scholarly journals ABSORPTION OF NEEM (Azadirachta indica) SEED OIL BY SPLIT-BAMBOO (Bambusa vulgaris) AT DIFFERENT TEMPERATURE REGIMES AND TREATMENT DURATIONS

FLORESTA ◽  
2012 ◽  
Vol 42 (2) ◽  
pp. 231 ◽  
Author(s):  
Andrew Agbontalor Erakhrumen
Keyword(s):  
Regression Model ◽  
Azadirachta Indica ◽  
Seed Oil ◽  
Treatment Temperature ◽  
Coefficient Of Determination ◽  
Temperature Regimes ◽  
Bambusa Vulgaris ◽  
Ambient Room Temperature ◽  
Made In

This study was carried out to evaluate absorption of neem (Azadirachta indica A. Juss) seed oil by split-bamboo (Bambusa vulgaris Schrad. ex J.C. Wendl.) samples at two different treatment temperature (TT) regimes and durations of treatment (DOT). A multivariate linear regression model was also developed for predicting oil absorption (OA) from TT and DOT. Split-bamboo specimens from the same source with same dimension were oven-dried at 103 ± 2 oC, conditioned to 11.76% mean moisture content, and treated by completely soaking a set in oil at an ambient room temperature of 25 ± 2 oC for 24 hours (A) and by soaking the other in hot oil at 60 oC for 4 hours (B). Results obtained showed that test specimens for A and B had mean OA values of 57.02 ± 3.23 and 124.30 ± 7.26 kgm-3 respectively. Regression model developed for predicting OA from TT and DOT had a coefficient of determination of 0.93 with a significant ANOVA result (p < 0.05). Implications of the results obtained were discussed while conclusions and recommendations were made in line with the outcome of the study.Keywords:  Lignocellulosic materials; vegetable oil; preservative properties; test variables; non-pressure treatment. ResumoAbsorção de óleo de sementes de nim (Azadirachta indica) por bambu fracionado (Bambusa vulgaris) em diferentes temperaturas e durações de tratamento. Este estudo foi realizado para avaliar a absorção de óleo de sementes de nim (Azadirachta indica A. Juss) por bambu fracionado (Bambusa vulgaris Schrad. Ex JC Wendl.) com amostras em duas temperaturas (TT) e diferentes durações do tratamento (DOT). Um modelo de regressão linear múltipla foi também desenvolvido para predizer a absorção de óleo (OA) de TT e DOT. Espécimes de bambu picado provenientes da mesma fonte com mesma dimensão foram secas em estufa a 103 ± 2 ºC, condicionados a 11,76% de teor médio de umidade e tratados por imersão em óleo a uma temperatura ambiente de 25 ± 2 ºC por 24 horas (A), embebendo-se o outro em óleo quente a 60 ºC durante 4 horas (B). Os resultados obtidos mostraram que as amostras de teste para A e B tinham valores médios de 57,02 OA ± 3,23 e 124,30 ± 7,26 kg.m-3, respectivamente. O modelo de regressão desenvolvido para predizer a OA de TT e DOT teve um coeficiente de determinação de 0,93, com um resultado da ANOVA significativo (p <0,05). Implicações dos resultados obtidos foram discutidos enquanto conclusões e recomendações foram feitas de acordo com o resultado do estudo.Palavras-chave:     Materiais lignocelulósicos; óleo vegetal; propriedades conservantes; variáveis de teste.

Can Gamification Concepts Work With E-Government?

2019 ◽  
Vol 12 (3) ◽  
pp. 44-59
Author(s):  
Emad Ahmed Abu-Shanab ◽  
Malak Rasheed Al-Sayed
Keyword(s):  
Regression Model ◽  
Negative Influence ◽  
Positive Influence ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Coefficient Of Determination ◽  
Intention To Use ◽  
Perceived Ease Of Use ◽  
Government Websites

This article predicts the adoption of e-government websites and services by focusing on gamification and enjoyment factors. The sample uses ranked use of points and coupons as the most suitable schemes, while excluding the use of quests and puzzles. In predicting the intention to use e-government, five constructs were used: perceived usefulness, perceived ease of use, enjoyment and innovation, positive influence on government image and negative influence of government images. Results indicated a significant role for enjoyment and innovation based on the gamification context. The influence on government image (positive and negative) were not significant in predicting the intention to use e-government. The coefficient of determination of the regression model was 0.655, which explains 65.5% of the variance in ITU.

