Search results for: multiple linear regression analysis

Authors: Rabia Khan, Nargis Munir, Suriya Gharib, Syeda Roshana Ali

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In this paper linear equations are discussed in detail along with elimination method. Gaussian elimination and Gauss Jordan schemes are carried out to solve the linear system of equation. This paper comprises of matrix introduction, and the direct methods for linear equations. The goal of this research was to analyze different elimination techniques of linear equations and measure the performance of Gaussian elimination and Gauss Jordan method, in order to find their relative importance and advantage in the field of symbolic and numeric computation. The purpose of this research is to revise an introductory concept of linear equations, matrix theory and forms of Gaussian elimination through which the performance of Gauss Jordan and Gaussian elimination can be measured.

Keywords: direct, indirect, backward stage, forward stage

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Authors: Danny Fraser, James Zhang

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Aim of the study: Our study aims to identify any statistically significant evidence as it relates to PROMs for mastectomy and implant-based reconstruction to guide future surgical management. Method: The demographic, pre and post-operative treatment and implant characteristics were collected of all patients at Basildon hospital who underwent breast reconstruction from 2017-2023. We used the Breast-Q psychosocial well-being, physical well-being, and satisfaction with breasts scales. An Independent t-test was conducted for each group, and linear regression of age and implant size. Results: 69 patients were contacted, and 39 PROMs returned. The mean age of patients was 57.6. 40% had smoked before, and 40.8% had BMI>30. 29 had pre-pectoral placement, and 40 had subpectoral placement. 17 had smooth implants, and 52 textured. Sub pectoral placement was associated with higher (75.7 vs. 61.9 p=0.046) psychosocial scores than pre pectoral, and textured implants were associated with a lower physical score than the smooth surface (34.7 VS 50.2 P=0.046). On linear regression, age was positively associated (p=0.007) with psychosocial score. Conclusion: We present a large cohort of patients who underwent breast reconstruction. Understanding the PROMs of these procedures can guide clinicians, patients and policy makers to be more informed of the course of rehabilitation of these operations. Significance: We have found that from a patient perspective subpectoral implant placement was associated with a statistically significant improvement in psychosocial scores.

Keywords: breast surgery, mastectomy, breast implants, oncology

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Authors: Gintarė Sauliutė, Gintaras Svecevičius

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Landfill leachates contain a number of persistent pollutants, including heavy metals. They have the ability to spread in ecosystems and accumulate in fish which most of them are classified as top-consumers of trophic chains. Fish are freely swimming organisms; but perhaps, due to their species-specific ecological and behavioral properties, they often prefer the most suitable biotopes and therefore, did not avoid harmful substances or environments. That is why it is necessary to evaluate the persistent pollutant dispersion in hydroecosystem using fish tissue metal concentration. In hydroecosystems of hybrid type (e.g. river-pond-river) the distance from the pollution source could be a perfect indicator of such a kind of metal distribution. The studies were carried out in the Kairiai landfill neighboring hybrid-type ecosystem which is located 5 km east of the Šiauliai City. Fish tissue (gills, liver, and muscle) metal concentration measurements were performed on two types of ecologically-different fishes according to their feeding characteristics: benthophagous (Gibel carp, roach) and predatory (Northern pike, perch). A number of mathematical models (linear, non-linear, using log and other transformations) have been applied in order to identify the most satisfactorily description of the interdependence between fish tissue metal concentration and the distance from the pollution source. However, the only one log-multiple regression model revealed the pattern that the distance from the pollution source is closely and positively correlated with metal concentration in all predatory fish tissues studied (gills, liver, and muscle).

