Search results for: genetic components

Authors: Lucjan Setlak, Emil Ruda

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The subject of this paper is to review, comparative analysis and simulation of selected components of power electronic systems (PES), consistent with the concept of a more electric aircraft (MEA). Comparative analysis and simulation in software environment MATLAB / Simulink were carried out based on a group of representatives of civil aircraft (B-787, A-380) and military (F-22 Raptor, F-35) in the context of multi-pulse converters used in them (6- and 12-pulse, and 18- and 24-pulse), which are key components of high-tech electronics on-board power systems of autonomous power systems (ASE) of modern aircraft (airplanes of the future).

Keywords: converters, electric machines, MEA (more electric aircraft), PES (power electronics systems)

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Authors: Rithvik Reddy Adapa, Xin Wang

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Phasor Measurement Units (PMUs) are very sophisticated measuring devices that find amplitude, phase and frequency of various voltages and currents in a power system. Particle filter is a state estimation technique that uses Bayesian inference. Particle filters are widely used in pose estimation and indoor navigation and are very reliable. This paper studies and compares four different particle filters as PMUs namely, generic particle filter (GPF), genetic algorithm particle filter (GAPF), particle swarm optimization particle filter (PSOPF) and adaptive particle filter (APF). Two different test signals are used to test the performance of the filters in terms of responsiveness and correctness of the estimates.

Keywords: phasor measurement unit, particle filter, genetic algorithm, particle swarm optimisation, state estimation

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Authors: H. Mousavi, M. Sharifi, H. Pourvaziri

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Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities.

Keywords: redundancy allocation problem, simulated annealing, cloud theory, monte carlo simulation

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Authors: Abeer Toheed Quadri, Maria Abubakar, Mehreen Sirshar

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Component Based Software Development (CBSD) is a new trend in software development. Selection of quality components is not enough to ensure software quality in Component Based Software System (CBSS). A software product is considered to be a quality product if it satisfies its customer’s needs and has minimum defects. Authors’ survey different research papers and analyzes various techniques which ensure software quality in component based software development. This paper includes an investigation about how to improve the quality of a component based software system without effecting quality attributes. The reported information is identified from literature survey. The developments of component based systems are rising as they reduce the development time, effort and cost by means of reuse. After analysis, it has been explored that in order to achieve the quality in a CBSS we need to have the components that are certified through software measure because the predictability of software quality attributes of system depend on the quality attributes of the constituent components, integration process and the framework used.

Keywords: CBSD (component based software development), CBSS (component based software system), quality components, SQA (software quality assurance)

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Authors: Michael Aupetit, Mahmoud Al-ismail, Khaled Mohamed

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Cancer is a major public health problem all over the world. Breast cancer has the highest incidence rate over all cancers for women in Qatar making its study a top priority of the country. Human cancer is a dynamic disease that develops over an extended period through the accumulation of a series of genetic alterations. A Darwinian process drives the tumor cells toward higher malignancy growing the branches of a progression tree in the space of genes expression. Although it is not possible to track these genetic alterations dynamically for one patient, it is possible to reconstruct the progression tree from the aggregation of thousands of tumor cells’ genetic profiles from thousands of different patients at different stages of the disease. Analyzing the progression tree is a way to detect pivotal molecular events that drive the malignant evolution and to provide a guide for the development of cancer diagnostics, prognostics and targeted therapeutics. In this work we present the development of a Visual Analytic web platform CanVis enabling users to upload gene-expression data and analyze their progression tree. The server computes the progression tree based on state-of-the-art techniques and allows an interactive visual exploration of this tree and the gene-expression data along its branching structure helping to discover potential driver genes.

Keywords: breast cancer, progression tree, visual analytics, web platform

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Authors: Fadilah Fadilah, Rochmadi Rochmadi, Siti Syamsiah, Djagal W. Marseno

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Glucomannan can be found in the tuber of porang together with starch and proteinaceous components which were regarded as impurities. An enzymatic process for obtaining higher glucomannan content from Porang flour have been conducted. Papain was used for hydrolysing proteinaceous components in Porang flour which was conducted after a simultaneous extraction of glucomannan and enzymatic starch hydrolysis. Three variables affecting the rate were studied, i.e. temperature, the amount of enzyme and the stirring speed. The ninhydrin method was used to determine degree of protein hydrolysis. Results showed that the rising of degree of hydrolysis were fast in the first ten minutes of the reaction and then proceeded slowly afterward. The optimum temperature for hydrolysis was 60 ^oC. Increasing the amount of enzyme showed a remarkable effect to degree of hydrolysis, but the stirring speed had no significant effect. This indicated that the reaction controlled the rate of hydrolysis.

