Search results for: general linear regression model

Authors: Chaimongkol Limpianchob

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This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case.

Keywords: aromatic coconut, supply chain management, production planning, mixed-integer linear programming

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Authors: Saima Shafiq, Najma Iqbal Malik

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This study was aimed to explore the relationship of perceived stigma, perception of burden and psychological distress among parents of intellectually disabled children. The study also aimed to explore the moderating role of perceived social support on all the variables of the study. The sample of the study comprised of (N = 250) parents of intellectually disabled children. The present study utilized the co-relational research design. It consists of two phases. Phase-I consisted of two steps which contained the translation of two scales that were used in the present study and tried out on the sample of parents (N = 70). The Affiliated Stigma Scale and Care Giver Burden Inventory were translated into Urdu for the present study. Phase-1 revealed that translated scaled entailed satisfactory psychometric properties. Phase -II of the study was carried out in order to test the hypothesis. Correlation, linear regression analysis, and t-test were computed for hypothesis testing. Hierarchical regression analysis was applied to study the moderating effect of perceived social support. Findings revealed that there was a positive relationship between perceived stigma and psychological distress, perception of burden and psychological distress. Linear regression analysis showed that perceived stigma and perception of burden were positive predictors of psychological distress. The study did not show the moderating role of perceived social support among variables of the present study. The major limitation of the study is the sample size and the major implication is awareness regarding problems of parents of intellectually disabled children.

Keywords: perceived stigma, perception of burden, psychological distress, perceived social support

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Authors: Syed Afrozuddin Ahmed, Junaid Saghir Siddiqi, Sabah Quaiser

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Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan. In this study it has been investigated that spatial pattern of environment causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. The climatic or environmental structure data and the Dengue Fever (DF) data was processed by coding, editing, tabulating, recoding, restructuring in terms of re-tabulating was carried out, and finally applying different statistical methods, techniques, and procedures for the evaluation. Five climatic variables which we have studied are precipitation (P), Maximum temperature (Mx), Minimum temperature (Mn), Humidity (H) and Wind speed (W) collected from 1980-2012. The dengue cases in Karachi from 2010 to 2012 are reported on weekly basis. Principal component analysis is applied to explore the climatic variables and/or the climatic (structure) which may influence in the increase or decrease in the number of dengue fever cases in Karachi. PC1 for all the period is General atmospheric condition. PC2 for dengue period is contrast between precipitation and wind speed. PC3 is the weighted difference between maximum temperature and wind speed. PC4 for dengue period contrast between maximum and wind speed. Negative binomial and Poisson regression model are used to correlate the dengue fever incidence to climatic variable and principal component score. Relative humidity is estimated to positively influence on the chances of dengue occurrence by 1.71% times. Maximum temperature positively influence on the chances dengue occurrence by 19.48% times. Minimum temperature affects positively on the chances of dengue occurrence by 11.51% times. Wind speed is effecting negatively on the weekly occurrence of dengue fever by 7.41% times.

Keywords: principal component analysis, dengue fever, negative binomial regression model, poisson regression model

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Authors: Dileep Kumar Gupta, Rajendra Prasad, Pradeep Kumar, Varun Narayan Mishra, Ajeet Kumar Vishwakarma, Prashant K. Srivastava

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An approach was evaluated for the retrieval of soil moisture of bare soil surface using bistatic scatterometer data in the angular range of 200 to 700 at VV- and HH- polarization. The microwave data was acquired by specially designed X-band (10 GHz) bistatic scatterometer. The linear regression analysis was done between scattering coefficients and soil moisture content to select the suitable incidence angle for retrieval of soil moisture content. The 250 incidence angle was found more suitable. The support vector regression analysis was used to approximate the function described by the input-output relationship between the scattering coefficient and corresponding measured values of the soil moisture content. The performance of support vector regression algorithm was evaluated by comparing the observed and the estimated soil moisture content by statistical performance indices %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 2.9451, 1.0986, and 0.9214, respectively at HH-polarization. At VV- polarization, the values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 3.6186, 0.9373, and 0.9428, respectively.