Keywords: bioaccumulation in fish, heavy metals, hydroecosystem, landfill leachate, mathematical model

Procedia PDF Downloads 286

Authors: Mohammad Karimizadehardakani, Hooman Minoonejad, Reza Rajabi, Ali Sharifnejad

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Background: Ankle sprain is one of the most important risk factor of anterior cruciate ligament (ACL) injury. Also, functional ankle instability (FAI) population has alterations in lower extremity sagittal plane biomechanics during landing task. We want to examine whether biomechanical alterations demonstrated by FAI patients are associated with the mechanism of ACL injury during high risk and sport related tasks. Methods: Sixteen basketball player with FAI and 16 non-injured control performed a single-leg cross drop landing. Knee sagittal and frontal (ATSF) was calculated. Independent t-tests, multiple linear regression, and Pearson correlation were used for analysis data. Result: Subject with FAI showed more peak ATFS, posterior ground reaction force (GRF) and less knee flexion, compared to the controls (P= 0.001, P= 0.004, P= 0.011). Knee flexion (r= −0.824, P = 0.011) and posterior GRF (r= 0.901, P = .001) were correlated with ATSF; Posterior GRF was factor that most explained the variance in ATSF (R2= 0.645; P = .001) in the FAI group. Conclusions: Result of our study showed there is a potential biomechanical relationship between the presence of FAI and risk factors associated with ACL injury mechanism.

Keywords: functional ankle instability, anterior cruciate ligament, biomechanics, risk factor

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Authors: Robert van Ling, Alexander Schwahn, Shanhua Lin, Ken Cook, Frank Steiner, Rowan Moore, Mauro de Pra

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Purpose: Demonstration of fast high resolution charge variant analysis for monoclonal antibody (mAb) therapeutics within 5 minutes. Methods: Three commercially available mAbs were used for all experiments. The charge variants of therapeutic mAbs (Bevacizumab, Cetuximab, Infliximab, and Trastuzumab) are analyzed on a strong cation exchange column with a linear pH gradient separation method. The linear gradient from pH 5.6 to pH 10.2 is generated over time by running a linear pump gradient from 100% Thermo Scientific™ CX-1 pH Gradient Buffer A (pH 5.6) to 100% CX-1 pH Gradient Buffer B (pH 10.2), using the Thermo Scientific™ Vanquish™ UHPLC system. Results: The pH gradient method is generally applicable to monoclonal antibody charge variant analysis. In conjunction with state-of-the-art column and UHPLC technology, ultra fast high-resolution separations are consistently achieved in under 5 minutes for all mAbs analyzed. Conclusion: The linear pH gradient method is a platform method for mAb charge variant analysis. The linear pH gradient method can be easily optimized to improve separations and shorten cycle times. Ultra-fast charge variant separation is facilitated with UHPLC that complements, and in some instances outperforms CE approaches in terms of both resolution and throughput.

Keywords: charge variants, ion exchange chromatography, monoclonal antibody, UHPLC

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Authors: Ozden Saygili, Eser Cakti

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The masonry building stock in Istanbul and in other cities of Turkey are exposed to significant earthquake hazard. Determination of the safety of masonry structures against earthquakes is a complex challenge. This study deals with experimental tests and non-linear dynamic analysis of masonry structures modeled through discrete element method. The 1:10 scale model of Mustafa Paşa Mosque was constructed and the data were obtained from the sensors on it during its testing on the shake table. The results were used in the calibration/validation of the numerical model created on the basis of the 1:10 scale model built for shake table testing. 3D distinct element model was developed that represents the linear and nonlinear behavior of the shake table model as closely as possible during experimental tests. Results of numerical analyses with those from the experimental program were compared and discussed.

Keywords: dynamic analysis, non-linear modeling, shake table tests, masonry

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Authors: Nagham Ismail, Djamel Ouahrani

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Thermal resilience in the built environment reflects the building's capacity to adapt to extreme climate changes. In hot climates, power outages in office buildings pose risks to the health and productivity of workers. Therefore, it is of interest to quantify the thermal resilience of office buildings by developing a user-friendly simplified model. This simplified model begins with creating an assessment metric of thermal resilience that measures the duration between the power outage and the point at which the thermal habitability condition is compromised, considering different power interruption times (morning, noon, and afternoon). In this context, energy simulations of an office building are conducted for Qatar's summer weather by changing different parameters that are related to the (i) wall characteristics, (ii) glazing characteristics, (iii) load, (iv) orientation and (v) air leakage. The simulation results are processed using SPSS to derive linear regression equations, aiding stakeholders in evaluating the performance of commercial buildings during different power interruption times. The findings reveal the significant influence of glazing characteristics on thermal resilience, with the morning power outage scenario posing the most detrimental impact in terms of the shortest duration before compromising thermal resilience.

