Search results for: predicted mean vote

Authors: Maryam Saeidi, Ehsan Motamedian, Seyed Abbas Shojaosadati

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Zymomonas mobilis is known as an example of the uncoupled growth phenomenon. This microorganism also has a unique metabolism that degrades glucose by the Entner–Doudoroff (ED) pathway. In this paper, a genome-scale metabolic model including 434 genes, 757 reactions and 691 metabolites was reconstructed to simulate uncoupled growth and study its effect on flux distribution in the central metabolism. The model properly predicted that ATPase was activated in experimental growth yields of Z. mobilis. Flux distribution obtained from model indicates that the major carbon flux passed through ED pathway that resulted in the production of ethanol. Small amounts of carbon source were entered into pentose phosphate pathway and TCA cycle to produce biomass precursors. Predicted flux distribution was in good agreement with experimental data. The model results also indicated that Z. mobilis metabolism is able to produce biomass with maximum growth yield of 123.7 g (mol glucose)-1 if ATP synthase is coupled with growth and produces 82 mmol ATP gDCW-1h-1. Coupling the growth and energy reduced ethanol secretion and changed the flux distribution to produce biomass precursors.

Keywords: genome-scale metabolic model, Zymomonas mobilis, uncoupled growth, flux distribution, ATP dissipation

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Authors: Hassnen M. Jafer, Khalid S. Hashim, W. Atherton, Ali W. Alattabi

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This study investigates the effect of two important parameters (length of curing period and percentage of the added binder) on the strength of soil treated with OPC. An intermediate plasticity silty clayey soil with medium organic content was used in this study. This soft soil was treated with different percentages of a commercially available cement type 32.5-N. laboratory experiments were carried out on the soil treated with 0, 1.5, 3, 6, 9, and 12% OPC by the dry weight to determine the effect of OPC on the compaction parameters, consistency limits, and the compressive strength. Unconfined compressive strength (UCS) test was carried out on cement-treated specimens after exposing them to different curing periods (1, 3, 7, 14, 28, and 90 days). The results of UCS test were used to develop a non-linear multi-regression model to find the relationship between the predicted and the measured maximum compressive strength of the treated soil (qu). The results indicated that there was a significant improvement in the index of plasticity (IP) by treating with OPC; IP was decreased from 20.2 to 14.1 by using 12% of OPC; this percentage was enough to increase the UCS of the treated soil up to 1362 kPa after 90 days of curing. With respect to the statistical model of the predicted qu, the results showed that the regression coefficients (R2) was equal to 0.8534 which indicates a good reproducibility for the constructed model.

Keywords: cement admixtures, soft soil stabilisation, geotechnical parameters, multi-regression model

Procedia PDF Downloads 336

Authors: Chakrit Chotamonsak

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In this study, the Weather Research and Forecasting (WRF) model was used as regional climate model to dynamically downscale the ECHAM5 Global Climate Model projection for the regional climate change impact on air quality–related meteorological conditions in the upper northern Thailand. The analyses were focused on meteorological variables that potentially impact on the regional air quality such as sea level pressure, planetary boundary layer height (PBLH), surface temperature, wind speed and ventilation. Comparisons were made between the present (1990–2009) and future (2045–2064) climate downscaling results during majority air pollution season (dry season, January-April). Analyses showed that the sea level pressure will be stronger in the future, suggesting more stable atmosphere. Increases in temperature were obvious observed throughout the region. Decreases in surface wind and PBLH were predicted during air pollution season, indicating weaker ventilation rate in this region. Consequently, air quality-related meteorological variables were predicted to change in almost part of the upper northern Thailand, yielding a favorable meteorological condition for pollutant accumulation in the future.

Keywords: climate change, climate impact, air quality, air pollution, Thailand

Procedia PDF Downloads 325

Authors: Asli Bugay Sökmez, Elif Manuoglu, Muhammet Coskun, Nebi̇ Sümer

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Decades of research have searched out the relationships between self-esteem, self-compassion, attachment, and rumination. Yet, unique mediated and moderated predictor power of these correlates of rumination has not been discovered yet. Moreover, no study examined whether these critical correlates of rumination specifically predict sub-dimensions of rumination that are reflection and brooding. Despite the broad range of study regarding predictors of rumination, a huge gap exists for the possible predictors of co-rumination. To address these issues, the present study mainly investigates the predictor roles of self-esteem, self-compassion, and attachment on dimensions of rumination (brooding and reflection) and co-rumination, especially the mediating and moderating roles of these predictor variables. 510 undergraduate and graduate students from different departments of a major state university in Turkey participated in the current study. The mean age of the participants was 21.8 (SD = 2.29) and 57.3% of them were female. Overall analyses revealed that self-compassion and attachment anxiety was negatively correlated with both co-rumination and brooding. Surprisingly, while attachment anxiety significantly and positively predicted reflection, attachment avoidance predicted reflection negatively. Moreover, anxiety, avoidance and self-compassion all were found to be significant predictor variables of co-rumination. Finally, as expected, a moderating effect of self-compassion revealed in predicting reflection and showed as a mediator in predicting brooding and co-rumination. All findings were discussed in light of the related literature.

