Search results for: genetic breeding models

Authors: Kumaran Narayanan, Andrew N. Osahor

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E. coli has been engineered by our group and by others as a vector to deliver DNA into cultured human and animal cells. However, so far conditions to improve gene delivery using this vector have not been investigated, resulting in a major gap in our understanding of the requirements for this vector to function optimally. Our group recently published novel data showing that simple addition of the DNA transfection reagent Lipofectamine increased the efficiency of the E. coli vector by almost 3-fold, providing the first strong evidence that further optimization of bactofection is possible. This presentation will discuss advances that demonstrate the effects of several intracellular strategies that improve the efficiency of this vector. Conditions that promote endosomal escape of internalized bacteria to evade lysosomal destruction after entry in the cell, a known obstacle limiting this vector, are elucidated. Further, treatments that increase bacterial lysis so that the vector can release its transgene into the mammalian environment for expression will be discussed. These experiments will provide valuable new insight to advance this E. coli system as an important class of vector technology for genetic correction of human disease models in cells and whole animals.

Keywords: DNA, E. coli, gene expression, vector

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Authors: Josip Vasilj, Petar Sarajcev, Damir Jakus

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One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind power generation. For this purpose, simulation models are required, reproducing the performance of wind power forecasts. This paper presents a wind power forecast error simulation models which are based on the stochastic process simulation. Proposed models capture the most important statistical parameters recognized in wind power forecast error time series. Furthermore, two distinct models are presented based on data availability. First model uses wind speed measurements on potential or existing wind power plant locations, while the seconds model uses statistical distribution of wind speeds.

Keywords: wind power, uncertainty, stochastic process, Monte Carlo simulation

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Authors: Rodrigo Antunes, Olga Borisevich, David Demange

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In future nuclear fusion reactors, tritium self-sufficiency will be ensured by tritium (3H) production via reactions between the fusion neutrons and lithium. To favor tritium breeding, a neutron multiplier must also be used. Both tritium breeder and neutron multiplier will be placed in the so-called Breeding Blanket (BB). For the European Helium-Cooled Pebble Bed (HCPB) BB concept, the tritium production and neutron multiplication will be ensured by neutron bombardment of Li4SiO4 and Be pebbles, respectively. The produced tritium is extracted from the pebbles by purging them with large flows of He (~ 104 Nm3h-1), doped with small amounts of H2 (~ 0.1 vol%) to promote tritium extraction via isotopic exchange (producing HT). Due to the presence of oxygen in the pebbles, production of tritiated water is unavoidable. Therefore, the purging gas downstream of the BB will be composed by Q2/Q2O/He (Q = 1H, 2H, 3H), with Q2/Q2O down to ppm levels, which must be further processed for tritium recovery. A two-stage continuous approach, where zeolite membranes (ZMs) are followed by a catalytic membrane reactor (CMR), has been recently proposed to fulfil this task. The tritium recovery from Q2/Q2O/He is ensured by the CMR, that requires a reduction of the gas flow coming from the BB and a pre-concentration of Q2 and Q2O to be efficient. For this reason, and to keep this stage with reasonable dimensions, ZMs are required upfront to reduce as much as possible the He flows and concentrate the Q2/Q2O species. Therefore, experimental activities have been carried out at the Tritium Laboratory Karlsruhe (TLK) to test the separation performances of different zeolite membranes for H2/H2O/He. First experiments have been performed with binary mixtures of H2/He and H2O/He with commercial MFI-ZSM5 and NaA zeolite-type membranes. Only the MFI-ZSM5 demonstrated selectivity towards H2, with a separation factor around 1.5, and H2 permeances around 0.72 µmolm-2s-1Pa-1, rather independent for feed concentrations in the range 0.1 vol%-10 vol% H2/He. The experiments with H2O/He have demonstrated that the separation factor towards H2O is highly dependent on the feed concentration and temperature. For instance, at 0.2 vol% H2O/He the separation factor with NaA is below 2 and around 1000 at 5 vol% H2O/He, at 30°C. Overall, both membranes demonstrated complementary results at equivalent temperatures. In fact, at low feed concentrations ( ≤ 1 vol% H2O/He) MFI-ZSM5 separates better than NaA, whereas the latter has higher separation factors for higher inlet water content ( ≥ 5 vol% H2O/He). In this contribution, the results obtained with both MFI-ZSM5 and NaA membranes for H2/He and H2O/H2 mixtures at different concentrations and temperatures are compared and discussed.

