Search results for: simple linear regression analysis

Authors: Kuixi Du, Thomas J. Lipscomb

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The proficiency in Mathematics skills is fundamental to success in the STEM disciplines. In the US, beginning college students who are placed in remedial/developmental Mathematics courses frequently struggle to achieve academic success. Therefore, Mathematics remediation in college has become an important concern, and providing Mathematics remediation is a prevalent way to help the students who may not be fully prepared for college-level courses. Programs vary, however, and the effectiveness of a particular remedial Mathematics program must be empirically demonstrated. The purpose of this study was to apply the sharp regression discontinuity (RD) technique to determine the effectiveness of the Jack Leaps Summer (JLS) Mathematic remediation program in supporting improved Mathematics learning outcomes among newly admitted Mathematics students in the South Dakota State University. The researchers studied the newly admitted Fall 2019 cohort of Mathematics majors (n=423). The results indicated that students whose pretest score was lower than the cut-off point and who were assigned to the JLS program experienced significantly higher scores on the post-test (Math 101 final score). Based on these results, there is evidence that the JLS program is effective in meeting its primary objective.

Keywords: causal inference, mathematisc remedial program evaluation, quasi-experimental research design, regression discontinuity design, cohort studies

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Authors: Mikiyas Adugna

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Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature.

Keywords: agile software development, effort estimation, elastic net regression, LASSO

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Authors: Ahmed Elsayed Ahmed, Ashraf Hafez, A. N. Ouda, Hossam Eldin Hussein Ahmed, Hala Mohamed ABD-Elkader

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Unmanned Aircraft Systems (UAS) are playing increasingly prominent roles in defense programs and defense strategies around the world. Technology advancements have enabled the development of it to do many excellent jobs as reconnaissance, surveillance, battle fighters, and communications relays. Simulating a small unmanned aerial vehicle (SUAV) dynamics and analyzing its behavior at the preflight stage is too important and more efficient. The first step in the UAV design is the mathematical modeling of the nonlinear equations of motion. In this paper, a survey with a standard method to obtain the full non-linear equations of motion is utilized,and then the linearization of the equations according to a steady state flight condition (trimming) is derived. This modeling technique is applied to an Ultrastick-25e fixed wing UAV to obtain the valued linear longitudinal and lateral models. At the end, the model is checked by matching between the behavior of the states of the non-linear UAV and the resulted linear model with doublet at the control surfaces.

Keywords: UAV, equations of motion, modeling, linearization

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Authors: Migena Ceyhan, Zeynep Orhan, Dimitrios Karras

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People in modern societies are continuously sharing their experiences, emotions, and thoughts in different areas of life. The information reaches almost everyone in real-time and can have an important impact in shaping people’s way of living. This phenomenon is very well recognized and advantageously used by the market representatives, trying to earn the most from this means. Given the abundance of information, people and organizations are looking for efficient tools that filter the countless data into important information, ready to analyze. This paper is a modest contribution in this field, describing the process of automatically classifying social media comments in the Turkish language into positive or negative. Once data is gathered and preprocessed, feature sets of selected single words or groups of words are build according to the characteristics of language used in the texts. These features are used later to train, and test a system according to different machine learning algorithms (Naïve Bayes, Sequential Minimal Optimization, J48, and Bayesian Linear Regression). The resultant high accuracies can be important feedback for decision-makers to improve the business strategies accordingly.

Keywords: feature selection, machine learning, natural language processing, sentiment analysis, social media reviews

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Authors: Dong Wook Lee

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This paper describes a method for analyzing penetration for composite material using an explicit nonlinear Finite Element Analysis (FEA). This method may be used in the early stage of design for the protection of military vehicles, aircraft engines and nuclear power plant structures made of composite materials. This paper deals with simple ballistic penetration tests for composite materials and the FEA modeling method and results. The FEA was performed to interpret the ballistic field test phenomenon regarding the damage propagation in the structure subjected to local foreign object impact.

