Search results for: non-dominated sorting genetic algorithm

Authors: Jianhua Zhang, Weiping Chen, Guanjie Chen, Jason Flannick, Emma Fikse, Glenda Smerin, Yanqin Yang, Yulong Li, John A. Hanover, William F. Simonds

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Ethnic disparities in many diseases are well recognized and reflect the consequences of genetic, behavior, and environmental factors. However, direct scientific evidence connecting the ethnic genetic variations and the disease disparities has been elusive, which may have led to the ethnic inequalities in large scale genetic studies. Through the genome-wide analysis of data representing 185,934 subjects, including 14,955 from our own studies of the African America Diabetes Mellitus, we discovered sets of genetic variants either unique to or conserved in all ethnicities. We further developed a quantitative gene function-based high-risk variant index (hrVI) of 20,428 genes to establish profiles that strongly correlate with the subjects' self-identified ethnicities. With respect to the ability to detect human essential and pathogenic genes, the hrVI analysis method is both comparable with and complementary to the well-known genetic analysis methods, pLI and VIRlof. Application of the ethnicity-specific hrVI analysis to the type 2 diabetes mellitus (T2DM) national repository, containing 20,791 cases and 24,440 controls, identified 114 candidate T2DM-associated genes, 8.8-fold greater than that of ethnicity-blind analysis. All the genes identified are defined as either pathogenic or likely-pathogenic in ClinVar database, with 33.3% diabetes-associated and 54.4% obesity-associated genes. These results demonstrate the utility of hrVI analysis and provide the first genetic evidence by clustering patterns of how genetic variations among ethnicities may impede the discovery of diabetes and foreseeably other disease-associated genes.

Keywords: diabetes-associated genes, ethnic health disparities, high-risk variant index, hrVI, T2DM

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Authors: Vaibhav Singh Rajput, Ravi Kumar Jatoth, Nagu Bhookya, Bhasker Boda

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In this paper proportional, integral, derivative (PID) controller is used to control the pitch angle of the aircraft when the elevation angle is changed or modified. The pitch angle is dependent on elevation angle; a change in one corresponds to a change in the other. The PID controller helps in restricted change of pitch rate in response to the elevation angle. The PID controller is dependent on different parameters like Kp, Ki, Kd which change the pitch rate as they change. Various methodologies are used for changing those parameters for getting a perfect time response pitch angle, as desired or wished by a concerned person. While reckoning the values of those parameters, trial and guessing may prove to be futile in order to provide comfort to passengers. So, using some metaheuristic techniques can be useful in handling these errors. Hybrid GA-PSO is one such powerful algorithm which can improve transient and steady state response and can give us more reliable results for PID gain scheduling problem.

Keywords: pitch rate, elevation angle, PID controller, genetic algorithm, particle swarm optimization, phugoid

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Authors: M. R. Naroui Rad, H. Fanaei, A. Ghalandarzehi

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To find out the yield of melon, the traits are vital. This research was performed with the objective to assess the impact of nine different morphological traits on the production of 20 melon landraces in the sistan weather region. For all the traits genetic variation was noted. Minimum genetical variance (9.66) along with high genetic interaction with the environment led to low heritability (0.24) of the yield. The broad sense heritability of the traits that were included into the differentiating model was more than it was in the production. In this study, the five selected traits, number of fruit, fruit weight, fruit width, flesh diameter and plant yield can differentiate the genotypes with high or low production. This demonstrated the significance of these 5 traits in plant breeding programs. Discriminant function of these 5 traits, particularly, the weight of the fruit, in case of the current outputs was employed as an all-inclusive parameter for pointing out landraces with the highest yield. 75% of variation in yield can be explained with this index, and the weight of fruit also has substantial relation with the total production (r=0.72**). This factor can be highly beneficial in case of future breeding program selections.

Keywords: melon, discriminant analysis, genetic components, yield, selection

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Authors: Elham Kazemi

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Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem.

