Search results for: fuzzy genetic network programming

Authors: Arshad Mehmood Ch, Riaz Ahmad, Imran Ali Ch, Waqas Durrani

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This study deals with the application of Ant Colony Optimization (ACO) approach to solve no-wait flowshop scheduling problem (NW-FSSP). ACO algorithm so developed has been coded on Matlab computer application. The paper covers detailed steps to apply ACO and focuses on judging the strength of ACO in relation to other solution techniques previously applied to solve no-wait flowshop problem. The general purpose approach was able to find reasonably accurate solutions for almost all the problems under consideration and was able to handle a fairly large spectrum of problems with far reduced CPU effort. Careful scrutiny of the results reveals that the algorithm presented results better than other approaches like Genetic algorithm and Tabu Search heuristics etc; earlier applied to solve NW-FSSP data sets.

Keywords: no-wait, flowshop, scheduling, ant colony optimization (ACO), makespan

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Authors: Pooja Mishra, Chris Pollett

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This paper explores efficient ways to implement various media-updating features like news aggregation, video conversion, and bulk email handling. All of these jobs share the property that they are periodic in nature, and they all benefit from being handled in a distributed fashion. The data for these jobs also often comes from a social or collaborative source. We isolate the class of periodic, one round map reduce jobs as a useful setting to describe and handle media updating tasks. As such tasks are simpler than general map reduce jobs, programming them in a general map reduce platform could easily become tedious. This paper presents a MediaUpdater module of the Yioop Open Source Search Engine Web Portal designed to handle such jobs via an extension of a PHP class. We describe how to implement various media-updating tasks in our system as well as experiments carried out using these implementations on an Amazon Web Services cluster.

Keywords: distributed jobs framework, news aggregation, video conversion, email

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Authors: Anikó Kaluzsa

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The article's author aims to introduce and analyze those microbiological safety hazards which indicate the presence of secondary contamination in the water supply system. Since drinking water belongs to primary foods and is the basic condition of life, special attention should be paid on its quality. There are such indicators among the microbiological features can be found in water, which are clear evidence of the presence of water contamination, and based on this there is no need to perform other diagnostics, because they prove properly the contamination of the given water supply section. Laboratory analysis can help - both technologically and temporally – to identify contamination, but it does matter how long takes the removal and if the disinfection process takes place in time. The identification of the factors that often occur in the same places or the chance of their occurrence is greater than the average, facilitates our work. The pathogen microbiological risk assessment by the help of several features determines the most likely occurring microbiological features in the Carpathian basin. From among all the microbiological indicators, that are recommended targets for routine inspection by the World Health Organization, there is a paramount importance of the appearance of Escherichia coli in the water network, as its presence indicates the potential ubietiy of enteric pathogens or other contaminants in the water network. In addition, the author presents the steps of microbiological risk assessment analyzing those pathogenic micro-organisms registered to be the most critical.

Keywords: drinking water, E. coli, microbiological indicators, risk assessment, water safety plan

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Authors: Jacky Hau

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The prevalence of emerging technologies disrupts various industries as well as consumer behavior. Data collection has been in the fingertip and inherited through enabled Internet-of-things (IOT) devices. Big data analytics (BDA) becomes possible and allows real-time demand network management (DNM) through leagile supply chain. To enhance further on its resilience and predictability, governance is going to be examined to promote supply chain transparency and trust in an efficient manner. Leagility combines lean thinking and agile techniques in supply chain management. It aims at reducing costs and waste, as well as maintaining responsiveness to any volatile consumer demand by means of adjusting the decoupling point where the product flow changes from push to pull. Leagility would only be successful when collaborative planning, forecasting, and replenishment (CPFR) process or alike is in place throughout the supply chain business entities. Governance and procurement of the supply chain, however, is crucial and challenging for the execution of CPFR as every entity has to walk-the-talk generously for the sake of overall benefits of supply chain performance, not to mention the complexity of exercising the polices at both of within across various supply chain business entities on account of organizational behavior and mutual trust. Empirical survey results showed that the effective timespan on demand forecasting had been drastically shortening in the magnitude of months to weeks planning horizon, thus agility shall come first and preferably following by lean approach in a timely manner.

