Search results for: protein secondary structure prediction

Authors: Thi Thao Ninh, Yuri Trusov, José Ramón Botella

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Heterotrimeric G proteins, comprised of three subunits, α, β and γ, are involved in signal transduction pathways that mediate a vast number of processes across the eukaryotic kingdom. 23 Gα subunits are present in humans whereas most plant genomes encode for only one canonical Gα. The disparity observed between Arabidopsis, rice, and maize Gα-deficient mutant phenotypes suggest that Gα functions have diversified between eudicots and monocots during evolution. Alternatively, since the only Gα mutations available in dicots have been produced in Arabidopsis, the possibility exists that this species might be an exception to the rule. In order to test this hypothesis, we studied the G protein α subunit (TGA1) in tomato. Four tga1 knockout lines were generated in tomato cultivar Moneymaker using CRISPR/Cas9. The tga1 mutants exhibit a number of auxin-related phenotypes including changes in leaf shape, reduced plant height, fruit size and number of seeds per fruit. In addition, tga1 mutants have increased sensitivity to abscisic acid during seed germination, reduced sensitivity to exogenous auxin during adventitious root formation from cotyledons and excised hypocotyl explants. Our results suggest that Gα mutant phenotypes in tomato are very similar to those observed in monocots, i.e. rice and maize, and cast doubts about the validity of using Arabidopsis as a model system for plant G protein studies.

Keywords: auxin-related phenotypes, CRISPR/Cas9, G protein α subunit, heterotrimeric G proteins, tomato

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Authors: J. M. Muñoz, Y. Vasquez, P. Oyola, M. Rubio

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All emissions of contaminants inside of homes, offices, school and another enclosure closer that affect the health of those who inhabit or use them are cataloged how indoor pollution. The importance of this study is because individuals spend most of their time in indoors ambient. The main indoor pollutants are oxides of nitrogen (NOₓ), sulfur dioxide (SO₂), carbon monoxide (CO) and particulate matter (PM). Combustion heaters are an important source of pollution indoors. It will be measured: NOₓ, SO₂, CO, PM₂,₅ y PM₁₀ continuous and discreet form at indoor and outdoor of two households with different heating energy; kerosene and electricity (control home) respectively, in addition to environmental parameters such as temperature. With the values obtained in the 'control home' it will be possible estimate the contaminants transport from outside to inside of the household and later the contribution generated by kerosene heating. Transporting the emissions from burning kerosene to a photochemical chamber coupled to a continuous and discreet measuring system of contaminants it will be evaluated the oxidation of the emissions and formation of secondary particulate matter. It will be expected watch a contaminants transport from outside to inside of the household and the kerosene emissions present a high potential of formation secondary particulate matter.

Keywords: heating, indoor pollution, kerosene, secondary particulate matter

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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Authors: Yuwaratu Syafira, Wahyu K. Y. Putra, Kusharisupeni Djokosujono

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Background/Objective: Body Mass Index (BMI) serves various purposes, including measuring the prevalence of obesity in a population, and also in formulating a patient’s diet at a hospital, and can be calculated with the equation = body weight (kg)/body height (m)². However, the BMI of an individual with difficulties in carrying their weight or standing up straight can not necessarily be measured. The aim of this study was to form a prediction model for the BMI of young adult students of Public Health Faculty of University of Indonesia. Subject/Method: This study used a cross sectional design, with a total sample of 132 respondents, consisted of 58 males and 74 females aged 21- 30. The dependent variable of this study was BMI, and the independent variables consisted of sex and anthropometric measurements, which included ulna length, arm length, tibia length, knee height, mid-upper arm circumference, and calf circumference. Anthropometric information was measured and recorded in a single sitting. Simple and multiple linear regression analysis were used to create the prediction equation for BMI. Results: The male respondents had an average BMI of 24.63 kg/m² and the female respondents had an average of 22.52 kg/m². A total of 17 variables were analysed for its correlation with BMI. Bivariate analysis showed the variable with the strongest correlation with BMI was Mid-Upper Arm Circumference/√Ulna Length (MUAC/√UL) (r = 0.926 for males and r = 0.886 for females). Furthermore, MUAC alone also has a very strong correlation with BMI (r = 0,913 for males and r = 0,877 for females). Prediction models formed from either MUAC/√UL or MUAC alone both produce highly accurate predictions of BMI. However, measuring MUAC/√UL is considered inconvenient, which may cause difficulties when applied on the field. Conclusion: The prediction model considered most ideal to estimate BMI is: Male BMI (kg/m²) = 1.109(MUAC (cm)) – 9.202 and Female BMI (kg/m²) = 0.236 + 0.825(MUAC (cm)), based on its high accuracy levels and the convenience of measuring MUAC on the field.

