Search results for: simple linear regression analysis

Authors: M. Berrehail, F. Benamira

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We study the quantum motion of a particle in the presence of a time- dependent linear potential using an operator invariant that is quadratic in p and linear in q within the framework of the Lewis-Riesenfeld invariant, The special invariant operator proposed in this work is demonstrated to be an Hermitian operator which has an Airy wave packet as its Eigenfunction

Keywords: airy wave packet, ivariant, time-dependent linear potential, unitary transformation

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Authors: Sajana Suwal, Ganesh R. Nhemafuki

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Evaluation of ground response under earthquake shaking is crucial in geotechnical earthquake engineering. Damage due to seismic excitation is mainly correlated to local geological and geotechnical conditions. It is evident from the past earthquakes (e.g. 1906 San Francisco, USA, 1923 Kanto, Japan) that the local geology has strong influence on amplitude and duration of ground motions. Since then significant studies has been conducted on ground motion amplification revealing the importance of influence of local geology on ground. Observations from the damaging earthquakes (e.g. Nigata and San Francisco, 1964; Irpinia, 1980; Mexico, 1985; Kobe, 1995; L’Aquila, 2009) divulged that non-uniform damage pattern, particularly in soft fluvio-lacustrine deposit is due to the local amplification of seismic ground motion. Non-uniform damage patterns are also observed in Kathmandu Valley during 1934 Bihar Nepal earthquake and recent 2015 Gorkha earthquake seemingly due to the modification of earthquake ground motion parameters. In this study, site effects resulting from amplification of soft soil in Kathmandu are presented. A large amount of subsoil data was collected and used for defining the appropriate subsoil model for the Kathamandu valley. A comparative study of one-dimensional total-stress equivalent linear and non-linear site response is performed using four strong ground motions for six sites of Kathmandu valley. In general, one-dimensional (1D) site-response analysis involves the excitation of a soil profile using the horizontal component and calculating the response at individual soil layers. In the present study, both equivalent linear and non-linear site response analyses were conducted using the computer program DEEPSOIL. The results show that there is no significant deviation between equivalent linear and non-linear site response models until the maximum strain reaches to 0.06-0.1%. Overall, it is clearly observed from the results that non-linear site response model perform better as compared to equivalent linear model. However, the significant deviation between two models is resulted from other influencing factors such as assumptions made in 1D site response, lack of accurate values of shear wave velocity and nonlinear properties of the soil deposit. The results are also presented in terms of amplification factors which are predicted to be around four times more in case of non-linear analysis as compared to equivalent linear analysis. Hence, the nonlinear behavior of soil prevails the urgent need of study of dynamic characteristics of the soft soil deposit that can specifically represent the site-specific design spectra for the Kathmandu valley for building resilient structures from future damaging earthquakes.

Keywords: deep soil, equivalent linear analysis, non-linear analysis, site response

