scholarly journals Comparison of Direct-reading and Gravimetric Methods of Particle Measurement in a Science Building, Silpakorn University

2021 ◽  
pp. 132-143
Author(s):  
Aungsiri Tipayarom ◽  
Prayad Sangngam ◽  
Siraphop Pinitkarn
Keyword(s):  
Gravimetric Method ◽  
Ordinary Least Squares ◽  
Gravimetric Analysis ◽  
Regression Equations ◽  
Direct Reading ◽  
Ols Regression ◽  
Pm10 And Pm2.5 ◽  
Science Building ◽  
Predicted Values ◽  
Comparison Of The Results

This study aimed to develop relationships between particulate matter (PM) concentrations obtained from a direct-reading instrument to those from a gravimetric method. TSI DustTrak II Aerosol Monitors (Model 8530), a direct-reading instrument for PM10 and PM2.5 measurement, together with personal air pumps connected to a Sensidyne cyclone and a SKC Personal Environmental Monitor (PEM) for gravimetric PM10 and PM2.5 measurements respectively were deployed in the Faculty of Science building, Silpakorn University, Nakhon Pathom, Thailand. Comparison of the results from each instrument indicated that PM10 and PM2.5 concentrations obtained from the TSI DustTrak were higher. The linear relationship from ordinary least squares (OLS) regression between PM10 data determined by TSI DustTrak (x) and Sensidyne cyclone (y ̂) was significant (R2=0.92) and could be represented as y ̂ = 0.272x. For PM2.5, the relationship between concentrations determined by TSI DustTrak (x) and SKC PEM (y ̂) was also significant (R2=0.92) and represented by y ̂ = 4.848√x. Validation of both equations was undertaken by comparing predicted values from these relationships against the actual concentrations found by gravimetric analysis, with R2=1.0 and 0.92 for PM10 and PM2.5, respectively. It is suggested that these site-specific OLS regression equations can provide fast and convenient estimation of concentrations derived by gravimetric analysis from direct-reading TSI DustTrak monitor data.

Robust SURE estimates of profitability in the Egyptian insurance market

2021 ◽  
pp. 1-13
Author(s):  
Ahmed H. Youssef ◽  
Amr R. Kamel ◽  
Mohamed R. Abonazel
Keyword(s):  
Robust Regression ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Insurance Market ◽  
Insurance Companies ◽  
Regression Equations ◽  
Robust Estimators ◽  
Exogenous Variables ◽  
Net Profit ◽  
Mm Estimation

This paper proposed three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.

scholarly journals Numerical investigation of the effects of unsupported railway sleepers on train-induced environmental vibrations

2017 ◽  
Vol 36 (2) ◽  
pp. 160-176 ◽  
Author(s):  
Seyed-Ali Mosayebi ◽  
Morteza Esmaeili ◽  
Jabbar-Ali Zakeri
Keyword(s):  
Finite Element ◽  
Element Model ◽  
Two Dimensions ◽  
Railway Track ◽  
Sensitivity Analyses ◽  
Regression Equations ◽  
The Finite Element Method ◽  
Technical Literature ◽  
Support Force ◽  
Comparison Of The Results

Review of technical literature regarding to train-induced vibrations shows that the effects of unsupported railway sleepers on this issue have been less investigated. So, the present study was devoted to numerical investigations of the mentioned issue. In this regard, first the problem of longitudinal train–track dynamic interaction was simulated in two dimensions by using the finite element method and the developed model was validated through comparison of the results with those obtained by previous researchers. In the next stage, a series of sensitivity analyses were accomplished to account for the effects of value of gap beneath the unsupported sleeper(s) and the track support stiffness on increasing the sleeper displacement and track support force. Moreover, the raised sleeper support force was introduced as applied load to a two-dimensional plane strain finite element model of track in lateral section and consequently the train-induced vibrations were assessed. As a result, a series of regression equations were established between the peak particle velocity in the surrounding environment of railway track and the sleeper support stiffness for tracks without unsupported sleepers and with one and two unsupported sleepers.