Keywords: bistatic scatterometer, soil moisture, support vector regression, RMSE, %Bias, NSE

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Authors: Manouchehr Amiri

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This article introduces a general notion of physical bit information that is compatible with the basics of quantum mechanics and incorporates the Shannon entropy as a special case. This notion of physical information leads to the Binary data matrix model (BDM), which predicts the basic results of quantum mechanics, general relativity, and black hole thermodynamics. The compatibility of the model with holographic, information conservation, and Landauer’s principles are investigated. After deriving the “Bit Information principle” as a consequence of BDM, the fundamental equations of Planck, De Broglie, Beckenstein, and mass-energy equivalence are derived.

Keywords: physical theory of information, binary data matrix model, Shannon information theory, bit information principle

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Authors: Kiavash Heidarzadeh

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The seismic behavior of portal frames is mainly based on the shape of their joints. In these structures, vertical and inclined connections are the two general forms of connections. The shapes of connections can make differences in seismic responses of portal frames. Hence, in this paper, for the first step, the non-linear performance of portal frames with vertical and inclined connections has been investigated by monotonic analysis. Also, the effect of section sizes is considered in this analysis. For comparison, hysteresis curves have been evaluated for two model frames with different forms of connections. Each model has three various sizes of the column and beam. Other geometrical parameters have been considered constant. In the second step, for every model, an appropriate size of sections has been selected from the previous step. Next, the seismic behavior of each model has been analyzed by the time history method under three near-fault earthquake records. Finite element ABAQUS software is used for simulation and analysis of samples. Outputs show that connections form can impact on reaction forces of portal frames under earthquake loads. Also, it is understood that the load capacity in frames with vertical connections is more than the frames with inclined connections.

Keywords: inclined connections, monotonic, portal frames, seismic behavior, time history, vertical connections

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Authors: Tamás Mankovits, Dávid Huri, Imre Kállai, Imre Kocsis, Tamás Szabó

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Non-linear FEM calculations are indispensable when important technical information like operating performance of a rubber component is desired. Rubber bumpers built into air-spring structures may undergo large deformations under load, which in itself shows non-linear behavior. The changing contact range between the parts and the incompressibility of the rubber increases this non-linear behavior further. The material characterization of an elastomeric component is also a demanding engineering task. In this paper, a comprehensive investigation is introduced including laboratory measurements, mesh density analysis and complex finite element simulations to obtain the load-displacement curve of the chosen rubber bumper. Contact and friction effects are also taken into consideration. The aim of this research is to elaborate an FEM model which is accurate and competitive for a future shape optimization task.

Keywords: rubber bumper, finite element analysis, compression test, Mooney-Rivlin material model

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Authors: Phanida Phukoetphim, Asaad Y. Shamseldin

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In this study, the novel Stochastic Gradient Boosting (SGB) combination method is addressed for producing daily river flows from four different rain-runoff models of Ohinemuri catchment, New Zealand. The selected rainfall-runoff models are two empirical black-box models: linear perturbation model and linear varying gain factor model, two conceptual models: soil moisture accounting and routing model and Nedbør-Afrstrømnings model. In this study, the simple average combination method and the weighted average combination method were used as a benchmark for comparing the results of the novel SGB combination method. The models and combination results are evaluated using statistical and graphical criteria. Overall results of this study show that the use of combination technique can certainly improve the simulated river flows of four selected models for Ohinemuri catchment, New Zealand. The results also indicate that the novel SGB combination method is capable of accurate prediction when used in a combination method of the simulated river flows in New Zealand.