Keywords: nuclear fusion, gas separation, tritium processes, zeolite membranes

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Authors: Mia Bahar, Özlem Bozkurt

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Obsessive-compulsive disorder (OCD) is a typical, persistent, and long-lasting mental health condition in which a person experiences uncontrollable, recurrent thoughts (or "obsessions") and/or activities (or "compulsions") that they feel compelled to engage in repeatedly. Obsessive-compulsive disorder is both underdiagnosed and undertreated. It frequently manifests in a variety of medical settings and is persistent, expensive, and burdensome. Obsessive-compulsive neurosis was long believed to be a condition that offered valuable insight into the inner workings of the unconscious mind. Obsessive-compulsive disorder is now recognized as a prime example of a neuropsychiatric condition susceptible to particular pharmacotherapeutic and psychotherapy therapies and mediated by pathology in particular neural circuits. An obsessive-compulsive disorder which is called OCD, usually has two components, one cognitive and the other behavioral, although either can occur alone. Obsessions are often repetitive and intrusive thoughts that invade consciousness. These obsessions are incredibly hard to control or dismiss. People who have OCD often engage in rituals to reduce anxiety associated with intrusive thoughts. Once the ritual is formed, the person may feel extreme relief and be free from anxiety until the thoughts of contamination intrude once again. These thoughts are strengthened through a manifestation of negative reinforcement because they allow the person to avoid anxiety and obscurity. These thoughts are described as autogenous, meaning they most likely come from nowhere. These unwelcome thoughts are related to actions which we can describe as Thought Action Fusion. The thought becomes equated with an action, such as if they refuse to perform the ritual, something bad might happen, and so people perform the ritual to escape the intrusive thought. In almost all cases of OCD, the person's life gets extremely disturbed by compulsions and obsessions. Studies show OCD is an estimated 1.1% prevalence, making it a challenging issue with high co-morbidities with other issues like depressive episodes, panic disorders, and specific phobias. The first to reveal brain anomalies in OCD were numerous CT investigations, although the results were inconsistent. A few studies have focused on the orbitofrontal cortex (OFC), anterior cingulate gyrus (AC), and thalamus, structures also implicated in the pathophysiology of OCD by functional neuroimaging studies, but few have found consistent results. However, some studies have found abnormalities in the basal ganglion. There have also been some discussions that OCD might be genetic. OCD has been linked to families in studies of family aggregation, and findings from twin studies show that this relationship is somewhat influenced by genetic variables. Some Research has shown that OCD is a heritable, polygenic condition that can result from de novo harmful mutations as well as common and unusual variants. Numerous studies have also presented solid evidence in favor of a significant additive genetic component to OCD risk, with distinct OCD symptom dimensions showing both common and individual genetic risks.

Keywords: compulsions, obsessions, neuropsychiatric, genetic

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Authors: Sudip Kumar Kundu, Charu Singh

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As a great physiographic divide, the Himalayas affecting a large system of water and air circulation which helps to determine the climatic condition in the Indian subcontinent to the south and mid-Asian highlands to the north. It creates obstacles by defending chill continental air from north side into India in winter and also defends rain-bearing southwesterly monsoon to give up maximum precipitation in that area in monsoon season. Nowadays extreme weather conditions such as heavy precipitation, cloudburst, flash flood, landslide and extreme avalanches are the regular happening incidents in the region of North Western Himalayan (NWH). The present study has been planned to investigate the suitable model(s) to find out the rainfall pattern over that region. For this investigation, selected models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and Coupled Model Intercomparison Project Phase 5 (CMIP5) has been utilized in a consistent framework for the period of 1976 to 2000 (historical). The ability of these driving models from CORDEX domain and CMIP5 has been examined according to their capability of the spatial distribution as well as time series plot of rainfall over NWH in the rainy season and compared with the ground-based Indian Meteorological Department (IMD) gridded rainfall data set. It is noted from the analysis that the models like MIROC5 and MPI-ESM-LR from the both CORDEX and CMIP5 provide the best spatial distribution of rainfall over NWH region. But the driving models from CORDEX underestimates the daily rainfall amount as compared to CMIP5 driving models as it is unable to capture daily rainfall data properly when it has been plotted for time series (TS) individually for the state of Uttarakhand (UK) and Himachal Pradesh (HP). So finally it can be said that the driving models from CMIP5 are better than CORDEX domain models to investigate the rainfall pattern over NWH region.