Keywords: Quadratic Assignment Problem (QAP), Discrete Cuckoo Optimization Algorithm (DCOA), meta-heuristic algorithms, optimization algorithms

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Authors: Sahar El Shair, Laura Greco, William Reay, Murray Cairns

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Background: Rheumatoid arthritis (RA) is a chronic, systematic, inflammatory, autoimmune disease that involves damages to joints and erosions to the associated bones and cartilage, resulting in reduced physical function and disability. RA is a multifactorial disorder influenced by heterogenous genetic and environmental factors. Whilst different medications have proven successful in reducing inflammation associated with RA, they often come with significant side effects and limited efficacy. To address this, the novel pharmagenic enrichment score (PES) algorithm was tested in self-reported RA patients from the UK Biobank (UKBB), which is a cohort of predominantly European ancestry, and identified individuals with a high genetic risk in clinically actionable biological pathways to identify novel opportunities for precision interventions and drug repurposing to treat RA. Methods and materials: Genetic association data for rheumatoid arthritis was derived from publicly available genome-wide association studies (GWAS) summary statistics (N=97173). The PES framework exploits competitive gene set enrichment to identify pathways that are associated with RA to explore novel treatment opportunities. This data is then integrated into WebGestalt, Drug Interaction database (DGIdb) and DrugBank databases to identify existing compounds with existing use or potential for repurposed use. The PES for each of these candidates was then profiled in individuals with RA in the UKBB (Ncases = 3,719, Ncontrols = 333,160). Results A total of 209 pathways with known drug targets after multiple testing correction were identified. Several pathways, including interferon gamma signaling and TID pathway (which relates to a chaperone that modulates interferon signaling), were significantly associated with self-reported RA in the UKBB when adjusting for age, sex, assessment centre month and location, RA polygenic risk and 10 principal components. These pathways have a major role in RA pathogenesis, including autoimmune attacks against certain citrullinated proteins, synovial inflammation, and bone loss. Encouragingly, many also relate to the mechanism of action of existing RA medications. The analyses also revealed statistically significant association between RA polygenic scores and self-reported RA with individual PES scorings, highlighting the potential utility of the PES algorithm in uncovering additional genetic insights that could aid in the identification of individuals at risk for RA and provide opportunities for more targeted interventions. Conclusions In this study, pharmacologically annotated genetic risk was explored through the PES framework to overcome inter-individual heterogeneity and enable precision drug repurposing in RA. The results showed a statistically significant association between RA polygenic scores and self-reported RA and individual PES scorings for 3,719 RA patients. Interestingly, several enriched PES pathways were targeted by already approved RA drugs. In addition, the analysis revealed genetically supported drug repurposing opportunities for future treatment of RA with a relatively safe profile.

Keywords: rheumatoid arthritis, precision medicine, drug repurposing, system biology, bioinformatics

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Aptin Rahnavard, Ghavamaldin Asadian, Khalil Pourshamsian, Mariamalsadat Taghavi

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Dog rose is one of the important rose species in Iran that the distant past had been considered due to nutritional value and medicinal. Despite its long history of use, due to poor information on the genetic modification of plants has been done resources inheritance. In this study was to assess the genetic diversity. Total of 30 genotypes Dog rose from areas of northern Iran in the Caspian region (provinces of Guilan and Mazandaran) were evaluated using 25 RAPD primers. The number of bands produced total of 202 and for each primer were measured in a bands with an average 8-band .The number of polymorphic bands per primer ranged from 1 to 13 and the bands were in the range of 300 to 3000 bp. Based on the results OPA-04 primer with 13 bands and PRA-1, E-09 and A-04 with 5-band were created maximum and minimum number of amplified fragments. Molecular marker genotypes showed a high degree of polymorphism. Studied genotypes based on RAPD results were divided into 2 groups and 2 subgroups. Most similar in subgroups A2 and B group was the lowest.

Keywords: rosa canina spp., RAPD marker, genetic variation, caspian climate

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Authors: Jerome G. Chandraseelan, Samuel M. D. Oliveira, Antti Häkkinen, Sofia Startceva, Andre S. Ribeiro

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We used live E. coli containing synthetic genetic oscillators to study how the degree of synchrony between the genetic circuits of sister cells changes with temperature. We found that both the mean and the variability of the degree of synchrony between the fluorescence signals from sister cells are affected by temperature. Also, while most pairs of sister cells were found to be highly synchronous in each condition, the number of asynchronous pairs increased with increasing temperature, which was found to be due to disruptions in the oscillations. Finally we provide evidence that these disruptions tend to affect multiple generations as opposed to individual cells. These findings provide insight in how to design more robust synthetic circuits and in how cell division can affect their dynamics.