Keywords: body mass index, mid-upper arm circumference, prediction model, ulna length

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Authors: Ejaz Ahmed, Huang Yong

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The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

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Authors: Babatunde Kasim Oladele

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This current study investigated students’ characteristics as factors that influence Mathematics Achievement of junior secondary school students. The study adopted a descriptive survey design. The population of the study was one hundred and twenty-three (123) JSS students of secondary schools in Ibadan North Local Government in Oyo State. A Mathematics achievement test and three questionnaires on student’s self-efficacy belief, attitude, and learning style were the instruments used. Prior to the administration of the constructed mathematics achievement test, 100-item mathematics was subjected to the expert review, and items analysis was carried out. Fifty items were retained. The Cronbach Alpha reliability coefficients of the instruments were 0.71, 0.76, and 0.83, respectively. Collected data were analysed using the frequency count, percentages, mean, standard deviation, and Path Analysis in Amos SPSS Version 20. Students characteristics: gender, age, self-efficacy, attitude and learning style had positive direct effects on students’ achievement in Mathematics as indicated by their respective beta weights (β = 0.36, 0.203, 0.92, 0.079, 0.69 p < 0.05). Consequently, the study concluded that student’s characteristics (Age, gender, and learning style) explained a significant part of the variability in students’ achievement in Mathematics.

Keywords: mathematics achievement, students’ characteristics, junior secondary school, Ibadan

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Authors: Hosia T. Pule, Julius T. Tjelele, Michelle J. Tedder, Dawood Hattas

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Acceptability of plant material to herbivores is influenced by, among other factors; nutrients, plant secondary metabolites and growth stage of the plants. However, the effect of these factors on Seriphium plumosum L. acceptability to livestock is still not clearly understood, despite its importance in managing its encroachment in grassland communities. The study used 2 x 2 x 2 factorial analysis of variance to investigate the effect of season (wet and dry), fire, slope position (top and bottom) and their interaction on Seriphium plumosum chemistry. We tested the hypothesis that S. plumosum chemistry varies temporally, spatially and pre- and post-fire treatment. Seriphium plumosum edible material was collected during the wet and dry season from burned and unburned areas on both top and bottom slopes before being analysed for protein (CP) content, neutral detergent fibre (NDF), total phenolics (TP) and condensed tannins (CT). Season had a significant effect on S. plumosum protein content, neutral detergent fibre, total phenolics and condensed tannins. Fire had a significant effect on CP. Interaction of season x fire had a significant effect on NDF and CP (p < 0.05). Seriphium plumosum in the wet season (6.69% ± 0.20 (SE)) had significantly higher CP than in the dry season (5.22% ± 0.13). NDF was significantly higher (58.01% ± 0.41) in the dry season than in the wet season (53.17% ± 0.34), while TP were significantly higher in the dry season (14.44 mg/gDw ± 1.03) than in the wet season (11.08 mg/gDw ± 1.07). CT in the wet season were significantly higher (1.56 mg/gDw ± 0.13) than in the dry season (1 mg/gDw ± 0.03). CP was significantly higher in burned (6. 31 % ± 0.22) than in unburned S. plumosum edible material (5.60 % ± 0.15). Seriphium plumosum CP was significantly higher in wet season x burned (7.34 % ± 0.31) than wet season x unburned (6.08 % ± 0.20) material and dry season x burned (5.34 % ± 0.18) and unburned (5.09 % ± 0.18) material were similar. NDF was similar in dry season x burned (58.31% ± 0.54) and dry season x unburned (57.69 % ± 0.62) material and significantly higher than similar wet season x burned (52.43% ± 0.45) and wet season x post-unburned (53.88% ± 0.47) material. This study suggests integrating fire, browsers, and supplements as encroacher S. plumosum control agents, especially in the wet season, following fire due to high S. plumosum CP content.