Keywords: computer aided engineering, finite element analysis, impact analysis, penetration analysis, composite material

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Authors: Hassan Samadi, Mustapha El Jarroudi

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In this work, we consider the homogenization of a non-linear problem in periodic medium with two periodic connected media exchanging a heat flux throughout their common interface. The interfacial exchange coefficient λ is assumed to tend to zero or to infinity following a rate λ=λ(ε) when the size ε of the basic cell tends to zero. Three homogenized problems are determined according to some critical value depending of λ and ε. Our method is based on Γ-Convergence techniques.

Keywords: variational methods, epiconvergence, homogenization, convergence technique

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Authors: Shankha Pratim Bhattacharya

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In general building repair and renovation projects are minor in nature. It requires less attention as the primary cost involvement is relatively small. Although the building repair and maintenance projects look simple, it involves much complexity during execution. Many of the present research indicate that few uncertain situations are usually linked with maintenance projects. Those may not be read properly in the planning stage of the projects, and finally, lead to time overrun. Building repair and maintenance become essential and periodical after commissioning of the building. In Institutional buildings, the regular maintenance projects also include addition –alteration, modification activities. Increase in the student admission, new departments, and sections, new laboratories and workshops, up gradation of existing laboratories are very common in the institutional buildings in the developing nations like India. The project becomes very critical because it undergoes space problem, architectural design issues, structural modification, etc. One of the prime factors in the institutional building maintenance and modification project is the time constraint. Mostly it required being executed a specific non-work time period. The present research considered only the institutional buildings of the Eastern part of India to analyse the repair and maintenance project delay. A general survey was conducted among the technical institutes to find the causes and corresponding nature of construction delay factors. Five technical institutes are considered in the present study with repair, renovation, modification and extension type of projects. Construction delay factors are categorically subdivided into four groups namely, material, manpower (works), Contract and Site. The survey data are collected for the nature of delay responsible for a specific project and the absolute amount of delay through proposed and actual duration of work. In the first stage of the paper, a relative importance index (RII) is proposed for the delay factors. The occurrence of the delay factors is also judged by its frequency-severity nature. Finally, the delay factors are then rated and linked with the type of work. In the second stage, a regression analysis is executed to establish an empirical relationship between the actual time of a project and the percentage of delay. It also indicates the impact of the factors for delay responsibility. Ultimately, the present paper makes an effort to identify the critical delay factors for the repair and renovation type project in the Eastern Indian Institutional building.

Keywords: delay factor, institutional building, maintenance, relative importance index, regression analysis, repair

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Authors: Adem Peker, Yüksel Eroğlu, İsmail Ay

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As the information and communication technologies have become embedded in everyday life of adolescents, both their possible benefits and risks to adolescents are being identified. The information and communication technologies provide opportunities for adolescents to connect with peers and to access to information. However, as with other social connections, users of information and communication devices have the potential to meet and interact with in harmful ways. One emerging example of such interaction is cyber bullying. Cyber bullying occurs when someone uses the information and communication technologies to harass or embarrass another person. Cyber bullying can take the form of malicious text messages and e-mails, spreading rumours, and excluding people from online groups. Cyber bullying has been linked to psychological problems for cyber bullies and victims. Therefore, it is important to determine how internet addiction contributes to cyber bullying. Building on this question, this study takes a closer look at the relationship between internet addiction and cyber bullying. For this purpose, in this study, based on descriptive relational model, it was hypothesized that loss of control, excessive desire to stay online, and negativity in social relationships, which are dimensions of internet addiction, would be associated positively with cyber bullying and victimization. Participants were 383 high school students (176 girls and 207 boys; mean age, 15.7 years). Internet addiction was measured by using Internet Addiction Scale. The Cyber Victim and Bullying Scale was utilized to measure cyber bullying and victimization. The scales were administered to the students in groups in the classrooms. In this study, stepwise regression analyses were utilized to examine the relationships between dimensions of internet addiction and cyber bullying and victimization. Before applying stepwise regression analysis, assumptions of regression were verified. According to stepwise regression analysis, cyber bullying was predicted by loss of control (β=.26, p<.001) and negativity in social relationships (β=.13, p<.001). These variables accounted for 9 % of the total variance, with the loss of control explaining the higher percentage (8 %). On the other hand, cyber victimization was predicted by loss of control (β=.19, p<.001) and negativity in social relationships (β=.12, p<.001). These variables altogether accounted for 8 % of the variance in cyber victimization, with the best predictor loss of control (7 % of the total variance). The results of this study demonstrated that, as expected, loss of control and negativity in social relationships predicted cyber bullying and victimization positively. However, excessive desire to stay online did not emerge a significant predictor of both cyberbullying and victimization. Consequently, this study would enhance our understanding of the predictors of cyber bullying and victimization since the results proposed that internet addiction is related with cyber bullying and victimization.