Keywords: thermal resilience, thermal envelope, energy modeling, building simulation, thermal comfort, power disruption, extreme weather

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Jia Li, Huacong Li, Xiaobao Han

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Due to the more variable geometry parameters of VCE (variable cycle aircraft engine), presents an adaptive controller method based on the full-state linear model of VCE and has simulated to solve the multivariate controller design problem of the whole flight envelops. First, analyzes the static and dynamic performances of bypass ratio and other state parameters caused by variable geometric components, and develops nonlinear component model of VCE. Then based on the component model, through small deviation linearization of main fuel (Wf), the area of tail nozzle throat (A8) and the angle of rear bypass ejector (A163), setting up multiple linear model which variable geometric parameters can be inputs. Second, designs the adaptive controllers for VCE linear models of different nominal points. Among them, considering of modeling uncertainties and external disturbances, derives the adaptive law by lyapunov function. The simulation results showed that, the adaptive controller method based on full-state linear model used the angle of rear bypass ejector as input and effectively solved the multivariate control problems of VCE. The performance of all nominal points could track the desired closed-loop reference instructions. The adjust time was less than 1.2s, and the system overshoot was less than 1%, at the same time, the errors of steady states were less than 0.5% and the dynamic tracking errors were less than 1%. In addition, the designed controller could effectively suppress interference and reached the desired commands with different external random noise signals.

Keywords: variable cycle engine (VCE), full-state linear model, adaptive control, by-pass ratio

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: Vandana T. Gawande, Kailash G. Bothara, Chandani O. Satija

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The present research work was aimed to determine stability of cefprozil monohydrate (CEFZ) as per various stress degradation conditions recommended by International Conference on Harmonization (ICH) guideline Q1A (R2). Forced degradation studies were carried out for hydrolytic, oxidative, photolytic and thermal stress conditions. The drug was found susceptible for degradation under all stress conditions. Separation was carried out by using High Performance Thin Layer Chromatographic System (HPTLC). Aluminum plates pre-coated with silica gel 60F254 were used as the stationary phase. The mobile phase consisted of ethyl acetate: acetone: methanol: water: glacial acetic acid (7.5:2.5:2.5:1.5:0.5v/v). Densitometric analysis was carried out at 280 nm. The system was found to give compact spot for cefprozil monohydrate (0.45 Rf). The linear regression analysis data showed good linear relationship in the concentration range 200-5.000 ng/band for cefprozil monohydrate. Percent recovery for the drug was found to be in the range of 98.78-101.24. Method was found to be reproducible with % relative standard deviation (%RSD) for intra- and inter-day precision to be < 1.5% over the said concentration range. The method was validated for precision, accuracy, specificity and robustness. The method has been successfully applied in the analysis of drug in tablet dosage form. Three unknown degradation products formed under various stress conditions were isolated by preparative HPTLC and characterized by mass spectroscopic studies.

Keywords: cefprozil monohydrate, degradation products, HPTLC, stress study, stability indicating method

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Authors: Qusay Alghazali, Tibor Cinkler, Abdulhalim Fayad

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In modern optical networks, the flex grid spectrum usage is most widespread, where higher bit rate streams get larger spectrum slices while lower bit rate traffic streams get smaller spectrum slices. To our practice, under the ITU-T recommendation, G.694.1, spectrum slices of 50, 75, and 100 GHz are being used with central frequency at 193.1 THz. However, when these spectrum slices are not sufficient, multiple spectrum slices can use either one next to another or anywhere in the optical wavelength. In this paper, we propose the analysis of the wavelength assignment problem. We compare different algorithms for this spectrum assignment with and without protection. As a reference for comparisons, we concluded that the Integer Linear Programming (ILP) provides the global optimum for all cases. The most scalable algorithm is the greedy one, which yields results in subsequent ranges even for more significant network instances. The algorithms’ benchmark implemented using the LEMON C++ optimization library and simulation runs based on a minimum number of spectrum slices assigned to lightpaths and their execution time.