Keywords: acceptability, chemistry, edible material, encroachment, phenolics, tannins

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Authors: Rasha Saad, Jean Frederic Weber, Fatimah Bebe, Sadia Sultan

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The goal of study was to screen the effects of candesartan and four endophytic fungi for their potential in microbial biotransformation. In this experiment, four types of unidentified fungi with the codes of TH2L1, TH2R10, TH1P35 and TH1S46 were used in screening process by MECFUS (Microtiter plate, Elicitors, Combination, Freeze-drying, UHPLC, Statistical analysis) protocol. The experiment was carried out by using 96-well microtiter plate (MTP) with different media and elicitors. Various media with two concentrations of Potato Dextrose Broth (PDB) and elicitors used were to induce the production of secondary metabolites from the fungi as well as the biotransformation of the drug compound. After incubation, cultures were extracted by freeze drying method and finally analyzed by ultra-High performance Liquid Chromatography (uHPLC). The extracts analyzed by uHPLC followed by LC/Ms, demonstrated the presence of biotransformation products from the drug compound and elicitation of the secondary metabolism from the fungi by the occurrence of the additional peaks. From the four fungi, TH1S46 showed highly potential produced secondary metabolites as well as the biotransformation of candesartan. For other fungi, they responded when candesartan was introduced. Moreover, the additional peaks produced in uHPLC need to be further investigation by using LC-MS or NMR.

Keywords: biotransformation, candesartan, endophytes, secondary metabolites

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Authors: K. B. Ghazaryan, R. A. Ghazaryan

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Based on the constitutive model and anti-plane equations of anisotropic elastic body of monoclinic symmetry we consider the problem of shear wave propagation in multi-layered disordered composite structure with point defect. Using transfer matrix method the analytic expression is obtained providing solutions of shear Floquet wave propagation in periodic disordered anisotropic structure. The usefulness of the obtained analytical expression was discussed also in reflection and refraction problems from multi-layered reflector as well as in vibration problem of multi-layered waveguides. Numerical results are presented highlighting the effects arising in disordered periodic structure due to defects of multi-layered structure.

Keywords: shear elastic waves, monoclinic anisotropic media, periodic structure, disordered multilayer laminae, multi-layered waveguide

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Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

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We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

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Authors: B. Poorna Chander

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Urban centre is one of the most important parts of the city where all the community activities take place. They are the active zones which enhance the structure of a city. The structure of the city refers to its form, mobility patterns, and concentration of people and lifestyles of people. The purpose of the research paper is to study how does the character or structure of city changes when a new centre is established. An attempt has been made to understand this by studying how the formation of centre has been changing the form or the structure of the city since the ancient times, what are the notions of a city and a centre by various architects, by studying the various models of the future city proposed by them. And then the data has been linked to how the formation of the new centres is changing the city. As the demands of the city are increasing, it also regulates how the new centres are formed. So both, the city and the centre are interdependent on each other.

Keywords: centre, activities, lifestyles, people, form

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Authors: Tomas Vampola, Michael Valášek

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On the basis of simulation-generated failure states of structural elements of a turboprop engine suitable for the busy-jet class of aircraft, an algorithm for early prediction of damage or reduction in functionality of structural elements of the engine is designed and verified with real data obtained at dynamometric testing facilities of aircraft engines. Based on an expanding database of experimentally determined data from temperature and pressure sensors during the operation of turboprop engines, this strategy is constantly modified with the aim of using the minimum number of sensors to detect an inadmissible or deteriorated operating mode of specific structural elements of an aircraft engine. The assembled algorithm for the early prediction of reduced functionality of the aircraft engine significantly contributes to the safety of air traffic and to a large extent, contributes to the economy of operation with positive effects on the reduction of the energy demand of operation and the elimination of adverse effects on the environment.