Keywords: global warming, rainfall, CMIP5, CORDEX, NWH

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

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The influence of inbreeding on growth traits in the New Zealand White rabbits maintained at Sheep Breeding and Research Station, Sandynallah, The Nilgiris, India was studied in a closed herd. Data were collected over a period of 15 years (1998 to 2012). The traits studied were 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. The effects of inbreeding along with other non-genetic factors (sex of the kit, season and period of birth of the kit) were analyzed using least-squares method. The inbreeding (F) and equivalent inbreeding (EF) coefficients were taken as fixed classes as well as covariates in separate analyses. When taken as covariate, the effect was analyzed as partial regression of respective growth trait on individual inbreeding coefficient (F or EF). The mean body weights at weaning, post-weaning and marketing were 0.715, 1.276 and 2.187 kg, respectively. The maximum growth efficiency was noticed between weaning and post-weaning. Season and period had highly significant influence on all the growth parameters studied and sex of the kit had significant influence on certain growth efficiency traits only. The average coefficients of inbreeding and equivalent inbreeding in the population were 13.233 and 17.585 percent, respectively. About 11.17 percent of total matings were highly inbred in which full-sib, half-sib and parent-offspring matings were 1.20, 6.30 and 3.67 percent, respectively. The regression of body weight traits on F and EF showed negative effect whereas most of the growth efficiency traits showed positive effects. Significant inbreeding depression was observed in W42 and W70. The depression in W42 was 0.214 kg and 0.139 kg and in W70 was 0.269 kg and 0.172 kg for every one unit increase in F and EF, respectively. Though the trait W135 showed positive value and ADG1 showed depression, the effects of inbreeding and equivalent inbreeding were non-significant in these traits. Higher values of inbreeding depression could be due to more variance of F or EF in the population. The analysis of the effect of level of inbreeding on growth traits revealed that the inbreeding class was significant on W70, ADG2, RGR2 and KR2 while EF classes had significant influence only on ADG2, RGR2 and KR2. Obviously, inbreeding does not have a positive effect, therefore, these results suggest that inbreeding had no effect on these traits.

Keywords: growth parameters, equivalent inbreeding, inbreeding effects, rabbit genetics

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Authors: Mariedel Autriz, Angel Lambio, Renato Vega, Severino Capitan, Rita Laude

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The study was conducted to associate genetic polymorphism of the leptin gene T3469C with reproductive performance in purebred sows. DNA were isolated from hair follicles of 29 Landrace and 24 Large White sows. Amplification of the leptin gene was done followed by Hinf1digestion to determine the base at the T3469C site. Electrophoresis of the digestion products revealed that there were 25 Landrace and 15 Large White sows with the TT genotype while there were 3 Landrace and 6 Large White TC. There was 1 CC for Landrace and 3 for Large White. Significant genotype associations were observed for total litter size born and total born alive. Significant breed differences, on the other hand, was observed for gestation length and average birth weight. Significant breed by genotype interaction was observed in litter size total born and litter size born alive.

Keywords: genetic polymorphism, leptin, swine, T3469C

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Andrej Pilipović, Branislav Kovačević, Marina Milović, Lazar Kesić, Saša Pekeč, Leopold Poljaković-Pajnik, Saša Orlović

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The effect of landfills on the environment reflects in the dispersion of the contaminants on surrounding soils by the groundwater plume. Such negative effect can be mitigated with the establishment of vegetative buffers surrounding landfills. The “TreeRemEnergy” project funded by the Science Fund of Republic of Serbia – Green program focuses on development of phytobuffers for landfill phytoremediation with the use of Short Rotation Woody Crops (SRWC) plantations that can be further used for the biomass for energy. One of the goals of the project is to select most appropriate poplar (Populus sp.) and willow (Salix sp.) clones through phytorecurrent selection that involves testing of various breeding traits. Physiological parameters serve as a significant contribution to the breeding process aimed to early detection of potential candidates. This study involved testing of the effect of the landfill soils on the photosynthetic processes of the selected poplar and willow candidates. For this purpose, measurements of the gas exchange, chlorophyll content and chlorophyll fluorescence were measured on the tested plants. Obtained results showed that there were differences in the influence of the controlled sources of variation on examined physiological parameters. The effect of clone was significant in all parameters, while the effect of the substrate was not statistically significant in any of measured parameters. However, the effect of interaction Clone×Substrate was significant in intercellular CO2 concentration(ci), stomatal conductance (gs) and transpiration rate (E), suggesting that water regime of the tested clones showed different response to the tested soils. Some clones showed more “generalist” behavior (380, 107/65/9, and PE19/66), while “specialist” behavior was recorded in clones PE4/68, S1-8, and 79/64/2. On the other hand, there was no significant effect of the tested substrate on the pigments content measured with SPAD meter. Results of this study allowed us to narrow the group of clones for further trails in field conditions.