Keywords: cyber bullying, internet addiction, adolescents, regression

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Authors: Matthew C. Best

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Linear Multi-Input Multi-Output (MIMO) dynamic models can be identified, with no a priori knowledge of model structure or order, using a new Generalised Identifying Filter (GIF). Based on an Extended Kalman Filter, the new filter identifies the model iteratively, in a continuous modal canonical form, using only input and output time histories. The filter’s self-propagating state error covariance matrix allows easy determination of convergence and conditioning, and by progressively increasing model order, the best fitting reduced-order model can be identified. The method is shown to be resistant to noise and can easily be extended to identification of smoothly nonlinear systems.

Keywords: system identification, Kalman filter, linear model, MIMO, model order reduction

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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Authors: Osamu Komori, Shinto Eguchi, Hiroshi Okamura, Momoko Ichinokawa

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The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting.

Keywords: double robust estimation, ecological binary data, mixed effect logistic regression model, propensity score

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Authors: Issam Aouari, Abdelmalek Abdelhamid

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For seismologists, the characterization of seismic demand should include the amplitude and duration of strong shaking in the system. The duration of ground shaking is one of the key parameters in earthquake resistant design of structures. This paper proposes a nonlinear statistical model to estimate earthquake ground motion duration in soft soils using multiple seismicity indicators. Three definitions of ground motion duration proposed by literature have been applied. With a comparative study, we select the most significant definition to use for predict the duration. A stochastic model is presented for the McCann and Shah Method using nonlinear regression analysis based on a data set for moment magnitude, source to site distance and site conditions. The data set applied is taken from PEER strong motion databank and contains shallow earthquakes from different regions in the world; America, Turkey, London, China, Italy, Chili, Mexico...etc. Main emphasis is placed on soft site condition. The predictive relationship has been developed based on 600 records and three input indicators. Results have been compared with others published models. It has been found that the proposed model can predict earthquake ground motion duration in soft soils for different regions and sites conditions.

Keywords: duration, earthquake, prediction, regression, soft soil

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Authors: Iliya Bitrus Abaje

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This study examines the evidence of climate change scenario in Kaduna State from the analysis of temperature and rainfall data (1976-2015) from three meteorological stations along a geographic transect from the southern part to the northern part of the State. Different statistical methods were used in determining the changes in both the temperature and rainfall series. The result of the linear trend lines revealed a mean increase in average temperature of 0.73oC for the 40 years period of study in the State. The plotted standard deviation for the temperature anomalies generally revealed that years of temperatures above the mean standard deviation (hotter than the normal conditions) in the last two decades (1996-2005 and 2006-2015) were more than those below (colder than the normal condition). The Cramer’s test and student’s t-test generally revealed an increasing temperature trend in the recent decades. The increased in temperature is an evidence that the earth’s atmosphere is getting warmer in recent years. The linear trend line equation of the annual rainfall for the period of study showed a mean increase of 316.25 mm for the State. Findings also revealed that the plotted standard deviation for the rainfall anomalies, and the 10-year non-overlapping and 30-year overlapping sub-periods analysis in all the three stations generally showed an increasing trend from the beginning of the data to the recent years. This is an evidence that the study area is now experiencing wetter conditions in recent years and hence climate change. The study recommends diversification of the economic base of the populace with emphasis on moving away from activities that are sensitive to temperature and rainfall extremes Also, appropriate strategies to ameliorate the scourge of climate change at all levels/sectors should always take into account the recent changes in temperature and rainfall amount in the area.