Keywords: detectability of malfunction, dynamometric testing, prediction of damage, turboprop engine

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Authors: Tagouelbe Tiho, Amissa Augustin Adima, Yao Casimir Brou, Nabayo Traore, Gouha Firmin Kouassi, Thierry Roland Kouame, Maryline Kouba

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The work aimed to study Borassus aethiopum Mart (B.a) dried pulp nutritional value for its incorporation in human and poultry diets. Firstly, the mature fruit macro-composition was assessed. Secondly, the pulp was dried at 40, 50, 60, 70, and 80ᵒC. Thereafter, the analysis was performed for fat, protein, total sugars, Ca, P, Mg, and fatty acid profile monitoring. As a result, the fruits weighed 1,591.35, delivered 516.73, and 677.82 grams of pulp and seeds, respectively. Mainly, increasing heat adversely affected the outputs. Consequently, the fat results were 14.12, 12.97, 8.93, 8.89ᶜ, and 5.56%; protein contents were 11.64, 10.15, 8.97, 8.84, and 8.42%; total sugar deliveries were 6.28, 6.05, 5.26, 5.02, and 4.76% (P < 0.01). Thereafter, the metabolizable energies were 3,785.22; 3,834.28; 3,616.62; 3,667.03; and 3,608.33 kcal/kg (DM). Additionally, Calcium (Ca) contents were 0.51, 0.55, 0.69, 0.77, and 0.81%, while phosphorus (P) mean was 0.17%, and the differences were not significant (P < 0.01). So, the Ca/P ratios were 2.79, 3.04, 4.10, 4.71, and 4.95. Finally, fatty acids (FA) assessments revealed 22.33 saturated (SFA), 77.67 unsaturated (UFA), within which 67.59% were monounsaturated (MUFA). Interestingly, the rising heat depressed n-6/n-3 ratios that were 1.1, 1.1, 0.45 and 0.38, respectively at 40, 50, 70 and 80ᵒC. In short, drying did not only enhance the product shelf life but it also improved the nutritional value. Thus, B.a mature fruit pulps dried at 70ᵒC are good functional foods, with more than 66% MUFA, and energy source for human and poultry nutrition.

Keywords: Borassus aethiopum Mart, fatty acids, metabolizable energy, minerals, protein

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Authors: Hamad Rafique

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The bioactive peptide RDFPITWPW (RW-9) identified from oat protein has been reported to be positive in memory deficits. However, no clarity on the mechanisms responsible for the neuroprotective effects of RW-9 peptide against AD-like symptoms. Herein, it found that RW-9 intervention showed various improving effects in cognitive-behavioral tests and alleviated oxidative stress and inflammation in the scopolamine-induced mice model. The hippocampus proteomics analysis revealed the upregulation of memory-related proteins, including Grin3a, Ppp2r1b, Stat6, Pik3cd, Slc5a7, Chrm2, mainly involved in cAMP signaling, PI3K-Akt signaling, and JAK-STAT signaling pathways. The administration of RW-9 significantly upregulated the neurotransmitters, including 5-HT, DA, and Arg, in mice brains. Moreover, it regulated the serum metabolic profile and increased the expression levels of ABC transporters, biosynthesis of amino acids, and Amino acyl-tRNA biosynthesis, among others. The 16s-rRNA results illustrated that the RW-9 restored the abundance of Muribaculaceae, Lachnospiraceae, Lactobacillus, Clostridia and Bactericides. Taken together, our results suggest that the RW-9 may prevent the AD-like symptoms via modulation of the gut-serum-brain axis.

Keywords: oat protein, active peptide, neuroprotective, gut-brain axis

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Authors: Kefaya Qaddoum

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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

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Authors: Hamed Alipour-Banaei, Farhad Mehdizadeh

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We report the design and characterization of ultra high quality factor filter based on one-dimensional photonic-crystal Thue-Morse sequence structure. The behavior of aperiodic array of photonic crystal structure is numerically investigated and we show that by changing the angle of incident wave, desired wavelengths could be tuned and a tunable filter is realized. Also it is shown that high quality factor filter be achieved in the telecommunication window around 1550 nm, with a device based on Thue-Morse structure. Simulation results show that the proposed structure has a quality factor more than 100000 and it is suitable for DWDM communication applications.