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Authors: Nanine Fouche

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The Cape Flats is a low-lying sand-covered expanse of approximately 460 square kilometres, situated to the southeast of the central business district of Cape Town in the Western Cape of South Africa. The aeolian sands masking this area are often loose and compressible in the upper 1m to 1.5m of the surface, and there is a general exceedance of the maximum allowable settlement in these sands. The settlement of shallow foundations on Cape Flats sands is commonly predicted using the results of in-situ tests such as the SPT or DPSH due to the difficulty of retrieving undisturbed samples for laboratory testing. Varying degrees of accuracy and reliability are associated with these methods. More recently, shear wave velocity (Vs) profiles obtained from seismic testing, such as continuous surface wave tests (CSW), are being used for settlement prediction. Such predictions have the advantage of considering non-linear stress-strain behaviour of soil and the degradation of stiffness with increasing strain. CSW tests are rarely executed in the Cape Flats, whereas SPT’s are commonly performed. For this reason, and to facilitate better settlement predictions in Cape Flats sand, equations representing shear wave velocity (Vs) as a function of SPT blow count (N60) and vertical effective stress (v’) were generated by statistical regression of site investigation data. To reveal the most appropriate method of overburden correction, analyses were performed with a separate overburden term (Pa/σ’v) as well as using stress corrected shear wave velocity and SPT blow counts (correcting Vs. and N60 to Vs1and (N1)60respectively). Shear wave velocity profiles and SPT blow count data from three sites masked by Cape Flats sands were utilised to generate 80 Vs-SPT N data pairs for analysis. Investigated terrains included sites in the suburbs of Athlone, Muizenburg, and Atlantis, all underlain by windblown deposits comprising fine and medium sand with varying fines contents. Elastic settlement analysis was also undertaken for the Cape Flats sands, using a non-linear stepwise method based on small-strain stiffness estimates, which was obtained from the best Vs-N60 model and compared to settlement estimates using the general elastic solution with stiffness profiles determined using Stroud’s (1989) and Webb’s (1969) SPT N60-E transformation models. Stroud’s method considers strain level indirectly whereasWebb’smethod does not take account of the variation in elastic modulus with strain. The expression of Vs. in terms of N60 and Pa/σv’ derived from the Atlantis data set revealed the best fit with R2 = 0.83 and a standard error of 83.5m/s. Less accurate Vs-SPT N relations associated with the combined data set is presumably the result of inversion routines used in the analysis of the CSW results showcasing significant variation in relative density and stiffness with depth. The regression analyses revealed that the inclusion of a separate overburden term in the regression of Vs and N60, produces improved fits, as opposed to the stress corrected equations in which the R2 of the regression is notably lower. It is the correction of Vs and N60 to Vs1 and (N1)60 with empirical constants ‘n’ and ‘m’ prior to regression, that introduces bias with respect to overburden pressure. When comparing settlement prediction methods, both Stroud’s method (considering strain level indirectly) and the small strain stiffness method predict higher stiffnesses for medium dense and dense profiles than Webb’s method, which takes no account of strain level in the determination of soil stiffness. Webb’s method appears to be suitable for loose sands only. The Versak software appears to underestimate differences in settlement between square and strip footings of similar width. In conclusion, settlement analysis using small-strain stiffness data from the proposed Vs-N60 model for Cape Flats sands provides a way to take account of the non-linear stress-strain behaviour of the sands when calculating settlement.

Keywords: sands, settlement prediction, continuous surface wave test, small-strain stiffness, shear wave velocity, penetration resistance

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Authors: Arindam Chaudhuri

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The research presents epsilon- hierarchical fuzzy twin support vector regression (epsilon-HFTSVR) based on epsilon-fuzzy twin support vector regression (epsilon-FTSVR) and epsilon-twin support vector regression (epsilon-TSVR). Epsilon-FTSVR is achieved by incorporating trapezoidal fuzzy numbers to epsilon-TSVR which takes care of uncertainty existing in forecasting problems. Epsilon-FTSVR determines a pair of epsilon-insensitive proximal functions by solving two related quadratic programming problems. The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon-FTSVR. This yields dual stable positive definite problems which improves regression performance. Epsilon-FTSVR is then reformulated as epsilon-HFTSVR consisting of a set of hierarchical layers each containing epsilon-FTSVR. Experimental results on both synthetic and real datasets reveal that epsilon-HFTSVR has remarkable generalization performance with minimum training time.

Keywords: regression, epsilon-TSVR, epsilon-FTSVR, epsilon-HFTSVR

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

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Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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Authors: Naushad Mamode Khan

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The Com-Poisson (CMP) model is one of the most popular discrete generalized linear models (GLMS) that handles both equi-, over- and under-dispersed data. In longitudinal context, an integer-valued autoregressive (INAR(1)) process that incorporates covariate specification has been developed to model longitudinal CMP counts. However, the joint likelihood CMP function is difficult to specify and thus restricts the likelihood based estimating methodology. The joint generalized quasilikelihood approach (GQL-I) was instead considered but is rather computationally intensive and may not even estimate the regression effects due to a complex and frequently ill conditioned covariance structure. This paper proposes a new GQL approach for estimating the regression parameters (GQLIII) that are based on a single score vector representation. The performance of GQL-III is compared with GQL-I and separate marginal GQLs (GQL-II) through some simulation experiments and is proved to yield equally efficient estimates as GQL-I and is far more computationally stable.