Keywords: anomalies, linear trend, rainfall, temperature

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Hui-Yen Lee, Hsiu-Yun Wei, Guey-Jen Lin, Pi-Yueh Lee Lee

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Background: Unplanned hospital readmissions increase healthcare costs and have been considered a marker of poor healthcare performance. The elderly face a higher risk of unplanned readmission due to elderly-specific characteristics such as deteriorating body functions and the relatively high incidence of complications after treatment of acute diseases. Purpose: The aim of this study was exploring the factors that relate to the unplanned readmission of elderly within 14 days of discharge at our hospital in southern Taiwan. Methods: We retrospectively reviewed the medical records of patients aged ≥65 years who had been re-admitted between January 2018 and December 2018.The Charlson Comorbidity score was calculated using previous used method. Related factors that affected the rate of unplanned readmission within 14 days of discharge were screened and analyzed using the chi-squared test and logistic regression analysis. Results: This study enrolled 829 subjects aged more than 65 years. The numbers of unplanned readmission patients within 14 days were 318 cases, while those did not belong to the unplanned readmission were 511 cases. In 2018, the rate of elderly patients in unplanned 14 days readmissions was 38.4%. The majority patients were females (166 cases, 52.2%), with an average age of 77.6 ± 7.90 years (65-98). The average value of Charlson Comorbidity score was 4.42±2.76. Using logistic regression analysis, we found that the gastric or peptic ulcer (OR=1.917 , P< 0.002), diabetes (OR= 0.722, P< 0.043), hemiplegia (OR= 2.292, P< 0.015), metastatic solid tumor (OR= 2.204, P< 0.025), hypertension (OR= 0.696, P< 0.044), and skin ulcer/cellulitis (OR= 2.747, P< 0.022) have significantly higher risk of 14-day readmissions. Conclusion: The results of the present study may assist the healthcare teams to understand the factors that may affect unplanned readmission in the elderly. We recommend that these teams give efficient approach in their medical practice, provide timely health education for elderly, and integrative healthcare for chronic diseases in order to reduce unplanned readmissions.

Keywords: unplanning readmission, elderly, Charlson comorbidity score, logistic regression analysis

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Authors: Jianfang Wang, Xianzhe Chen, Zhuoliang Liu, Cheng-An Tao, Yujiao Li

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Organophosphorus (OPs) pesticide used as insecticides are widely used in agricultural pest control, household and storage deworming. The detection of pesticides needs more simple and efficient methods. One of the best ways is to make electrochemical biosensors. In this paper, an electrochemical enzyme biosensor based on acetylcholine esterase (AChE) was constructed, and its sensing properties and sensing mechanisms were studied. Reduced graphene oxide-polydopamine complexes (RGO-PDA), gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were prepared firstly and composited with AChE and chitosan (CS), then fixed on the glassy carbon electrode (GCE) surface to construct the biosensor GCE/RGO-PDA-AuNPs-AgNPs-AChE-CS by one-pot method. The results show that graphene oxide (GO) can be reduced by dopamine (DA) and dispersed well in RGO-PDA complexes. And the composites have a synergistic catalysis effect and can improve the surface resistance of GCE. The biosensor selectively can detect acetylcholine (ACh) and OPs pesticide with good linear range and high sensitivity. The performance of the biosensor is affected by the ratio and adding ways of AChE and the adding of AuNPs and AChE. And the biosensor can achieve a detection limit of 2.4 ng/L for methyl parathion and a wide linear detection range of 0.02 ng/L ~ 80 ng/L, and has excellent stability, good anti-interference ability, and excellent preservation performance, indicating that the sensor has practical value.