Keywords: multi-model combination, rainfall-runoff modeling, stochastic gradient boosting, bioinformatics

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Authors: Segen Asayehegn

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Introduction: In Tigray, Ethiopia, next to cervical cancer, breast cancer is one of the most common cancer health problems for women. Objectives: This article is proposed to identify the prospective and potential risk factors affecting the time-to-first-recurrence of breast cancer patients in Tigray, Ethiopia. Methods: The data were taken from the patient’s medical record that registered from January 2010 to January 2020. The study considered a sample size of 1842 breast cancer patients. Powerful non-parametric and parametric shared frailty survival regression models (FSRM) were applied, and model comparisons were performed. Results: Out of 1842 breast cancer patients, about 1290 (70.02%) recovered/cured the disease. The median cure time from breast cancer is found at 12.8 months. The model comparison suggested that the lognormal parametric shared a frailty survival regression model predicted that treatment, stage of breast cancer, smoking habit, and marital status significantly affects the first recurrence of breast cancer. Conclusion: Factors like treatment, stages of cancer, and marital status were improved while smoking habits worsened the time to cure breast cancer. Recommendation: Thus, the authors recommend reducing breast cancer health problems, the regional health sector facilities need to be improved. More importantly, concerned bodies and medical doctors should emphasize the identified factors during treatment. Furthermore, general awareness programs should be given to the community on the identified factors.

Keywords: acceleration factor, breast cancer, Ethiopia, shared frailty survival models, Tigray

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Authors: Mark Ashton Newman, Richard Blagrove, Jonathan Folland

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The force-velocity profile of an individual has been shown to influence success in ballistic movements, independent of the individuals' maximal power output; therefore, effective and accurate evaluation of an individual’s F-V characteristics and not solely maximal power output is important. The relatively narrow range of loads typically utilised during force-velocity profiling protocols due to the difficulty in obtaining force data at high velocities may bring into question the accuracy of the F-V slope along with predictions pertaining to the maximum force that the system can produce at a velocity of null (F₀) and the theoretical maximum velocity against no load (V₀). As such, the reliability of the slope of the force-velocity profile, as well as V₀, has been shown to be relatively poor in comparison to F₀ and maximal power, and it has been recommended to assess velocity at loads closer to both F₀ and V₀. The aim of the present study was to assess the relative and absolute reliability of an instrumented novel leg press machine which enables the assessment of force and velocity data at loads equivalent to ≤ 10% of one repetition maximum (1RM) through to 1RM during a ballistic leg press movement. The reliability of maximal and mean force, velocity, and power, as well as the respective force-velocity and power-velocity relationships and the linearity of the force-velocity relationship, were evaluated. Sixteen male strength-trained individuals (23.6 ± 4.1 years; 177.1 ± 7.0 cm; 80.0 ± 10.8 kg) attended four sessions; during the initial visit, participants were familiarised with the leg press, modified to include a mounted force plate (Type SP3949, Force Logic, Berkshire, UK) and a Micro-Epsilon WDS-2500-P96 linear positional transducer (LPT) (Micro-Epsilon, Merseyside, UK). Peak isometric force (IsoMax) and a dynamic 1RM, both from a starting position of 81% leg length, were recorded for the dominant leg. Visits two to four saw the participants carry out the leg press movement at loads equivalent to ≤ 10%, 30%, 50%, 70%, and 90% 1RM. IsoMax was recorded during each testing visit prior to dynamic F-V profiling repetitions. The novel leg press machine used in the present study appears to be a reliable tool for measuring F and V-related variables across a range of loads, including velocities closer to V₀ when compared to some of the findings within the published literature. Both linear and polynomial models demonstrated good to excellent levels of reliability for SFV and F₀ respectively, with reliability for V₀ being good using a linear model but poor using a 2nd order polynomial model. As such, a polynomial regression model may be most appropriate when using a similar unilateral leg press setup to predict maximal force production capabilities due to only a 5% difference between F₀ and obtained IsoMax values with a linear model being best suited to predict V₀.

Keywords: force-velocity, leg-press, power-velocity, profiling, reliability

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

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

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

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Authors: Hazem Al-Mofleh, John Daniels, Joseph McKean

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Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares.