Keywords: governance, leagility, procure-to-pay, source-to-contract

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Authors: M. Ananthakrishnan, Sunil K Patil, Koti Naveen, Inuganti Hemanth Kumar

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State estimation output of EMS forms the base case for all other advanced applications used in real time by a power system operator. Ensuring tuning of state estimator is a repeated process and cannot be left once a good solution is obtained. This paper attempts to demonstrate methods to improve state estimator solution by identifying incorrect modelling and telemetry inputs to the application. In this work, identification of database topology modelling error by plotting static network using node-to-node connection details is demonstrated with examples. Analytical methods to identify wrong transmission parameters, incorrect limits and mistakes in pseudo load and generator modelling are explained with various cases observed. Further, methods used for active and reactive power tuning using bus summation display, reactive power absorption summary, and transformer tap correction are also described. In a large power system, verifying all network static data and modelling parameter on regular basis is difficult .The proposed tuning methods can be easily used by operators to quickly identify errors to obtain the best possible state estimation performance. This, in turn, can lead to improved decision-support capabilities, ultimately enhancing the safety and reliability of the power grid.

Keywords: active power tuning, database modelling, reactive power, state estimator

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Authors: Saeedeh Ahmadi Basir, Mohammad Mahdavi Mazdeh, Mohammad Namakshenas

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Manufacturers often dispatch jobs in batches to reduce delivery costs. However, sending several jobs in batches can have a negative effect on other scheduling-related objective functions such as minimizing the number of tardy jobs which is often used to rate managers’ performance in many manufacturing environments. This paper aims to minimize the number of weighted tardy jobs and the sum of delivery costs of a two-stage assembly flow-shop problem in a batch delivery system. We present a mixed-integer linear programming (MILP) model to solve the problem. As this is an MILP model, the commercial solver (the CPLEX solver) is not guaranteed to find the optimal solution for large-size problems at a reasonable amount of time. We present several numerical examples to confirm the accuracy of the model.

Keywords: scheduling, two-stage assembly flow-shop, tardy jobs, batched delivery system

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Authors: Feng Yang

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Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: Diego De Castro Rodrigues, Marcelo Lisboa Rocha, Daniela M. De Q. Trevisan, Marcos Dias Da Conceicao, Gabriel Rosa, Rommel M. Barbosa

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The use of association rules algorithms within data mining is recognized as being of great value in the knowledge discovery in databases. Very often, the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantity. The purpose of this research is to present a method for reducing the quantity of rules generated with association algorithms. Therefore, a computational algorithm was developed with the use of a Weka Application Programming Interface, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model, and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence, and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.

Keywords: data mining, association rules, rules reduction, artificial intelligence

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Authors: Dimitrios Binas, Marianna Konidari, Charis Bourgioti, Lia Angela Moulopoulou, Theodore Economopoulos, George Matsopoulos

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High grade ovarian epithelial cancer (OEC) is fatal gynecological cancer and the poor prognosis of this entity is closely related to considerable intratumoral genetic heterogeneity. By examining imaging data, it is possible to assess the heterogeneity of tumorous tissue. This study proposes a methodology for aligning, segmenting and finally visualizing information from various magnetic resonance imaging series in order to construct 3D models of heterogeneity maps from the same tumor in OEC patients. The proposed system may be used as an adjunct digital tool by health professionals for personalized medicine, as it allows for an easy visual assessment of the heterogeneity of the examined tumor.

Keywords: image segmentation, ovarian epithelial cancer, quantitative characteristics, image registration, tumor visualization