Keywords: longitudinal, com-Poisson, ill-conditioned, INAR(1), GLMS, GQL

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Authors: Muhammad Tariq A. Chaudhary

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Soil-structure interaction (SSI) in bridges under seismic excitation is a complex phenomenon which involves coupling between the non-linear behavior of bridge pier columns and SSI in the soil-foundation part. It is a common practice in the study of SSI to model the bridge piers as linear elastic while treating the soil and foundation with a non-linear or an equivalent linear modeling approach. Consequently, the contribution of soil and foundation to the SSI phenomenon is disproportionately highlighted. The present study considered non-linear behavior of bridge piers in FEM model of a 4-span, pile-supported bridge that was designed for five different soil conditions in a moderate seismic zone. The FEM model of the bridge system was subjected to a suite of 21 actual ground motions representative of three levels of earthquake hazard (i.e. Design Basis Earthquake, Functional Evaluation Earthquake and Maximum Considered Earthquake). Results of the FEM analysis were used to delineate the influence of pier column non-linearity and SSI on critical design parameters of the bridge system. It was found that pier column non-linearity influenced the bridge lateral displacement and base shear more than SSI for majority of the analysis cases for the class of bridge investigated in the study.

Keywords: bridge, FEM model, reinforced concrete pier, pile foundation, seismic loading, soil-structure interaction

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Authors: Ivan Poza Martínez

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The international energy landscape has been significantly affected by the Covid-19 pandemic and te conflict in Ukraine. The Vaca Muerta sedimentary formation in Argentina´s Neuquén province has become a crucial area for energy production, specifically in the shale gas ad shale oil sectors. The massive investment required for theexploitation of this reserve make it essential to understand te determinants of the investment in the upstream sector at both local ad international levels. The aim of this study is to identify the qualitative and quantitative determinants of investment in Vaca Muerta. The research methodolody employs both quantiative ( econometrics ) and qualitative approaches. A linear regression model is used to analyze the impact in non-conventional hydrocarbons. The study highlights that, in addition to quantitative factors, qualitative variables, particularly the design of a regulatory framework, significantly influence the level of the investment in Vaca Muerta. The analysis reveals the importance of attracting both domestic and foreign capital investment. This research contributes to understanding the factors influencing investment inthe Vaca Muerta regioncomapred to other published studies. It emphasizes to role of qualitative varibles, such as regulatory frameworks, in the development of the shale gas and oil sectors. The study uses a combination ofquantitative data , such a investment figures, and qualitative data, such a regulatory frameworks. The data is collected from various rpeorts and industry publications. The linear regression model is used to analyze the relationship between the variables and the investment in Vaca Muerta. The research addresses the question of what factors drive investment in the Vaca Muerta region, both from a quantitative and qualitative perspective. The study concludes that a combination of quantitative and qualitative factors, including the design of a regulatory framework, plays a significant role in attracting investment in Vaca Muerta. It highlights the importance of these determinants in the developmentof the local energy sector and the potential economic benefits for Argentina and the Southern Cone region.

Keywords: vaca muerta, FDI, shale gas, shale oil, YPF

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Authors: Mojtaba Rezaei, Elham Heydari

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This paper highlights the empirical results of analyzing the correlation between accounting information and systematic risk. This association is analyzed among financial ratios and systematic risk by considering the financial statement of 39 companies listed on the Tehran Stock Exchange (TSE) for five years (2014-2018). Financial ratios have been categorized into four groups and to describe the special features, as representative of accounting information we selected: Return on Asset (ROA), Debt Ratio (Total Debt to Total Asset), Current Ratio (current assets to current debt), Asset Turnover (Net sales to Total assets), and Total Assets. The hypotheses were tested through simple and multiple linear regression and T-student test. The findings illustrate that there is no significant relationship between accounting information and market risk. This indicates that in the selected sample, historical accounting information does not fully reflect the price of stocks.

Keywords: accounting information, market risk, systematic risk, stock return, efficient market hypothesis, EMH, Tehran stock exchange, TSE

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Authors: Nureni O. Adeboye, Dawud A. Agunbiade

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This research attempts to investigate the effects of heteroscedasticity and periodicity in a Panel Data Regression Model (PDRM) by extending previous works on balanced panel data estimation within the context of fitting PDRM for Banks audit fee. The estimation of such model was achieved through the derivation of Joint Lagrange Multiplier (LM) test for homoscedasticity and zero-serial correlation, a conditional LM test for zero serial correlation given heteroscedasticity of varying degrees as well as conditional LM test for homoscedasticity given first order positive serial correlation via a two-way error component model. Monte Carlo simulations were carried out for 81 different variations, of which its design assumed a uniform distribution under a linear heteroscedasticity function. Each of the variation was iterated 1000 times and the assessment of the three estimators considered are based on Variance, Absolute bias (ABIAS), Mean square error (MSE) and the Root Mean Square (RMSE) of parameters estimates. Eighteen different models at different specified conditions were fitted, and the best-fitted model is that of within estimator when heteroscedasticity is severe at either zero or positive serial correlation value. LM test results showed that the tests have good size and power as all the three tests are significant at 5% for the specified linear form of heteroscedasticity function which established the facts that Banks operations are severely heteroscedastic in nature with little or no periodicity effects.