Keywords: rumination, co-rumination, attachment, self-compassion, self-esteem

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Authors: Ting Shen

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Whether resource matters to school has been a topic of intense debate since 1960s. Educational researchers and policy makers have been particularly interested in knowing the return or payoff of Per-Pupil Expenditure (PPE) on improving students’ achievement. However, the evidence on the effect of PPE has been mixed and the size of the effect is also unknown. With regard to the methods, it is well-known that meta-analysis study is superior to individual study and it is also preferred to vote counting method in terms of scientifically weighting the evidence by the sample size. This meta-analysis study aims to provide a synthesized evidence on the correlation between PPE and student academic achievement using recent study data from 1990s to 2010s. Meta-analytical approach of fixed- and random-effects models will be utilized in addition to a meta regression with predictors of year, location, region and school type. A preliminary result indicates that by and large there is no statistically significant relationship between per pupil expenditure and student achievement, but location seems to have a mediating effect.

Keywords: per pupil expenditure, student academic achievement, multilevel model, meta-analysis

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Authors: Nweke Kingsley

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From the times of ancient Greece to modern times, democratic elections have been associated with a higher number of participants. Sequel to this, politicians globally and incumbent governments appear concerned with understanding the personality traits that may assure them of unflinching support by electorates. The study examined how openness to experience predicted electoral behaviour among senior non-teaching employees of Nnamdi Azikiwe University Awka. One hundred and thirty-three non-teaching employees who volunteered were randomly selected for the study. Two instruments were used for data collection: The Electoral Behaviour Scale, and the Openness to Experience dimension of the Personality Trait Inventory. A correlational design was adopted for the study, and the data generated were statistically analyzed using Pearson Product Moment and linear regression statistics. Results revealed that Openness to Experience positively and significantly predicted Electoral Behaviour among senior non-teaching employees of Nnamdi Azikiwe University, Awka. It was recommended that politicians and stakeholders hold town hall meetings and seminars to increase awareness of the electoral perception of electorates with the Openness to Experience trait as this will increase their support and yield successive results during elections.

Keywords: electoral-behaviour, employees, non-teaching, openness-to-experience

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Authors: Ahmad Amiri, Hamed Khajeh Arzani, Md. Salim Newaz Kazi, Bee Teng Chew

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Obviously, the behavior of thermophysical properties of covalently functionalized MWNT-based water nanofluids cannot be predicted from the predicted models. We present a study of the specific heat capacity, effective thermal conductivity, density and viscosity of coolants containing functionalized multi-wall carbon nanotubes (MWNT-COOH) with carboxyl groups at different temperatures. After synthesizing of MWNT-COOH-based water, measurements on the prepared coolants were made at various concentrations by different experimental methods. While thermal conductivity of nanofluids illustrated a significant increase, the specific heat capacity of the samples showed a downward behavior with increasing temperature. The viscosity was investigated in different shear rates and temperatures. Interestingly, the specific heat capacity of all prepared nanofluids was decreased with increasing concentration. Also, the density of the MWNT-COOH-based water nanofluids increased and decreased smoothly with increasing MWNT-COOH concentration and temperature, respectively.

Keywords: carbon nanotubes, coolant, heat capacity, density, viscosity, thermal conductivity

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Authors: Lamis Naddaf, Yuval Tabach

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In this research, we identified the missense genetic variants that have the potential to enhance resistance against cancer. Such field has not been widely explored, as researchers tend to investigate mutations that cause diseases, in response to the suffering of patients, rather than those mutations that protect from them. In conjunction with the genomic revolution, and the advances in genetic engineering and synthetic biology, identifying the protective variants will increase the power of genotype-phenotype predictions and can have significant implications on improved risk estimation, diagnostics, prognosis and even for personalized therapy and drug discovery. To approach our goal, we systematically investigated the sites of the coding genomes and picked up the alleles that showed a correlation with the species’ cancer resistance. We predicted 250 protecting variants (PVs) with a 0.01 false discovery rate and more than 20 thousand PVs with a 0.25 false discovery rate. Cancer resistance in Mammals and reptiles was significantly predicted by the number of PVs a species has. Moreover, Genes enriched with the protecting variants are enriched in pathways relevant to tumor suppression like pathways of Hedgehog signaling and silencing, which its improper activation is associated with the most common form of cancer malignancy. We also showed that the PVs are more abundant in healthy people compared to cancer patients within different human races.

Keywords: comparative genomics, machine learning, cancer resistance, cancer-protecting alleles

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Authors: Okoh Charity N.