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Authors: Siming Xie

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In social networks, the term homophily refers to the tendency of agents with similar characteristics to link with one another and is so robustly observed across many contexts and dimensions. The starting point of my research is the observation that the “type” of agents is not a single exogenous variable. Agents, despite their differences in race, religion, and other hard to alter characteristics, may share interests and engage in activities that cut across those predetermined lines. This research aims to capture the interactions of homophily effects in a model where agents have two-dimension characteristics (i.e., race and personal hobbies such as basketball, which one either likes or dislikes) and with biases in meeting opportunities and in favor of same-type friendships. A novel feature of my model is providing a matching process with biased meeting probability on different dimensions, which could help to understand the structuring process in multidimensional networks without missing layer interdependencies. The main contribution of this study is providing a welfare based matching process for agents with multi-dimensional characteristics. In particular, this research shows that the biases in meeting opportunities on one dimension would lead to the emergence of homophily on the other dimension. The objective of this research is to determine the pattern of homophily in network formations, which will shed light on our understanding of segregation and its remedies. By constructing a two-dimension matching process, this study explores a method to describe agents’ homophilous behavior in a social network with multidimension and construct a game in which the minorities and majorities play different strategies in a society. It also shows that the optimal strategy is determined by the relative group size, where society would suffer more from social segregation if the two racial groups have a similar size. The research also has political implications—cultivating the same characteristics among agents helps diminishing social segregation, but only if the minority group is small enough. This research includes both theoretical models and empirical analysis. Providing the friendship formation model, the author first uses MATLAB to perform iteration calculations, then derives corresponding mathematical proof on previous results, and last shows that the model is consistent with empirical evidence from high school friendships. The anonymous data comes from The National Longitudinal Study of Adolescent Health (Add Health).

Keywords: homophily, multidimension, social networks, friendships

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Authors: Ridwan Dewayanto Rusli

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We show how prisoner's dilemma-type competition problems can be mitigated through rapid platform diversification and ecosystem expansion. We analyze a ride-hailing company in Southeast Asia, Gojek, whose network grew to more than 170 million users comprising consumers, partner drivers, merchants, and complementors within a few years and has already achieved higher contribution margins than ride-hailing peers Uber and Lyft. Its ecosystem integrates ride-hailing, food delivery and logistics, merchant solutions, e-commerce, marketplace and advertising, payments, and fintech offerings. The company continues growing its network of complementors and App developers, expanding content and gaining critical mass in consumer data analytics and advertising. We compare the company's growth and diversification trajectory with those of its main international rivals and peers. The company's rapid growth and future potential are analyzed using Cusumano's (2012) Staying Power and Six Principles, Hax and Wilde's (2003) and Hax's (2010) The Delta Model as well as Santos' (2016) home-market advantages frameworks. The recently announced multi-billion-dollar merger with one of Southeast Asia's largest e-commerce majors lends additional support to the above arguments.

Keywords: ride-hailing, prisoner's dilemma, platform and ecosystem strategy, digital applications, diversification, home market advantages, e-commerce

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Authors: Amy L. Irvine, Gabriel Reygondeau, Derek P. Tittensor

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Marine protected areas (MPAs) within Canada’s exclusive economic zone help ensure the conservation and sustainability of marine ecosystems and the continued provision of ecosystem services to society (e.g., food, carbon sequestration). With ongoing and accelerating climate change, however, MPAs may become undermined in terms of their effectiveness at fulfilling these outcomes. Many populations of species, especially those at their thermal range limits, may shift to cooler waters or become extirpated due to climate change, resulting in new species compositions and ecological interactions within static MPA boundaries. While Canadian MPA management follows international guidelines for marine conservation, no consistent approach exists for adapting MPA networks to climate change and the resulting altered ecosystem conditions. To fill this gap, projected climate-driven shifts in species distributions on Canada’s east coast were analyzed to identify when native species emigrate and novel species immigrate within the network and how high mitigation and carbon emission scenarios influence these timelines. Indicators of the ecological changes caused by these species' shifts in the biological community were also developed. Overall, our research provides projections of climate change impacts and helps to guide adaptive management responses within the Canadian east coast MPA network.

Keywords: climate change, ecosystem modeling, marine protected areas, management

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Authors: Abul Bashar

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The recent surge in the adoption of cloud computing systems by various organizations has brought forth the challenge of evaluating their performance. One of the major issues faced by the cloud service providers and customers is to assess the ability of cloud computing systems to provide the desired services in accordance to the QoS and SLA constraints. To this end, an opportunity exists to develop means to ensure that the desired performance levels of such systems are met under simulated environments. This will eventually minimize the service disruptions and performance degradation issues during the commissioning and operational phase of cloud computing infrastructure. However, it is observed that several simulators and modelers are available for simulating the cloud computing systems. Therefore, this paper presents a critical evaluation of the state-of-the-art modeling and simulation frameworks applicable to cloud computing systems. It compares the prominent simulation frameworks in terms of the API features, programming flexibility, operating system requirements, supported services, licensing needs and popularity. Subsequently, it provides recommendations regarding the choice of the most appropriate framework for researchers, administrators and managers of cloud computing systems.