Keywords: acetylcholine esterase, electrochemical biosensor, nanoparticles, organophosphates, reduced graphene oxide

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Y. Heyrani Birak, R. Hizaji, J. Shahkarami

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Reinforced Concrete (RC) deep beams are special structural elements because of their geometry and behavior under loads. For example, assumption of strain- stress distribution is not linear in the cross section. These types of beams may have simple supports or fixed supports. A lot of research works have been conducted on simply supported deep beams, but little study has been done in the fixed-end RC deep beams behavior. Recently, using of fixed-ended deep beams has been widely increased in structures. In this study, the behavior of fixed-ended deep beams is investigated, and the important parameters in capacity of this type of beams are mentioned.

Keywords: deep beam, capacity, reinforced concrete, fixed-ended

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Authors: J. Ritonja, R. Brezovnik, M. Petrun, B. Polajžer

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Many modern synchronous generators in power systems are extremely weakly damped. The reasons are cost optimization of the machine building and introduction of the additional control equipment into power systems. Oscillations of the synchronous generators and related stability problems of the power systems are harmful and can lead to failures in operation and to damages. The only useful solution to increase damping of the unwanted oscillations represents the implementation of the power system stabilizers. Power system stabilizers generate the additional control signal which changes synchronous generator field excitation voltage. Modern power system stabilizers are integrated into static excitation systems of the synchronous generators. Available commercial power system stabilizers are based on linear control theory. Due to the nonlinear dynamics of the synchronous generator, current stabilizers do not assure optimal damping of the synchronous generator’s oscillations in the entire operating range. For that reason the use of the robust power system stabilizers which are convenient for the entire operating range is reasonable. There are numerous robust techniques applicable for the power system stabilizers. In this paper the use of sliding mode control for synchronous generator stability improvement is studied. On the basis of the sliding mode theory, the robust power system stabilizer was developed. The main advantages of the sliding mode controller are simple realization of the control algorithm, robustness to parameter variations and elimination of disturbances. The advantage of the proposed sliding mode controller against conventional linear controller was tested for damping of the synchronous generator oscillations in the entire operating range. Obtained results show the improved damping in the entire operating range of the synchronous generator and the increase of the power system stability. The proposed study contributes to the progress in the development of the advanced stabilizer, which will replace conventional linear stabilizers and improve damping of the synchronous generators.

Keywords: control theory, power system stabilizer, robust control, sliding mode control, stability, synchronous generator

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Authors: Udomporn Yingpaisuk, Premwadee Karuhadej

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The purposed of this research was to study the eating habit of teenage pregnancy and its relationship to the knowledge of nutrition during pregnancy. The 100 samples were derived from simple random sampling technique of the teenage pregnancy in Bangkae District. The questionnaire was used to collect data with the reliability of 0.8. The data were analyzed by SPSS for Windows with multiple regression technique. Percentage, mean and the relationship of knowledge of eating and eating behavior were obtained. The research results revealed that their knowledge in nutrition was at the average of 4.07 and their eating habit that they mentioned most was to refrain from alcohol and caffeine at 82% and the knowledge in nutrition influenced their eating habits at 54% with the statistically significant level of 0.001.

Keywords: teenage pregnancy, knowledge of eating, eating behavior, alcohol, caffeine

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Authors: Luciano Oliveira, Elsa Vásquez-Alvarez

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The increase in rail freight transport in Brazil in recent years requires new railway lines and the maintenance of existing ones, which generates high costs for concessionaires. It is in this context that this work is inserted, whose objective is to propose a method that uses Binary Linear Programming for the choice of sleeper and rail fastening, from various options, including the way to apply these materials, with focus to minimize costs. Unit value information, the life cycle each of material type, and service expenses are considered. The model was implemented in commercial software using real data for its validation. The formulated model can be replicated to support decision-making for other railway projects in the choice of sleepers and rail fastening with lowest cost.