Keywords: clones, net photosynthesis, WUE, transpiration, stomatal conductance, SPAD

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Authors: H. C. Chinwenyi, H. D. Ibrahim, F. A. Ahmed

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In modern financial mathematics, valuing derivatives such as options is often a tedious task. This is simply because their fair and correct prices in the future are often probabilistic. This paper examines three different Stochastic Differential Equation (SDE) models in finance; the Constant Elasticity of Variance (CEV) model, the Balck-Karasinski model, and the Heston model. The various Martingales option price valuation formulas for these three models were obtained using the replicating portfolio method. Also, the numerical solution of the derived Martingales options price valuation equations for the SDEs models was carried out using the Monte Carlo method which was implemented using MATLAB. Furthermore, results from the numerical examples using published data from the Nigeria Stock Exchange (NSE), all share index data show the effect of increase in the underlying asset value (stock price) on the value of the European Put Option for these models. From the results obtained, we see that an increase in the stock price yields a decrease in the value of the European put option price. Hence, this guides the option holder in making a quality decision by not exercising his right on the option.

Keywords: equivalent martingale measure, European put option, girsanov theorem, martingales, monte carlo method, option price valuation formula

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Authors: Adilson Elias Xavier, Otto Corrêa Rotunno Filho, Paulo Canedo De Magalhães

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This paper addresses the issue of automatic parameter estimation in conceptual rainfall-runoff (CRR) models. Due to threshold structures commonly occurring in CRR models, the associated mathematical optimization problems have the significant characteristic of being strongly non-differentiable. In order to face this enormous task, the resolution method proposed adopts a smoothing strategy using a special C∞ differentiable class function. The final estimation solution is obtained by solving a sequence of differentiable subproblems which gradually approach the original conceptual problem. The use of this technique, called Hyperbolic Smoothing Method (HSM), makes possible the application of the most powerful minimization algorithms, and also allows for the main difficulties presented by the original CRR problem to be overcome. A set of computational experiments is presented for the purpose of illustrating both the reliability and the efficiency of the proposed approach.

Keywords: rainfall-runoff models, automatic calibration, hyperbolic smoothing method

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Authors: Iva Košek Bartošová, Andrea Jokešová, Eva Kozlová, Helena Matějová

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The paper presents results of a research team from Faculty of Education, University of Hradec Králové in the Czech Republic. It introduces with the most reading methods used in the 1^st classes of a primary school and presents results of a pilot research focused on mastering reading techniques and the quality of reading comprehension of pupils in the first half of a school year during training in teaching reading by an analytic-synthetic method and by a genetic method. These methods of practicing reading skills are the most used ones in the Czech Republic. During the school year 2015/16 there has been a measurement made of two groups of pupils of the 1^st year and monitoring of quantitative and qualitative parameters of reading pupils’ outputs by several methods. Both of these methods are based on different theoretical basis and each of them has a specific educational and methodical procedure. This contribution represents results during a piloting project and draws pilot conclusions which will be verified in the subsequent broader research at the end of the school year of the first class of primary school.

Keywords: analytic-synthetic method of reading, genetic method of reading, reading comprehension, reading literacy, reading methods, reading speed

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Authors: Mehdi Seifbarghy, Zahra Mansouri

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In this paper a few location-allocation models are developed in a multi-echelon supply chain including suppliers, manufacturers, distributors and retailers. The objectives are maximizing demand coverage, minimizing the total distance of distributors from suppliers, minimizing some facility establishment costs and minimizing the environmental effects. Since nature of the given models is multi-objective, we suggest a number of goal-based solution techniques such L-P metric, goal programming, multi-choice goal programming and goal attainment in order to solve the problems.