Keywords: non-linear wilcoxon, robust estimation, variogram estimation, wilcoxon norm

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Authors: Suman Bala, Sunil Kamboj, Vipin Saini

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Quantitative Structure Activity Relationship (QSAR) analysis has been developed to relate antifungal activity of novel substituted 1,3,4-oxadiazole against Candida albicans and Aspergillus niger using computer assisted multiple regression analysis. The study has shown the better relationship between antifungal activities with respect to various descriptors established by multiple regression analysis. The analysis has shown statistically significant correlation with R² values 0.932 and 0.782 against Candida albicans and Aspergillus niger respectively. These derivatives were further subjected to molecular docking studies to investigate the interactions between the target compounds and amino acid residues present in the active site of glucosamine-6-phosphate synthase. All the synthesized compounds have better docking score as compared to standard fluconazole. Our results could be used for the further design as well as development of optimal and potential antifungal agents.

Keywords: 1, 3, 4-oxadiazole, QSAR, multiple linear regression, docking, glucosamine-6-phosphate synthase

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Authors: Shittu Bisi Agnes

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Studies on households’ willingness to pay for environmental and general health safety in the advent of Ebola virus Diseases in Nigeria was carried out. This is aimed at revealing the means by which the virus was eventually eradicated in Nigeria as widely claimed in the media. This study therefore attempted to determine the environmental and general health condition in the State Of Osun, how socio-economic characteristics of the people affected willingness to pay. And also provide platform for the reduction of environmental and general health problems. Data were collected with the aid of well-structured questionnaire and administer 150 randomly selected people of study area, and oral interview was also utilized. Data collected were analyzed using both descriptive tools and inferential statistics vis-a-viz regression analysis. Findings showed 92.5% of respondents was aware of ebola virus diseases outbreak in Nigeria, 8.5% was unaware of any disease outbreak. And 65.7% of respondents was strongly willing to pay for environmental and general health safety 27.1% was fairly willing, 5.7% was indifferent and 1.7% was unwilling to pay. 5% rated the level of environmental and general health condition in the area has been good, 53.6% rated theirs has been fair, 33.6% as been poor. The average willingness to pay per household per month were #500.00, #250.00, #150.00 and #100.00 respectively for the four categories. It was recommended that policy instruments to increase peoples' income will accelerate eradication of environmental and general health problems, environmental health education in form of talk shop, workshop, lectures and seminars could be organized at the political ward levels, churches, mosque, and at schools. Environmental and general health safety related information could be disseminated through mass media, market women, and functional unions.

Keywords: ebola virus diseases (EVD), socio-economic, safety, pay, Osun

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Authors: Georges Jabbour

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Background and aims: This two-year follow-up study explores the long-term sustainability of physical activity (PA) levels in young people with type 1 diabetes, focusing on the relationship between PA, hypoglycemia, and behavioral scores. The literature highlights the importance of PA and its health benefits, as well as the barriers to engaging in PA practices. Studies have shown that individuals with high levels of vigorous physical activity have higher fear of hypoglycemia (FOH) scores and more hypoglycemia episodes. Considering that hypoglycemia episodes are a major barrier to physical activity, and many studies reported a negative association between PA and high FOH scores, it cannot be guaranteed that those experiencing hypoglycemia over a long period will remain active. Building on that, the present work assesses whether high PA levels, despite elevated hypoglycemia risk, can be maintained over time. The study tracks PA levels at one and two years, correlating them with hypoglycemia instances and Fear of Hypoglycemia (FOH) scores. Materials and methods: A self-administered questionnaire was completed by 61 youth with T1D, and their PA was assessed. Hypoglycemia episodes, fear of hypoglycemia scores and HbA1C levels were collected. All assessments were realized at baseline (visit 0: V0), one year (V1) and two years later (V2). For the purpose of the present work, we explore the relationships between PA levels, hypoglycemia episodes, and FOH scores at each time point. We used multiple linear regression to model the mean outcomes for each exposure of interest. Results: Findings indicate no changes in total moderate to vigorous PA (MVPA) and VPA levels among visits, and HbA1c (%) was negatively correlated with the total amount of VPA per day in minutes (β= -0.44; p=0.01, β= -0.37; p=0.04, and β= -0.66; p=0.01 for V0, V1, and V2, respectively). Our linear regression model reported a significant negative correlation between VPA and FOH across the visits (β=-0.59, p=0.01; β= -0.44, p=0.01; and β= -0.34, p=0.03 for V0, V1, and V2, respectively), and HbA1c (%) was influenced by both the number of hypoglycemic episodes and FOH score at V2 (β=0.48; p=0.02 and β=0.38; p=0.03, respectively). Conclusion: The sustainability of PA levels and HbA1c (%) in young individuals with type 1 diabetes is influenced by various factors, including fear of hypoglycemia. Understanding these complex interactions is essential for developing effective interventions to promote sustained PA levels in this population. Our results underline the necessity of a multi-strategic approach to promoting active lifestyles among diabetic youths. This approach should synergize PA enhancement with vigilant glucose monitoring and effective FOH management.