Real-Ear-to-Coupler Difference Predictions as a Function of Age for Two Coupling Procedures

2002 ◽  
Vol 13 (08) ◽  
pp. 407-415 ◽  
Author(s):  
Marlene P. Bagatto ◽  
Susan D. Scollie ◽  
Richard C. Seewald ◽  
K. Shane Moodie ◽  
Brenda M. Hoover
Keyword(s):  
Young Children ◽  
Infants And Children ◽  
Regression Equations ◽  
Predictive Equations ◽  
Subject Variability ◽  
Predicted Values ◽  
Age Range ◽  
Infants And Young Children

The predicted real-ear-to-coupler difference (RECD) values currently used in pediatric hearing instrument prescription methods are based on 12-month age range categories and were derived from measures using standard acoustic immittance probe tips. Consequently, the purpose of this study was to develop normative RECD predicted values for foam/acoustic immittance tips and custom earmolds across the age continuum. To this end, RECD data were collected on 392 infants and children (141 with acoustic immittance tips, 251 with earmolds) to develop normative regression equations for use in deriving continuous age predictions of RECDs for foam/acoustic immittance tips and earmolds. Owing to the substantial between-subject variability observed in the data, the predictive equations of RECDs by age (in months) resulted in only gross estimates of RECD values (i.e., within ± 4.4 dB for 95% of acoustic immittance tip measures; within ± 5.4 dB in 95% of measures with custom ear molds) across frequency. Thus, it is concluded that the estimates derived from this study should not be used to replace the more precise individual RECD measurements. Relative to previously available normative RECD values for infants and young children, however, the estimates derived through this study provide somewhat more accurate predicted values for use under those circumstances for which individual RECD measurements cannot be made.

scholarly journals Cratering for Impact of Hypervelocity Projectiles into Granite Targets within a Velocity Range of 1.91–3.99 km/s: Experiments and Analysis

2020 ◽  
Vol 10 (4) ◽  
pp. 1393
Author(s):  
Xiaofeng Wang ◽  
Jingbo Liu ◽  
Biao Wu ◽  
Defeng Kong ◽  
Jiarong Huang ◽  
...  
Keyword(s):  
Impact Velocity ◽  
Scaling Law ◽  
Density Ratio ◽  
Hypervelocity Impact ◽  
Experimental Results ◽  
Tungsten Alloy ◽  
Regression Equations ◽  
Velocity Range ◽  
Predicted Values ◽  
The Impact

To understand and analyze crater damage of rocks under hypervelocity impact, the hypervelocity impact cratering of 15 shots of hemispherical-nosed cylindrical projectiles into granite targets was studied within the impact velocity range of 1.91–3.99 km/s. The mass of each projectile was 40 g, and the length–diameter ratio was 2. Three types of metal material were adopted for the projectiles, including titanium alloy with a density of 4.44 g/cm3, steel alloy with a density of 7.81 g/cm3, and tungsten alloy with a density of 17.78 g/cm3. The projectile–target density ratio (ρp/ρt) ranged from 1.71 to 6.86. The depth–diameter ratios (H/D) of the craters yielded from the experiments were between 0.14 and 0.24. The effects of ρp/ρt and the impact velocity on the morphologies of the crater were evaluated. According to the experimental results, H/D of craters is negatively correlated with the impact velocity, whereas the correlation between H/D and ρp/ρt is weak positive. The crater parameters were expressed as power law relations of impact parameters by using scaling law analysis. The multiple regression analysis was utilized to obtain the coefficients and the exponents of the relation equations. The predicted values of the regression equations were close to the experimental results.

India-BIMSTEC Bilateral Trade Activities

2020 ◽  
pp. 126-145
Author(s):  
Gurpreet Kaur ◽  
Akriti Gupta
Keyword(s):  
Bay Of Bengal ◽  
Fixed Effects ◽  
Bilateral Trade ◽  
Moving Average ◽  
Trade Agreements ◽  
Ordinary Least Squares ◽  
Regression Equations ◽  
Economic Interests ◽  
Auto Regressive ◽  
Regional Dummy

The Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) is one of the solutions to converge the economic interests of India's Look East Policy and Thailand's Look West Policy. Its objective is to integrate the regions on both sides of the Bay of Bengal. The development of BIMSTEC countries is indispensable for the forward march of Asia as a whole. This chapter analyzes the India-BIMSTEC trade activities after the establishment of BIMSTEC bloc. Gravity model and Auto-Regressive Integrated Moving Average (ARIMA) are used. The model estimates the sets of regression equations to measure the effects of regional trade agreements using ordinary least squares with nation dummies to capture country-specific fixed effects. The study reveals that all coefficients of regional dummy variables are mostly positive and significant, indicating the agreements that tend to enhance more trade than bilateral trade agreements. The authors state that based on India's trade with the BIMSTEC region, there exists a scope for intraregional trade in the future.