Keywords: Thue-Morse, filter, quality factor, photonic

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Anna Perrone, Federico Martinelli

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Olive fruit is well-known for the high content in secondary metabolites with high interest at nutritional, nutraceutical, antioxidant, and healthy levels. The content of secondary metabolites in olive at harvest may be affected by different water regimes, with significant effects on olive oil composition and quality and, consequently, on its healthy and nutritional features. In this work, a summary of several research studies dealing with the biosynthesis of healthy and nutraceutical metabolites of the secondary metabolism in olive fruit will be reported. The phytochemical findings have been correlated with the expression of key genes involved in polyphenol, terpenoid, and carotenoid biosynthesis and metabolism in response to different development stages and water regimes. Flavonoids were highest in immature fruits, while anthocyanins increased at ripening. In epicarp tissue, this was clearly associated with an up-regulation of the UFGT gene. Olive fruits cultivated under different water regimes were analyzed by metabolomics. This method identified several hundred metabolites in the ripe mesocarp. Among them, 46 were differentially accumulated in the comparison between rain-fed and irrigated conditions. Well-known healthy metabolites were more abundant at a higher level of water regimes. Increased content of polyphenols was observed in the rain-fed fruit; particularly, anthocyanin concentration was higher at ripening. Several secondary metabolites were differentially accumulated between different irrigation conditions. These results showed that these metabolic approaches could be efficiently used to determine the effects of agronomic treatments on olive fruit physiology and, consequently, on nutritional and healthy properties of the obtained extra-virgin olive oil.

Keywords: olea europea, anthocyanins, polyphenols, water regimes

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Authors: Phawichsak Prapassornpitaya, Wanida Jinsart

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Daily monitoring PM₁₀ and PM₂.₅ data from 2007 to 2017 were analyzed to provide baseline data for prediction of the air pollution in Bangkok in the period of 2018 -2020. Two statistical models, Autoregressive Integrated Moving Average model (ARIMA) were used to evaluate the trends of pollutions. The prediction concentrations were tested by root means square error (RMSE) and index of agreement (IOA). This evaluation of the traffic PM₂.₅ and PM₁₀ were studied in association with the regulatory control and emission standard changes. The emission factors of particulate matter from diesel vehicles were decreased when applied higher number of euro standard. The trends of ambient air pollutions were expected to decrease. However, the Bangkok smog episode in February 2018 with temperature inversion caused high concentration of PM₂.₅ in the air environment of Bangkok. The impact of traffic pollutants was depended upon the emission sources, temperature variations, and metrological conditions.

Keywords: fine particulate matter, ARIMA, RMSE, Bangkok

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

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Authors: Kazui Sasakii, Seira Mormune-Moriya, Hiroaki Tanahashi, Shigeji Kongaya

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Poly (3.4-ethylenedioxythiophene) doped with poly (4-styrene sulfonate) (PEDOT: PSS) attracted a great deal of attention because of its unique characteristics of flexibility, optical properties, heat resistance and colloidal dispersion in water. It is well known that when high boiling solvents such as ethylene glycol or dimethyl sulfoxide are added as a secondary dopant to the micellar structure, PEDOT microcrystallizes and becomes highly conductive. In previous study bis(4-hydroxyphenyl) sulfone (BPS) was used as a secondary dopant for PEDOT:PSS and the enhancement of the conductivity was revealed. However, ductility is one of the serious issues which limited the application of PEDOT:PSS/BPS. So far, the composition with polymer binders has been conducted, however, polymer binders decrease the conductivity of the materials. In this study, PEDOT: PSS composites with polyester (PEs) were prepared by a simple aqueous process using PEs emulsion. The structural studies revealed that PEDOT:PSS and PEs were homogeneously distributed in the composites. It was found that the properties of PEDOT:PSS were remarkably enhanced by the incorporation of PEs. According to the tensile test, the ductility of PEDOT:PSS was remarkably improved. Interestingly, the conductivity of PEDOT:PSS/PEs composites was higher than that of neat PEDOT:PSS. For example, the conductivity increased by 8% at PEs content of 25 wt%. Since PEDOT:PSS were homogeneously dispersed on the surface of PEs particles, it was assumed that the conductive pathway was constructed by PEs particles in the nanocomposites. Therefore, a significant increase in conductivity was achieved.