Keywords: physical activity, hypoglycemia, fear of hypoglycemia, youth

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Authors: Maoxin Ye

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This study examines the relationship between social network sites (SNS) usage and social capital. Because SNS usage can expand the users’ networks, and people who are connected in this networks may become resources to SNS users and lead them to advantage in some situation, it is important to estimate the relationship between SNS usage and ‘who’ is connected or what resources the SNS users can get. Additionally, ‘who’ can be divided in two aspects – people who possess high position and people who are different, hence, it is important to estimate the relationship between SNS usage and high position people and different people. This study adapts Lin’s definition of social capital and the measurement of position generator which tells us who was connected, and can be divided into the same two aspects as well. A national data of America (N = 2,255) collected by Pew Research Center is utilized to do a general regression analysis about SNS usage and social capital. The results indicate that SNS usage is negatively associated with each factor of social capital, and it suggests that, in fact, comparing with non-users, although SNS users can get more connections, the variety and resources of these connections are fewer. For this reason, we could lose benefits through SNS usage.

Keywords: social network sites, social capital, position generator, general regression

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Authors: Hisham M. Soliman, Hassan Yousef

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This manuscript presents new results on design saturated power system stabilizers (PSS) to assign system poles within a desired region for achieving good dynamic performance. The regional pole placement is accomplished against model uncertainties caused by different load conditions. The design is based on a sufficient condition in the form of linear matrix inequalities (LMI) which forces the saturated nonlinear controller to lie within the linear zone. The controller effectiveness is demonstrated on a single machine infinite bus system.

Keywords: power system stabilizer, saturated control, robust control, regional pole placement, linear matrix inequality (LMI)

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Authors: Jamilatuzzahro, Rezzy Eko Caraka

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The yield curve is the plot of the yield to maturity of zero-coupon bonds against maturity. In practice, the yield curve is not observed but must be extracted from observed bond prices for a set of (usually) incomplete maturities. There exist many methodologies and theory to analyze of yield curve. We use two methods (the Nelson-Siegel Method, the Svensson Method, and the SVR method) in order to construct and compare our zero-coupon yield curves. The objectives of this research were: (i) to study the adequacy of NSS model and SVR to Indonesian government bonds data, (ii) to choose the best optimization or estimation method for NSS model and SVR. To obtain that objective, this research was done by the following steps: data preparation, cleaning or filtering data, modeling, and model evaluation.

Keywords: support vector regression, Nelson-Siegel-Svensson, yield curve, Indonesian government

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Authors: Seyedtaghi Mirmohammadi, Jamshid Yazdani, Syavash Etemadi Nejad

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One of the main indoor air parameters in classrooms is dust pollution and it depends on the particle size and exposure duration. However, there is a lake of data about the exposure level to PM2.5 concentrations in rural area classrooms. The objective of the current study was exposure assessment for PM2.5 for students in the classrooms. One year monitoring was carried out for fifteen schools by time-series sampling to evaluate the indoor air PM2.5 in the rural district of Sari city, Iran. A hygrometer and thermometer were used to measure some psychrometric parameters (temperature, relative humidity, and wind speed) and Real-Time Dust Monitor, (MicroDust Pro, Casella, UK) was used to monitor particulate matters (PM2.5) concentration. The results show the mean indoor PM2.5 concentration in the studied classrooms was 135µg/m3. The regression model indicated that a positive correlation between indoor PM2.5 concentration and relative humidity, also with distance from city center and classroom size. Meanwhile, the regression model revealed that the indoor PM2.5 concentration, the relative humidity, and dry bulb temperature was significant at 0.05, 0.035, and 0.05 levels, respectively. A statistical predictive model was obtained from multiple regressions modeling for indoor PM2.5 concentration and indoor psychrometric parameters conditions.