Keywords: cloud computing, modeling framework, performance evaluation, simulation tools

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Bongmun Kang, Kagnari Yamakawa, Yoshihisa Hagihara, Yuji Ito, Michimasa Kishimoto, Yoichi Kumada

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The PMMA-tag was genetically fused with the C-terminal region of VHH molecules. This antibody, VHH, is known as a single-chain domain, which is devoid of light chains. The PMMA-tag, which could affect the isoelectric point (pI) changeable with a charge of amino acid in VHHs were closely related to the solubility of VHH molecules during refolding. The genetic fusion of PMMA-tag to C-terminal region of VHHs significantly affects the recovery of their soluble protein during refolding by 50 mM TAPS at pH 8.5. It could be refolded with a recovery of more than 95% by dialysis at pH 8.5. A marked difference in the antigen-binding activities in the adsorption state was significantly high in VHH-PM compared to the wild type of VHH. There are approximately 8-fold differences in the antigen-binding activities in the adsorption state between VHH-PM and VHH.

Keywords: VHH, PMMA-tag, isoelectric point, pH, Solubility, refolding, immobilization, ELISA

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Authors: Yuan-Lin Chen

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This paper presents an approaching forward collision probability index (AFCPI) for alerting and assisting driver in keeping safety distance to avoid the forward collision accident in highway driving. The time to collision (TTC) and time headway (TH) are used to evaluate the TTC forward collision probability index (TFCPI) and the TH forward collision probability index (HFCPI), respectively. The Mamdani fuzzy inference algorithm is presented combining TFCPI and HFCPI to calculate the approaching collision probability index of the vehicle. The AFCPI is easier to understand for the driver who did not even have any professional knowledge in vehicle professional field. At the same time, the driver’s behavior is taken into account for suiting each driver. For the approaching index, the value 0 is indicating the 0% probability of forward collision, and the values 0.5 and 1 are indicating the 50% and 100% probabilities of forward collision, respectively. The AFCPI is useful and easy-to-understand for alerting driver to avoid the forward collision accidents when driving in highway.

Keywords: approaching index, forward collision probability, time to collision, time headway

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Authors: M. Mokhber, J. Hedayati

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From the managerial perspective, it is important to devise an operational plan based on top priorities due to limited resources, diversity of measures and high costs needed to improve safety in infrastructure. Dealing with the high-risk corridors across Iran, this study prioritized the corridors according to statistical data on accidents involving fatalities, injury or damage over three consecutive years. In collaboration with the Iranian Police Department, data were collected and modified. Then, the prioritization criteria were specified based on the expertise opinions and international standards. In this study, the prioritization criteria included accident severity and accident density. Finally, the criteria were standardized and weighted (equal weights) to score each high-risk corridor. The prioritization phase involved the scoring and weighting procedure. The high-risk corridors were divided into twelve groups out of 50. The results of data analysis for a three-year span suggested that the first three groups (150 corridors) along with a quarter of Iranian road network length account for nearly 60% of traffic accidents. In the next step, according to variables including weather conditions particular roads for the purpose of winter safety measures were extracted from the abovementioned categories. According to the results ranking, ‎‏9‏‎ roads with the overall ‎length of about ‎‎‏1000‏‎ Km of high-risk corridors are considered as preferences of ‎safety measures‎.

Keywords: high-risk corridors, HRCs, road safety rating, road scoring, winter safety measures