Keywords: location, multi-echelon supply chain, covering, goal programming

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Nodelman V.

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Despite serious difficulties in the assimilation of the conceptual system of Calculus, software in the educational process is used only occasionally, and even then, mainly for illustration purposes. The following are a few reasons: The non-trivial nature of the studied material, Lack of skills in working with software, Fear of losing time working with software, The variety of the software itself, the corresponding interface, syntax, and the methods of working with the software, The need to find suitable models, and familiarize yourself with working with them, Incomplete compatibility of the found models with the content and teaching methods of the studied material. This paper proposes an active use of the developed non-commercial software VusuMatica, which allows removing these restrictions through Broad support for the studied mathematical material (and not only Calculus). As a result - no need to select the right software, Emphasizing the unity of mathematics, its intrasubject and interdisciplinary relations, User-friendly interface, Absence of special syntax in defining mathematical objects, Ease of building models of the studied material and manipulating them, Unlimited flexibility of models thanks to the ability to redefine objects, which allows exploring objects characteristics, and considering examples and counterexamples of the concepts under study. The construction of models is based on an original approach to the analysis of the structure of the studied concepts. Thanks to the ease of construction, students are able not only to use ready-made models but also to create them on their own and explore the material studied with their help. The presentation includes examples of using VusuMatica in studying the concepts of limit and continuity of a function, its derivative, and integral.

Keywords: counterexamples, limitations and requirements, software, teaching and learning calculus, user-friendly interface and syntax

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Authors: Anida Causevic-Ramosevac, Sabina Semiz

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The aim of this study was to investigate the association of four single nucleotide polymorphisms (SNPs) - rs673548, rs693 in ApoB gene, rs1800775 in CETP gene and rs4846914 in GALNT2 gene with parameters of type 2 diabetes (T2D) and diabetic dyslipidemia in the population of Bosnia and Herzegovina (BH). Materials and methods: Our study involved 352 patients with T2D and 156 healthy subjects. Biochemical and anthropometric parameters were measured in all participants. DNA was extracted from the peripheral blood for the purpose of genetic testing. Polymorphisms in ApoB (rs673548, rs693), CETP (rs1800775) and GALNT2 (rs4846914) genes were analyzed by using Sequenom IPLEX platform. Results: Our results demonstrated significant associations for rs180075 polymorphism in CETP gene with levels of fasting insulin (p = 0.020; p = 0.027; p = 0.044), triglycerides (p = 0.046) and ALT (p = 0.031) activity in control group. In group of diabetic patients, results showed a significant association of rs673548 in ApoB gene with levels of fasting insulin (p = 0.008), HOMA-IR (p = 0.013), VLDL-C (p = 0.037) and CRP (p = 0.029) and rs693 in ApoB gene with BMI (p = 0.025), systolic blood pressure (p = 0.027), fasting insulin (p = 0.037) and HOMA-IR (p = 0.023) levels. Significant associations were also observed for rs1800775 in CETP gene with triglyceride (p = 0.023) levels and rs4846914 in GALNT2 gene with HbA1C (p = 0.013) and triglyceride (p = 0.043) levels. Conclusion: In conclusion, this is the first study that examined the impact of variations of candidate genes on a wide range of metabolic parameters in BH population. Our results suggest an association of variations of ApoB, CETP and GALNT2 genes with specific markers of T2D and dyslipidemia. Further studies would be needed in order to confirm these genetic effects in other ethnic groups as well.

Keywords: ApoB, CETP, dyslipidemia, GALNT2, type 2 diabetes

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Authors: Yushinta Fujaya, Andi Ivo Asphama, Andi Parenrengi, Andi Tenriulo