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This study investigated the predictive power of preschoolers’ involvement in indoor and outdoor learning activities on their social learning skills in Niger state, Nigeria. Two research questions and two null hypotheses guided the study. Correlational research design was employed in the study. The population of the study consisted of 8,568 Nursery III preschoolers across the 549 preschools in the five Local Education Authorities in Niger State. A sample of 390 preschoolers drawn through multistage sampling procedure. Two instruments; Preschoolers’ Learning Activities Rating Scale (PLARS) and Preschoolers’ Social Learning Skills Rating Scale (PSLSRS) developed by the researcher were used for data collection. The reliability coefficients obtained for the PLARS and PSLSRS were 0.83 and 0.82, respectively. Data collected were analyzed using simple linear regression. Results showed that 37% of preschoolers’ social learning skills are predicted by their involvement in indoor learning activities, which is statistically significant (p < 0.05). It also shows that 11% of preschoolers’ social learning skills are predicted by their involvement in outdoor learning activities, which is statistically significant (p < 0.05). Therefore, it was recommended among others, that government and school administrators should employ qualified teachers who will stand as role models for preschoolers’ social skills development and provide indoor and outdoor activities and materials for preschoolers in schools.

Keywords: preschooler, social learning, indoor activities, outdoor activities

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Authors: Lamis Naddaf, Yuval Tabach

Abstract:

We identified missense genetic variants with the potential to enhance resistance against cancer. Such a field has not been widely explored as researchers tend to investigate the mutations that cause diseases, in response to the suffering of patients, rather than those mutations that protect from them. In conjunction with the genomic revolution and the advances in genetic engineering and synthetic biology, identifying the protective variants will increase the power of genotype-phenotype predictions and have significant implications for improved risk estimation, diagnostics, prognosis, and even personalized therapy and drug discovery. To approach our goal, we systematically investigated the sites of the coding genomes and selected the alleles that showed a correlation with the species’ cancer resistance. Interestingly, we found several amino acids that are more generally preferred (like the Proline) or avoided (like the Cysteine) by the resistant species. Furthermore, Cancer resistance in mammals and reptiles is significantly predicted by the number of the predicted protecting variants (PVs) a species has. Moreover, PVs-enriched-genes are enriched in pathways relevant to tumor suppression. For example, they are enriched in the Hedgehog signaling and silencing pathways, which its improper activation is associated with the most common form of cancer malignancy. We also showed that the PVs are mostly more abundant in healthy people compared to cancer patients within different human races.

Keywords: cancer resistance, protecting variant, naked mole rat, comparative genomics

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Authors: Hassan. A. Abas, Saad A. Aiban

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Soil improvement by means of stone column method is used to improve sabkha soils in order to limit total and differential settlement and to achieve the required bearing capacity. Full-scale plate test was performed on site to confirm the achievement of required bearing capacity at the specified settlement. Despite the fact that this technique is widely used to improve sabkha soils, there are no studies focusing on the behavior of stone columns in such problematic soils. Sabkha soils are known for its high compressibility, low strength and water sensitivity due to loss of salt cementation upon flooding during installation of stone columns. Numerical modeling of plate load test assist to understand complicated behavior of sabkha – stone column interaction. This paper presents a three-dimensional Finite element model, using PLAXIS 3D software, to simulate vertical plate load tests on a stone column installed in sabkha. The predicted settlement values are in reasonable agreement with the field measure values and the field load - settlement curve can be predicted with good accuracy.

Keywords: soil improvement, stone column, sabkha, PLAXIS 3D

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Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

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The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

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Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

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Authors: Robert Cui

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COVID-19 has drastically changed the way students interact and engage with their environments. Students, in particular, have been forced to change from in-person to online learning. How can we ensure that students continue to remain motivated even as their mode of education transitions to online learning? In this study conducted on high school students from a small private school (n = 50), we investigate the factors that predict student motivation during online learning. Using the framework of self-determination theory, we examine the three facets of student motivation during online learning: engagement, autonomy, and competence. We find that students' perception of their peers' engagement with the curriculum, feelings of parental academic expectations, perceptions of favoritism by the teacher, and perceived clarity of instruction given by the teacher all predict student engagement in online learning. Student autonomy is predicted by the amount of parental control a student feels, the clarity of instruction given by the teacher, and also the amount to which a student is perceiving their peers to be paying attention. Finally, competence is predicted by favoritism a student perceives from a teacher and also the amount of which a student is perceiving their peers to be paying attention. Based on these findings, we provide insights on how three important stakeholders –parents, teachers, and peers can enhance students' motivation during online learning.