Keywords: linear programming, rail fastening, rail sleeper, railway

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Authors: Yash M. Gupta, Kittisak Buddhachat, Surin Peyachoknagul, Somjit Homchan

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The house cricket, Acheta domesticus is one of the commonly found species of field crickets. Although it is very commonly used as food and feed, the genomic information of house cricket is still missing for genetic investigation. DNA sequencing technology has evolved over the decades, and it has also revolutionized the molecular marker development for genetic analysis. In the present study, we have sequenced the whole genome of A. domesticus using illumina platform based HiSeq X Ten sequencing technology for searching simple sequence repeats (SSRs) in DNA to develop polymorphic microsatellite markers for population genetic analysis. A total of 112,157 SSRs with primer pairs were identified, 91 randomly selected SSRs used to check DNA amplification, of which nine primers were polymorphic. These microsatellite markers have shown cross-amplification with other three species of crickets which are Gryllus bimaculatus, Gryllus testaceus and Brachytrupes portentosus. These nine polymorphic microsatellite markers were used to check genetic variation for forty-five individuals of A. domesticus, Phitsanulok population, Thailand. For nine loci, the number of alleles was ranging from 5 to 15. The observed heterozygosity was ranged from 0.4091 to 0.7556. These microsatellite markers will facilitate population genetic analysis for future studies of A. domesticus populations. Moreover, the transferability of these SSR makers would also enable researchers to conduct genetic studies for other closely related species.

Keywords: cross-amplification, microsatellite markers, observed heterozygosity, population genetic, simple sequence repeats

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Authors: Khaled Ahmed Kadouh, Kamal Ali Albashiri

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This paper presents a designed algorithm involves improvement of transferring data over Simple Object Access Protocol (SOAP). The aim of this work is to establish whether using SOAP in exchanging XML messages has any added advantages or not. The results showed that XML messages without SOAP take longer time and consume more memory, especially with binary data.

Keywords: JAX-WS, SMTP, SOAP, web service, XML

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Authors: Mohammad Karimi Moridani, Mohammad Abdi Zadeh, Zahra Shahiazar Mazraeh

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In recent years, the use of intelligent systems in biomedical engineering has increased dramatically, especially in the diagnosis of various diseases. Also, due to the relatively simple recording of the electrocardiogram signal (ECG), this signal is a good tool to show the function of the heart and diseases associated with it. The aim of this paper is to design an intelligent system for automatically detecting a normal electrocardiogram signal from abnormal one. Using this diagnostic system, it is possible to identify a person's heart condition in a very short time and with high accuracy. The data used in this article are from the Physionet database, available in 2016 for use by researchers to provide the best method for detecting normal signals from abnormalities. Data is of both genders and the data recording time varies between several seconds to several minutes. All data is also labeled normal or abnormal. Due to the low positional accuracy and ECG signal time limit and the similarity of the signal in some diseases with the normal signal, the heart rate variability (HRV) signal was used. Measuring and analyzing the heart rate variability with time to evaluate the activity of the heart and differentiating different types of heart failure from one another is of interest to the experts. In the preprocessing stage, after noise cancelation by the adaptive Kalman filter and extracting the R wave by the Pan and Tampkinz algorithm, R-R intervals were extracted and the HRV signal was generated. In the process of processing this paper, a new idea was presented that, in addition to using the statistical characteristics of the signal to create a return map and extraction of nonlinear characteristics of the HRV signal due to the nonlinear nature of the signal. Finally, the artificial neural networks widely used in the field of ECG signal processing as well as distinctive features were used to classify the normal signals from abnormal ones. To evaluate the efficiency of proposed classifiers in this paper, the area under curve ROC was used. The results of the simulation in the MATLAB environment showed that the AUC of the MLP and SVM neural network was 0.893 and 0.947, respectively. As well as, the results of the proposed algorithm in this paper indicated that the more use of nonlinear characteristics in normal signal classification of the patient showed better performance. Today, research is aimed at quantitatively analyzing the linear and non-linear or descriptive and random nature of the heart rate variability signal, because it has been shown that the amount of these properties can be used to indicate the health status of the individual's heart. The study of nonlinear behavior and dynamics of the heart's neural control system in the short and long-term provides new information on how the cardiovascular system functions, and has led to the development of research in this field. Given that the ECG signal contains important information and is one of the common tools used by physicians to diagnose heart disease, but due to the limited accuracy of time and the fact that some information about this signal is hidden from the viewpoint of physicians, the design of the intelligent system proposed in this paper can help physicians with greater speed and accuracy in the diagnosis of normal and patient individuals and can be used as a complementary system in the treatment centers.