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

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Authors: Brahim-Fares Zaidi, Malika Boudraa, Sid-Ahmed Selouani

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Our work aims to improve our Automatic Recognition System for Dysarthria Speech (ARSDS) based on the Hidden Models of Markov (HMM) and the Hidden Markov Model Toolkit (HTK) to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients (MFCC's) and Perceptual Linear Prediction (PLP's) and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers.

Keywords: hidden Markov model toolkit (HTK), hidden models of Markov (HMM), Mel-frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP’s)

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Authors: Minto Rattan, Tania Bose, Neeraj Chamoli

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The present paper investigates the effect of linear thermal gradient on the steady-state creep behavior of rotating isotropic disc using threshold stress based Sherby’s creep law. The composite discs made of aluminum matrix reinforced with silicon carbide particulate has been taken for analysis. The stress and strain rate distributions have been calculated for discs rotating at linear thermal gradation using von Mises’ yield criterion. The material parameters have been estimated by regression fit of the available experimental data. The results are displayed and compared graphically in designer friendly format for the above said temperature profile with the disc operating under uniform temperature profile. It is observed that radial and tangential stresses show minor variation and the strain rates vary significantly in the presence of thermal gradation as compared to disc having uniform temperature.

Keywords: creep, isotropic, steady-state, thermal gradient

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Authors: Khadidja Bellifa, Sidi Mohamed Mekelleche

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

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

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Authors: Md. Nazmul Haque, Mahinur Rahman

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The objective of the research is to determine the quantity of household waste generated and forecast the future condition of Ward No 24 (Nirala). For performing that, three core issues are focused: (i) the capacity and service area of the dumping stations; (ii) the present waste generation amount per capita per day; (iii) the responsibility of the local authority in the household waste collection. This research relied on field survey-based data collection from all stakeholders and GIS-based secondary analysis of waste collection points and their coverage. However, these studies are mostly based on the inherent forecasting approaches, cannot predict the amount of waste correctly. The findings of this study suggest that Nirala is a formal residential area introducing a better approach to the waste collection - self-controlled and collection system. Here, a forecasting model proposed for waste generation as Y = -2250387 + 1146.1 * X, where X = year.

Keywords: eco-friendly environment, household waste, linear regression, waste management

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Authors: Mohadeseh Shojaei Shahrokhabadi, Ding-Geng (Din) Chen

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Positive continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical cost data. To jointly model semi-continuous longitudinal cost data and survival data and to provide marginalized covariate effect estimates, a marginalized two-part joint model (MTJM) has been developed for outcome variables with lognormal distributions. In this paper, we propose MTJM models for outcome variables from a generalized gamma (GG) family of distributions. The GG distribution constitutes a general family that includes approximately all of the most frequently used distributions like the Gamma, Exponential, Weibull, and Log Normal. In the proposed MTJM-GG model, the conditional mean from a conventional two-part model with a three-parameter GG distribution is parameterized to provide the marginal interpretation for regression coefficients. In addition, MTJM-gamma and MTJM-Weibull are developed as special cases of MTJM-GG. To illustrate the applicability of the MTJM-GG, we applied the model to a set of real electronic health record data recently collected in Iran, and we provided SAS code for application. The simulation results showed that when the outcome distribution is unknown or misspecified, which is usually the case in real data sets, the MTJM-GG consistently outperforms other models. The GG family of distribution facilitates estimating a model with improved fit over the MTJM-gamma, standard Weibull, or Log-Normal distributions.