Keywords: audit fee lagrange multiplier test, heteroscedasticity, lagrange multiplier test, Monte-Carlo scheme, periodicity

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Authors: Ali Etemadi, Polat H. Gulkan

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The drift spectrum derived through the continuous shear-beam and wave propagation theory is known to be useful appliance to measure of the demand of pulse like near field ground motions on building structures. As regards, many of old frame buildings with poor or non-ductile column elements, pass the elastic limits and blurt the post yielding hysteresis degradation responses when subjected to such impulsive ground motions. The drift spectrum which, is based on a linear system cannot be predicted the overestimate drift demands arising from inelasticity in an elastic plastic systems. A simple procedure to estimate the drift demands in shear-type frames which, respond over the elastic limits is described and effect of hysteresis degradation behavior on seismic demands is clarified. Whereupon the modification factors are proposed to incorporate the hysteresis degradation effects parametrically. These factors are defined with respected to the linear systems. The method can be applicable for rapid assessment of existing poor detailed, non-ductile buildings.

Keywords: drift spectrum, shear-type frame, stiffness and strength degradation, pinching, smooth hysteretic model, quasi static analysis

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Authors: Debasis Sarkar, Jatan Talati

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This paper is an attempt to develop an Integrated Mass Rapid Transit System (MRTS) for a smart city project in Western India. Integrated transportation is one of the enablers of smart transportation for providing a seamless intercity as well as regional level transportation experience. The success of a smart city project at the city level for transportation is providing proper integration to different mass rapid transit modes by way of integrating information, physical, network of routes fares, etc. The methodology adopted for this study was primary data research through questionnaire survey. The respondents of the questionnaire survey have responded on the issues about their perceptions on the ways and means to improve public transport services in urban cities. The respondents were also required to identify the factors and attributes which might motivate more people to shift towards the public mode. Also, the respondents were questioned about the factors which they feel might restrain the integration of various modes of MRTS. Furthermore, this study also focuses on developing a utility equation for respondents with the help of multiple linear regression analysis and its probability to shift to public transport for certain factors listed in the questionnaire. It has been observed that for shifting to public transport, the most important factors that need to be considered were travel time saving and comfort rating. Also, an Integrated MRTS can be obtained by combining metro rail with BRTS, metro rail with monorail, monorail with BRTS and metro rail with Indian railways. Providing a common smart card to transport users for accessing all the different available modes would be a pragmatic solution towards integration of the available modes of MRTS.

Keywords: mass rapid transit systems, smart city, metro rail, bus rapid transit system, multiple linear regression, smart card, automated fare collection system

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Authors: Sujeet Kumar Singh, Shiv Prasad Yadav

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This paper develops an approach for solving intuitionistic fuzzy linear fractional programming (IFLFP) problem where the cost of the objective function, the resources, and the technological coefficients are triangular intuitionistic fuzzy numbers. Here, the IFLFP problem is transformed into an equivalent crisp multi-objective linear fractional programming (MOLFP) problem. By using fuzzy mathematical programming approach the transformed MOLFP problem is reduced into a single objective linear programming (LP) problem. The proposed procedure is illustrated through a numerical example.

Keywords: triangular intuitionistic fuzzy number, linear programming problem, multi objective linear programming problem, fuzzy mathematical programming, membership function

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Authors: Tulin Coskun

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We investigate the approximation of analytic functions of several variables in polydisc by the sequences of linear k-positive operators in Gadjiev sence. The approximation of analytic functions of complex variable by linear k-positive operators was tackled, and k-positive operators and formulated theorems of Korovkin's type for these operators in the space of analytic functions on the unit disc were introduced in the past. Recently, very general results on convergence of the sequences of linear k-positive operators on a simply connected bounded domain within the space of analytic functions were proved. In this presentation, we extend some of these results to the approximation of analytic functions of several complex variables by sequences of linear k-positive operators.

