siamese neural network, few-shot learning, meta-learning, metric-based learning, thick data transformation and analytics

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Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

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The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.

Keywords: melanoma, convolutional neural network, momentum, batch hyperparameter

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Authors: Dana M. Ragab, Jasim A. Ghaeb

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The three-phase power system suffers from different challenging problems, e.g. voltage unbalance conditions at the load side. The voltage unbalance usually degrades the power quality of the electric power system. Several techniques can be considered for load balancing including load reconfiguration, static synchronous compensator and static reactive power compensator. In this work an efficient neural network is designed to control the unbalanced condition in the Aqaba-Qatrana-South Amman (AQSA) electric power system. It is designed for highly enhanced response time of the reactive compensator for voltage balancing. The neural network is developed to determine the appropriate set of firing angles required for the thyristor-controlled reactor to balance the three load voltages accurately and quickly. The parameters of AQSA power system are considered in the laboratory model, and several test cases have been conducted to test and validate the proposed technique capabilities. The results have shown a high performance of the proposed Neural Network Control (NNC) technique for correcting the voltage unbalance conditions at three-phase load based on accuracy and response time.

Keywords: three-phase power system, reactive power control, voltage unbalance factor, neural network, power quality

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Authors: Hidayah Triana, Mahir S. Gani, Asmi Citra Malina, Hamka

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This study was conducted to determine genetic diversity of tiger groupers that are resistant to V. parahaemolyticus and tolerant to low extreme salinities. This research is useful to obtain superior broodstock of fish. Tiger grouper used were 6 to 8 cm obtained from Brackish Water Aquaculture Research Center Gondol (Bali). This study consists of four stages: preliminary stage was adaptation of fish exposed to several concentrations of V. parahaemolyticus (103, 104, 105, 106, and 107 CFU / ml); second stage was test of Lethal Concentration (LC50) of bacteria to fish; third stage was salinity tolerance test (low salinity 12, 14 and 16 ppt) and fourth stage was analysis of DNA profiles. For DNA profiles analysis, genomic DNA of fish were extracted for PCR using primers YNZ-22 and UBC-122 and visualized by electrophoresis method. The results showed that Lethal concentration of bacteria (LC50) to fish was 1,56x106 CFU/ml. Furthermore, survival rate of groupers exposed with low salinities (12, 14, 16 ppt) survival rates were found to be 54,17 %, 66,67 % and 79,16 % respectively. Average of DNA fragment (5 fragments) generated from primer UBC-122 in the group of fish resistant to V.parahaemolyticus and tolerant to low salinities was similar to group of susceptible to low salinities. Primer YNZ-22 generated more diverse of DNA fragments (8,0 and 5,8 fragments) both in the group of fish tolerant and susceptible to low salinities compared to primer UBC-122 (5,0 fragments). Size of DNA 1.5 kb resulted from primer YNZ-22. Primer YNZ-22 generated 4 (50 %) and 3 (42,8 %) polymorfic fragments in the group of fish tolerant and susceptible to low salinities, respectively. Four (4) monomorfic fragments were found both in the group of fish tolerant and susceptible to low salinities. Primer UBC-122 generated 6 (85,7 %) and 9 (90,0 %) polymorfic fragments in the fish tolerant and susceptible to low salinities, respectively.

Keywords: genetic diversity, epinephelus fuscoguttatus, V. parahaemolyticus, PCR-RAPD, low extreme salinity

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Authors: M. Khouil, N. Saber, M. Mestari

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This study aimed to propose, a different architecture of a Path Planning using the NECMOP. where several nonlinear objective functions must be optimized in a conflicting situation. The ability to detect and avoid collision is very important for mobile intelligent machines. However, many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons linear and threshold logic, which simplified the actual implementation of all the networks proposed. This article represents a comprehensive algorithm that determine through the AMAXNET network a measure (a mini-maximum point) in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: path planning, collision detection, convex polyhedron, neural network

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Authors: Javeria Arain, Saad Malik