Keywords: spectrum assignment, integer linear programming, greedy algorithm, international telecommunication union, library for efficient modeling and optimization in networks

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Authors: Nicolas R. Finkler, Wesley A. Saltarelli, Taison A. Bortolin, Vania E. Schneider, Davi G. F. Cunha

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Estimating the relative contribution of nonpoint or point sources of pollution in low-orders streams is an important tool for the water resources management. The location of headwaters in areas with anthropogenic impacts from urbanization and agriculture is a common scenario in developing countries. This condition can lead to conflicts among different water users and compromise ecosystem services. Water pollution also contributes to exporting organic loads to downstream areas, including higher order rivers. The purpose of this research is to preliminarily assess nutrients and solids loads exported by water bodies located in watersheds with different types of land uses in São Carlos - SP (Latitude. -22.0087; Longitude. -47.8909) and Caxias do Sul - RS (Latitude. -29.1634, Longitude. -51.1796), Brazil, using regression analysis. The variables analyzed in this study were Total Kjeldahl Nitrogen (TKN), Nitrate (NO3-), Total Phosphorus (TP) and Total Suspended Solids (TSS). Data were obtained in October and December 2015 for São Carlos (SC) and in November 2012 and March 2013 for Caxias do Sul (CXS). Such periods had similar weather patterns regarding precipitation and temperature. Altogether, 11 sites were divided into two groups, some classified as more pristine (SC1, SC4, SC5, SC6 and CXS2), with predominance of native forest; and others considered as impacted (SC2, SC3, CXS1, CXS3, CXS4 and CXS5), presenting larger urban and/or agricultural areas. Previous linear regression was applied for data on flow and drainage area of each site (R² = 0.9741), suggesting that the loads to be assessed had a significant relationship with the drainage areas. Thereafter, regression analysis was conducted between the drainage areas and the total loads for the two land use groups. The R² values were 0.070, 0.830, 0.752 e 0.455 respectively for SST, TKN, NO3- and TP loads in the more preserved areas, suggesting that the loads generated by runoff are significant in these locations. However, the respective R² values for sites located in impacted areas were respectively 0.488, 0.054, 0.519 e 0.059 for SST, TKN, NO3- and P loads, indicating a less important relationship between total loads and runoff as compared to the previous scenario. This study suggests three possible conclusions that will be further explored in the full-text article, with more sampling sites and periods: a) In preserved areas, nonpoint sources of pollution are more significant in determining water quality in relation to the studied variables; b) The nutrient (TKN and P) loads in impacted areas may be associated with point sources such as domestic wastewater discharges with inadequate treatment levels; and c) The presence of NO3- in impacted areas can be associated to the runoff, particularly in agricultural areas, where the application of fertilizers is common at certain times of the year.

Keywords: land use, linear regression, point and non-point pollution sources, streams, water resources management

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Authors: Intan Maisarah Abd Rahim, Herlina Abdul Rahim, Rashidah Ghazali

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Diabetes is a medical condition that can lead to various diseases such as stroke, heart disease, blindness and obesity. In clinical practice, the concern of the diabetic patients towards the blood glucose examination is rather alarming as some of the individual describing it as something painful with pinprick and pinch. As for some patient with high level of glucose level, pricking the fingers multiple times a day with the conventional glucose meter for close monitoring can be tiresome, time consuming and painful. With these concerns, several non-invasive techniques were used by researchers in measuring the glucose level in blood, including ultrasonic sensor implementation, multisensory systems, absorbance of transmittance, bio-impedance, voltage intensity, and thermography. This paper is discussing the application of the near-infrared (NIR) spectroscopy as a non-invasive method in measuring the glucose level and the implementation of the linear system identification model in predicting the output data for the NIR measurement. In this study, the wavelengths considered are at the 1450 nm and 1950 nm. Both of these wavelengths showed the most reliable information on the glucose presence in blood. Then, the linear Autoregressive Moving Average Exogenous model (ARMAX) model with both un-regularized and regularized methods was implemented in predicting the output result for the NIR measurement in order to investigate the practicality of the linear system in this study. However, the result showed only 50.11% accuracy obtained from the system which is far from the satisfying results that should be obtained.