Keywords: analytic functions, approximation of analytic functions, Linear k-positive operators, Korovkin type theorems

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: S. Oudjemia, F. Alim, S. Seddiki

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This paper shows the application of multifractal analysis for additional help in cancer diagnosis. The medical image processing is a very important discipline in which many existing methods are in search of solutions to real problems of medicine. In this work, we present results of multifractal analysis of brain MRI images. The purpose of this analysis was to separate between healthy and cancerous tissue of the brain. A nonlinear method based on multifractal detrending moving average (MFDMA) which is a generalization of the detrending fluctuations analysis (DFA) is used for the detection of abnormalities in these images. The proposed method could make separation of the two types of brain tissue with success. It is very important to note that the choice of this non-linear method is due to the complexity and irregularity of tumor tissue that linear and classical nonlinear methods seem difficult to characterize completely. In order to show the performance of this method, we compared its results with those of the conventional method box-counting.

Keywords: irregularity, nonlinearity, MRI brain images, multifractal analysis, brain tumor

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Authors: Zia R. Tahir, P. Mandal

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

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

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Caroline Nnokwe, Iheanyi Eneremadu

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Teachers and principals are vital and integral parts of the educational system. For educational objectives to be met, the role of teachers and the functions of the principals are not to be overlooked. However, the inability of teachers and principals to carry out their roles effectively has impacted the outcome of the students’ performance. The study, therefore, examined teachers’ roles and principal’s administrative functions as correlates of effective academic performance of public secondary school students in Imo state, Nigeria. Four research questions and two hypotheses guided the study. The study adopted a correlation research design. The sample size was 5,438 respondents via the Yaro-Yamane technique, which consists of 175 teachers, 13 principals and 5,250 students using the proportional stratified random sampling technique. The instruments for data collection were a researcher-made questionnaire titled Teachers’ Role/Principals’ Administrative Functions Questionnaire (TRPAFQ) with a Cronbach Alpha coefficient of .82 and student's internal results obtained from the school authorities. Data collected were analyzed using the Pearson product-moment correlation coefficient and simple linear regression. Research questions were answered using Pearson Product Moment Correlation statistics, while the hypotheses were tested at 0.05 level of significance using regression analysis. The findings of the study showed that the educational qualification of teachers, organizing, and planning correlated student’s academic performance to a great extent, while availability and proper use of instructional materials by teachers correlated the academic performance of students to a very high extent. The findings also revealed that there is a significant relationship between teachers’ role, principals’ administrative functions and student’s academic performance of public secondary schools in Imo State, The study recommended among others that there is the need for government, through the ministry of education, and education authorities to adequately staff their supervisory department in order to carry out proper supervision of secondary school teachers, and also provide adequate instructional materials to ensure greater academic performance among secondary school students of Imo state, Nigeria.

Keywords: instructional materials, principals’ administrative functions, students’ academic performance, teacher role

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Authors: Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari, Mohammad Ensaf

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This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Keywords: Gaussian process regression, ensemble kernels, bayesian optimization, pharmaceutical sales analysis, time series forecasting, data analysis

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Authors: Woo-Young Jung, Minho Kwon

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Seismic design criteria based on performance of structures have recently been adopted by practicing engineers in response to destructive earthquakes. A simple but efficient structural-analysis tool capable of predicting both the strength and ductility is needed to analyze reinforced concrete (RC) structures under such event. A three-dimensional lattice model is developed in this study to analyze torsions in high-strength RC members. Optimization techniques for determining optimal variables in each lattice model are introduced. Pure torsion tests of RC members are performed to validate the proposed model. Correlation studies between the numerical and experimental results confirm that the proposed model is well capable of representing salient features of the experimental results.

Keywords: torsion, non-linear analysis, three-dimensional lattice, high-strength concrete

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Authors: Peyman Sindareh Esfahani, Jeffery Kurt Pieper

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In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the l₂-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem.