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The management of solid waste is a crucial and essential aspect of urban environmental management especially in a city with an ever increasing population such as Karachi. The total amount of municipal solid waste generated from Gulshan e Iqbal town on average is 444.48 tons per day and landfill sites are a widely accepted solution for final disposal of this waste. However, an improperly selected site can have immense environmental, economical and ecological impacts. To select an appropriate landfill site a number of factors should be kept into consideration to minimize the potential hazards of solid waste. The purpose of this research is to analyse the study area for the construction of an appropriate landfill site for disposal of municipal solid waste generated from Gulshan e-Iqbal Town by using geospatial techniques considering hydrological, geological, social and geomorphological factors. This was achieved using analytical hierarchy process and fuzzy analysis as a decision support tool with integration of geographic information sciences techniques. Eight most critical parameters, relevant to the study area, were selected. After generation of thematic layers for each parameter, overlay analysis was performed in ArcGIS 10.0 software. The results produced by both methods were then compared with each other and the final suitability map using AHP shows that 19% of the total area is Least Suitable, 6% is Suitable but avoided, 46% is Moderately Suitable, 26% is Suitable, 2% is Most Suitable and 1% is Restricted. In comparison the output map of fuzzy set theory is not in crisp logic rather it provides an output map with a range of 0-1, where 0 indicates least suitable and 1 indicates most suitable site. Considering the results it is deduced that the northern part of the city is appropriate for constructing the landfill site though a final decision for an optimal site could be made after field survey and considering economical and political factors.

Keywords: Analytical Hierarchy Process (AHP), fuzzy set theory, Geographic Information Sciences (GIS), Multi-Criteria Decision Analysis (MCDA)

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Authors: Epaminondas G. Kyriakidis, Theodosis D. Dimitrakos, Constantinos C. Karamatsoukis

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The vehicle routing problem (VRP) is a well-known problem in Operations Research and has been widely studied during the last fifty-five years. The context of the VRP is that of delivering products located at a central depot to customers who are scattered in a geographical area and have placed orders for these products. A vehicle or a fleet of vehicles start their routes from the depot and visit the customers in order to satisfy their demands. Special attention has been given to the capacitated VRP in which the vehicles have limited carrying capacity of the goods that must be delivered. In the present work, we present a specific capacitated stochastic vehicle routing problem which has realistic applications to distributions of materials to shops or to healthcare facilities or to military units. A vehicle starts its route from a depot loaded with items of two similar but not identical products. We name these products, product 1 and product 2. The vehicle must deliver the products to N customers according to a predefined sequence. This means that first customer 1 must be serviced, then customer 2 must be serviced, then customer 3 must be serviced and so on. The vehicle has a finite capacity and after servicing all customers it returns to the depot. It is assumed that each customer prefers either product 1 or product 2 with known probabilities. The actual preference of each customer becomes known when the vehicle visits the customer. It is also assumed that the quantity that each customer demands is a random variable with known distribution. The actual demand is revealed upon the vehicle’s arrival at customer’s site. The demand of each customer cannot exceed the vehicle capacity and the vehicle is allowed during its route to return to the depot to restock with quantities of both products. The travel costs between consecutive customers and the travel costs between the customers and the depot are known. If there is shortage for the desired product, it is permitted to deliver the other product at a reduced price. The objective is to find the optimal routing strategy, i.e. the routing strategy that minimizes the expected total cost among all possible strategies. It is possible to find the optimal routing strategy using a suitable stochastic dynamic programming algorithm. It is also possible to prove that the optimal routing strategy has a specific threshold-type structure, i.e. it is characterized by critical numbers. This structural result enables us to construct an efficient special-purpose dynamic programming algorithm that operates only over those routing strategies having this structure. The findings of the present study lead us to the conclusion that the dynamic programming method may be a very useful tool for the solution of specific vehicle routing problems. A problem for future research could be the study of a similar stochastic vehicle routing problem in which the vehicle instead of delivering, it collects products from ordered customers.

