Search results for: panel data regression models

Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

Abstract:

In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r2) and the cross-validated coefficient (q2) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions.

Keywords: 3D QSAR, CoMSIA, triazoles, novel heterocycles

Procedia PDF Downloads 429

Authors: Valentina Marutyan

Abstract:

Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

Procedia PDF Downloads 52

Authors: Reza Mohammadzadeh

Abstract:

The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

Procedia PDF Downloads 252

Authors: Bahram Fathi, Seyedhossein Sajadifar, Naser Khiabani

Abstract:

Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as byproducts of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 12 OPEC countries and the results obtained are presented.

Keywords: energy efficiency, undesirable outputs, data envelopment analysis

Procedia PDF Downloads 715

Authors: Priyoth Kittiteerasack, Alana Steffen, Alicia K. Matthews

Abstract:

Depression is a leading cause of the worldwide burden of disability and disease burden. Notably, lesbian, gay, bisexual, and transgender (LGBT) populations are more likely to be a high-risk group for depression compared to their heterosexual and cisgender counterparts. To date, little is known about the rates and predictors of depression among Thai LGBT populations. As such, the purpose of this study was to: 1) measure the prevalence of depression among a diverse sample of Thai LGBT adults and 2) determine the influence of minority stress variables (discrimination, victimization, internalized homophobia, and identity concealment), general stress (stress and loneliness), and coping strategies (problem-focused, avoidance, and seeking social support) on depression outcomes. This study was guided by the Minority Stress Model (MSM). The MSM posits that elevated rates of mental health problems among LGBT populations stem from increased exposures to social stigma due to their membership in a stigmatized minority group. Social stigma, including discrimination and violence, represents unique sources of stress for LGBT individuals and have a direct impact on mental health. This study was conducted as part of a larger descriptive study of mental health among Thai LGBT adults. Standardized measures consistent with the MSM were selected and translated into the Thai language by a panel of LGBT experts using the forward and backward translation technique. The psychometric properties of translated instruments were tested and acceptable (Cronbach’s alpha > .8 and Content Validity Index = 1). Study participants were recruited using convenience and snowball sampling methods. Self-administered survey data were collected via an online survey and via in-person data collection conducted at a leading Thai LGBT organization. Descriptive statistics and multivariate analyses using multiple linear regression models were conducted to analyze study data. The mean age of participants (n = 411) was 29.5 years (S.D. = 7.4). Participants were primarily male (90.5%), homosexual (79.3%), and cisgender (76.6%). The mean score for depression of study participant was 9.46 (SD = 8.43). Forty-three percent of LGBT participants reported clinically significant levels of depression as measured by the Beck Depression Inventory. In multivariate models, the combined influence of demographic, stress, coping, and minority stressors explained 47.2% of the variance in depression scores (F(16,367) = 20.48, p < .001). Minority stressors independently associated with depression included discrimination (β = .43, p < .01) victimization (β = 1.53, p < .05), and identity concealment (β = -.54, p < .05). In addition, stress (β = .81, p < .001), history of a chronic disease (β = 1.20, p < .05), and coping strategies (problem-focused coping β = -1.88, p < .01, seeking social support β = -1.12, p < .05, and avoidance coping β = 2.85, p < .001) predicted depression scores. The study outcomes emphasized that minority stressors uniquely contributed to depression levels among Thai LGBT participants over and above typical non-minority stressors. Study findings have important implications for nursing practice and the development of intervention research.