Keywords: linear fractional transformation, linear matrix inequality, robust model predictive control, state feedback control

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Authors: Kornelius Ronald Demu, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Human Development Index (HDI) is a standard measurement for a country's human development. Several factors may have influenced it, such as life expectancy, gross domestic product (GDP) based on the province's annual expenditure, the number of poor people, and the percentage of an illiterate people. The scatter plot between HDI and the influenced factors show that the plot does not follow a specific pattern or form. Therefore, the HDI's data in Indonesia can be applied with a nonparametric regression model. The estimation of the regression curve in the nonparametric regression model is flexible because it follows the shape of the data pattern. One of the nonparametric regression's method is a truncated spline. Truncated spline regression is one of the nonparametric approach, which is a modification of the segmented polynomial functions. The estimator of a truncated spline regression model was affected by the selection of the optimal knots point. Knot points is a focus point of spline truncated functions. The optimal knots point was determined by the minimum value of generalized cross validation (GCV). In this article were applied the data of Human Development Index with a truncated spline nonparametric regression model. The results of this research were obtained the best-truncated spline regression model to the HDI's data in Indonesia with the combination of optimal knots point 5-5-5-4. Life expectancy and the percentage of an illiterate people were the significant factors depend to the HDI in Indonesia. The coefficient of determination is 94.54%. This means the regression model is good enough to applied on the data of HDI in Indonesia.

Keywords: generalized cross validation (GCV), Human Development Index (HDI), knots point, nonparametric regression, truncated spline

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: Daniele Losanno, Giorgio Serino

Abstract:

This paper focuses on parametric analysis of reinforced concrete structures equipped with supplemental damping braces. Practitioners still luck sufficient data for current design of damper added structures and often reduce the real model to a pure damper braced structure even if this assumption is neither realistic nor conservative. In the present study, the damping brace is modelled as made by a linear supporting brace connected in series with the viscous/hysteretic damper. Deformation capacity of existing structures is usually not adequate to undergo the design earthquake. In spite of this, additional dampers could be introduced strongly limiting structural damage to acceptable values, or in some cases, reducing frame response to elastic behavior. This work is aimed at providing useful considerations for retrofit of existing buildings by means of supplemental damping braces. The study explicitly takes into consideration variability of (a) relative frame to supporting brace stiffness, (b) dampers’ coefficient (viscous coefficient or yielding force) and (c) non-linear frame behavior. Non-linear time history analysis has been run to account for both dampers’ behavior and non-linear plastic hinges modelled by Pivot hysteretic type. Parametric analysis based on previous studies on SDOF or MDOF linear frames provide reference values for nearly optimal damping systems design. With respect to bare frame configuration, seismic response of the damper-added frame is strongly improved, limiting deformations to acceptable values far below ultimate capacity. Results of the analysis also demonstrated the beneficial effect of stiffer supporting braces, thus highlighting inadequacy of simplified pure damper models. At the same time, the effect of variable damping coefficient and yielding force has to be treated as an optimization problem.

Keywords: brace stiffness, dissipative braces, non-linear analysis, plastic hinges, reinforced concrete frames

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Authors: B. G. Abiona, A. A. Onaseso, T. D. Odetayo, J. Yila, O. E. Fapojuwo, K. G. Adeosun

Abstract:

This study determines the perceived effects of work-family balance on employees’ job satisfaction among Extension Agents in the Agricultural Development Programme (ADP) in southwest Nigeria. A multistage sampling technique was used to select 256 respondents for the study. Data on personal characteristics, work-family balance domain, and job satisfaction were collected. The collected data were analysed using descriptive statistics, Chi-square, Pearson Product Moment Correlation (PPMC), multiple linear regression, and Student T-test. Results revealed that the mean age of the respondents was 40 years; the majority (59.3%) of the respondents were male, and slightly above half (51.6%) of the respondents had MSc as their highest academic qualification. Findings revealed that turnover intention (x ̅ = 3.20) and work-role conflict (x ̅ = 3.06) were the major perceived work-family balance domain in the studied areas. Further, the result showed that the respondents have a high (79%) level of job satisfaction. Multiple linear regression revealed that job involvement (ß=0.167, p<0.01) and work-role conflict (ß= -0.221, p<0.05) contributed significantly to employees’ level of job satisfaction. The results of the Student T-test revealed a significant difference in the perceived work-family balance domain (t = 0.43, p<0.05) between the two studied areas. The study concluded that work-role conflict among employees causes work-family imbalance and, therefore, negatively affects employees’ job satisfaction. The definition of job design among the respondents that will create a balance between work and family is highly recommended.