Keywords: repressilator, robustness, synchrony, synthetic biology

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Authors: Chaghoub Soraya, Zhang Xiaoyan

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This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques.

Keywords: approximation algorithms, buy-at-bulk, combinatorial optimization, network design, p-median

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Authors: Rajagopal Kavitha, Van Lun Low, Mohd Sofian-Azirun, Chee Dhang Chen, Mohd Yusof Farida Zuraina, Mohd Salleh Ahmad Firdaus, Navaratnam Shanti, Abdul Haiyee Zaibunnisa

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This study investigated the hidden genetic lineages in the oriental latrine fly Chrysomya megacephala (Fabricius) across four states (i.e., Johore, Pahang, Perak and Selangor) and a federal territory (i.e., Kuala Lumpur) in Malaysia using Cytochrome b (Cyt b) genetic marker. The Cyt b phylogenetic tree and haplotype network revealed three distinct genetic lineages of Ch. megacephala. Lineage A, the basal clade was restricted to flies that originated from Kuala Lumpur and Selangor, while Lineages B and C, comprised of flies from all studied populations. An overlap of the three genetically divergent groups of Ch. megacephala was observed. However, the flies from both Kuala Lumpur and Selangor populations consisted of three different lineages, indicating that they are genetically diverse compared to those from Pahang, Perak and Johore.

Keywords: forensic entomology, calliphoridae, mitochondrial DNA, cryptic lineage

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Authors: Willian C. De Brito, Hernan D. C. Munoz, Erlan V. C. Carvalho, Helder L. C. De Oliveira

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In order to improve commute time for small distance trips and relieve large cities traffic, a new transport category has been the subject of research and new designs worldwide. The air taxi travel market promises to change the way people live and commute by using the concept of vehicles with the ability to take-off and land vertically and to provide passenger’s transport equivalent to a car, with mobility within large cities and between cities. Today’s civil air transport remains costly and accounts for 2% of the man-made CO₂ emissions. Taking advantage of this scenario, many companies have developed their own Vertical Take Off and Landing (VTOL) design, seeking to meet comfort, safety, low cost and flight time requirements in a sustainable way. Thus, the use of green power supplies, especially batteries, and fully electric power plants is the most common choice for these arising aircrafts. However, it is still a challenge finding a feasible way to handle with the use of batteries rather than conventional petroleum-based fuels. The batteries are heavy and have an energy density still below from those of gasoline, diesel or kerosene. Therefore, despite all the clear advantages, all electric aircrafts (AEA) still have low flight autonomy and high operational cost, since the batteries must be recharged or replaced. In this sense, this paper addresses a way to optimize the energy consumption in a typical mission of an aerial taxi aircraft. The approach and landing procedure was chosen to be the subject of an optimization genetic algorithm, while final programming can be adapted for take-off and flight level changes as well. A real tilt rotor aircraft with fully electric power plant data was used to fit the derived dynamic equations of motion. Although a tilt rotor design is used as a proof of concept, it is possible to change the optimization to be applied for other design concepts, even those with independent motors for hover and cruise flight phases. For a given trajectory, the best set of control variables are calculated to provide the time history response for aircraft´s attitude, rotors RPM and thrust direction (or vertical and horizontal thrust, for independent motors designs) that, if followed, results in the minimum electric power consumption through that landing path. Safety, comfort and design constraints are assumed to give representativeness to the solution. Results are highly dependent on these constraints. For the tested cases, performance improvement ranged from 5 to 10% changing initial airspeed, altitude, flight path angle, and attitude.