Keywords: diabetes, glucose level, linear, near-infrared, non-invasive, prediction system

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Authors: Lahlou Dahmani, Warda Mekiri, Ahmed Boudjemia

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This scientific paper explores the comparison between the ultimate buckling load obtained through the Eurocode 3 methodology and the ultimate buckling load obtained through finite element simulations for steel columns under compression. The study aims to provide insights into the adequacy of the design rules proposed in Eurocode 3 for different slenderness ratios. The finite element simulations with the Ansys commercial program involve a geometrical and material non-linear analysis of the columns with imperfections. The loss of equilibrium is generally caused by the geometrically nonlinear effects where the column begins to buckle and lose its stability when the load reaches a certain critical value. The linear buckling analysis predicts the theoretical buckling strength of an elastic structure but the nonlinear one is more accurate with taking into account the initial imperfection.

Keywords: Ansys, linear buckling, eigen value, nonlinear buckling, slenderness ratio, Eurocode 3

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Authors: Alev Atak

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We study the endogeneity in the cryptocurrency market over the branching ratio of the Hawkes process and evaluate the movement of self-excitability in the financial markets. We consider a semi-parametric self-exciting point process regression model where the excitation function is assumed to be smooth and decreasing but otherwise unspecified, and the baseline intensity is assumed to be a linear function of the regressors. We apply the empirical analysis to the three largest crypto assets, i.e. Bitcoin - Ethereum - Ripple, and provide a comparison with other financial assets such as SP500, Gold, and the volatility index VIX observed from January 2015 to December 2020. The results depict variable and high levels of endogeneity in the basket of cryptocurrencies under investigation, underlining the evidence of a significant role of endogenous feedback mechanisms in the price formation process.

Keywords: hawkes process, cryptocurrency, endogeneity, reflexivity

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

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Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Punit Kumar, Niraj Kumar

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The traction behavior of lubricants with the linear pressure-viscosity response in EHL line contacts is investigated numerically for smooth as well as rough surfaces. The analysis involves the simultaneous solution of Reynolds, elasticity and energy equations along with the computation of lubricant properties and surface temperatures. The temperature modified Doolittle-Tait equations are used to calculate viscosity and density as functions of fluid pressure and temperature, while Carreau model is used to describe the lubricant rheology. The surface roughness is assumed to be sinusoidal and it is present on the nearly stationary surface in near-pure sliding EHL conjunction. The linear P-V oil is found to yield much lower traction coefficients and slightly thicker EHL films as compared to the synthetic oil for a given set of dimensionless speed and load parameters. Besides, the increase in traction coefficient attributed to surface roughness is much lower for the former case. The present analysis emphasizes the importance of employing realistic pressure-viscosity response for accurate prediction of EHL traction.

Keywords: EHL, linear pressure-viscosity, surface roughness, traction, water/glycol

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Authors: Guillermo Manuel Flores Figueroa, Juan Alejandro Vazquez Feijoo, Jose Navarro Antonio

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A continuous nonlinear system response may be obtained by an infinite sum of the so-called Volterra operators. Each operator is obtained from multidimensional convolution of nth-order between the nth-order Volterra kernel and the system input. These operators can also be obtained from the Associated Linear Equations (ALEs) that are linear models of subsystems which inputs and outputs are of the same nth-order. Each ALEs produces a particular nth-Volterra operator. As linear models a unitary impulse response can be obtained from them. This work shows the relationship between this unitary impulse responses and the corresponding order Volterra kernel.