Keywords: degree of hydrolysis, ninhydrin, papain, porang flour, proteinaceous components

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Authors: I. Gahlouz, A. Chellil

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The paper concerns the estimation of system components faults by using an unknown inputs observer. To reach this goal, we used the Bond Graph approach to physical modelling. We showed that this graphical tool is allowing the representation of system components faults as unknown inputs within the state representation of the considered physical system. The study of the causal and structural features of the system (controllability, observability, finite structure, and infinite structure) based on the Bond Graph approach was hence fulfilled in order to design an unknown inputs observer which is used for the system component fault estimation.

Keywords: estimation, bond graph, controllability, observability

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Authors: Hamed K. Esfahani, Bithin Datta

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Transport of reactive chemical contaminant species in groundwater aquifers is a complex and highly non-linear physical and geochemical process especially for real life scenarios. Simulating this transport process involves solving complex nonlinear equations and generally requires huge computational time for a given aquifer study area. Development of optimal remediation strategies in aquifers may require repeated solution of such complex numerical simulation models. To overcome this computational limitation and improve the computational feasibility of large number of repeated simulations, Genetic Programming based trained surrogate models are developed to approximately simulate such complex transport processes. Transport process of acid mine drainage, a hazardous pollutant is first simulated using a numerical simulated model: HYDROGEOCHEM 5.0 for a contaminated aquifer in a historic mine site. Simulation model solution results for an illustrative contaminated aquifer site is then approximated by training and testing a Genetic Programming (GP) based surrogate model. Performance evaluation of the ensemble GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. The results show the efficiency and feasibility of using ensemble GP surrogate models as approximate simulators of complex hydrogeologic and geochemical processes in a contaminated groundwater aquifer incorporating uncertainties in historic mine site.

Keywords: geochemical transport simulation, acid mine drainage, surrogate models, ensemble genetic programming, contaminated aquifers, mine sites

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Authors: Seyed Habib Shojaei, Reza Eivazi, Mir Sajad Shojaei, Alireza Akbari, Pooria Mazloom, Seyede Mitra Sadati, Mir Zeinalabedin Shojaei, Farnaz Farbakhsh

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To study the genetic diversity in rapeseeds and agronomic traits, an experiment was conducted using multivariate statistical methods at Agricultural Research Station of Kashmar in 2012-2013.In this experiment, ten genotypes of rapeseed in a Randomized Complete Block designs with three replications were evaluated. The following traits were studied: seed yield, number of days to the fifty percent of flowering, plant height, number of pods on main stem, length of the pod, seed yield per plant, number of seed in pod, harvest index, weight of 100 seeds, number of pods on lateral branch, number of lateral branches. In analyzing the variance, differences between cultivars were significant. The average comparative revealed that the most valuable variety was Licord regarding to the traits while the least valuable variety was Opera. In stepwise regression, harvest index, grain yield per plant and number of pods per lateral branches were entering to model. Correlation analysis showed that the grain yield with the number of pods per lateral branches and seed yield per plant have positive and significant correlation. In the factor analysis, the first five components explained more than 83% of the variance in the data. In the first factor, seed yield and the number of pods per lateral branches were of the highest importance. The traits, seed yield per plant, and pod per main stem were of a great significance in the second factor. Moreover, in the third factor, plant height and the number of lateral branches were more important. In the fourth factor, plant height and one hundred seeds weight were of the highest variance. Finally, days to fifty percent of flowering and one hundred seeds weight were more important in fifth factor.

Keywords: rapeseed, variance analysis, regression, factor analysis

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Authors: A. Mansouri, F. Krim

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This paper presents two techniques for DC motor parameters identification. We propose a numerical method using the adaptive extensive recursive least squares (AERLS) algorithm for real time parameters estimation. This algorithm, based on minimization of quadratic criterion, is realized in simulation for parameters identification of DC motor autoregressive moving average with extra inputs (ARMAX). As advanced technique, we use genetic algorithms (GA) identification with biased estimation for high dynamic performance speed regulation. DC motors are extensively used in variable speed drives, for robot and solar panel trajectory control. GA effectiveness is derived through comparison of the two approaches.