Keywords: marginalized two-part model, zero-inflated, right-skewed, semi-continuous, generalized gamma

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Authors: Feyza Gökce, Jochen Gensichen, Antonius Schneider, Karolina de Valerio, Gabriele Pitschel-Walz

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People with depressive symptoms often first consult a General Practitioner (GP) before making use of other treatment options. The present study shows the adaptation and evaluation of a psychoeducational program for patients with depressive symptoms that are treated by GPs in Bavaria, Germany. The adaptation of an existing psychoeducational program, that is used in inpatient psychiatric settings, was performed in exchange with experts (psychotherapists, general practitioners, and a patient representative). As a result, a program consisting of 4 psychoeducational sessions was developed, which is carried out in individual settings in GP practices by the practitioners themselves. This program will be compared to treatment as usual that patients with depression receive by GPs. Data is collected at 3 measurement points (baseline, 3-months-follow-up, 6-months-follow-up) using different questionnaires (BDI-II, D-Lit-R German, FERUS, PAM13-D, PHQ-9, GAD-7, PHQ-15, PC-PTSD-5). In addition to the change in depressive symptoms, changes in depression knowledge, self-efficacy, and patient activation will be analyzed, and the feasibility of the program and the subjective benefit for GPs and patients will be assessed. By now, 84 patients have been recruited by 20 cluster-randomized GP practices, with 73.5% of the participants being female and 26.5% being males. The average age was M= 50.1 (SD= 14.67) years. The change in depression symptoms over the 3-month period will be compared between the two study conditions by using a linear mixed model by the end of data collection (December 2023). The subjective benefit for the patients and GPs will be assessed via feedback questionnaires. Results will be presentable by the beginning of 2024 and will provide indications for further development and barriers to the implementation of such a program for GP practices.

Keywords: depression, general practice, psychoeducation, primary care

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

Abstract:

Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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Authors: K. Godazandeh, K. Sadeghy

Abstract:

In the present study, the effect of magnetic field on the hydrodynamic instability of Taylor-Couette flow between two concentric rotating cylinders has been numerically investigated. At the beginning the basic flow has been solved using continuity, Cauchy equations (with regards to Lorentz force) and the constitutive equations of a viscoelastic model called "Giesekus" model. Small perturbations, considered to be normal mode, have been superimposed to the basic flow and the unsteady perturbation equations have been derived consequently. Neglecting non-linear terms, the general eigenvalue problem obtained has been solved using pseudo spectral method (combination of Chebyshev polynomials). The objective of the calculations is to study the effect of magnetic fields on the onset of first mode of instability (axisymmetric mode) for different dimensionless parameters of the flow. The results show that the stability picture is highly influenced by the magnetic field. When magnetic field increases, it first has a destabilization effect which changes to stabilization effect due to more increase of magnetic fields. Therefor there is a critical magnetic number (Hartmann number) for instability of Taylor-Couette flow. Also, the effect of magnetic field is more dominant in large gaps. Also based on the results obtained, magnetic field shows a more considerable effect on the stability at higher Weissenberg numbers (at higher elasticity), while the "mobility factor" changes show no dominant role on the intense of suction and injection effect on the flow's instability.

Keywords: magnetic field, Taylor-Couette flow, Giesekus model, pseudo spectral method, Chebyshev polynomials, Hartmann number, Weissenberg number, mobility factor

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Authors: Jonathan Iworiso

Abstract:

Forecasting the equity premium out-of-sample is a major concern to researchers in finance and emerging markets. The quest for a superior model that can forecast the equity premium with significant economic gains has resulted in several controversies on the choice of variables and suitable techniques among scholars. This research focuses mainly on the application of Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regression models involving model complexity was employed. The RT models include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net, and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the RT models demonstrates significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk.

Keywords: regression training, out-of-sample forecasts, expanding window, statistical predictability, economic significance, utility gains

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Authors: Gintarė Sauliutė, Gintaras Svecevičius

Abstract:

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