Keywords: polymer composites, conductivity, PEDOT:PSS, polyester

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Muqaddas Butt, Allah Bakhsh Malik

Abstract:

The changing educational environment, the diverse educational needs of students, the high expectations from the public, and the policy reports demand a strong change & innovation in the curriculum. Effective change and innovation could not be possible without the involvement of two major tenants i.e. teachers and educational managers. Thus, the locus of this research was to explore the secondary school principals and teachers priorities regarding change and innovation in curriculum. The main research objectives were included to explore the secondary school teachers’ and principals’ views about existing Humanities group curriculum; to identify their priorities regarding change and innovation in curriculum and to make a comparison between the priorities of both (the teachers & principals). A total sample of 150 Secondary school teachers and 24 principals from Federal Government Secondary Schools was drawn. The data was obtained through a five point Likert scale questionnaire. The findings indicated a huge difference between principals and teachers priorities. Related to prevailed curriculum, teachers showed more satisfactory views than principals. It was also found that the principals in comparison with teachers showed more inclination towards change and innovation and emphasized on an interdisciplinary, practical and ICT Integrated curriculum. Inclusion of local environmental issues; creativity based and practical activities based curriculum; and orientation to citizenship education into curriculum were some of the aspects highly prioritized by both teachers and principals.

Keywords: curriculum change, curriculum innovation, humanities curriculum, curriculum priorities

Procedia PDF Downloads 443

Authors: Mikhail Gritskevich, Sebastian Hohenstein

Abstract:

The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: discrete holes film cooling, Reynolds Averaged Navier-Stokes (RANS), Reynolds stress tensor anisotropy, turbulent heat transfer

Procedia PDF Downloads 416

Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

Procedia PDF Downloads 341

Authors: A. Hakimiyan, H. Namazi

Abstract:

Human behavior is defined as a range of behaviors exhibited by humans who are influenced by different internal or external sources. Human behavior is the subject of much research in different areas of psychology and neuroscience. Despite some advances in studies related to forecasting of human behavior, there are not many researches which consider the effect of the time delay between the presence of stimulus and the related human response. Analysis of EEG signal as a fractal time series is one of the major tools for studying the human behavior. In the other words, the human brain activity is reflected in his EEG signal. Artificial Neural Network has been proved useful in forecasting of different systems’ behavior especially in engineering areas. In this research, a time delay neural network is trained and tested in order to forecast the human EEG signal and subsequently human behavior. This neural network, by introducing a time delay, takes care of the lagging time between the occurrence of the stimulus and the rise of the subsequent action potential. The results of this study are useful not only for the fundamental understanding of human behavior forecasting, but shall be very useful in different areas of brain research such as seizure prediction.

Keywords: human behavior, EEG signal, time delay neural network, prediction, lagging time

Procedia PDF Downloads 657

Authors: Zi-Gui Huang, Shen-Hsien Hu

Abstract:

This study proposes a fabrication of phononic-crystal acoustic wave device. A graphene-like atomic structure was adopted as the main research subject, and a graphene-like structure was designed using piezoelectric material zinc oxide and its periodic boundary conditions were defined using the finite element method. The effects of a hexagonal honeycomb structure were investigated regarding the band gap phenomenon. The use of micro-electromechanical systems process technology to make the film etched micron graphics, designed to produce four kinds of different piezoelectric structure (plat, periodic, single defect and double defects). Frequency response signals and phase change were also measured in this paper.

Keywords: MEMS, phononic crystal, piezoelectric material, Zinc oxide

Procedia PDF Downloads 530

Authors: Innocent C. Ezenwa, Takashi Yoshino

Abstract:

Knowledge of the transport properties of Fe and its alloys at extreme high pressure (P), temperature (T) conditions are essential for understanding the generation and sustainability of the magnetic field of the rocky planets with a metallic core. Since Pt, an unfilled d-band late transition metal with an electronic structure of Xe4f¹⁴5d⁹6s¹, is paramagnetic and remains close-packed structure at ambient conditions and high P-T, it is expected that its transport properties at these conditions would be similar to those of ε-Fe. We investigated the T-dependent electrical resistivity of solid and liquid Pt up to 8 GPa and found it constant along its melting curve both on the liquid and solid sides in agreement with theoretical prediction and experimental results estimated from thermal conductivity measurements. Our results suggest that the T-dependent resistivity of ε-Fe is linear and would not saturate at high P, T conditions. This, in turn, suggests that the thermal conductivity of liquid Fe at Earth’s core conditions may not be as high as previously suggested by models employing saturation resistivity. Hence, thermal convection could have powered the geodynamo before the birth of the inner core. The electrical resistivity and thermal conductivity on the liquid and solid sides of the inner core boundary of the Earth would be significantly different in values.

Keywords: electrical resistivity, thermal conductivity, transport properties, geodynamo and geomagnetic field

Procedia PDF Downloads 138