scholarly journals The Application of a Logistic Regression Model for Predicting Preferences of Transport System Users

2018 ◽  
Vol 64 (4) ◽  
pp. 145-159
Author(s):  
A. Brzeziński ◽  
K. Brzeziński ◽  
T. Dybicz ◽  
Ł. Szymański
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Transport System ◽  
Urban Areas ◽  
Logistic Regression Model ◽  
Probabilistic Approach ◽  
The Impact ◽  
Selection Of ◽  
Made In

AbstractWithin the INMOP 3 research project, an attempt was made to solve a number of problems associated with the methodology of modelling travel in urban areas and the application of intermodal models. One of these is the ability to describe the behaviour of transport system users, when it comes to making decisions regarding the selection of means of transport and searching for relationships between travel describing factors and the decisions made in regard of means of transport choice.The paper describes a probabilistic approach to the determination of modal split, and the application of a logistic regression model to determine the impact of variables describing individual and mass transport travels on the probability of selecting specific means of transport. Travels in local model of Warsaw city divided into 9 motivation groups were tested, for which ultimately 8 models were developed, out of which 7 were deemed very well fitted (obtained pseudo R2 was well above 0.2).

scholarly journals PENGEMBANGAN MODEL REGRESI POLINOMIAL BERGANDA PADA KASUS DATA PEMASARAN

2013 ◽  
Vol 12 (2) ◽  
pp. 149 ◽  
Author(s):  
Julyanti S Malensang ◽  
Hanny Komalig ◽  
Djoni Hatidja
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Polynomial Regression ◽  
Predictor Variable ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
A Value ◽  
Marketing Data ◽  
Set Up

PENGEMBANGAN MODEL REGRESI POLINOMIAL BERGANDA PADA KASUS DATA PEMASARANABSTRAK Regresi polinomial merupakan regresi linier berganda yang dibentuk dengan menjumlahkan pengaruh variabel prediktor (X) yang dipangkatkan secara meningkat sampai orde ke-k. Model regresi polinomial, struktur analisisnya sama dengan model regresi linier berganda. Artinya, setiap pangkat atau orde variabel prediktor (X) pada model polinomial, merupakan transformasi variabel awal dan dipandang sebagai sebuah variabel prediktor (X) baru dalam linier berganda. Model terbaik dari kelima model yang telah diuji adalah persamaan regresi model ke-5. Hal ini dapat dilihat dari nilai koefisien determinasi sebesar 99,1% dan nilai R-Sq(adj) = 98,8%, karena nilai R2 mendekati nilai yang telah diatur dan berdasarkan pengujian yang dilakukan ternyata seluruh koefisien-koefisien dari setiap variabel bebas signifikan serta ada kelengkungan yang bersifat kubik (pangkat 3) terhadap data X3 terhadap Y. Kata kunci: Pemasaran, Regresi polynomial. DEVELOPMENT OF MULTIPOLYNOMIAL REGRESSION MODEL ON MARKETING DATA CASE ABSTRACT Polynomial regression is linear regression multiple were created by summing the effect of each predictor variable (X) is raised to increase to the order of the k.  Polynomial regression model, has the same structure with linear regression models. That is, any rank or order predictor variable (X) in polynomial models, an initial variable transformation and is seen as a predictor variable (X) has the linear regression. The best model of the six models tested were equation regression model to-5.  It can be seen from the value of the coefficient of determination of 99.1% and a value of R-Sq (adj) = 98.8%, due to the value of R2 close to the value that has been set up and based on tests performed turns all the coefficients of each independent variable significantly and there are cubic curvature (rank 3) to the data X3 to Y. Keywords : Marketing, Polynomial regression.

scholarly journals Provide a model to identify the effect of the occurrence of business cycles on financial uncertainty in the stock market Developed countries