Impact Evaluation Using Analysis of Covariance With Error-Prone Covariates That Violate Surrogacy

2019 ◽  
Vol 43 (6) ◽  
pp. 335-369
Author(s):  
J. R. Lockwood ◽  
Daniel F. McCaffrey
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Ordinary Least Squares ◽  
Analysis Of Covariance ◽  
Asymptotic Results ◽  
Teacher Effects ◽  
Intervention Effects ◽  
Ols Regression ◽  
Ancova Model

Background: Analysis of covariance (ANCOVA) is commonly used to adjust for potential confounders in observational studies of intervention effects. Measurement error in the covariates used in ANCOVA models can lead to inconsistent estimators of intervention effects. While errors-in-variables (EIV) regression can restore consistency, it requires surrogacy assumptions for the error-prone covariates that may be violated in practical settings. Objectives: The objectives of this article are (1) to derive asymptotic results for ANCOVA using EIV regression when measurement errors may not satisfy the standard surrogacy assumptions and (2) to demonstrate how these results can be used to explore the potential bias from ANCOVA models that either ignore measurement error by using ordinary least squares (OLS) regression or use EIV regression when its required assumptions do not hold. Results: The article derives asymptotic results for ANCOVA with error-prone covariates that cover a variety of cases relevant to applications. It then uses the results in a case study of choosing among ANCOVA model specifications for estimating teacher effects using longitudinal data from a large urban school system. It finds evidence that estimates of teacher effects computed using EIV regression may have smaller bias than estimates computed using OLS regression when the data available for adjusting for students’ prior achievement are limited.

scholarly journals Identifying the Driving Factors of Black Bloom in Lake Bay through Bayesian LASSO

2019 ◽  
Vol 16 (14) ◽  
pp. 2492 ◽  
Author(s):  
Wang ◽  
Wang ◽  
Cheng ◽  
Cheng
Keyword(s):  
Policy Development ◽  
Sediment Concentration ◽  
Ordinary Least Squares ◽  
Complex Problem ◽  
Regression Equation ◽  
Management Policy ◽  
Major Influence ◽  
Chaohu Lake ◽  
Regression Equations ◽  
Lasso Regression

Black blooms are a serious and complex problem for lake bays, with far-reaching implications for water quality and drinking safety. While Fe(II) and S(−II) have been reported as the most important triggers of this phenomenon, little effort has been devoted in investigating the relationships between Fe(II) and S(−II) and the host of potentially important aquatic factors. However, a model involving many putative predictors and their interactions will be oversaturated and ill-defined, making ordinary least squares (OLS) estimation unfeasible. In such a case, sparsity assumption is typically required to exclude the redundant predictors from the model, either through variable selection or regularization. In this study, Bayesian least absolute shrinkage and selection operator (LASSO) regression was employed to identify the major influence variables from 11 aquatic factors for Fe(II), S(−II), and suspended sediment concentration (SSC) in the Chaohu Lake (Eastern of China) bay during black bloom maintenance. Both the main effects and the interactions between these factors were studied. The method successfully screened the most important variables from many items. The determination coefficients (R2) and adjusted determination coefficients (Adjust R2) showed that all regression equations for Fe(II), S(-II), and SSC were in good agreement with the situation observed in the Chaohu Lake. The outcome of correlation and LASSO regression indicated that total phosphorus (TP) was the single most important factor for Fe(II), S(-II), and SSC in black bloom with explanation ratios (ERs) of 76.1% , 37.0%, and 12.9%, respectively. The regression results showed that the interaction items previously deemed negligible have significant effects on Fe(II), S(−II), and SSC. For the Fe(II) equation, total nitrogen (TN) × dissolved oxygen (DO) and chlorophyll a (CHLA) × oxidation reduction potential (ORP), which contributed 10.6% and 13.3% ERs, respectively, were important interaction variables. TP emerged in each key interaction item of the regression equation for S(−II). Water depth (DEP) × Fe(II) (30.7% ER) was not only the main interaction item, but DEP (5.6% ER) was also an important single factor for the SSC regression equation. It also indicated that the sediment in shallow bay is an important source for SSC in water. The uncertainty of these relationships was also estimated by the posterior distribution and coefficient of variation (CV) of these items. Overall, our results suggest that TP concentration is the most important driver of black blooms in a lake bay, whereas the other factors, such as DO, DEP, and CHLA act in concert with other aquatic factors. There results provide a basis for the further control and management policy development of black blooms.

scholarly journals Gravimetric analysis of the autocatalytic growth of copper microparticles in aqueous solution

RSC Advances ◽  
2019 ◽  
Vol 9 (65) ◽  
pp. 37895-37900
Author(s):  
Jinuk Byun ◽  
Kwang Hawn Kim ◽  
Byung Keun Kim ◽  
Ji Woong Chang ◽  
Sung Ki Cho ◽  
...  
Keyword(s):  
Aqueous Solution ◽  
Growth Kinetics ◽  
Gravimetric Method ◽  
Gravimetric Analysis ◽  
Kinetics Of ◽  
Autocatalytic Growth