Keywords: neart rate variability, signal processing, linear and non-linear features, classification methods, ROC Curve

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Authors: Yousef Al-Qaryouti, Besan Alagawani

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The main concern of this study is to evaluate the effect of adding horizontal steel bracing system to ordinary moment resisting steel frames subjected to wind load. Similar frames without bracing systems are also to be compared. A general analytical study was carried out to obtain the influence of such system in resisting wind load. Linear static analysis has been carried out using ETABS software by applying fixed wind load defined according to ASCE7-10 for three-, six-, nine-, and twelve-story ordinary moment steel frame buildings including and not including horizontal steel bracing system. The results showed that the lateral drift due to wind load decreased by adding horizontal bracing system. Also, the results show that effect of such system is more efficient to low-rise buildings.

Keywords: horizontal bracing system, steel moment frames, wind load resisting system, linear static analysis

Procedia PDF Downloads 273

Authors: Alfredo Reyes-Salazar, Mario D. Llanes-Tizoc, Eden Bojorquez, Federico Valenzuela-Beltran, Juan Bojorquez, Jose R. Gaxiola-Camacho, Achintya Haldar

Abstract:

Seismic analysis of steel buildings is usually based on the use of the concentrated mass (ML) matrix and the Rayleigh damping matrix (C). Similarly, the initial stiffness matrix (KO) and the first two modes associated with lateral vibrations are commonly used to develop matrix C. The evaluation of the accuracy of these practices for the particular case of steel buildings with moment-resisting steel frames constitutes the main objective of this research. For this, the non-linear seismic responses of three models of steel frames, representing low-, medium- and high-rise steel buildings, are considered. Results indicate that if the ML matrix is used, shears and bending moments in columns are underestimated by up to 30% and 65%, respectively when compared to the corresponding results obtained with the consistent mass matrix (MC). It is also shown that if KO is used in C instead of the tangent stiffness matrix (Kt), axial loads in columns are underestimated by up to 80%. It is concluded that the consistent mass matrix should be used in the structural modelling of moment-resisting steel frames and that the tangent stiffness matrix should be used to develop the Rayleigh damping matrix.

Keywords: moment-resisting steel frames, consistent and concentrated mass matrices, non-linear seismic response, Rayleigh damping

Procedia PDF Downloads 135

Authors: Seyedtaghi Mirmohammadi, J. Yazdani, S. M. Asadi, M. Rokni, A. Toosi

Abstract:

There are many indoor air quality studies with an emphasis on indoor particulate matters (PM2.5) monitoring. Whereas, there is a lake of data about indoor PM2.5 concentrations in rural area schools (especially in classrooms), since preliminary children are assumed to be more defenseless to health hazards and spend a large part of their time in classrooms. The objective of this study was indoor PM2.5 concentration quality assessment. Fifteen preliminary schools by time-series sampling were selected to evaluate the indoor air quality in the rural district of Sari city, Iran. Data on indoor air climate parameters (temperature, relative humidity and wind speed) were measured by a hygrometer and thermometer. Particulate matters (PM2.5) were collected and assessed by Real Time Dust Monitor, (MicroDust Pro, Casella, UK). The mean indoor PM2.5 concentration in the studied classrooms was 135µg/m3 in average. The multiple linear regression revealed that a correlation between PM2.5 concentration and relative humidity, distance from city center and classroom size. Classroom size yields reasonable negative relationship, the PM2.5 concentration was ranged from 65 to 540μg/m3 and statistically significant at 0.05 level and the relative humidity was ranged from 70 to 85% and dry bulb temperature ranged from 28 to 29°C were statistically significant at 0.035 and 0.05 level, respectively. A statistical predictive model was obtained from multiple regressions modeling for PM2.5 and indoor psychrometric parameters.