Keywords: depression, LGBT, minority stress, sexual and gender minority, Thailand

Procedia PDF Downloads 111

Authors: Min Kyung An

Abstract:

In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks

Procedia PDF Downloads 201

Authors: Adeleke Omolade

Abstract:

The study investigated the impacts of capital goods' import and human capital on the economic growth of the Sub Sahahra Africa (SSA). 30 countries were used in the Panel- ARDL analysis where economic growth is the dependent variables and capital goods' import, human capital, primary export, investment exchange rate, among others were used as the independent variables. The result from the panel analysis indicates that capital goods' import will significantly and positively influence economic growth but human capital fails to have significant positive impact on economic growth of the SSA. Earlier the trend analysis and the correlation results have shown that there is a weak association between capital goods' import and human capital in the SSA. The results offer an expository analysis that reveals that the quality of the human capital is very germane to the effective utilization of capital goods' import for the purpose of growth in a primary goods' export dominated region like the SSA.

Keywords: capital goods import, economic growth, human capital, Sub-Sahara Africa

Procedia PDF Downloads 214

Authors: Salahaeddine Sbai

Abstract:

Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

Procedia PDF Downloads 68

Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

Abstract:

There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

Procedia PDF Downloads 151

Authors: Shahadut Hossain, Jacek Wesolowski, Zahirul Hoque

Abstract:

In many epidemiological and environmental studies covariate measurements are subject to the detection limit. In most applications, covariate measurements are usually truncated from below which is known as left-truncation. Because the measuring device, which we use to measure the covariate, fails to detect values falling below the certain threshold. In regression analyses, it causes inflated bias and inaccurate mean squared error (MSE) to the estimators. This paper suggests a response-based regression calibration method to correct the deleterious impact introduced by the covariate detection limit in the estimators of the parameters of simple logistic regression model. Compared to the maximum likelihood method, the proposed method is computationally simpler, and hence easier to implement. It is robust to the violation of distributional assumption about the covariate of interest. In producing correct inference, the performance of the proposed method compared to the other competing methods has been investigated through extensive simulations. A real-life application of the method is also shown using data from a population-based case-control study of non-Hodgkin lymphoma.

Keywords: environmental exposure, detection limit, left truncation, bias, ad-hoc substitution

Procedia PDF Downloads 222

Authors: Khadidja Bellifa, Sidi Mohamed Mekelleche

Abstract:

Phenols and especially halogenated phenols represent a substantial part of the chemicals produced worldwide and are known as aquatic pollutants. Quantitative structure–toxicity relationship (QSTR) models are useful for understanding how chemical structure relates to the toxicity of chemicals. In the present study, the acute toxicities of 45 halogenated phenols to Tetrahymena Pyriformis are estimated using no cost semi-empirical quantum chemistry methods. QSTR models were established using the multiple linear regression technique and the predictive ability of the models was evaluated by the internal cross-validation, the Y-randomization and the external validation. Their structural chemical domain has been defined by the leverage approach. The results show that the best model is obtained with the AM1 method (R²= 0.91, R²CV= 0.90, SD= 0.20 for the training set and R²= 0.96, SD= 0.11 for the test set). Moreover, all the Tropsha’ criteria for a predictive QSTR model are verified.

Keywords: halogenated phenols, toxicity mechanism, hydrophobicity, electrophilicity index, quantitative stucture-toxicity relationships

Procedia PDF Downloads 280

Authors: D. S. Gomes, A. T. Silva

Abstract:

Analysis of the uncertainty quantification related to nuclear safety margins applied to the nuclear reactor is an important concept to prevent future radioactive accidents. The nuclear fuel performance code may involve the tolerance level determined by traditional deterministic models producing acceptable results at burn cycles under 62 GWd/MTU. The behavior of nuclear fuel can simulate applying a series of material properties under irradiation and physics models to calculate the safety limits. In this study, theoretical predictions of nuclear fuel failure under transient conditions investigate extended radiation cycles at 75 GWd/MTU, considering the behavior of fuel rods in light-water reactors under reactivity accident conditions. The fuel pellet can melt due to the quick increase of reactivity during a transient. Large power excursions in the reactor are the subject of interest bringing to a treatment that is known as the Fuchs-Hansen model. The point kinetic neutron equations show similar characteristics of non-linear differential equations. In this investigation, the multivariate logistic regression is employed to a probabilistic forecast of fuel failure. A comparison of computational simulation and experimental results was acceptable. The experiments carried out use the pre-irradiated fuels rods subjected to a rapid energy pulse which exhibits the same behavior during a nuclear accident. The propagation of uncertainty utilizes the Wilk's formulation. The variables chosen as essential to failure prediction were the fuel burnup, the applied peak power, the pulse width, the oxidation layer thickness, and the cladding type.