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Blue Swimming crab (Portunus pelagicus) is an important commercial species throughout the subtropical waters and as such constitutes part of the fisheries resources. Data are lacking on the morphological variations of selected fast-growing crabs reared in a pond. This study aimed to analyze the morphological and molecular character of a selected fast-growing crab reared in ponds in South of Sulawesi. The crab seeds were obtained from local fish-trap and hatchery. A study on the growth was carried out in the population of crabs. The dimensions analyzed were carapace width (CW) measured after 3 months of grow out. Morphological character states were examined based on the pattern of spots on the carapace. Molecular analysis was performed using RAPD (Random Amplified Polymorphic DNA). Genetic distance was analysed using TFPGA (Tools for Population Genetic Analyses) version 1.3. The results showed that there were variations in the growth of crabs. These crabs clustered morphologically into three quite distinct groups. The crab with white spots irregularly spread over its carapace was the largest size while the crab with large white spots scattered over the carapace was the smaller size (3%). The crab with small white spots scattered over the carapace was the smallest size found in this study. Molecular analysis showed that there are morphologically and genetically different between groups of crabs. Genetic distances among crabs ranged from 0.1527 to 0.5856. Thus, this study provides information the use of white spots pattern over carapace as indicators to identify the type of blue swimming crabs.

Keywords: crab, portunus pelagicus, morphology, RAPD, Carapace

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Authors: Khalil Trigui, Rafik Ben Said, Fourat Akrout, Neji Aloui

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In this study, we examined in the first part the eco-biology of Artemia (A.salina) collected from Sahline Salt Lake (governorate of Monastir: Tunisia) during an annual cycle. The correlations between environmental factors and some biological parameters of Artemia were determined. The results obtained showed that the environmental factors affected the biology of Artemia. The highest abundance was recorded in May (550 ± 2,16 ind/l) and all life history stages existed with different seasonal proportions. The Artemia population is bisexual with ovoviviparous reproduction at the beginning and oviparous at the end of the life cycle. We also recorded the dominance of males at the start and the females at the end of the cycle. During all the study period, the size of mature females is bigger than that of males. The fertility obtained resulted in a significant production of cysts compared to the nauplii. A negative correlation with highly significant effect was deduced between environmental factors (temperature and salinity) and the production of nauplii (ovoviviparity) in contrast with dissolved oxygen. In the second part of our work is consecrated to the mastery of breeding Artemia. For this, we tested the effect of five external factors (temperature, salinity, dissolved oxygen, light intensity and food) on the survival of this crustacean. Thereby, the survival rates of Artemia were affected by the different values of studied factors. The recorded results showed that Artemia salina has an optimum temperature ranged from 25 to 27°C with a survival rate ranging from 84 to 88%. The optimal salinity to breed Artemia salina was 37 psu (62 ± 0,23%). Nevertheless, this crustacean was able to survive and withstand the salinity of 0 psu (freshwater). The optimum concentration of dissolved oxygen was 7mg/l with a survival rate of 87,11 ± 0,04%. An optimum light intensity of 10 lux revealed a survival rate equal to 85,33 ± 0,01%. The results also showed that the preferred micro-algae by Artemia is Dunaliella salina with a maximum survival rate of the order of 80 ± 0,15%. There is a significant effect for all experienced parameters on the survival of Artemia reared except the nature of food.

Keywords: Artemia salina, biology, breeding, ecology, Sahline salt lake

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: H. Djebar, L. Khene, M. Boucenna, M. R. Djebar, M. N. Khebbeb, M. Djekoun

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Currently in toxicology, use of alternative models permits to understand the mechanisms of toxicity at different levels of cells. Objectives of our research concern the determination of NPs ZnO, TiO2, AlO2, and FeO2 effect on ciliate protist freshwater Paramecium sp and Helix aspersa. The result obtained show that NPs increased antioxidative enzyme activity like catalase, glutathione –S-transferase and level GSH. Also, cells treated with high concentrations of NPs showed a high level of MDA. In conclusion, observations from growth and enzymatic parameters suggest on one hand that treatment with NPs provokes an oxidative stress and on the other that snale and paramecium are excellent alternatives models for ecotoxicological studies.

Keywords: NPs, GST, catalase, GSH, MDA, toxicity, snale and paramecium

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Authors: Zelikha Labbani

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Since its creation in 1964 by Guha and Maheshwari in India on Datura innoxia Mill, in vitro androgenesis has become the method of choice in the production of doubled haploid in many species. However in durum wheat, the Doubled haploid plant breeding programs remained limited due to the low production of androgenetic embryos and converting them into fertile green plants. We describe here an efficient method for inducing embryos and regenerating green plants directly from isolated microspores of durum wheat.