Keywords: air taxi travel, all electric aircraft, batteries, energy consumption, genetic algorithm, landing performance, optimization, performance improvement, tilt rotor, VTOL design

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Authors: Younis Elhaddad, Alfonso Ortega

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Oil production by means of gas lift is a standard technique in oil production industry. To optimize the total amount of oil production in terms of the amount of gas injected is a key question in this domain. Different methods have been tested to propose a general methodology. Many of them apply well-known numerical methods. Some of them have taken into account the power of evolutionary approaches. Our goal is to provide the experts of the domain with a powerful automatic searching engine into which they can introduce their knowledge in a format close to the one used in their domain, and get solutions comprehensible in the same terms, as well. These proposals introduced in the genetic engine the most expressive formal models to represent the solutions to the problem. These algorithms have proven to be as effective as other genetic systems but more flexible and comfortable for the researcher although they usually require huge search spaces to justify their use due to the computational resources involved in the formal models. The first step to evaluate the viability of applying our approaches to this realm is to fully understand the domain and to select an instance of the problem (gas lift optimization) in which applying genetic approaches could seem promising. After analyzing the state of the art of this topic, we have decided to choose a previous work from the literature that faces the problem by means of numerical methods. This contribution includes details enough to be reproduced and complete data to be carefully analyzed. We have designed a classical, simple genetic algorithm just to try to get the same results and to understand the problem in depth. We could easily incorporate the well mathematical model, and the well data used by the authors and easily translate their mathematical model, to be numerically optimized, into a proper fitness function. We have analyzed the 100 curves they use in their experiment, similar results were observed, in addition, our system has automatically inferred an optimum total amount of injected gas for the field compatible with the addition of the optimum gas injected in each well by them. We have identified several constraints that could be interesting to incorporate to the optimization process but that could be difficult to numerically express. It could be interesting to automatically propose other mathematical models to fit both, individual well curves and also the behaviour of the complete field. All these facts and conclusions justify continuing exploring the viability of applying the approaches more sophisticated previously proposed by our research group.

Keywords: evolutionary automatic programming, gas lift, genetic algorithms, oil production

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Authors: Guo Yan

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Clothing recycling bin is a traditional way to collect textile waste in many areas. In the clothing recycling business, the transportation cost normally takes over 50% of total costs. This case gives a good way to reduce transportation cost by reverse logistics system. In this reverse logistics system, there are offline strategic alliance partners, such as transport firms, convenience stores, laundries, and post office which are integrated onto the mobile APP. Offline strategic alliance partners provide the service of textile waste collection, and transportation by their vacant vehicles return journey from convenience stores, laundries and post offices to sorting centers. The results of the case study provide the strategic alliance with a valuable and light - asset business model by using the logistics of offline memberships. The company in this case just focuses on textile waste sorting, reuse, recycling etc. The research method of this paper is a case study of a clothing recycling company in Chengdu by field research and interview; the analysis is based on the theory of the reverse logistics system.

Keywords: closed-loop recycles system, clothing recycling, end-of-life clothing, sharing economy, strategic alliance, reverse logistics.

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: degree, initial cluster center, k-means, minimum spanning tree

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Authors: Acheampong Addo, Emmanuel Odartei Armah, Seth Koranteng Agyakwah, Ruby Asmah, Emmanuel Tetteh-Doku Mensah, Rhoda Lims Diyie, Sena Amewu, Catherine Ragasa, Edward Kofi Abban, Mike Yaw Osei-Atweneboana

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The study investigated genetic variation and relationships among populations of Nile tilapia cultured in small-scale fish farms in selected regions of Ghana. A total of 700 samples were collected. All samples were screened with five microsatellite markers and results were analyzed using (Genetic Analysis in Excel), (Molecular and Evolutionary Genetic Analysis software, and Genpop on the web for Heterozygosity and Shannon diversity, (Analysis of Molecular Variance), and (Principal Coordinate Analysis). Fish from the 16 populations (made up of 14 farms and 2 selectively bred populations) clustered into three groups: 7 populations clustered with the GIFT-derived strain, 4 populations clustered with the Akosombo strain, and three populations were in a separate cluster. The clustering pattern indicated groups of different strains of Nile tilapia cultured. Mantel correlation test also showed low genetic variations among the 16 populations hence the need to boost seed quality in order to accelerate aquaculture production in Ghana.