Keywords: logistic regression, reactivity-initiated accident, safety margins, uncertainty propagation

Procedia PDF Downloads 278

Authors: Mokhtar Kouki Inès Rekik

Abstract:

This paper focuses on the assessment of the air pollution and morbidity relationship in Tunisia. Air pollution is measured by ozone air concentration and the morbidity is measured by the number of respiratory-related restricted activity days during the 2-week period prior to the interview. Socioeconomic data are also collected in order to adjust for any confounding covariates. Our sample is composed by 407 Tunisian respondents; 44.7% are women, the average age is 35.2, near 69% are living in a house built after the 1980, and 27.8% have reported at least one day of respiratory-related restricted activity. The model consists on the regression of the number of respiratory-related restricted activity days on the air quality measure and the socioeconomic covariates. In order to correct for zero-inflation and heterogeneity, we estimate several models (Poisson, Negative binomial, Zero inflated Poisson, Poisson hurdle, Negative binomial hurdle and finite mixture Poisson models). Bootstrapping and post-stratification techniques are used in order to correct for any sample bias. According to the Akaike information criteria, the hurdle negative binomial model has the greatest goodness of fit. The main result indicates that, after adjusting for socioeconomic data, the ozone concentration increases the probability of positive number of restricted activity days.

Keywords: bootstrapping, hurdle negbin model, overdispersion, ozone concentration, respiratory-related restricted activity days

Procedia PDF Downloads 242

Authors: Zichun Guo

Abstract:

Guangzhou's house price has long been lower than the other three major cities, with the gradual increase of Guangzhou's house price, the influencing factors of house price have gradually been paid attention to, this paper tries to use house price data and POI data, and explores the distribution of house price and influencing factors by applying the Kriging spatial interpolation method and geographically weighted regression model in Arcgis. The results show that the interpolation result of house price has a significant relationship with the economic development and development potential of the region, and that different POI types have different impacts on the growth of house price in different regions.

Keywords: housing prices, spatial heterogeneity, Guangzhou, POI

Procedia PDF Downloads 24

Authors: Yazhi Huang, Jing Yang, Dingge Ying, Yan Zhang, Vorasuk Shotelersuk, Nattiya Hirankarn, Pak Chung Sham, Yu Lung Lau, Wanling Yang

Abstract:

Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT.

Keywords: human leukocyte antigens, next generation sequencing, whole exome sequencing, HLA typing

Procedia PDF Downloads 648

Authors: Anthony Usoro

Abstract:

In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data.

Keywords: autoregressive model, bilinear autoregressive model, bilinear moving average model, moving average model

Procedia PDF Downloads 388

Authors: H. Taylan Selamlar, I. Yavuz, G. Yapar

Abstract:

It is difficult to make predictions especially about the future and making accurate predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. Numerous number of forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), and still new methods are derived or inspired from them. After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness and accuracy as automatic forecasting procedures especially in the famous M-Competitions. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this study will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have similar structural forms and ATA can be easily adapted to all of the individual ES models however ATA has many advantages due to its innovative new weighting scheme. In this paper, the focus is on modeling the trend component and handling seasonality patterns by utilizing classical decomposition. Therefore, ATA method is expanded to higher order ES methods for additive, multiplicative, additive damped and multiplicative damped trend components. The proposed models are called ATA trended models and their predictive performances are compared to their counter ES models on the M3 competition data set since it is still the most recent and comprehensive time-series data collection available. It is shown that the models outperform their counters on almost all settings and when a model selection is carried out amongst these trended models ATA outperforms all of the competitors in the M3- competition for both short term and long term forecasting horizons when the models’ forecasting accuracies are compared based on popular error metrics.