Keywords: ARMAX model, DC motor, AERLS, GA, optimization, parameter identification, PID speed regulation

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Authors: Benjamin A. Ha, Marco Morselli, Xinhui Paige Zhang, Elizabeth A. C. Heath-Heckman, Jonathan B. Puritz, David K. Jacobs

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Next-generation sequencing has enhanced the ability to acquire massive amounts of sequence data to address classic population genetic questions for non-model organisms. Targeted approaches allow for cost effective or more precise analyses of relevant sequences; although, many such techniques require a known genome and it can be costly to purchase probes from a company. This is challenging for non-model organisms with no published genome and can be expensive for large population genetic studies. Expressed exome capture sequencing (EecSeq) synthesizes probes in the lab from expressed mRNA, which is used to capture and sequence the coding regions of genomic DNA from a pooled suite of samples. A normalization step produces probes to recover transcripts from a wide range of expression levels. This approach offers low cost recovery of a broad range of genes in the genome. This research project expands on EecSeq to investigate if mRNA from one taxon may be used to capture relevant sequences from a series of increasingly less closely related taxa. For this purpose, we propose to use the endangered Northern Tidewater goby, Eucyclogobius newberryi, a non-model organism that inhabits California coastal lagoons. mRNA will be extracted from E. newberryi to create probes and capture exomes from eight other taxa, including the more at-risk Southern Tidewater goby, E. kristinae, and more divergent species. Captured exomes will be sequenced, analyzed bioinformatically and phylogenetically, then compared to previously generated phylogenies across this group of gobies. This will provide an assessment of the utility of the technique in cross-species studies and for analyzing low genetic variation within species as is the case for E. kristinae. This method has potential applications to provide economical ways to expand population genetic and evolutionary biology studies for non-model organisms.

Keywords: coastal lagoons, endangered species, non-model organism, target capture method

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Authors: A. Ozkose

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This study was conducted to determination of yield and yield components of some fodder beet types (Amarilla Barres, Feldherr, Kyros, Magnum, and Rota) under the Konya region conditions. Fodder beet was obtained from the Selcuk University, Faculty of Agriculture, at 2006-2007 season and the experiment was established in a randomized complete block design with three replicates. Differences among the averages of the fodder beet cultivars are statistically important in terms of all the characteristics investigated. Leaf attitude value was 1.2–2.2 (1=erect; 5= prostrate), root shape scale value was (1=spheroidal – 9=cylindrical), root diameter 11.0–12.2 cm, remaining part of root on the ground was 6.3–13.7 cm, root length was 21.4 – 29.6 cm, leaf yield 1592 – 1917 kg/da, root yield was 10083–12258 kg/da, root dry matter content was %8.2– 18.6 and root dry matter yield was 889–1887 kg/da. As a result of the study, it was determined that fodder beet cultivars are different conditions in terms of yield and yield components. Therefore, determination of appropriate cultivars for each region affect crop yield importantly.

Keywords: fedder beet, root yield, yield components, Konya, agriculture

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Authors: Atefeh Esmailnejad, Gholam Reza Nikbakht Brujeni

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The major histocompatibility complex (MHC) is the best-characterized genetic region associated with susceptibility and/or resistance to a wide range of infectious diseases, autoimmune diseases and immune responses to vaccines. It has been demonstrated that there is an association between the MHC and resistance to Marek disease, Newcastle disease, Rous sarcoma tumor, Avian leucosis, Fowl cholera, Salmonellosis and Pasteurellosis in chicken. The present study evaluated the MHC polymorphism and its association with antibody response to Newcastle (ND) vaccine in Iranian native chickens. The MHC polymorphism was investigated using LEI0258 microsatellite locus by PCR-based fragment analysis. LEI0258 microsatellite marker is a genetic indicator for MHC, which is located on microchromosome 16 and strongly associated with serologically defined MHC haplotypes. Antibody titer against ND vaccine was measured by Haemaglutination Inhibition (HI) assay. Statistical analysis was performed using SPSS software (version 21). Total of 13 LEI0258 microsatellite alleles were identified in 72 samples which indicated a high genetic diversity in the population. The association study revealed a significant influence of MHC alleles on immune responses to Newcastle vaccine. 311 and 313 bp alleles were significantly associated with elevated immune responses to Newcastle vaccine (p<0.05). These results would be applicable in designing and improving the populations under selective breeding.