Keywords: microsatellites, small- scale, Nile tilapia, akosombo strain, GIFT strain

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Authors: Archana Singh, Jyoti Agarwal, Ajay Rana

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Data Mining is an efficient technology to discover patterns in large databases. Association Rule Mining techniques are used to find the correlation between the various item sets in a database, and this co-relation between various item sets are used in decision making and pattern analysis. In recent years, the problem of finding association rules from large datasets has been proposed by many researchers. Various research papers on association rule mining (ARM) are studied and analyzed first to understand the existing algorithms. Apriori algorithm is the basic ARM algorithm, but it requires so many database scans. In DIC algorithm, less amount of database scan is needed but complex data structure lattice is used. The main focus of this paper is to propose a new optimized algorithm (Friendly Algorithm) and compare its performance with the existing algorithms A data set is used to find out frequent itemsets and association rules with the help of existing and proposed (Friendly Algorithm) and it has been observed that the proposed algorithm also finds all the frequent itemsets and essential association rules from databases as compared to existing algorithms in less amount of database scan. In the proposed algorithm, an optimized data structure is used i.e. Graph and Adjacency Matrix.

Keywords: association rules, data mining, dynamic item set counting, FP-growth, friendly algorithm, graph

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Authors: Kassaye Balkew Workagegn, Gunnar Klemetsdal, Hans Magnus Gjøen

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In this study, the first objective was to investigate whether non-lethal or non-invasive methods, utilizing body measurements, could be used to efficiently predict fillet weight and fillet yield for a complete diallel cross of three Nile tilapia (Oreochromis niloticus) strains collected from three Ethiopian Rift Valley lakes, Lakes Ziway, Koka and Chamo. The second objective was to estimate heritability of body weight, actual and predicted fillet traits, as well as genetic correlations between these traits. A third goal was to estimate additive, reciprocal, and heterosis effects for body weight and the various fillet traits. As in females, early sexual maturation was widespread, only 958 male fish from 81 full-sib families were used, both for the prediction of fillet traits and in genetic analysis. The prediction equations from body measurements were established by forward regression analysis, choosing models with the least predicted residual error sums of squares (PRESS). The results revealed that body measurements on live Nile tilapia is well suited to predict fillet weight but not fillet yield (R²= 0.945 and 0.209, respectively), but both models were seemingly unbiased. The genetic analyses were carried out with bivariate, multibreed models. Body weight, fillet weight, and predicted fillet weight were all estimated with a heritability ranged from 0.23 to 0.28, and with genetic correlations close to one. Contrary, fillet yield was only to a minor degree heritable (0.05), while predicted fillet yield obtained a heritability of 0.19, being a resultant of two body weight variables known to have high heritability. The latter trait was estimated with genetic correlations to body weight and fillet weight traits larger than 0.82. No significant differences among strains were found for their additive genetic, reciprocal, or heterosis effects, while total heterosis effects were estimated as positive and significant (P < 0.05). As a conclusion, prediction of prediction of fillet weight based on body measurements is possible, but not for fillet yield.

Keywords: additive, fillet traits, genetic correlation, heritability, heterosis, prediction, reciprocal

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Authors: Ji Eun Shin, Dong Hoon Lim

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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

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Authors: M. Sakthivel, A. Devaki, D. Balasubramanyam, P. Kumarasamy, A. Raja, R. Anilkumar, H. Gopi

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The genetic parameters of growth traits in the New Zealand White rabbits maintained at Sheep Breeding and Research Station, Sandynallah, The Nilgiris, India were estimated by partitioning the variance and covariance components. The (co)variance components of body weights at weaning (W42), post-weaning (W70) and marketing (W135) age and growth efficiency traits viz., average daily gain (ADG), relative growth rate (RGR) and Kleiber ratio (KR) estimated on a daily basis at different age intervals (1=42 to 70 days; 2=70 to 135 days and 3=42 to 135 days) from weaning to marketing were estimated by restricted maximum likelihood, fitting six animal models with various combinations of direct and maternal effects. Data were collected over a period of 15 years (1998 to 2012). A log-likelihood ratio test was used to select the most appropriate univariate model for each trait, which was subsequently used in bivariate analysis. Heritability estimates for W42, W70 and W135 were 0.42 ± 0.07, 0.40 ± 0.08 and 0.27 ± 0.07, respectively. Heritability estimates of growth efficiency traits were moderate to high (0.18 to 0.42). Of the total phenotypic variation, maternal genetic effect contributed 14 to 32% for early body weight traits (W42 and W70) and ADG1. The contribution of maternal permanent environmental effect varied from 6 to 18% for W42 and for all the growth efficiency traits except for KR2. Maternal permanent environmental effect on most of the growth efficiency traits was a carryover effect of maternal care during weaning. Direct maternal genetic correlations, for the traits in which maternal genetic effect was significant, were moderate to high in magnitude and negative in direction. Maternal effect declined as the age of the animal increased. The estimates of total heritability and maternal across year repeatability for growth traits were moderate and an optimum rate of genetic progress seems possible in the herd by mass selection. The estimates of genetic and phenotypic correlations among body weight traits were moderate to high and positive; among growth efficiency traits were low to high with varying directions; between body weights and growth efficiency traits were very low to high in magnitude and mostly negative in direction. Moderate to high heritability and higher genetic correlation in body weight traits promise good scope for genetic improvement provided measures are taken to keep the inbreeding at the lowest level.