Keywords: particulate matters, classrooms, regression, concentration, humidity

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Authors: N. Casado-Sanz, B. Guirao

Abstract:

The population aged 65 and over is projected to double in the coming decades. Due to this increase, driver population is expected to grow and in the near future, all countries will be faced with population aging of varying intensity and in unique time frames. This is the greatest challenge facing industrialized nations and due to this fact, the study of the relationships of dependency between population aging and road safety is becoming increasingly relevant. Although the deterioration of driving skills in the elderly has been analyzed in depth, to our knowledge few research studies have focused on the road infrastructure and the mobility of this particular group of users. In Spain, crosstown roads have one of the highest fatality rates. These rural routes have a higher percentage of elderly people who are more dependent on driving due to the absence or limitations of urban public transportation. Analysing road safety in these routes is very complex because of the variety of the features, the dispersion of the data and the complete lack of related literature. The objective of this paper is to identify key factors that cause traffic accidents. The individuals under study were the accidents with killed or seriously injured in Spanish crosstown roads during the period 2006-2015. Latent cluster analysis was applied as a preliminary tool for segmentation of accidents, considering population aging as the main input among other socioeconomic indicators. Subsequently, a linear regression analysis was carried out to estimate the degree of dependence between the accident rate and the variables that define each group. The results show that segmenting the data is very interesting and provides further information. Additionally, the results revealed the clear influence of the aging variable in the clusters obtained. Other variables related to infrastructure and mobility levels, such as the crosstown roads layout and the traffic intensity aimed to be one of the key factors in the causality of road accidents.

Keywords: cluster analysis, population ageing, rural roads, road safety

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Authors: Mohammad Mehdi Mazarei, Ali Asghar Behroozpoor

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In this paper, we define an optimization problem corresponding to smooth and nonsmooth functions which its optimal solution is the first derivative of these functions in a domain. For this purpose, a linear programming problem corresponding to optimization problem is obtained. The optimal solution of this linear programming problem is the approximate generalized first derivative. In fact, we approximate generalized first derivative of nonsmooth functions as tailor series. We show the efficiency of our approach by some smooth and nonsmooth functions in some examples.

Keywords: general derivative, linear programming, optimization problem, smooth and nonsmooth functions

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Authors: Chunhua Ma

Abstract:

Background: There is a relationship between cultural identity and psychological health. Resilience is an important factor of psychological health, and cultural identity will protect the resilience. The research showed that the cultural identity, resilience, and self-concordance of students from different cultures. It should be a theoretical basis to improve mental health of different nationalities students. And the role of resilience factors for adults’ cultural identity and self-concordance was deserve studied. Aims: The current study aimed to examine the relationship between cultural identity and self-concordance among Chinese academician from 3 minorities, postulating mediating by resilience. Methods: This study used cross-sectional and correlational design. Participants were 328 Chinese aged between 18 and 25 years. Data was collected via self-reports including both closed and opened questions. Results: Linear regression analysis controlling for age, gender, the result showed that: (a) Cultural identity was related to self-concordance, resilience was related to self-concordance and cultural identity was related to resilience, (b) Resilience mediated the link between cultural identity and self-concordance, respectively. Discussion: Our findings suggested that resilience and cultural identity are important factors in self-concordance. If minority college students realized the heterogeneous culture, it would alleviate their psychological conflict, stimulate their strength potential and improve their self-concordance.

Keywords: cultural identity, resilience, self-concordance, mediating effect

Procedia PDF Downloads 388