Keywords: accuracy, exponential smoothing, forecasting, initial value

Procedia PDF Downloads 164

Authors: Anthony J. Kondracki, Bonzo I. Reddick, Jennifer L. Barkin

Abstract:

We sought to examine the association of maternal Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and treponema pallidum (TP) (syphilis) infections with preterm birth (PTB) (<37 weeks gestation), low birth weight (LBW) (<2500 grams) and prenatal care (PNC) attendance. This cross-sectional study was based on data drawn from the 2020 United States National Center for Health Statistics (NCHS) Natality File. We estimated the prevalence of all births, early/late PTBs, moderately/very LBW, and the distribution of sexually transmitted infections (STIs) according to maternal characteristics in the sample. In multivariable logistic regression models, we examined adjusted odds ratios (aORs) and their corresponding 95% confidence intervals (CIs) of PTB and LBW subcategories in the association with maternal/infant characteristics, PNC status, and maternal CT, NG, and TP infections. In separate logistic regression models, we assessed the risk of these newborn outcomes stratified by PNC status. Adjustments were made for race/ethnicity, age, education, marital status, health insurance, liveborn parity, previous preterm birth, gestational hypertension, gestational diabetes, PNC status, smoking, and infant sex. Additionally, in a sensitivity analysis, we assessed the association with early, full, and late term births and the potential impact of unmeasured confounding using the E-value. CT (1.8%) was most prevalent STI in pregnancy, followed by NG (0.3%), and TP (0.1%). Non-Hispanic Black women, 20-24 years old, with a high school education, and on Medicaid had the highest rate of STIs. Around 96.6% of women reported receiving PNC and about 60.0% initiated PNC early in pregnancy. PTB and LBW were strongly associated with NG infection (12.2% and 12.1%, respectively) and late initiation/no PNC (8.5% and 7.6%, respectively), and ≤10 prenatal visits received (13.1% and 10.3%, respectively). The odds of PTB and LBW were 2.5- to 3-foldhigher for each STI among women who received ≤10 prenatal visits than >10 visits. Adequate prenatal care utilization and timely screening and treatment of maternal STIs can substantially reduce the burden of adverse newborn outcomes.

Keywords: low birthweight, prenatal care, preterm birth, sexually transmitted infections

Procedia PDF Downloads 161

Authors: Zainab Almukhtar, Adel Merabet

Abstract:

In this paper, a method for maximum power point tracking of a photovoltaic energy conversion system is presented. This method is based on using the difference between the power from the solar panel and an estimated power value to control the DC-DC converter of the photovoltaic system. The difference is continuously compared with a preset error permitted value. If the power difference is more than the error, the estimated power is multiplied by a factor and the operation is repeated until the difference is less or equal to the threshold error. The difference in power will be used to trigger a DC-DC boost converter in order to raise the voltage to where the maximum power point is achieved. The proposed method was experimentally verified through a PV energy conversion system driven by the OPAL-RT real time controller. The method was tested on varying radiation conditions and load requirements, and the Photovoltaic Panel was operated at its maximum power in different conditions of irradiation.

Keywords: control system, error, solar panel, MPPT tracking

Procedia PDF Downloads 255

Authors: Salwa Aftab, Sehrish Riaz

Abstract:

The objective of this study was to check the impact of grade sensitivity on learning motivation and academic performance of students and to remove the degree of difference that exists among students regarding the cause of their learning motivation and also to gain knowledge about this matter since it has not been adequately researched. Data collection was primarily done through the academic sector of Pakistan and was depended upon the responses given by students solely. A sample size of 208 university students was selected. Both paper and online surveys were used to collect data from respondents. The results of the study revealed that grade sensitivity has a positive relationship with the learning motivation of students and their academic performance. These findings were carried out through systematic correlation and regression analysis.