Keywords: genetic parameters, growth traits, maternal effects, rabbit genetics

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Authors: Halil Ibrahim Demir, Caner Erden, Mumtaz Ipek, Ozer Uygun

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Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied.

Keywords: process planning, weighted scheduling, weighted due-date assignment, genetic search, simulated annealing, hybrid meta-heuristics

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Authors: Muhammad R. Bello, Mamman A. Wasagu, Yahya M. Kamar

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The study investigated the effects of computer-aided instructional package (CAIP) on performance and retention of genetic concepts among secondary school students in Niger State. Quasi-experimental research design i.e. pre-test-post-test experimental and control groups were adopted for the study. The population of the study was all senior secondary school three (SS3) students’ offering biology. A sample of 223 students was randomly drawn from six purposively selected secondary schools. The researchers’ developed computer aided instructional package (CAIP) on genetic concepts was used as treatment instrument for the experimental group while the control group was exposed to the conventional lecture method (CLM). The instrument for data collection was a Genetic Performance Test (GEPET) that had 50 multiple-choice questions which were validated by science educators. A Reliability coefficient of 0.92 was obtained for GEPET using Pearson Product Moment Correlation (PPMC). The data collected were analyzed using IBM SPSS Version 20 package for computation of Means, Standard deviation, t-test, and analysis of covariance (ANCOVA). The ANOVA analysis (Fcal (220) = 27.147, P < 0.05) shows that students who received instruction with CAIP outperformed the students who received instruction with CLM and also had higher retention. The findings also revealed no significant difference in performance and retention between male and female students (tcal (103) = -1.429, P > 0.05). It was recommended amongst others that teachers should use computer-aided instructional package in teaching genetic concepts in order to improve students’ performance and retention in biology subject. Keywords: Computer-aided Instructional Package, Performance, Retention and Genetic Concepts.

Keywords: computer aided instructional package, performance, retention, genetic concepts, senior secondary school students

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Authors: I. Kade Wiratama

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As a result of the significant growth of wind turbines in size, blade load control has become the main challenge for large wind turbines. Many advanced techniques have been investigated aiming at developing control devices to ease blade loading. Amongst them, trailing edge flaps have been proven as effective devices for load alleviation. The present study aims at investigating the potential benefits of flaps in enhancing the energy capture capabilities rather than blade load alleviation. A software tool is especially developed for the aerodynamic simulation of wind turbines utilising blades equipped with flaps. As part of the aerodynamic simulation of these wind turbines, the control system must be also simulated. The simulation of the control system is carried out via solving an optimisation problem which gives the best value for the controlling parameter at each wind turbine run condition. Developing a genetic algorithm optimisation tool which is especially designed for wind turbine blades and integrating it with the aerodynamic performance evaluator, a design optimisation tool for blades equipped with flaps is constructed. The design optimisation tool is employed to carry out design case studies. The results of design case studies on wind turbine AWT 27 reveal that, as expected, the location of flap is a key parameter influencing the amount of improvement in the power extraction. The best location for placing a flap is at about 70% of the blade span from the root of the blade. The size of the flap has also significant effect on the amount of enhancement in the average power. This effect, however, reduces dramatically as the size increases. For constant speed rotors, adding flaps without re-designing the topology of the blade can improve the power extraction capability as high as of about 5%. However, with re-designing the blade pretwist the overall improvement can be reached as high as 12%.