Keywords: Durum wheat, haploid embryos, on in vitro, pretreatment

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Authors: Seyedmirsajad Mokhtarimousavi, Danial Talebi, Hamidreza Asgari

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Gate Assignment Problem (GAP) is one of the most substantial issues in airport operation. In principle, GAP intends to maintain the maximum capacity of the airport through the best possible allocation of the resources (gates) in order to reach the optimum outcome. The problem involves a wide range of dependent and independent resources and their limitations, which add to the complexity of GAP from both theoretical and practical perspective. In this study, GAP was mathematically formulated as a three-objective problem. The preliminary goal of multi-objective formulation was to address a higher number of objectives that can be simultaneously optimized and therefore increase the practical efficiency of the final solution. The problem is solved by applying the second version of Non-dominated Sorting Genetic Algorithm (NSGA-II). Results showed that the proposed mathematical model could address most of major criteria in the decision-making process in airport management in terms of minimizing both airport/airline cost and passenger walking distance time. Moreover, the proposed approach could properly find acceptable possible answers.

Keywords: airport management, gate assignment problem, mathematical modeling, genetic algorithm, NSGA-II

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Authors: Yake Zhang, Peng Xu

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The development of building energy models (BEM) required for architectural design and analysis is a time-consuming and complex process, demanding a deep understanding and proficient use of simulation software. To streamline the generation of complex building energy models, this study proposes an automated method for generating building energy models using a large language model and the BEM library aimed at improving the efficiency of model generation. This method leverages a large language model to parse user-specified requirements for target building models, extracting key features such as building location, window-to-wall ratio, and thermal performance of the building envelope. The BEM library is utilized to retrieve energy models that match the target building’s characteristics, serving as reference information for the large language model to enhance the accuracy and relevance of the generated model, allowing for the creation of a building energy model that adapts to the user’s modeling requirements. This study enables the automatic creation of building energy models based on natural language inputs, reducing the professional expertise required for model development while significantly decreasing the time and complexity of manual configuration. In summary, this study provides an efficient and intelligent solution for building energy analysis and simulation, demonstrating the potential of a large language model in the field of building simulation and performance modeling.

Keywords: artificial intelligence, building energy modelling, building simulation, large language model

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Authors: Fernando L. P. Santos, Luiz G. Lyra, Helenice O. Florentino, Daniela R. Cantane

Abstract:

Dengue is a febrile infectious disease caused by a virus of the family Flaviridae. It is transmitted by the bite of mosquitoes, usually of the genus Aedes aegypti. It occurs in tropical and subtropical areas of the world. This disease has been a major public health problem worldwide, especially in tropical countries such as Brazil, and its incidence has increased in recent years. Dengue is a subject of intense research. Efficient forms of mosquito control must be considered. In this work, the mono-objective optimal control problem was solved for analysing the dengue disease problem. Chemical and biological controls were considered in the mathematical aspect. This model describes the dynamics of mosquitoes in water and winged phases. We applied the genetic algorithms (GA) to obtain optimal strategies for the control of dengue. Numerical simulations have been performed to verify the versatility and the applicability of this algorithm. On the basis of the present results we may recommend the GA to solve optimal control problem with a large region of feasibility.

Keywords: genetic algorithm, dengue, Aedes aegypti, biological control, chemical control

Procedia PDF Downloads 349

Authors: Raninder Kaur Dhillon, Mayur Jethwa, Hardeep Singh Rai

Abstract:

Information technology has made a pivotal progress across disparate disciplines, one of which is AEC (Architecture, Engineering and Construction) industry. CAD is a form of computer-aided building modulation that architects, engineers and contractors use to create and view two- and three-dimensional models. The AEC industry also uses building information modeling (BIM), a newer computerized modeling system that can create four-dimensional models; this software can greatly increase productivity in the AEC industry. BIM models generate open source IFC (Industry Foundation Classes) files which aim for interoperability for exchanging information throughout the project lifecycle among various disciplines. The methods developed in previous studies require either an IFC schema or MVD and software applications, such as an IFC model server or a Building Information Modeling (BIM) authoring tool, to extract a partial or complete IFC instance model. This paper proposes an efficient algorithm for extracting a partial and total model from an Industry Foundation Classes (IFC) instance model without an IFC schema or a complete IFC model view definition (MVD).