Keywords: academic performance, correlation, grade sensitivity, learning motivation, regression

Procedia PDF Downloads 379

Authors: Jianzhou Ni, Hua Fan

Abstract:

It has been determined that supervisor support has a major influence on postgraduate students' academic emotions and is considered a method of successfully anticipating postgraduates' good psychological well-being levels. As a result, by assessing the mediating influence upon academic emotions for contemporary postgraduates in China, this study investigated the tight reciprocal relationship between psychological empowerment and positive mental well-being among postgraduates. To that end, a help enables a theoretical analysis of role clarity, academic emotion, and positive psychological health was developed, and its validity and reliability were demonstrated for the first time using the normalized postgrad relationship with supervisor scale, academic emotion scale, and positive mental scale, as well as questionnaire data from Chinese postgraduate students. This study used the cross-lagged (ARCL) panel model data to longitudinally measure 798 valid data from two survey questions polls taken in 2019 (T1) and 2021 (T2) to investigate the link between supervisor support and positive graduate student mental well-being in a bidirectional relationship of influence. The study discovered that mentor assistance could have a considerable beneficial impact on graduate students' academic emotions and, as a result, indirectly help learners attain positive mental health development. This verifies the theoretical premise that academic emotions partially mediate the effect of mentor support on positive mental health development and argues for the coexistence of the two. The outcomes of this study can help researchers gain a better knowledge of the dynamic interplay among three different research variables: supervisor support, academic emotions, and positive mental health, as well as fill gaps in previous research. In this regard, the study indicated that mentor assistance directly stimulates students' academic drive and assists graduate students in developing good academic emotions, which contributes to the development of positive mental health. However, given the restricted measurement time in this study's cross-lagged panel data and the potential effect of moderating effects other than academic mood on graduate students' good mental health, the results of this study need to be more fully understood and validated.

Keywords: supervisor support, academic emotions, positive mental health, interaction effects, longitudinal cross-lagged measurements

Procedia PDF Downloads 73

Authors: Strahinja Kovačević, Lidija Jevrić, Miloš Kuzmanović, Sanja Podunavac-Kuzmanović

Abstract:

In this paper, various derivatives of benzimidazole have been evaluated against Gram-negative bacteria Escherichia coli. For all investigated compounds the minimum inhibitory concentration (MIC) was determined. Quantitative structure-activity relationships (QSAR) attempts to find consistent relationships between the variations in the values of molecular properties and the biological activity for a series of compounds so that these rules can be used to evaluate new chemical entities. The correlation between MIC and some absorption, distribution, metabolism and excretion (ADME) parameters was investigated, and the mathematical models for predicting the antibacterial activity of this class of compounds were developed. The quality of the multiple linear regression (MLR) models was validated by the leave-one-out (LOO) technique, as well as by the calculation of the statistical parameters for the developed models and the results are discussed on the basis of the statistical data. The results of this study indicate that ADME parameters have a significant effect on the antibacterial activity of this class of compounds. Principal component analysis (PCA) and agglomerative hierarchical clustering algorithms (HCA) confirmed that the investigated molecules can be classified into groups on the basis of the ADME parameters: Madin-Darby Canine Kidney cell permeability (MDCK), Plasma protein binding (PPB%), human intestinal absorption (HIA%) and human colon carcinoma cell permeability (Caco-2).