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Authors: Azadeh Davachi

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Although there is a growing interest in Iranian female migration and gender roles, little attention has been paid to how Iranian migrant women in Australia access and sustain social networks, both locally and spatially dispersed over time. Social network theories have much to offer an analysis of migrant’s social ties and interpersonal relationships. Thus, it is important to note that social media are not only new communication channels in a migration network but also that they actively transform the nature of these networks and thereby facilitate migration for migrants. Drawing on that, this article will focus on Iranian women migrants and the use of social media in migration in Australia. Based on the case of main social networks such as Facebook and Instagram; this paper will investigate that how women migrants use these networks to facilitate the process of migration and integration. In addition, with the use of social networks, they could promote their home business and as a result become more engaged economically in Australian society. This paper will focus on three main Iranian pages in Instagram and Facebook, they will contend that compared to men, women are more active in these social networks. Consequently, as this article will discuss with the use of these social media Iranian migrant women can become more engaged and overcome post migration hardships, thus, gender plays a key role in using social media in migrant communities. Based on these findings from these social media pages, this paper will conclude that social media are transforming migration networks and thereby lowering the threshold for migration. It also will be demonstrated that these networks boost Iranian women’s confidence and lead them to become more visible in Iranian migrant communities comparing to men.

Keywords: integration, gender, migration, women migrants

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Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: Amna Abdalla Mohammed Khalid, Pietro Parisse, Silvia Onesti, Loredana Casalis

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Basic cellular processes as DNA replication are crucial to cell life. Understanding at the molecular level the mechanisms that govern DNA replication in proliferating cells is fundamental to understand disease connected to genomic instabilities, as a genetic disease and cancer. A key step for DNA replication to take place, is unwinding the DNA double helix and this carried out by proteins called helicases. The archaeal MCM (minichromosome maintenance) complex from Methanothermobacter thermautotrophicus have being studied using Atomic Force Microscopy (AFM), imaging in air and liquid (Physiological environment). The accurate analysis of AFM topographic images allowed to understand the static conformations as well the interaction dynamic of MCM and DNA double helix in the present of ATP.

Keywords: DNA, protein-DNA interaction, MCM (mini chromosome manteinance) complex, atomic force microscopy (AFM)

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: David Bywaters, Paweł Młodkowski

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The paper assesses the roles of resource growth and technology in development for Nigeria, India, and the UK and concludes by assessing the contribution of technological progress in the economic function of exchange on technological progress in the economic function of production. The evidence suggests for Nigeria, India, and the UK that increases in specialisation, as measured by the variable denoted exchtech in this paper, are the most statistically significant explanation of production technology, compared to education and capital per worker. This paper builds on theoretical work by the authors, which shows that reductions in resource use per transaction are a cause of economic growth, as well as increases in resource quantity and improvements in production technology using mathematical programming. That work is briefly reviewed and then extended to include the economic activities of consumption, from stock, production, and exchange to government, investing activities, financing activities, and exchange within and outside one economy to facilitate application to national accounts and other data.

Keywords: economic growth, Nigeria, transactions costs, Ronald Coase

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Authors: Deniz Dincel, Sena Ardicli, Hale Samli, Mustafa Ogan, Faruk Balci

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The aim of the study was to determine the effects of some environmental and genetic factors on growth, fertility traits, milk yield and composition in Saanen goats. For this purpose, the total of 173 Saanen goats and kids were investigated for growth, fertility and milk traits in Marmara Region of Turkey. Fertility parameters (n=70) were evaluated during two years. Milk samples were collected during the lactation and the milk yield/components (n=59) of each goat were calculated. In terms of CSN3 and AGPAT6 gene; the genotypes were defined by PCR-RFLP. Saanen kids (n=86-112) were measured from birth to 6 months of life. The birth, weaning, 60ᵗʰ, 90ᵗʰ, 120ᵗʰ and 180tᵗʰ days of average live weights were calculated. The effects of maternal age on pregnancy rate (p < 0.05), birth rate (p < 0.05), infertility rate (p < 0.05), single born kidding (p < 0.001), twinning rate (p < 0.05), triplet rate (p < 0.05), survival rate of kids until weaning (p < 0.05), number of kids per parturition (p < 0.01) and number of kids per mating (p < 0.01) were found significant. The impacts of year on birth rate (p < 0.05), abortion rate (p < 0.001), single born kidding (p < 0.01), survival rate of kids until weaning (p < 0.01), number of kids per mating (p < 0.01) were found significant for fertility traits. The impacts of lactation length on all milk yield parameters (lactation milk, protein, fat, totally solid, solid not fat, casein and lactose yield) (p < 0.001) were found significant. The effects of age on all milk yield parameters (lactation milk, protein, fat, total solid, solid not fat, casein and lactose yield) (p < 0.001), protein rate (p < 0.05), fat rate (p < 0.05), total solid rate (p < 0.01), solid not fat rate (p < 0.05), casein rate (p < 0.05) and lactation length (p < 0.01), were found significant too. However, the effect of AGPAT6 gene on milk yield and composition was not found significant in Saanen goats. The herd was found monomorphic (FF) for CSN3 gene. The effects of sex on live weights until 90ᵗʰ days of life (birth, weaning and 60ᵗʰ day of average weight) were found significant statistically (p < 0.001). The maternal age affected only birth weight (p < 0,001). The effects month at birth on all of the investigated day [the birth, 120ᵗʰ, 180ᵗʰ days (p < 0.05); the weaning, 60ᵗʰ, 90ᵗʰ days (p < 0,001)] were found significant. The birth type was found significant on the birth (p < 0,001), weaning (p < 0,01), 60ᵗʰ (p < 0,01) and 90ᵗʰ (p < 0,01) days of average live weights. As a result, screening the other regions of CSN3, AGPAT6 gene and also investigation the phenotypic association of them should be useful to clarify the efficiency of target genes. Environmental factors such as maternal age, year, sex and birth type were found significant on some growth, fertility and milk traits in Saanen goats. So consideration of these factors could be used as selection criteria in dairy goat breeding.