Keywords: Volterra series, frequency response functions FRF, associated linear equations ALEs, unitary response function, Voterra kernel

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Authors: Muhammad G. Almatar, Mohammad A. Alnasrallah

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This research focuses on the study of three main issues: 1) The temporal analysis of leukemia for a period of six years (2006-2012), 2) spatial analysis by investigating this phenomenon in the Kuwaiti society spatially in the residential areas within the six governorates, 3) the use of Geographic Information System technology in investigating the hypothesis of the research and its variables using the linear regression, to show the pattern of linear relationship. The study depends on utilizing the map to understand the distribution of blood cancer in Kuwait. Several geodatabases were created for the number of patients and air pollution. Spatial interpolation models were used to generate layers of air pollution in the study area. These geodatabases were tested over the past six years to reach the conclusion: Is there a relationship with significant significance between the two main variables of the study: blood cancer and air pollution? This study is the first to our best knowledge. As far as the researchers know, the distribution of this disease has not been studied geographically at the level of regions in Kuwait within six years and in specific areas as described above. This study investigates the concentration of this type of disease. The study found that there is no relationship of significant value between the two variables studied, and this may be due to the nature of the disease, which are often hereditary. On the other hand, this study has reached a number of suggestions and recommendations that may be useful to decision-makers and interested in the study of leukemia in Kuwait by focusing on the study of genetic diseases, which may be a cause of leukemia rather than air pollution.

Keywords: Kuwait, GIS, cancer, geography

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Authors: Khalid Kaddoura, Ahmed Assad, Tarek Zayed

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Sewer pipelines are prone to deterioration over-time. In fact, their deterioration does not follow a fixed downward pattern. This is in fact due to the defects that propagate through their service life. Sewer pipeline defects are categorized into distinct groups. However, the main two groups are the structural and operational defects. By definition, the structural defects influence the structural integrity of the sewer pipelines such as deformation, cracks, fractures, holes, etc. However, the operational defects are the ones that affect the flow of the sewer medium in the pipelines such as: roots, debris, attached deposits, infiltration, etc. Yet, the process for each defect to emerge follows a cause and effect relationship. Deformation, which is the change of the sewer pipeline geometry, is one type of an influencing defect that could be found in many sewer pipelines due to many surrounding factors. This defect could lead to collapse if the percentage exceeds 15%. Therefore, it is essential to predict the deformation percentage before confronting such a situation. Accordingly, this study will predict the percentage of the deformation defect in sewer pipelines adopting the multiple regression analysis. Several factors will be considered in establishing the model, which are expected to influence the defamation defect severity. Besides, this study will construct a time-based curve to understand how the defect would evolve overtime. Thus, this study is expected to be an asset for decision-makers as it will provide informative conclusions about the deformation defect severity. As a result, inspections will be minimized and so the budgets.

Keywords: deformation, prediction, regression analysis, sewer pipelines

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Authors: Yoshihiro Horio

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Many young offenders grow up in difficult environments. It has often been suggested that many young offenders are victims of abuse. However, there were restricted to abuse or family’s problem. Little research has examined data on ‘multiple victimization’ experiences of young offenders. Thus, this study investigated the victimization experiences of young offenders, including child abuse at home, bullying at school, and crime in the community. Specifically, the number of victimization experiences of young offenders was compared with those of non-delinquents at home, school, and in the community. It was found that young offenders experienced significantly more victimization than non-delinquents. Additionally, the influence of childhood victimization on later misconduct and/or delinquency was examined, then it was founded that victimization experiences to be a risk factor for subsequent delinquency. The hierarchical multiple regression analysis showed that young offenders who had a strong emotional reaction to their experience of abuse began their misconduct at an earlier age. If juveniles start their misconduct early, the degree of delinquency will increase. The anger of young offenders was stronger than that of non-delinquents. A strong emotion of anger may be related to juvenile delinquency.

Keywords: abuse, bullying, delinquency, victimization, young offenders

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Authors: Hyun-Woo Cho

Abstract:

Unexpected events may occur with serious impacts on industrial process. This work utilizes a data representation technique to model and to analyze process data pattern for the purpose of diagnosis. In this work, the use of triangular representation of process data is evaluated using simulation process. Furthermore, the effect of using different pre-treatment techniques based on such as linear or nonlinear reduced spaces was compared. This work extracted the fault pattern in the reduced space, not in the original data space. The results have shown that the non-linear technique based diagnosis method produced more reliable results and outperforms linear method.