Keywords: academic performance, motivation, online learning, parental influence, teacher, peers

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Authors: Shih-Yu Wang, Shun-Wen Hsiao

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In the field of cybersecurity, there exists many vendors giving malware samples classified results, namely naming after the label that contains some important information which is also called AV label. Lots of researchers relay on AV labels for research. Unfortunately, AV labels are too cluttered. They do not have a fixed format and fixed naming rules because the naming results were based on each classifiers' viewpoints. A way to fix the problem is taking a majority vote. However, voting can sometimes create problems of bias. Thus, we create a novel ensemble approach which does not rely on the cacophonous naming result but depend on group identification to aggregate everyone's opinion. To achieve this purpose, we develop an scoring system called Pairwise Consensus Score (PCS) to calculate result similarity. The entire method architecture combine Genetic Algorithm and PCS to find maximum consensus in the group. Experimental results revealed that our method outperformed the majority voting by 10% in term of the score.

Keywords: genetic algorithm, ensemble learning, malware family, malware labeling, AV labels

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Authors: Zia R. Tahir, P. Mandal

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This paper presents the details of a numerical study of buckling and post buckling behaviour of laminated carbon fiber reinforced plastic (CFRP) thin-walled cylindrical shell under axial compression using asymmetric meshing technique (AMT) by ABAQUS. AMT is considered to be a new perturbation method to introduce disturbance without changing geometry, boundary conditions or loading conditions. Asymmetric meshing affects both predicted buckling load and buckling mode shapes. Cylindrical shell having lay-up orientation [0°/+45°/-45°/0°] with radius to thickness ratio (R/t) equal to 265 and length to radius ratio (L/R) equal to 1.5 is analysed numerically. A series of numerical simulations (experiments) are carried out with symmetric and asymmetric meshing to study the effect of asymmetric meshing on predicted buckling behaviour. Asymmetric meshing technique is employed in both axial direction and circumferential direction separately using two different methods, first by changing the shell element size and varying the total number elements, and second by varying the shell element size and keeping total number of elements constant. The results of linear analysis (Eigenvalue analysis) and non-linear analysis (Riks analysis) using symmetric meshing agree well with analytical results. The results of numerical analysis are presented in form of non-dimensional load factor, which is the ratio of buckling load using asymmetric meshing technique to buckling load using symmetric meshing technique. Using AMT, load factor has about 2% variation for linear eigenvalue analysis and about 2% variation for non-linear Riks analysis. The behaviour of load end-shortening curve for pre-buckling is same for both symmetric and asymmetric meshing but for asymmetric meshing curve behaviour in post-buckling becomes extraordinarily complex. The major conclusions are: different methods of AMT have small influence on predicted buckling load and significant influence on load displacement curve behaviour in post buckling; AMT in axial direction and AMT in circumferential direction have different influence on buckling load and load displacement curve in post-buckling.

Keywords: CFRP composite cylindrical shell, asymmetric meshing technique, primary buckling, secondary buckling, linear eigenvalue analysis, non-linear riks analysis

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Authors: Waleed Basuliman

Abstract:

Anthropometric dimensions are considered one of the important factors when designing human-machine systems. In this study, the estimation of anthropometric dimensions has been improved by using Artificial Neural Network (ANN) model that is able to predict the anthropometric measurements of Saudi males in Riyadh City. A total of 1427 Saudi males aged 6 to 60 years participated in measuring 20 anthropometric dimensions. These anthropometric measurements are considered important for designing the work and life applications in Saudi Arabia. The data were collected during eight months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining 15 dimensions were set to be the measured variables (Model’s outcomes). The hidden layers varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was able to estimate the body dimensions of Saudi male population in Riyadh City. The network's mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found to be 0.0348 and 3.225, respectively. These results were found less, and then better, than the errors found in the literature. Finally, the accuracy of the developed neural network was evaluated by comparing the predicted outcomes with regression model. The ANN model showed higher coefficient of determination (R2) between the predicted and actual dimensions than the regression model.

Keywords: artificial neural network, anthropometric measurements, back-propagation

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Authors: Shahnam Behnam Malekzadeh, Ian Kerr, Tyson Kaempffer, Teague Harper, Andrew Watson

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The Site C Hydroelectric dam is currently being constructed in north-eastern British Columbia on sub-horizontal sedimentary strata that dip approximately 15 meters from one bank of the Peace River to the other. More than 615 pressure sensors (Vibrating Wire Piezometers) have been installed on bedding planes (BPs) since construction began, with over 80 more planned before project completion. These pressure measurements are essential to monitor the stability of the rock foundation during and after construction and for dam safety purposes. BPs are identified by their clay gouge infilling, which varies in thickness from less than 1 to 20 mm and can be challenging to identify as the core drilling process often disturbs or washes away the gouge material. Without the use of depth predictions from nearby boreholes, stratigraphic markers, and downhole geophysical data, it is difficult to confidently identify BP targets for the sensors. In this paper, a Case-Based Reasoning (CBR) method was used to develop an empirical model called the Bedding Plane Elevation Prediction (BPEP) to help geologists and geotechnical engineers to predict geological features and bedding planes at new locations in a fast and accurate manner. To develop CBR, a database was developed based on 64 pressure sensors already installed on key bedding planes BP25, BP28, and BP31 on the Right Bank, including bedding plane elevations and coordinates. Thirteen (20%) of the most recent cases were selected to validate and evaluate the accuracy of the developed model, while the similarity was defined as the distance between previous cases and recent cases to predict the depth of significant BPs. The average difference between actual BP elevations and predicted elevations for above BPs was ±55cm, while the actual results showed that 69% of predicted elevations were within ±79 cm of actual BP elevations while 100% of predicted elevations for new cases were within ±99cm range. Eventually, the actual results will be used to develop the database and improve BPEP to perform as a learning machine to predict more accurate BP elevations for future sensor installations.