Keywords: flaps, design blade, optimisation, simulation, genetic algorithm, WTAero

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Authors: Arman S. Kussainov, Altynbek K. Beisekov

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This work focuses on building an efficient universal procedure to construct a single density of states from the multiple pieces of data provided by the parallel implementation of the Wang Landau Monte Carlo based algorithm. The Ising and Pott models were used as the examples of the two-dimensional spin lattices to construct their densities of states. Sampled energy space was distributed between the individual walkers with certain overlaps. This was made to include the latest development of the algorithm as the density of states replica exchange technique. Several factors of immediate importance for the seamless stitching process have being considered. These include but not limited to the speed and universality of the initial parallel algorithm implementation as well as the data post-processing to produce the expected smooth density of states.

Keywords: density of states, Monte Carlo, parallel algorithm, Wang Landau algorithm

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Authors: Reza Dinarvand, Mahdi Sadeghian, Somaye Sadeghian

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Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.

Keywords: artificial neural network, clayey soil, imperialist competition algorithm, lateral bearing capacity, short pile

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Authors: Girija Madhavanprabhakaran, Frincy Franacis, Sheeba Elizabeth John

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Introduction: A wide range of prenatal genetic screening is introduced with increasing incidences of congenital anomalies even in low-risk pregnancies and is an emerging standard of care. Being frontline caretakers, the role and responsibilities of nurses and midwives are critical as they are working along with couples to provide evidence-based supportive educative care. The increasing genetic disorders and advances in prenatal genetic screening with limited genetic counselling facilities urge nurses and midwifery nurses with essential competencies to help couples to take informed decision. Objective: This integrative literature review aimed to explore nurse midwives’ knowledge and role in prenatal screening and genetic counselling competency and the challenges faced by them to cater to all pregnant women to empower their autonomy in decision making and ensuring psychological comfort. Method: An electronic search using keywords prenatal screening, genetic counselling, prenatal counselling, nurse midwife, nursing education, genetics, and genomics were done in the PUBMED, SCOPUS and Medline, Google Scholar. Finally, based on inclusion criteria, 8 relevant articles were included. Results: The main review results suggest that nurses and midwives lack essential support, knowledge, or confidence to be able to provide genetic counselling and help the couples ethically to ensure client autonomy and decision making. The majority of nurses and midwives reported inadequate levels of knowledge on genetic screening and their roles in obtaining family history, pedigrees, and providing genetic information for an affected client or high-risk families. The deficiency of well-recognized and influential clinical academic midwives in midwifery practice is also reported. Evidence recommended to update and provide sound educational training to improve nurse-midwife competence and confidence. Conclusion: Overcoming the challenges to achieving informed choices about fetal anomaly screening globally is a major concern. Lack of adequate knowledge and counselling competency, communication insufficiency, need for education and policy are major areas to address. Prenatal nurses' and midwives’ knowledge on prenatal genetic screening and essential counselling competencies can ensure services to the majority of pregnant women around the globe to be better-informed decision-makers and enhances their autonomy, and reduces ethical dilemmas.

Keywords: challenges, genetic counselling, prenatal screening, prenatal counselling

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Authors: Dimitri Golenko-Ginzburg

Abstract:

A CPM network project with deterministic activity durations, in which activities require homogenous resources with fixed capacities, is considered. The problem is to determine the optimal schedule of starting times for all network activities within their maximal allowable limits (in order not to exceed the network's critical time) to minimize the maximum required resources for the project at any point in time. In case when a non-critical activity may start only at discrete moments with the pregiven time span, the problem becomes NP-complete and an optimal solution may be obtained via a look-over algorithm. For the case when a look-over requires much computational time an approximate algorithm is suggested. The algorithm's performance ratio, i.e., the relative accuracy error, is determined. Experimentation has been undertaken to verify the suggested algorithm.