Keywords: fertility, growth, milk yield, Saanen goats

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Authors: Selcuk Aslan, Dervis Karaboga, Celal Ozturk

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Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm.

Keywords: Artificial Bee Colony algorithm, GPU computing, swarm intelligence, parallelization

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Authors: Yan Torres, Fernanda Simoes, Francisco Petrucci-Fonseca, Freddie-Jeanne Richard

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The non-invasive samples are an alternative of collecting genetic samples directly. Non-invasive samples are collected without the manipulation of the animal (e.g., scats, feathers and hairs). Nevertheless, the use of non-invasive samples has some limitations. The main issue is degraded DNA, leading to poorer extraction efficiency and genotyping. Those errors delayed for some years a widespread use of non-invasive genetic information. Possibilities to limit genotyping errors can be done using analysis methods that can assimilate the errors and singularities of non-invasive samples. Genotype matching and population estimation algorithms can be highlighted as important analysis tools that have been adapted to deal with those errors. Although, this recent development of analysis methods there is still a lack of empirical performance comparison of them. A comparison of methods with dataset different in size and structure can be useful for future studies since non-invasive samples are a powerful tool for getting information specially for endangered and rare populations. To compare the analysis methods, four different datasets used were obtained from the Dryad digital repository were used. Three different matching algorithms (Cervus, Colony and Error Tolerant Likelihood Matching - ETLM) are used for matching genotypes and two different ones for population estimation (Capwire and BayesN). The three matching algorithms showed different patterns of results. The ETLM produced less number of unique individuals and recaptures. A similarity in the matched genotypes between Colony and Cervus was observed. That is not a surprise since the similarity between those methods on the likelihood pairwise and clustering algorithms. The matching of ETLM showed almost no similarity with the genotypes that were matched with the other methods. The different cluster algorithm system and error model of ETLM seems to lead to a more criterious selection, although the processing time and interface friendly of ETLM were the worst between the compared methods. The population estimators performed differently regarding the datasets. There was a consensus between the different estimators only for the one dataset. The BayesN showed higher and lower estimations when compared with Capwire. The BayesN does not consider the total number of recaptures like Capwire only the recapture events. So, this makes the estimator sensitive to data heterogeneity. Heterogeneity in the sense means different capture rates between individuals. In those examples, the tolerance for homogeneity seems to be crucial for BayesN work properly. Both methods are user-friendly and have reasonable processing time. An amplified analysis with simulated genotype data can clarify the sensibility of the algorithms. The present comparison of the matching methods indicates that Colony seems to be more appropriated for general use considering a time/interface/robustness balance. The heterogeneity of the recaptures affected strongly the BayesN estimations, leading to over and underestimations population numbers. Capwire is then advisable to general use since it performs better in a wide range of situations.