Keywords: case-based reasoning, geological feature, geology, piezometer, pressure sensor, core logging, dam construction

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Authors: M. A. Fernández-Vilar, M. D. Galián, E. Ato

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The aim of this work was to analyze the relation between children´s effortful control and Maths achievement in a sample of 447 Spanish children aged between 6 and 8 years. Traditionally, the literature confirms that higher level of effortful control has been associated with higher academic achievement, but there are few studies that include the effect that children´s social competence exert to this relation. To measure children’s effortful control parents were given the TMCQ (Temperament in Middle Childhood Questionnaire), and Maths achievement was taken from teacher´s rates. To measure social competence, we used the nominations method in the classroom context. Results confirmed that higher effortful control predicted a better maths achievement, whereas lower effortful control scores predicted lower Maths scores. Using a statistical modeling approach, we tested a mediation model that revealed the mediating role of social competence (popularity and rejection) in the relation between effortful control and Maths achievement. Concretely, higher social competence (higher popularity and lower rejection) seems to mediate the better Maths achievement showed by better self´regulated children. Therefore, an adequate social competence mediates the positive effect that self-regulatory capacity exerts to academic achievement. The clinical implications of the present findings should be considered. Specifically, rejected children must be detected and evaluated in community settings, such as school or community programs, due the relevant role of social competence in the relation between temperament and academic achievement.

Keywords: effortful control, maths achievement, social competence, mediation

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Authors: Hari Bezwada, Michelle Cheon, Ryan Divan, Hannah Escritor, Michelle Kagramian, Isha Korgaonkar, Maya MacAdams, Udgita Pamidigantam, Richard Pilny, Eleanor Race, Angadh Singh, Nathan Zhang, LeeAnn Nguyen, Fina Liotta

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Malaria is a deadly disease caused by the Plasmodium parasite, which continues to develop resistance to current antimalarial drugs. In this research project, the effectiveness of numerous thiazole derivatives was explored in inhibiting the PfPKG, a crucial part of the Plasmodium life cycle. This study involved the synthesis of six thiazole-derived amides to inhibit the PfPKG pathway. Nuclear Magnetic Resonance (NMR) spectroscopy and Infrared (IR) spectroscopy were used to characterize these compounds. Furthermore, AutoDocking software was used to predict binding affinities of these thiazole-derived amides in silico. In silico, compound 6 exhibited the highest predicted binding affinity to PfPKG, while compound 5 had the lowest affinity. Compounds 1-4 displayed varying degrees of predicted binding affinity. In-vitro, it was found that compound 4 had the best percent inhibition, while compound 5 had the worst percent inhibition. Overall, all six compounds had weak inhibition (approximately 30-39% at 10 μM), but these results provide a foundation for future drug discovery experiments.

Keywords: Medicinal Chemistry, Malaria, drug discovery, PfPKG, Thiazole, Plasmodium

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Authors: Bidyadhar Dehury, Sanjay Kumar Mohanty

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According to the Right to Education Act of India, education is the fundamental right of all children of age group 6-14 year irrespective of their status. Using the unit level data from India Human Development Survey (IHDS), we tried to understand the inter-relationship between the level of poverty and the academic performance of the children aged 8-11 years. The level of multidimensional poverty is measured using five dimensions and 10 indicators using Alkire-Foster approach. The weighted deprivation score was obtained by giving equal weight to each dimension and indicators within the dimension. The weighted deprivation score varies from 0 to 1 and grouped into four categories as non-poor, vulnerable, multidimensional poor and sever multidimensional poor. The academic performance index was measured using three variables reading skills, math skills and writing skills using PCA. The bivariate and multivariate analysis was used in the analysis. The outcome variable was ordinal. So the predicted probabilities were calculated using the ordinal logistic regression. The predicted probabilities of good academic performance index was 0.202 if the child was sever multidimensional poor, 0.235 if the child was multidimensional poor, 0.264 if the child was vulnerable, and 0.316 if the child was non-poor. Hence, if the level of poverty among the children decreases from sever multidimensional poor to non-poor, the probability of good academic performance increases.