Keywords: collection of similar products, dynamic programming, stochastic demands, stochastic preferences, vehicle routing problem

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Authors: G. Blanco-Lizana, C. García-Fernández, J. A. Sánchez

Abstract:

Growth is a productive trait regulated by a large and complex gene network with very different effect. Some of they (candidate genes) have a higher effect and are excellent resources to search in them polymorphisms correlated with differences in growth rates. This study was focused on the identification of single nucleotide polymorphism (SNP) in MyoD-1 and MyoD-2 genes, members of the family of myogenic regulatory genes with a key role in the differentiation and development of muscular tissue.(MFRs), and its evaluation as potential markers in genetic selection programs for growth in gilthead sea bream (Sparus aurata). Through a sequencing in 30 seabream (classified as unrelated by microsatellite markers) of 1.968bp in MyoD-1 gene [AF478568 .1] and 1.963bp in MyoD-2 gene [AF478569.1], three SNPs were identified in each gene (SaMyoD-1 D2100A (D indicate a deletion) SaMyoD-1 A2143G and SaMyoD-1 A2404G and SaMyoD-2_A785C, SaMyoD-2_C1982T and SaMyoD-2_A2031T). The relationships between SNPs and body weight were evaluated by SNP genotyping of 53 breeders from two broodstocks (A:18♀-9♂; B:16♀-10♂) and 389 offspring divided into two groups (slow- and fast-growth) with significant differences in growth at 18 months of development (A18Slow: N=107, A18Fast: N=103, B18Slow: N=92 and B18Fast: N=87) (Borrell et al., 2011). Haplotype and diplotype were reconstructed from genotype data by Phase 2.1 software. Differences among means of different diplotypes were calculated by one-way ANOVA followed by post-hoc Tukey test. Association analysis indicated that single SNP did not show significant effect on body weight. However, when the analysis is carried out considering haplotype data it was observed that the DGG haplotipe of MyoD-1 gen and CCA haplotipe of MyoD- 2gen were associated to with lower body weight. This haplotype combination always showed the lowest mean body weight (P<0.05) in three (A18Slow, A18Fast & B18Slow) of the four groups tested. Individuals with DGG haplotipe of MyoD-1 gen have a 25,5% and those with CCA haplotipe of MyoD- 2gen showed 14-18% less on mean body weight. Although further studies are need to validate the role of these 3 SNPs as marker for body weight, the polymorphism-trait association established in this work create promising expectations on the use of these variants as genetic tool for future giltead seabream breeding programs.

Keywords: growth, MyoD-1 and MyoD-2 genes, selective breeding, SNP-haplotype

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Authors: Danilo López, Nelson Vera, Luis Pedraza

Abstract:

This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.

Keywords: neural networks, multilayer perceptron, long short-term memory, recurrent neuronal network, mathematical analysis

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Authors: Doreen Duri, Danai Zhou, Babil Stray-Pedersen, Collet Dandara

Abstract:

Given the high prevalence of HIV/AIDS in sub-Saharan Africa, and the elusive search for a cure, understanding the pharmacogenetics of currently used drugs is critical in populations from the most affected regions. Compared to Asian and Caucasian populations, African population groups are more genetically diverse, making it difficult to extrapolate findings from one ethnic group to another. This study aimed to investigate the role of genetic variation in CYP2B6 (c.516G>T and c.983T>C) single nucleotide polymorphisms on plasma nevirapine levels among HIV-infected adult Zimbabwean patients. Using a cross-sectional study, patients on nevirapine-containing HAART, having reached steady state (more than six weeks on treatment) were recruited to participate. Blood samples were collected after patients provided consent and samples were used to extract DNA for genetic analysis or to measure plasma nevirapine levels. Genetic analysis was carried out using PCR and RFLP or Snapshot for the two single nucleotide polymorphisms; CYP2B6 c.516G>T and c.983T>C, while LC-MS/MS was used in analyzing nevirapine concentration. CYP2B6 c.516G>T and c.983T>C significantly predicted plasma nevirapine concentration with the c.516T and c.983T being associated with elevated plasma nevirapine concentrations. Comparisons of the variant allele frequencies observed in this group to those reported in some African, Caucasian and Asian populations showed significant differences. We conclude that pharmacogenetics of nevirapine can be creatively used to determine patients who are likely to develop nevirapine-associated side effects as well as too low plasma concentrations for viral suppression.

Keywords: allele frequencies, genetically diverse, nevirapine, single nucleotide polymorphism

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

Abstract:

Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions.

Keywords: dialogue management, response generation, deep learning, evaluation

Procedia PDF Downloads 172