Keywords: BIM, CAD, IFC, MVD

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Authors: Daniel Fulus Fom, Gau Patrick Damulak

Abstract:

In this study, the Autoregressive Fractional Integrated Moving Average (ARFIMA) and Jordan Recurrent Neural Network (JRNN) models were employed to model the forecasting performance of the daily turbidity flow of White Clay Creek (WCC). The two methods were applied to the log difference series of the daily turbidity flow series of WCC. The measurements of error employed to investigate the forecasting performance of the ARFIMA and JRNN models are the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The outcome of the investigation revealed that the forecasting performance of the JRNN technique is better than the forecasting performance of the ARFIMA technique in the mean square error sense. The results of the ARFIMA and JRNN models were obtained by the simulation of the models using MATLAB version 8.03. The significance of using the log difference series rather than the difference series is that the log difference series stabilizes the turbidity flow series than the difference series on the ARFIMA and JRNN.

Keywords: auto regressive, mean absolute error, neural network, root square mean error

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Authors: Thiago Victor Cordeiro Marcos, Joao Felipe de Araujo Martos, Ronaldo de Lima Cardoso, David Romanelli Pinto, Paulo Gilberto de Paula Toro, Israel da Silveira Rego, Antonio Carlos de Oliveira

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Currently, the use of 3D rapid prototyping, also known as 3D printing, has been investigated by some universities around the world as an innovative technique, fast, flexible and cheap for a direct plastic models manufacturing that are lighter and with complex geometries to be tested for hypersonic shock tunnel. Initially, the purpose is integrated prototyped parts with metal models that actually are manufactured through of the conventional machining and hereafter replace them with completely prototyped models. The mechanical design models to be tested in hypersonic shock tunnel are based on conventional manufacturing processes, therefore are limited forms and standard geometries. The use of 3D rapid prototyping offers a range of options that enables geometries innovation and ways to be used for the design new models. The conception and project of a prototyped model for hypersonic shock tunnel should be rethought and adapted when comparing the conventional manufacturing processes, in order to fully exploit the creativity and flexibility that are allowed by the 3D prototyping process. The objective of this paper is to compare the conception and project of a 3D rapid prototyping model and a conventional machining model, while showing the advantages and disadvantages of each process and the benefits that 3D prototyping can bring to the manufacture of models to be tested in hypersonic shock tunnel.

Keywords: 3D printing, 3D prototyping, experimental research, hypersonic shock tunnel

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Authors: Salim Omar Raoof

Abstract:

This study was conducted at the Al-Salam cows’ station for milk production located in Al-Latifiya district - Al-Mahmudiyah district (25 km south of Baghdad governorate) on a sample of (180) Holstein cows imported from Germany by Taj Al-Nahrain company in order to study the effect of the sequence, season and calving year on Total Milk Production (TMP). The lactation period (LP), calving interval, Services per conception and the estimate of the heritability of the studied traits. The results showed that the overall mean of TMP and LP were 3172.53 kg and 237.09-day respectively. The parity effect on TMP in Holstein cows was highly significant (P≤0.01). Total Milk production increased with the advance of parity and mostly reached its maximum value in the 4th and 3rd parity being 3305.87 kg and3286.35 kg per day, respectively. Season of calving has a highly significant (P≤0.01), effect on (TMP). Cows calved in spring had a highest milk production than those calved in other seasons. Season of calving had a highly significant (P≤0.01) effect on services per conception. The result of the study showed the heritability values for TMP, LP, SPC and CL were 0.21, 0.08, 0.08 and 0.07, respectively.

Keywords: cows, non genetic, milk production, heritability

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Authors: Fouzi Aboura

Abstract:

The fractional order proportional-integral-derivative (FOPID) controller or fractional order (PIλDµ) is a proportional-integral-derivative (PID) controller where integral order (λ) and derivative order (µ) are fractional, one of the important application of classical PID is the Automatic Voltage Regulator (AVR).The FOPID controller needs five parameters optimization while the design of conventional PID controller needs only three parameters to be optimized. In our paper we have proposed a comparison between algorithms Differential Evolution (DE) and Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) ,we have studied theirs characteristics and performance analysis to find an optimum parameters of the FOPID controller, a new objective function is also proposed to take into account the relation between the performance criteria’s.

Keywords: FOPID controller, fractional order, AVR system, objective function, optimization, GA, PSO, HGAPSO

Procedia PDF Downloads 90