Keywords: benzimidazoles, QSAR, ADME, in silico

Procedia PDF Downloads 358

Authors: Muhammad Tahir

Abstract:

Purpose The purpose of the study is to find anchoring bias behavior in mutual fund return with its parent firm performance in Pakistan. Research Methodology The paper used monthly returns of equity funds whose parent firm exist from 2011 to 2021, along with parent firm return. Proximity to 52-week highest return calculated by dividing fund return by parent firm 52-week highest return. Control variables are also taken and used pannel regression model to estimate our results. For robust results, we also used feasible generalize least square (FGLS) model. Findings The results showed that there exist anchoring biased in mutual fund return with its parent firm performance. The FGLS results reaffirms the same results as obtained from panner regression results. Proximity to 52-week highest Xc is significant in both models. Research Implication Since most of mutual funds has a parent firm, anchoring behavior biased found in mutual fund with its parent firm performance. Practical Implication Mutual fund investors in Pakistan invest in equity funds in which behavioral bias exist, although there might be better opportunity in market. Originality/Value Addition Our research is a pioneer study to investigate anchoring bias in mutual fund return with its parent firm performance. Research limitations Our sample is limited to only 23 equity funds, which has a parent firm and data was available from 2011 to 2021.

Keywords: mutual fund, anchoring bias, 52-week high return, proximity to 52-week high, parent firm performance, pannel regression, FGLS

Procedia PDF Downloads 102

Authors: Abdol-Hassan Doulah

Abstract:

In this research, some of the inorganic complexes of uranyl with N- donor ligands were synthesized. Complexes were characteriezed by FT-IR and UV spectra, ¹HNMR, ¹³CNMR and some physical properties. The uranyl unit (UO2) is composed of a center of uranium atom with the charge (+6) and two oxygen atom by forming two U=O double bonds. The structure is linear (O=U=O, 180) and usually stable. So other ligands often coordinate to the U atom in the plane perpendicularly to the O=U=O axis. The antitumor activity of some of ligand and their complexes against a panel of human tumor cell lines (HT29: Haman colon adenocarcinoma cell line T47D: human breast adenocarcinoma cell line) were determined by MTT(3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl-tetrazolium bromide) assay. These data suggest that some of these compounds provide good models for the further design of potent antitumor compounds.

Keywords: inorganic, uranyl complex-donor ligands, Schiff bases, anticancer activity

Procedia PDF Downloads 441

Authors: Arega Shumetie Ademe, Belay Kassa, Degye Goshu, Majaliwa Mwanjalolo

Abstract:

Currently the world economy is suffering from one critical problem, climate change. Some studies done before identified that impact of the problem is region specific means in some part of the world (temperate zone) there is improvement in agricultural performance but in some others like in the tropics there is drastic reduction in crop production and crop income. Climate variability is becoming dominant cause of short-term fluctuation in rain-fed agricultural production and income of developing countries. The purely rain-fed Ethiopian agriculture is the most vulnerable sector to the risks and impacts of climate variability. Thus, this study tried to identify impact of climate variability on crop income of smallholders in Ethiopia. The research used eight rounded unbalanced panel data from 1994- 2014 collected from six villages in the study area. After having all diagnostic tests the research used fixed effect method of regression. Based on the regression result rainfall and temperature deviation from their respective long term averages have negative and significant effect on crop income. Other extreme devastating shocks like flood, storm and frost, which are sourced from climate variability, have significant and negative effect on crop income of households’. Parameters that notify rainfall inconsistency like late start, variation in availability at growing season, and early cessation are critical problems for crop income of smallholder households as to the model result. Given this, impact of climate variability is not consistent in different agro-ecologies of the country. Rainfall variability has similar impact on crop income in different agro-ecology, but variation in temperature affects cold agro-ecology villages negatively and significantly, while it has positive effect in warm villages. Parameters that represent rainfall inconsistency have similar impact in both agro-ecologies and the aggregate model regression. This implies climate variability sourced from rainfall inconsistency is the main problem of Ethiopian agriculture especially the crop production sub-sector of smallholder households.