Keywords: multidimensional poverty, academic performance index, reading skills, math skills, writing skills, India

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Authors: Jiahui Song

Abstract:

The application of an electric field can cause poration at cell membranes. This includes the outer plasma membrane, as well as the membranes of intracellular organelles. In order to analyze and predict such electroporation effects, it becomes necessary to first evaluate the electric fields and the transmembrane voltages. This information can then be used to assess changes in the pore formation energy that finally yields the pore distributions and their radii based on the Smolchowski equation. The dynamic pore model can be achieved by including a dynamic aspect and a dependence on the pore population density into the pore formation energy equation. These changes make the pore formation energy E(r) self-adjusting in response to pore formation without causing uncontrolled growth and expansion. By using dynamic membrane tension, membrane electroporation in response to a 180kV/cm trapezoidal pulse with a 10 ns on time and 1.5 ns rise- and fall-times is discussed. Poration is predicted to occur at times beyond the peak at around 9.2 ns. Modeling also yields time-dependent distributions of the membrane pore population after multiple pulses. It shows that the pore distribution shifts to larger values of the radius with multiple pulsing. Molecular dynamics (MD) simulations are also carried out for a fixed field of 0.5 V/nm to demonstrate nanopore formation from a microscopic point of view. The result shows that the pore is predicted to be about 0.9 nm in diameter and somewhat narrower at the central point.

Keywords: high-intensity, nanosecond, dynamics, electroporation

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Authors: Ruhollah Heydari Sheikh Ahmad, Sara Alaie Khoraem

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Introduction: Substance use disorder is conceptualized as a chronic relapsing condition where relapse is usually defined as the return to problematic substance use following treatment. An issue of great importance is the identification of the predictors of relapse and the development of treatments that may help prevent relapse. One of the strongest predictors of relapse is craving. The purpose of the present study was to study the effect of anxiety, anxiety sensitivity, and coping motives on craving. Materials and method: Participants (n=74) were male opiate users recruited from a semi-private clinic providing de-toxification and treatment services for substance users. Anxiety, anxiety sensitivity, coping motives and craving were assessed using relevant questionnaires. The addiction severity index was used to assess addiction severity. Results: All patients were methadone maintained and one year after detoxification, 36 patients (48.64%) relapsed. Stress and anxiety, anxiety sensitivity, addiction severity and coping motives predicted craving and relapse. Anxiety sensitivity specifically predicted early relapse. Conclusion: Substance use is a severe mental disorder, with high relapse rates. Substance users high in anxiety sensitivity are particularly prone to relapse during the first six months of treatment. Addiction severity and coping motives need to be taken into account when providing interventional services for substance users. Findings imply the significance of additional psychological attention to methadone maintained patients to prevent craving and relapse.

Keywords: anxiety sensitivity, coping motives, relapse, substance use craving

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Authors: Yori Gidron, Maartje Mol, Norbert Foudraine, Frits Van Osch, Joop Van Den Bergh, Moshe Farchi, Maud Straus

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Background: The worldwide pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-COV2), which began in 2019, also known as Covid-19, has infected over 136 million people and tragically took the lives of over 2.9 million people worldwide. Many of the complications and deaths are predicted by the inflammatory “cytokine storm.” One way to progress in the prevention of death is by finding a predictive and protective factor that inhibits inflammation, on the one hand, and which also increases anti-viral immunity on the other hand. The vagal nerve does precisely both actions. This study examined whether vagal nerve activity, indexed by heart-rate variability (HRV), predicts survival in patients with Covid-19. Method: We performed a pseudo-prospective study, where we retroactively obtained ECGs of 271 Covid-19 patients arriving at a large regional hospital in The Netherlands. HRV was indexed by the standard deviation of the intervals between normal heartbeats (SDNN). We examined patients’ survival at 3 weeks and took into account multiple confounders and known prognostic factors (e.g., age, heart disease, diabetes, hypertension). Results: Patients’ mean age was 68 (range: 25-95) and nearly 22% of the patients had died by 3 weeks. Their mean SDNN (17.47msec) was far below the norm (50msec). Importantly, relatively higher HRV significantly predicted a higher chance of survival, after statistically controlling for patients’ age, cardiac diseases, hypertension and diabetes (relative risk, H.R, and 95% confidence interval (95%CI): H.R = 0.49, 95%CI: 0.26 – 0.95, p < 0.05). However, since HRV declines rapidly with age and since age is a profound predictor in Covid-19, we split the sample by median age (40). Subsequently, we found that higher HRV significantly predicted greater survival in patients older than 70 (H.R = 0.35, 95%CI: 0.16 – 0.78, p = 0.01), but HRV did not predict survival in patients below age 70 years (H.R = 1.11, 95%CI: 0.37 – 3.28, p > 0.05). Conclusions: To the best of our knowledge, this is the first study showing that higher vagal nerve activity, as indexed by HRV, is an independent predictor of higher chances for survival in Covid-19. The results are in line with the protective role of the vagal nerve in diseases and extend this to a severe infectious illness. Studies should replicate these findings and then test in controlled trials whether activating the vagus nerve may prevent mortality in Covid-19.