Keywords: work-life, conflict, job satisfaction, extension agent

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Authors: Hadush Kidane Meresa

Abstract:

The hydrological extremes frequency analysis is the foundation for the hydraulic engineering design, flood protection, drought management and water resources management and planning to utilize the available water resource to meet the desired objectives of different organizations and sectors in a country. This spatial variation of the statistical characteristics of the extreme flood and drought events are key practice for regional flood and drought analysis and mitigation management. For different hydro-climate of the regions, where the data set is short, scarcity, poor quality and insufficient, the regionalization methods are applied to transfer at-site data to a region. This study aims in regional high and low flow frequency analysis for Poland River Basins. Due to high frequent occurring of hydrological extremes in the region and rapid water resources development in this basin have caused serious concerns over the flood and drought magnitude and frequencies of the river in Poland. The magnitude and frequency result of high and low flows in the basin is needed for flood and drought planning, management and protection at present and future. Hydrological homogeneous high and low flow regions are formed by the cluster analysis of site characteristics, using the hierarchical and C- mean clustering and PCA method. Statistical tests for regional homogeneity are utilized, by Discordancy and Heterogeneity measure tests. In compliance with results of the tests, the region river basin has been divided into ten homogeneous regions. In this study, frequency analysis of high and low flows using AM for high flow and 7-day minimum low flow series is conducted using six statistical distributions. The use of L-moment and LL-moment method showed a homogeneous region over entire province with Generalized logistic (GLOG), Generalized extreme value (GEV), Pearson type III (P-III), Generalized Pareto (GPAR), Weibull (WEI) and Power (PR) distributions as the regional drought and flood frequency distributions. The 95% percentile and Flow duration curves of 1, 7, 10, 30 days have been plotted for 10 stations. However, the cluster analysis performed two regions in west and east of the province where L-moment and LL-moment method demonstrated the homogeneity of the regions and GLOG and Pearson Type III (PIII) distributions as regional frequency distributions for each region, respectively. The spatial variation and regional frequency distribution of flood and drought characteristics for 10 best catchment from the whole region was selected and beside the main variable (streamflow: high and low) we used variables which are more related to physiographic and drainage characteristics for identify and delineate homogeneous pools and to derive best regression models for ungauged sites. Those are mean annual rainfall, seasonal flow, average slope, NDVI, aspect, flow length, flow direction, maximum soil moisture, elevation, and drainage order. The regional high-flow or low-flow relationship among one streamflow characteristics with (AM or 7-day mean annual low flows) some basin characteristics is developed using Generalized Linear Mixed Model (GLMM) and Generalized Least Square (GLS) regression model, providing a simple and effective method for estimation of flood and drought of desired return periods for ungauged catchments.

Keywords: flood , drought, frequency, magnitude, regionalization, stochastic, ungauged, Poland

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

Abstract:

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

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

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Authors: Seong-Jin Cho, Jin Ho Kim

Abstract:

Energy harvesting is the technology which gathers and converts external energies such as light, vibration and heat which are disposed into reusable electrical energy and uses such electrical energy. The vibration energy harvesting is very interesting technology because it produces very high density of energy and unaffected by the climate. Vibration energy can be harvested by the electrostatic, electromagnetic and piezoelectric systems. The electrostatic system has low energy conversion efficiency, and the piezoelectric system is expensive and needs the frequent maintenance because it is made of piezoelectric ceramic. On the other hand, the electromagnetic system has a long life time and high harvesting efficiency, and it is relatively cheap. The electromagnetic harvesting system includes the linear generator and the rotary-type generator. The rotary-type generators require the additional mechanical conversion device if it uses linear motion of vibration. But, the linear generator uses directly linear motion of vibration without a mechanical conversion device, and it has uncomplicated structure and light weight compared with the rotary-type generator. Therefore, the linear electromagnetic generator can be useful in using vibration energy harvesting. The pole transformer systems need electricity sensor system for sending voltage and power information to administrator. Therefore, the battery is essential, and its regular maintenance of replacement is required. In case of the transformer of high location in mountainous areas, the person can’t easily access it resulting in high maintenance cost. To overcome these problems, we designed and developed the linear electromagnetic generator which can replace battery in electricity sensor system for sending voltage and power information of the pole transformer. And, it uses vibration energy of frequency more than 50 Hz by the pole transformer. In order to analyze the electromagnetic characteristics of small linear electric generator, a commercial electromagnetic finite element analysis program "MAXWELL" was used. Then, through the actual production and experiment of linear generator, we confirmed output power of linear generator.

Keywords: energy harvesting, frequency, linear generator, experiment

Procedia PDF Downloads 260