The growth kinetics of copper microparticles was analysed by using the gravimetric method.

scholarly journals Modeling energy expenditure in children and adolescents using quantile regression

2013 ◽  
Vol 115 (2) ◽  
pp. 251-259 ◽  
Author(s):  
Yunwen Yang ◽  
Anne L. Adolph ◽  
Maurice R. Puyau ◽  
Firoz A. Vohra ◽  
Nancy F. Butte ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Quantile Regression ◽  
Ordinary Least Squares ◽  
Prediction Errors ◽  
Obese Children ◽  
Study Objective ◽  
Ols Regression ◽  
Physiological Processes ◽  
Covariate Effects ◽  
Children First

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the covariate effects of weight, PA, and HR across the conditional EE distribution. QR and ordinary least squares (OLS) regressions are estimated in 109 children, aged 5–18 yr. QR modeling of EE outperformed OLS regression for both nonobese and obese populations. Average prediction errors for QR compared with OLS were not only smaller at the median τ = 0.5 (18.6 vs. 21.4%), but also substantially smaller at the tails of the distribution (10.2 vs. 39.2% at τ = 0.1 and 8.7 vs. 19.8% at τ = 0.9). Covariate effects of weight, PA, and HR on EE for the nonobese and obese children differed across quantiles ( P < 0.05). The associations (linear and quadratic) between PA and HR with EE were stronger for the obese than nonobese population ( P < 0.05). In conclusion, QR provided more accurate predictions of EE compared with conventional OLS regression, especially at the tails of the distribution, and revealed substantially different covariate effects of weight, PA, and HR on EE in nonobese and obese children.

DEVELOPING PREDICTION EQUATIONS AND OPTIMIZING PRODUCTION OF THREE AM FUNGAL INOCULA UNDER ON-FARM CONDITIONS

2011 ◽  
Vol 47 (3) ◽  
pp. 529-537 ◽  
Author(s):  
MAHAVEER P. SHARMA ◽  
ALOK ADHOLEYA
Keyword(s):  
Mycorrhizal Fungi ◽  
Glomus Intraradices ◽  
Maximum Yield ◽  
Arbuscular Mycorrhizal ◽  
Total Production ◽  
Regression Equations ◽  
Farm Production ◽  
Predicted Values ◽  
Time Required ◽  
On Farm

SUMMARYThe production potential of three arbuscular mycorrhizal fungi (AMF), AM-1004 (Glomus intraradices), AM-1209 (mixed indigenous AMF) and AM-1207 (Mycorise, commercial inocula), were examined separately in three fractions/forms (root-based, soil-based and mixture of roots + soil) at 40, 60, 80 and 105 days in raised beds. The beds were amended with organic matter to develop regression equations for predicting optimal AM production vis-à-vis time required for particular inocula using infectious propagules (IP) as the independent variable. The IP production observed in the system was found to vary among the different inocula used. AM-1004 and AM-1207 produced significantly higher propagule counts in root or soil-based samples and a mixture of both at 105 days as compared to AM-1209. Based on two-way ANOVA, irrespective of time, AM-1004 (root/soil-based) produced a significantly larger number of propagules, whereas propagules in the crude inoculum (roots + soil) of all three inocula were not significantly different. On the other hand, irrespective of AMF, significantly more propagules (in all forms) were observed at 105 days. Similarly, irrespective of time, AM-1004 produced significantly higher root colonization (MCP, mycorrhizal colonization percentage) in all three forms (roots: 65.95%; soil: 24.32; soil + roots: 58.03%). The MCP in roots was increased significantly with time of multiplication. However, there was not much improvement in the MCP of soil or in soil + roots fractions beyond 80 days. Further, prediction of the number of IP for the three AM inocula was mathematically derived separately from the Mitscherlish-Bray equation (Y=a–b*exp (–cD). Based on the maximum yield of propagules of the three inocula observed and fitted into equations, root-based AM-1004 and AM-1209 inocula were found to be more efficient in producing propagules in 65 days as compared to AM-1207, which produced propagules in 76 days. While comparing the overall combinations, AM-1004 and AM-1209 inocula used either as roots, soil or a mixture of both and have greater potential in producing more propagules in the shortest span of time. While taking into account the predicted values of AM-1209 crude inoculum, about 12 IP g−1substrate can be achieved in 72 days. Therefore, if a farmer uses crude inocula (having zero time IP of about 0.8/g substrate) of AM 1209, a total production of about 12.12 million IP/m3can be achieved in 72 days. These can be used for on-farm production.

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