Keywords: process monitoring, data analysis, pattern modeling, fault, nonlinear techniques

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Authors: Goksel Ezgi Guzey, Bihrat Onoz

Abstract:

The scarcity of streamflow gauging stations and the increasing effects of global warming cause designing water management systems to be very difficult. This study is a significant contribution to assessing regional regression models for estimating streamflow. In this study, simulated meteorological data was related to the observed streamflow data from 1971 to 2020 for 33 stream gauging stations of the Euphrates-Tigris Basin. Ordinary least squares regression was used to predict flow for 2020-2100 with the simulated meteorological data. CORDEX- EURO and CORDEX-MENA domains were used with 0.11 and 0.22 grids, respectively, to estimate climate conditions under certain climate scenarios. Twelve meteorological variables simulated by two regional climate models, RCA4 and RegCM4, were used as independent variables in the ordinary least squares regression, where the observed streamflow was the dependent variable. The variability of streamflow was then calculated with 5-6 meteorological variables and watershed characteristics such as area and height prior to the application. Of the regression analysis of 31 stream gauging stations' data, the stations were subjected to a clustering analysis, which grouped the stations in two clusters in terms of their hydrometeorological properties. Two streamflow equations were found for the two clusters of stream gauging stations for every domain and every regional climate model, which increased the efficiency of streamflow estimation by a range of 10-15% for all the models. This study underlines the importance of homogeneity of a region in estimating streamflow not only in terms of the geographical location but also in terms of the meteorological characteristics of that region.

Keywords: hydrology, streamflow estimation, climate change, hydrologic modeling, HBV, hydropower

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Authors: Aouaouche Elmaouhab, Hassina Beggar

Abstract:

Solving multi-objective discrete optimization applications has always been limited by the resources of one machine: By computing power or by memory, most often both. To speed up the calculations, the grid computing represents a primary solution for the treatment of these applications through the parallelization of these resolution methods. In this work, we are interested in the study of some methods for solving multiple objective integer linear programming problem based on Branch-and-Bound and the study of grid computing technology. This study allowed us to propose an implementation of the method of Abbas and Al on the grid by reducing the execution time. To enhance our contribution, the main results are presented.

Keywords: multi-objective optimization, integer linear programming, grid computing, parallel computing

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Authors: Sie Long Kek, Wah June Leong, Kok Lay Teo

Abstract:

Linear quadratic Gaussian model is a standard mathematical model for the stochastic optimal control problem. The combination of the linear quadratic estimation and the linear quadratic regulator allows the state estimation and the optimal control policy to be designed separately. This is known as the separation principle. In this paper, an efficient computational method is proposed to solve the linear quadratic Gaussian problem. In our approach, the Hamiltonian function is defined, and the necessary conditions are derived. In addition to this, the output error is defined and the least-square optimization problem is introduced. By determining the first-order necessary condition, the gradient of the sum squares of output error is established. On this point of view, the stochastic approximation approach is employed such that the optimal control policy is updated. Within a given tolerance, the iteration procedure would be stopped and the optimal solution of the linear-quadratic Gaussian problem is obtained. For illustration, an example of the linear-quadratic Gaussian problem is studied. The result shows the efficiency of the approach proposed. In conclusion, the applicability of the approach proposed for solving the linear quadratic Gaussian problem is highly demonstrated.

Keywords: iteration procedure, least squares solution, linear quadratic Gaussian, output error, stochastic approximation

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Authors: S. Bangphan, P. Bangphan, C. Ketsombun, T. Sammana

Abstract:

Optimization of response surface methodology (RSM) was employed to study the effects of three factor (rubber of clearance, spindle of speed, and rice of moisture) in brown rice peeling machine of the optimal good rice yield (99.67, average of three repeats). The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α=0.05, the values of Regression coefficient, R2 adjust were 96.55% and standard deviation were 1.05056. The independent variables are initial rubber of clearance, spindle of speed and rice of moisture parameters namely. The investigating responses are final rubber clearance, spindle of speed and moisture of rice.

Keywords: brown rice, response surface methodology (RSM), peeling machine, optimization, paddy husker

Procedia PDF Downloads 574