Keywords: Covid-19, heart-rate Variability, prognosis, survival, vagal nerve

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Authors: B. Van Kessel, P. T. Kockelkorn, T. R. Speelman, T. C. Wierikx, C. Mai Van, T. A. Bogaard

Abstract:

Saltwater intrusion is a common problem in estuaries around the world, as it could hinder the freshwater supply of coastal zones. This problem is likely to grow due to climate change and sea-level rise. The influence of these factors on the saltwater intrusion was investigated for the Cai River in the Khanh Hoa province in Vietnam. In addition, the Cai River has high seasonal fluctuations in discharge, leading to increased saltwater intrusion during the dry season. Sea level rise, river discharge changes, river mouth widening and a proposed saltwater intrusion prevention dam can have influences on the saltwater intrusion but have not been quantified for the Cai River estuary. This research used both an analytical and numerical model to investigate the effect of the aforementioned factors. The analytical model was based on a model proposed by Savenije and was calibrated using limited in situ data. The numerical model was a 3D hydrodynamic model made using the Delft3D4 software. The analytical model and numerical model agreed with in situ data, mostly for tidally average data. Both models indicated a roughly similar dependence on discharge, also agreeing that this parameter had the most severe influence on the modeled saltwater intrusion. Especially for discharges below 10 m/s3, the saltwater was predicted to reach further than 10 km. In the models, both sea-level rise and river widening mainly resulted in salinity increments up to 3 kg/m3 in the middle part of the river. The predicted sea-level rise in 2070 was simulated to lead to an increase of 0.5 km in saltwater intrusion length. Furthermore, the effect of the saltwater intrusion dam seemed significant in the model used, but only for the highest position of the gate.

Keywords: Cai River, hydraulic models, river discharge, saltwater intrusion, tidal barriers

Procedia PDF Downloads 83

Authors: Harish Rajak, Preeti Patel

Abstract:

The application of histone deacetylase inhibitors is a well-known strategy in prevention of cancer which shows acceptable preclinical antitumor activity due to its ability of growth inhibition and apoptosis induction of cancer cell. Molecular docking were performed using Histone Deacetylase protein (PDB ID:1t69) and prepared series of hydroxamic acid based HDACIs. On the basis of docking study, it was predicted that compound 1 has significant binding interaction with HDAC protein and three hydrogen bond interactions takes place, which are essential for antitumor activity. On docking, most of the compounds exhibited better glide score values between -8 to -10 which is close to the glide score value of suberoylanilide hydroxamic acid. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. The 3D-QSAR models provided a good correlation between predicted and actual anticancer activity. Best QSAR model showed Q2 (0.7974), R2 (0.9200) and standard deviation (0.2308). QSAR visualization maps suggest that hydrogen bond acceptor groups at carbonyl group of cap region and hydrophobic groups at ortho, meta, para position of R9 were favorable for HDAC inhibitory activity. We established structure activity correlation using docking, pharmacophore modeling and atom based 3D QSAR model for hydroxamic acid based HDACIs.

Keywords: HDACIs, QSAR, e-pharmacophore, docking, suberoylanilide hydroxamic acid

Procedia PDF Downloads 263

Authors: Abu Salim Mustafa

Abstract:

The comparisons of mycobacterial genomes have identified several Mycobacterium tuberculosis-specific genomic regions that are absent in other mycobacteria and are known as regions of differences. Due to M. tuberculosis-specificity, the peptides encoded by these regions could be useful in the specific diagnosis of tuberculosis. To explore this possibility, overlapping synthetic peptides corresponding to 39 proteins predicted to be encoded by genes present in regions of differences were tested for antibody-reactivity with sera from tuberculosis patients and healthy subjects. The results identified four immunodominant peptides corresponding to four different proteins, with three of the peptides showing significantly stronger antibody reactivity and rate of positivity with sera from tuberculosis patients than healthy subjects. The fourth peptide was recognized equally well by the sera of tuberculosis patients as well as healthy subjects. Predication of antibody epitopes by bioinformatics analyses using ABCpred server predicted multiple linear epitopes in each peptide. Furthermore, peptide sequence analysis for sequence identity using BLAST suggested M. tuberculosis-specificity for the three peptides that had preferential reactivity with sera from tuberculosis patients, but the peptide with equal reactivity with sera of TB patients and healthy subjects showed significant identity with sequences present in nob-tuberculous mycobacteria. The three identified M. tuberculosis-specific immunodominant peptides may be useful in the serological diagnosis of tuberculosis.