Keywords: chicken, LEI0258, MHC, Newcastle vaccine

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Authors: Saad M. Howladar, Mike Dennett

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At first harvest, results showed that salinity (tap water, 100 and 200 mM NaCl) induced a significant decrease in all growth parameters in both Yecora Rojo and Paragon cultivars. The greatest effect of salinity was a decrease in leaf area. The same tendency was observed with specific leaf area, and total fresh and dry weights and their components. Green leaf and tiller numbers were reduced by the same extent in both cultivars. The corresponding final harvest, all growth parameters also reduced with increased salinity. Yield and yield components were also reduced by salinity with similar effects in both cultivars. Chlorophyll fluorescence, expressed as Fv/Fm, and gas exchange parameters were decreased significantly with increase in salinity in both cultivars. In contrast, seed protein content was increased significantly with increase in salinity. Salicylic acid (SA) application induced no significant improvements in growth parameters and yield components.

Keywords: salinity, salicylic acid, growth, chlorophyll fluorescence, gas exchange, yield

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Authors: Agostinho Antunes

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The completion of the human genome sequencing in 2003 opened a new perspective into the importance of whole genome sequencing projects, and currently multiple species are having their genomes completed sequenced, from simple organisms, such as bacteria, to more complex taxa, such as mammals. This voluminous sequencing data generated across multiple organisms provides also the framework to better understand the genetic makeup of such species and related ones, allowing to explore the genetic changes underlining the evolution of diverse phenotypic traits. Here, recent results from our group retrieved from comparative evolutionary genomic analyses of selected marine animal species will be considered to exemplify how gene novelty and gene enhancement by positive selection might have been determinant in the success of adaptive radiations into diverse habitats and lifestyles.

Keywords: marine genomics, evolutionary bioinformatics, human genome sequencing, genomic analyses

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Authors: Maximilian Winkens, Peter Nyhuis

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Components with sensory properties such as gentelligent components developed at the Collaborative Research Center 653 offer a new angle on the full utilization of the remaining service life in case of a preventive maintenance. The developed methodology of component status driven maintenance analyses the stress data obtained during the component's useful life and on the basis of this knowledge assesses the type of maintenance called for in this case. The procedure is derived from the case-based reasoning method and will be elucidated in detail. The method's functionality is demonstrated with real-life data obtained during test runs of a racing car prototype.

Keywords: gentelligent component, preventive maintenance, case-based reasoning, sensory

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Authors: Sarah Odofin, Zhiwei Gao

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Operations and maintenance of wind turbine have received much attention by researcher due to rapid expansion of wind farms. This paper explores a novel fault diagnosis that is designed and optimized to be very sensitive to faults and robust to disturbances. The faults considered are the sensor faults of which the augmented observer is considered to enlarge faults and to be robust to disturbance. A qualitative model based analysis is proposed for early fault diagnosis to minimize downtime mostly caused by components breakdown and exploit productivity. Simulation results are computed validating the models provided which demonstrates system performance using practical application of fault type examples. The results demonstrate the effectiveness of the developed techniques investigated in a Matlab/Simulink environment.

Keywords: wind turbine, condition monitoring, genetic algorithm, fault diagnosis, augmented observer, disturbance robustness, fault estimation, sensor monitoring

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Authors: Debabrata Biswas

Abstract:

The current trend in the development of antibiotic resistance by multiple bacterial pathogens has resulted in a troubling loss of effective antibiotic options for human. The emergence of multi-drug-resistant pathogens has necessitated higher dosages and combinations of multiple antibiotics, further exacerbating the problem of antibiotic resistance. Zoonotic bacterial pathogens, such as Salmonella, Campylobacter, Shiga toxin-producing Escherichia coli (such as enterohaemorrhagic E. coli or EHEC), and Listeria are the most common and predominant foodborne enteric infectious agents. It was observed that these pathogens gained/developed their ability to survive in the presence of antibiotics either in farm animal gut or farm environment and researchers believe that therapeutic and sub-therapeutic antibiotic use in farm animal production might play an important role in it. The mechanism of action of antimicrobial components used in farm animal production in genomic interplay in the gut and farm environment, has not been fully characterized. Even the risk of promoting the exchange of mobile genetic elements between microbes specifically pathogens needs to be evaluated in depth, to ensure sustainable farm animal production, safety of our food and to mitigate/limit the enteric infection with multiple antibiotic resistant bacterial pathogens. Due to the consumer’s demand and considering the current emerging situation, many countries are in process to withdraw antibiotic use in farm animal production. Before withdrawing use of the sub-therapeutic antibiotic or restricting the use of therapeutic antibiotics in farm animal production, it is essential to find alternative natural antimicrobials for promoting the growth of farm animal and/or treating animal diseases. Further, it is also necessary to consider whether that compound(s) has the potential to trigger the acquisition or loss of genetic materials in zoonotic and any other bacterial pathogens. Development of alternative therapeutic and sub-therapeutic antimicrobials for farm animal production and food processing and preservation and their effective implementation for sustainable strategies for farm animal production as well as the possible risk for horizontal gene transfer in major enteric pathogens will be focus in the study.

Keywords: food safety, natural antimicrobial, sustainable farming, antibiotic resistance

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Authors: Saad Howladar, Mike Dennett

Abstract:

At first harvest, results showed that salinity (tap water, 100 and 200 mM NaCl) induced a significant decrease in all growth parameters in both Yecora Rojo and Paragon cultivars. The greatest effect of salinity was a decrease in leaf area. The same tendency was observed with specific leaf area, and total fresh and dry weights and their components. Green leaf and tiller numbers were reduced by the same extent in both cultivars. The corresponding final harvest, all growth parameters also reduced with increased salinity. Yield and yield components were also reduced by salinity with similar effects in both cultivars. Chlorophyll fluorescence, expressed as Fv/Fm, and gas exchange parameters were decreased significantly with increase in salinity in both cultivars. In contrast, seed protein content was increased significantly with increase in salinity. Salicylic acid (SA) application induced no significant improvements in growth parameters and yield components.

Keywords: salinity, salicylic acid, growth, chlorophyll fluorescence, gas exchange, yield

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Authors: Talal Alsulaiman, Khaldoun Khashanah

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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

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Authors: Yessengali Ussenbekov, Valery Terletskiy, Orik Zhanserkenova, Shynar Kasymbekova, Indira Beyshova, Aitkali Imanbayev, Almas Serikov

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Currently, there are 46 hereditary diseases afflicting cattle with known molecular genetic diagnostic methods developed for them. Genetic anomalies frequently occur in the Holstein cattle breeds from American and Canadian bloodlines. The data on the incidence of BLAD, CVM, DUMPS and BC autosomal recessive lethal mutations in pedigree animals are discordant, the detrimental allele incidence rates are high for the Holstein cattle breed, whereas the incidence rates of these mutations are low in some breeds or they are completely absent. Data were obtained on the basis of frozen semen of stud bulls. DNA was extracted from the semen with the DNA-Sorb-B extraction kit. The lethal mutation in the genes CD18, SLC35A3, UMP and ASS of Alatau stud bulls (N=124) was detected by polymerase chain reaction and RFLP analysis. It was established that stud bulls of the local Alatau breed were not carriers of the BLAD, CVM, DUMPS, and BC detrimental mutations. However, with a view to preventing the dissemination of hereditary diseases it is recommended to monitor the pedigree stock using molecular genetic methods.

Keywords: PCR, autosomal recessive point mutation, BLAD, CVM, DUMPS, BC, stud bulls

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Authors: Radhey Sham Rajoria

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Pressure to increase the competitiveness in the manufacturing sector and for the survival in the market has led to the development of machining centres, which enhance productivity, improve quality, shorten the lead time, and reduce the manufacturing cost. With the innovation of machining centres in the manufacturing sector the production lines have been replaced by these machining centers, having the ability to machine various processes and multiple tooling with automatic tool changer (ATC) for the same part. Also the process plans can be easily generated for complex components. Some means are required to utilize the machining center at its best. The present work is concentrated on the automated part program generation, and in turn automated process plan generation for the turned components on Denford “MIRAC” 8 stations ATC lathe machining centre. A package in C++ on DOS platform is developed which generates the complete CNC part program, process plan and process sequence for the turned components. The input to this system is in the form of a blueprint in graphical format with machining parameters and variables, and the output is the CNC part program which is stored in a .mir file, ready for execution on the machining centre.