Keywords: climate variability, crop income, household, rainfall, temperature

Procedia PDF Downloads 350

Authors: B. Padmaja, V. V. Rama Prasad, K. V. N. Sunitha, E. Krishna Rao Patro

Abstract:

Stress is a psychological condition that reduces the quality of sleep and affects every facet of life. Constant exposure to stress is detrimental not only for mind but also body. Nevertheless, to cope with stress, one should first identify it. This paper provides an effective method for the cognitive stress level detection by using data provided from a physical activity tracker device Fitbit. This device gathers people’s daily activities of food, weight, sleep, heart rate, and physical activities. In this paper, four major stressors like physical activities, sleep patterns, working hours and change in heart rate are used to assess the stress levels of individuals. The main motive of this system is to use machine learning approach in stress detection with the help of Smartphone sensor technology. Individually, the effect of each stressor is evaluated using logistic regression and then combined model is built and assessed using variants of ordinal logistic regression models like logit, probit and complementary log-log. Then the quality of each model is evaluated using Akaike Information Criterion (AIC) and probit is assessed as the more suitable model for our dataset. This system is experimented and evaluated in a real time environment by taking data from adults working in IT and other sectors in India. The novelty of this work lies in the fact that stress detection system should be less invasive as possible for the users.

Keywords: physical activity tracker, sleep pattern, working hours, heart rate, smartphone sensor

Procedia PDF Downloads 241

Authors: Sarma Binti Aralas

Abstract:

The relationship between property rights and trade specialization is examined for developing and developed countries using panel data analysis. Property rights is measured using the international property rights index while trade specialization is measured using the comparative advantage index. Cross country differences in property rights are hypothesized to lead to differences in trade specialization. Based on the argument that a weak protection of natural resources implies greater trade in resource-intensive goods, developing countries with less defined property rights are hypothesized to have a comparative advantage in resource-based exports while countries with more defined property rights will not have an advantage in resource-intensive goods. Evidence suggests that developing countries with weaker environmental protection index but are rich in natural resources do specialize in the trade of resource-intensive goods. The finding suggests that institutional frameworks to increase the stringency of environmental protection of resources may be needed to diversify exports away from the trade of resource-intensive goods.

Keywords: environmental protection, panel data, renewable resources, trade specialization

Procedia PDF Downloads 429

Authors: John P. O’Sullivan, Thomas M. P. Ratouis, Michael J. O’Sullivan

Abstract:

As the use of geothermal energy grows internationally more effort is required to monitor and protect areas with rare and important geothermal surface features. A number of approaches are presented for developing and calibrating numerical geothermal reservoir models that are capable of accurately representing geothermal surface features. The approaches are discussed in the context of cases studies of the Rotorua geothermal system and the Orakei-korako geothermal system, both of which contain important surface features. The results show that models are able to match the available field data accurately and hence can be used as valuable tools for predicting the future response of the systems to changes in use.

Keywords: geothermal reservoir models, surface features, monitoring, TOUGH2

Procedia PDF Downloads 391

Authors: Ramon Santana

Abstract:

The use of biometric identifiers in the field of information security, access control to resources, authentication in ATMs and banking among others, are of great concern because of the safety of biometric data. In the general architecture of a biometric system have been detected eight vulnerabilities, six of them allow obtaining minutiae template in plain text. The main consequence of obtaining minutia templates is the loss of biometric identifier for life. To mitigate these vulnerabilities several models to protect minutiae templates have been proposed. Several vulnerabilities in the cryptographic security of these models allow to obtain biometric data in plain text. In order to increase the cryptographic security and ease of reversibility, a minutiae templates protection model is proposed. The model aims to make the cryptographic protection and facilitate the reversibility of data using two levels of security. The first level of security is the data transformation level. In this level generates invariant data to rotation and translation, further transformation is irreversible. The second level of security is the evaluation level, where the encryption key is generated and data is evaluated using a defined evaluation function. The model is aimed at mitigating known vulnerabilities of the proposed models, basing its security on the impossibility of the polynomial reconstruction.

Keywords: fingerprint, template protection, bio-cryptography, minutiae protection

Procedia PDF Downloads 157

Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

Abstract:

Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

Procedia PDF Downloads 410