Keywords: genomic regions of differences, Mycobacterium tuberculossis, peptides, serodiagnosis

Procedia PDF Downloads 160

Authors: Carolina S. Monteiro, Eugénia Pinto, Miguel A. Faria, Sara C. Cunha

Abstract:

Rice (Oryza sativa) production plays a vital role in reducing hunger and poverty and assumes particular importance in low-income and developing countries. Rice is a sensitive plant, and production occurs strictly where suitable temperature and water conditions are found. Climatic changes are likely to affect worldwide, and some models have predicted increased temperatures, variations in atmospheric CO₂ concentrations and modification in precipitation patterns. Therefore, the ongoing climatic changes threaten rice production by increasing biotic and abiotic stress factors, and crops will grow in different environmental conditions in the following years. Around the world, the effects will be regional and can be detrimental or advantageous depending on the region. Mediterranean zones have been identified as possible hot spots, where dramatic temperature changes, modifications of CO₂ levels, and rainfall patterns are predicted. The actual estimated atmospheric CO₂ concentration is around 400 ppm, and it is predicted that it can reach up to 1000–1200 ppm, which can lead to a temperature increase of 2–4 °C. Alongside, rainfall patterns are also expected to change, with more extreme wet/dry episodes taking place. As a result, it could increase the migration of pathogens, and a shift in the occurrence of mycotoxins, concerning their types and concentrations, is expected. Mycotoxigenic spoilage fungi can colonize the crops and be present in all rice food chain supplies, especially Penicillium species, mainly resulting in ochratoxin A (OTA) contamination. In this scenario, the objectives of the present study are evaluating the effect of temperature (20 vs. 25 °C), CO₂ (400 vs. 1000 ppm), and water stress (0.93 vs 0.95 water activity) on growth and OTA production by a Penicillium nordicum strain in vitro on rice-based media and when colonizing layers of raw rice. Results demonstrate the effect of temperature, CO₂ and drought on the OTA production in a rice-based environment, thus contributing to the development of mycotoxins predictive models in climate change scenarios. As a result, improving mycotoxins' surveillance and monitoring systems, whose occurrence can be more frequent due to climatic changes, seems relevant and necessary. The development of prediction models for hazard contaminants presents in foods highly sensitive to climatic changes, such as mycotoxins, in the highly probable new agricultural scenarios is of paramount importance.

Keywords: climate changes, ochratoxin A, penicillium, rice

Procedia PDF Downloads 31

Authors: A. M. Khourshid, A. M. El-Kassas, I. Sabry

Abstract:

The objective of this work was to investigate the mechanical properties in order to demonstrate the feasibility of friction stir welding for joining Al 6061 aluminium alloys. Welding was performed on pipe with different thickness (2, 3 and 4 mm), five rotational speeds (485, 710, 910, 1120 and 1400 rpm) and a traverse speed of 4mm/min. This work focuses on two methods which are artificial neural networks using software and Response Surface Methodology (RSM) to predict the tensile strength, the percentage of elongation and hardness of friction stir welded 6061 aluminium alloy. An Artificial Neural Network (ANN) model was developed for the analysis of the friction stir welding parameters of 6061 pipe. Tensile strength, the percentage of elongation and hardness of weld joints were predicted by taking the parameters tool rotation speed, material thickness and axial force as a function. A comparison was made between measured and predicted data. Response Surface Methodology (RSM) was also developed and the values obtained for the response tensile strength, the percentage of elongation and hardness are compared with measured values. The effect of FSW process parameters on mechanical properties of 6061 aluminium alloy has been analysed in detail.

Keywords: friction stir welding, aluminium alloy, response surface methodology, artificial neural network

Procedia PDF Downloads 260

Authors: Abdul Karim Zulkarnain, Marchaban, Subagus Wahyono, Ratna Asmah Susidarti

Abstract:

Mahkota Dewa contains phalerin which has activity as sun screen. In this study, 13 formulations of cream oil in water (o/w) were prepared and tested for their physical characteristics. The physical characteristics were then used for determining the optimum formula. This study aimed to explore the physical stability of optimized formulation of cream, its sun protecting factor (SPF) values using in vitro and in vivo tests. The optimum formula of o/w cream were prepared based on Simplex Lattice Design (LSD) method using software Design Expert®. The formulation of o/w cream were varied based on the proportion of cetyl alcohol, mineral oil and tween 80. The difference of physical characteristic of optimum and predicted formula was tested using t-test with significant level of 95%. The optimum formula of o/w cream was the formula which consists of cetyl alcohol 9.71%, mineral oil, 29%, and tween 80 3.29. Based on t-test, there was no significant difference of physical characteristics of optimum and predicted formulation. Viscosity, spread power, adhesive power, and separation volume ratio of o/w at week 0-4 were relatively stable. The o/w creams were relatively stable at extreme temperature. The o/w creams from mahkota dewa, phalerin, and benzophenone have SPF values of 21.32, 33.12, and 42.49, respectively. The formulas did not irritate the skin based on in vivo test.

Keywords: cream, stability, In vitro, In vivo

Procedia PDF Downloads 198