Keywords: CNC, MIRAC, ATC, process planning

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Authors: Mary Rose Eunice S. Gundayao, Manolo M. Fernandez

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Metachromatic leukodystrophy (MLD) is a rare lysosomal storage disorder with an autosomal recessive inheritance pattern. Lysosomal storage disorders are often severe, follow a progressively neurodegenerative path, and may result in multi-organ failure, potentially leading to death within 5 to 6 years in cases of early-onset forms. There are limited data regarding cases of MLD in Filipino children. This is the case of a 2-year-old Filipino girl who presented with progressive neurological deterioration and was diagnosed with metachromatic leukodystrophy by molecular genetic testing. This case report aims to present this patient’s clinical history, neurological findings, diagnosis and novel genetic mutations causing MLD. A concise review of updated literature on MLD will be discussed.

Keywords: metachromatic leukodystrophy, ARSA gene, peripheral neuropathy, case report, demyelinating disease

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Authors: Xiao Zhang, Wensheng Xiao, Junguo Cui, Hongmin Wang

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Submersible permanent magnet linear synchronous motors (SPMLSMs) are electromagnetic devices, which can directly drive plunger pump to obtain the crude oil. Those motors have been gradually applied in oil fields due to high thrust force density and high efficiency. Since the force performance closely depends on the concrete structural parameters, the seven different structural parameters are investigated in detail. This paper presents an optimum design of an SPMLSM to minimize the detent force and maximize the thrust by using design of experiment (DOE) and genetic algorithm (GA). The three significant structural parameters (air-gap length, slot width, pole-arc coefficient) are separately screened using 27 1/16 fractional factorial design (FFD) to investigate the significant effect of seven parameters used in this research on the force performance. Response surface methodology (RSM) is well adapted to make analytical model of thrust and detent force with constraints of corresponding significant parameters and enable objective function to be easily created, respectively. GA is performed as a searching tool to search for the Pareto-optimal solutions. By finite element analysis, the proposed PMLSM shows merits in improving thrust and reducing the detent force dramatically.

Keywords: optimization, force performance, design of experiment (DOE), genetic algorithm (GA)

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Authors: Toshinori Nawata

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In this paper a nonlinear feedback control called augmented automatic choosing control (AACC) for a class of nonlinear systems with constrained input is presented. When designing the control, a constant term which arises from linearization of a given nonlinear system is treated as a coefficient of a stable zero dynamics. Parameters of the control are suboptimally selected by maximizing the stable region in the sense of Lyapunov with the aid of a genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.

Keywords: augmented automatic choosing control, nonlinear control, genetic algorithm, zero dynamics

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Authors: Surekha Tony, Roshan Mevada

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Wiskott-Aldrich syndrome (WAS) is a primary immunodeficiency disease resulting in recurrent infections, eczema, and microthrombocytopenia. In its classical form, significant combined immune deficiency, autoimmune complications, and risk of hematological malignancy necessitate early correction, preferably before 2 years of age, with hematopoietic stem cell transplant (HSCT) or gene therapy. Clinical features and severity are varied, making the diagnosis difficult in milder cases. We report an Omani boy diagnosed in early infancy with WAS based on clinical presentation and confirmed by genetic diagnosis with cure by HSCT from an HLA-identical sibling donor.

Keywords: genetic diagnosis, hematopoietic stem cell transplant, infant, Wiskott-Aldrich syndrome

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Authors: D. Mohammadzadeh S., J. Bolouri Bazaz

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This paper presents a new prediction model for compression index of fine-grained soils using multi-gene genetic programming (MGGP) technique. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. Several laboratory test results for fine-grained were used to develop the models. Various criteria were considered to check the validity of the model. The parametric and sensitivity analyses were performed and discussed. The MGGP method was found to be very effective for predicting the soil compression index. A comparative study was further performed to prove the superiority of the MGGP model to the existing soft computing and traditional empirical equations.