Keywords: algorithms, genetics, matching, population

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

Abstract:

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

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

Procedia PDF Downloads 451

Authors: M. Chňapek, M. Tomka, R. Peroutková, Z. Gálová

Abstract:

Processes of plant breeding, testing and licensing of new varieties, patent protection in seed production, relations in trade and protection of copyright are dependent on identification, differentiation and characterization of plant genotypes. Therefore, we focused our research on utilization of wheat storage proteins as genetic markers suitable not only for differentiation of individual genotypes, but also for identification and characterization of their considerable properties. We analyzed a collection of 102 genotypes of bread wheat (Triticum aestivum L.), 41 genotypes of spelt wheat (Triticum spelta L.), and 35 genotypes of durum wheat (Triticum durum Desf.), in this study. Our results show, that genotypes of bread wheat and durum wheat were homogenous and single line, but spelt wheat genotypes were heterogenous. We observed variability of HMW-GS composition according to environmental factors and level of breeding and predict technological quality on the basis of Glu-score calculation.

Keywords: genotype identification, HMW-GS, wheat quality, polymorphism

Procedia PDF Downloads 468

Authors: Masahiro Miyaji

Abstract:

When a driver happens to be involved in some traffic congestion or after traffic incidents, the driver may fall in a state of anger. State of anger may encounter decisive risk resulting in severer traffic accidents. Preventive safety function using driver’s psychosomatic state with regard to anger may be one of solutions which would avoid that kind of risks. Identifying driver’s anger state is important to create countermeasures to prevent the risk of traffic accidents. As a first step, this research figured out root cause of traffic incidents by means of using Internet survey. From statistical analysis of the survey, dominant psychosomatic states immediately before traffic incidents were haste, distraction, drowsiness and anger. Then, we replicated anger state of a driver while driving, and then, replicated it by means of using driving simulator on bench test basis. Six types of facial expressions including anger were introduced as alternative characteristics. Kohonen neural network was adopted to classify anger state. Then, we created a methodology to detect anger state of a driver in high accuracy. We presented a driving support safety function. The function adapts driver’s anger state in cooperation with an autonomous driving unit to reduce the number of traffic accidents. Consequently, e evaluated reduction rate of driver’s anger in the traffic accident. To validate the estimation results, we referred the reduction rate of Advanced Safety Vehicle (ASV) as well as Intelligent Transportation Systems (ITS).

Keywords: Kohonen neural network, driver’s anger state, reduction of traffic accidents, driver’s state adaptive driving support safety

Procedia PDF Downloads 360

Authors: Ouaabbou Noureddine

Abstract:

Application design and development engineering represents a critical intersection between software engineering principles and user-centric design methodologies. This area meets the growing demand for professionals who can design, develop and deploy sophisticated software applications while ensuring optimal user experience. The curriculum encompasses basic programming concepts, database management, systems architecture, UI/UX design principles, and software testing methodologies. Students gain hands-on experience through real-world projects that reflect real-world software development cycles, from initial needs analysis to final deployment. This program prepares graduates for roles in software development, application architecture, and technical project management in response to the growing complexity of modern software applications and the evolving needs of the digital economy.

Keywords: software engineering, application development, user experience, system architecture, software design methodologies

Procedia PDF Downloads 10

Authors: Catherine K. Derow

Abstract:

Possibly a slime mold somehow mutated or already was mutated at progeniture and so stayed as a metazoan when it developed into the fruiting stage and so the slime mold(s) we are evolved and similar to are genetically differ from the slime molds in existence now. This may be why there are genetic links between humans and other metazoa now alive and slime molds now alive but we are now divergent branches of the evolutionary tree compared to the original slime mold, or perhaps slime mold-like organisms, that gave rise to metazoan animalia and perhaps algae and plantae as slime molds were undifferentiated enough in many ways that could allow their descendants to evolve into these three separate phylogenetic categories. Or it may be a slime mold was born or somehow progenated as multicellular, as the particular organism was mutated enough to have say divided in a a 'pseudo-embryonic' stage, and this could have happened for algae, plantae as well as animalia or all the branches may be from the same line but the missing link might be covered in 'phylogenetic sequence comparison noise'.

Keywords: metazoan evolution, unicellular bridge to metazoans, evolution, slime mold

Procedia PDF Downloads 231