Keywords: resource smoothing problem, CPM network, lookover algorithm, lexicographical order, approximate algorithm, accuracy estimate

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Authors: Asiye Ciftci, Zeki Kaya

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Populus nigra is one of the most economically and ecologically important forest trees in Turkey, well known for its rapid growth, good ability to vegetative propagation and the extreme uses of its wood. Due to overexploitation, loss of natural distribution area and extreme hybridization and introgression, Populus nigra is one of the most threatened tree species in Turkey and Europe. Using 20 nuclear microsatellite loci, the genetic structure of European black poplar populations along the two largest rivers of Turkey was analyzed. All tested loci were highly polymorphic, displaying 5 to 15 alleles per locus. Observed heterozygosity (overall Ho = 0.79) has been higher than the expected (overall He = 0.58) in each population. Low level of genetic differentiation among populations (FST= 0,03) and excess of heterozygotes for each river were found. Human-mediated dispersal, phenotypic selection, high level of gene flow and extensive circulations of clonal materials may cause those situations. The genetic data obtained from this study could provide the basis for efficient in situ and ex-situ conservation and restoration of species natural populations in its natural habitat as well as having sustainable breeding and poplar plantations in the future.

Keywords: populus, clonal, loci, ex situ

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Authors: Thiago Ribeiro De Alencar, Jacyro Gramulia Junior, Patricia Teixeira Leite Asano

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The area of the electricity sector that deals with energy needs by the hydropower and thermoelectric in a coordinated way is called Planning Operating Hydrothermal Power Systems. The aim of this area is to find a political operative to provide electrical power to the system in a specified period with minimization of operating cost. This article proposes a computational tool for solving the planning problem. In addition, this article will be introducing a methodology to find new transfer points between reservoirs increasing energy production in hydroelectric power plants cascade systems. The computational tool proposed in this article applies: i) genetic algorithms to optimize the water transfer and operation of hydroelectric plants systems; and ii) Ant Colony algorithm to find the trajectory with the least energy pumping for the construction of pipes transfer between reservoirs considering the topography of the region. The computational tool has a database consisting of 35 hydropower plants and 41 reservoirs, which are part of the southeastern Brazilian system, which has been implemented in an individualized way.

Keywords: ant colony system, genetic algorithms, hydroelectric, hydrothermal systems, optimization, water transfer between rivers

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Authors: Andi Darmawan, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Diabetes mellitus (DM) is a chronic disease in human that occurred if pancreas cannot produce enough of insulin hormone or the body uses ineffectively insulin hormone which causes increasing level of glucose in the blood, or it was called hyperglycemia. In Indonesia, DM is a serious disease on health because it can cause blindness, kidney disease, diabetic feet (gangrene), and stroke. The type of DM criteria can also be divided based on the main causes; they are DM type 1, type 2, and gestational. Diabetes type 1 or previously known as insulin-independent diabetes is due to a lack of production of insulin hormone. Diabetes type 2 or previously known as non-insulin dependent diabetes is due to ineffective use of insulin while gestational diabetes is a hyperglycemia that found during pregnancy. The most one type commonly found in patient is DM type 2. The main factors of this disease are genetic (A) and life style (E). Those disease with 2 factors can be constructed with additive genetic and unique environment (AE) model. In this article was discussed parameter estimation of AE model using Bayesian method and the inheritance character simulation on parent-offspring. On the AE model, there are response variable, predictor variables, and parameters were capable of representing the number of population on research. The population can be measured through a taken random sample. The response and predictor variables can be determined by sample while the parameters are unknown, so it was required to estimate the parameters based on the sample. Estimation of AE model parameters was obtained based on a joint posterior distribution. The simulation was conducted to get the value of genetic variance and life style variance. The results of simulation are 0.3600 for genetic variance and 0.0899 for life style variance. Therefore, the variance of genetic factor in DM type 2 is greater than life style.

Keywords: AE model, Bayesian method, diabetes mellitus type 2, genetic, life style

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Authors: Lu Yang, Meiyang Song, Xiang Yu, Wenhao Zhou, Chuntao Feng

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To address the problem that the FM continuous-wave radar (FMCW) extracts human vital sign signals which are susceptible to noise interference and low reconstruction accuracy, a new detection scheme for the sign signals is proposed. Firstly, an improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) algorithm is applied to decompose the radar-extracted thoracic signals to obtain several intrinsic modal functions (IMF) with different spatial scales, and then the IMF components are optimized by a BP neural network improved by immune genetic algorithm (IGA). The simulation results show that this scheme can effectively separate the noise and accurately extract the respiratory and heartbeat signals and improve the reconstruction accuracy and signal-to-noise ratio of the sign signals.

Keywords: frequency modulated continuous wave radar, ICEEMDAN, BP neural network, vital signs signal

Procedia PDF Downloads 166