Keywords: new prediction model, compression index soil, multi-gene genetic programming, MGGP

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Authors: Garima Anand, Rupam Kapoor

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Alternaria leaf spot caused by Alternaria carthami is one of the most devastating diseases of safflower. It has resulted in huge losses in crop production and cultivation leading to a fall out of India’s rank as the leading producer of safflower in the world. Understanding the diversity of any pathogen is essential for its management and for the development of disease control strategies. The diversity of A. carthami was therefore analysed on the basis of biochemical, pathogenicity and genetic lines using ISSR markers. Collections and isolations of 95 isolates of A. carthami were made from major safflower producing states of India. Virulence was analysed to evaluate the pathogenic potential of these isolates. The isolates from Bijapur, Dharwad districts (Karnataka), and Parbhani and Solapur districts (Maharashtra) were found to be highly virulent. The virulence assays showed low virulence levels (42%) for the largest part of the population. Biochemical characterization to assess aggressiveness of these isolates was done by estimating the activity of cell wall degrading enzymes where isolates from districts Dharwad, Bijapur of Karnataka and districts Parbhani and Latur of Maharashtra were found to be most aggressive. Genetic diversity among isolates of A. carthami was determined using eighteen ISSR markers. Distance analysis using neighbour joining method and PCoA analysis of the ISSR profiles divided the isolates into three sub-populations. The most virulent isolates clustered in one group in the dendrogram. The study provided no evidence for geographical clustering indicating that isolates are randomly spread across the states, signifying the high potential of the fungus to adapt to diverse regions. The study can, therefore, aid in the breeding and deployment of A. carthami resistant safflower varieties and in the management of Alternaria leaf spot disease.

Keywords: alternaria leaf spot, genetic diversity, pathogenic potential, virulence

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Authors: Aizati N. A. Daud, Jorieke E. H. Bergman, Wilhelmina S. Kerstjens-Frederikse, Pieter Van Der Vlies, Eelko Hak, Rolf M. F. Berger, Henk Groen, Bob Wilffert

Abstract:

Prenatal use of SRIs was previously associated with Congenital Heart Anomalies (CHA). The aim of the study is to explore whether pharmacogenetics plays a role in this teratogenicity using a gene-environment interaction study. A total of 33 case-mother dyads and 2 mother-only (children deceased) registered in EUROCAT Northern Netherlands were included in a case-only study. Five case-mother dyads and two mothers-only were exposed to SRIs (paroxetine=3, fluoxetine=2, venlafaxine=1, paroxetine and venlafaxine=1) in the first trimester of pregnancy. The remaining 28 case-mother dyads were not exposed to SRIs. Ten genes that encode the enzymes or proteins important in determining fetal exposure to SRIs or its mechanism of action were selected: CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6), ABCB1 (placental P-glycoprotein), SLC6A4 (serotonin transporter) and serotonin receptor genes (HTR1A, HTR1B, HTR2A, and HTR3B). All included subjects were genotyped for 58 genetic variations in these ten genes. Logistic regression analyses were performed to determine the interaction odds ratio (OR) between genetic variations and SRIs exposure on the risk of CHA. Due to low phenotype frequencies of CYP450 poor metabolizers among exposed cases, the OR cannot be calculated. For ABCB1, there was no indication of changes in the risk of CHA with any of the ABCB1 SNPs in the children and their mothers. Several genetic variations of the serotonin transporter and receptors (SLC6A4 5-HTTLPR and 5-HTTVNTR, HTR1A rs1364043, HTR1B rs6296 & rs6298, HTR3B rs1176744) were associated with an increased risk of CHA, but with too limited sample size to reach statistical significance. For SLC6A4 genetic variations, the mean genetic scores of the exposed case-mothers tended to be higher than the unexposed mothers (2.5 ± 0.8 and 1.88 ± 0.7, respectively; p=0.061). For SNPs of the serotonin receptors, the mean genetic score for exposed cases (children) tended to be higher than the unexposed cases (3.4 ± 2.2, and 1.9 ± 1.6, respectively; p=0.065). This study might be among the first to explore the potential gene-environment interaction between pharmacogenetic determinants and SRIs use on the risk of CHA. With small sample sizes, it was not possible to find a significant interaction. However, there were indications for a role of serotonin receptor polymorphisms in fetuses exposed to SRIs on fetal risk of CHA which warrants further investigation.

Keywords: gene-environment interaction, heart defects, pharmacogenetics, serotonin reuptake inhibitors, teratogenicity

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Authors: Rujia Chen, Ajit Narayanan

Abstract:

Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

Procedia PDF Downloads 186