2021 ◽  
Vol 9 (SPE1) ◽  
Author(s):  
Hossein Shirbandi ◽  
Farzad Moayeri ◽  
Ataullah Mohammadi Malqarni
Keyword(s):  
Growth Rate ◽  
Stock Market ◽  
Business Cycles ◽  
Regression Model ◽  
Developed Countries ◽  
Coefficient Of Determination ◽  
Fisher Test ◽  
Financial Uncertainty ◽  
P Index

On financial uncertainty (variance of S&P index growth rate). Also, considering that the coefficient of determination of the regression model (R2) is equal to 0.984 and is close to the number one, that is, the Fisher test (F) is significant (its probability is less than 0.05), so the regression model is justifiable and acceptable. Hypothesis H0 is therefore rejected and Hypothesis H1 is accepted with 95% probability, or in other words, business cycles have a significant effect on financial uncertainty in the stock market of developed countrie

Hygroscopic property of Neem (Azadirachta indica A. Juss.): an experimental determination of the Shrinkage characteristics

2013 ◽  
Vol 9 (2) ◽  
pp. 152-158
Keyword(s):  
Experimental Determination ◽  
Analysis Of Variance ◽  
Azadirachta Indica ◽  
Linear Shrinkage ◽  
Vegetation Zones ◽  
North Eastern ◽  
Pioneer Effort ◽  
Sudan Savanna ◽  
Made In

A pioneer effort is made in this study to carry out an experimental determination of shrinkage characteristics of neem (Azadirachta indica A. Juss) wood on its linear, volumetric, and coefficient values with the hope of ascertaining its utilization potential as timber. Three study locations were randomly selected from defined vegetation zones of north eastern Nigeria for the study. These are Maiduguri (Sahel savanna) Yola (Sudan savanna), and Bauchi (Guinea savanna). Forty five (45) tree samples of neem trees were randomly selected and felled, from which 135 wood specimens were extracted and prepared using Romanian Standard for the research. The analysis of variance (ANOVA) was used to analyze the obtained data. Results showed that the tree species has an average tangential linear shrinkage of 12.74%, radial linear shrinkage of 6.26%, longitudinal linear shrinkage of 1.15%, and volumetric shrinkage of 19.12%. The coefficients of tangential, radial, and longitudinal shrinkage were 0.00674, 0.00339, and 0.00061 respectively. The analysis of variance revealed insignificant differences of shrinkage between the three vegetation zones, the sampled trees, as well as between the tree trunk sections. Since the shrinkage value of neem wood compares favorably with some local wood species used for timber, neem wood could be considered suitable for timber production.

scholarly journals Predicting Heart Rate Variability Parameters in Healthy Korean Adults: A Preliminary Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110562
Author(s):  
Sung-Woo Kim ◽  
Hun-Young Park ◽  
Won-Sang Jung ◽  
Kiwon Lim
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Regression ◽  
Regression Model ◽  
Frequency Domain ◽  
Time Domain ◽  
Multiple Linear Regression Model ◽  
Coefficient Of Determination ◽  
Independent Variables

The purpose of the study was to examine the development of a multiple linear regression model to estimate heart rate variability (HRV) parameters using easy-to-measure independent variables in preliminary experiments. HRV parameters (time domain: SDNN, RMSSD, NN50, pNN50; frequency domain: TP, VLF, LF, HF) and the independent variables (e.g., sex, age, body height, body weight, BMI, HR, HRmax, HRR) were measured in 75 healthy adults (male n = 27, female n = 48) for estimating HRV. The HRV estimation multiple linear regression model was developed using the backward elimination technique. The regression model’s coefficient of determination for the time domain variables was significantly high (SDNN = R2: 72.2%, adjusted R2: 69.8%, P < .001; RMSSD = R2: 93.1%, adjusted R2: 92.1%, P < .001; NN50 = R2: 78.0%, adjusted R2: 74.9%, P < .001; pNN50 = R2: 89.1%, adjusted R2: 87.4%, P < .001). The coefficient of determination of the regression model for the frequency domain variable was moderate (TP = R2: 75.6%, adjusted R2: 72.6%, P < .001; VLF = R2: 41.6%, adjusted R2: 40.3%, P < .001; LF = R2: 54.6%, adjusted R2: 49.2%, P < .001; HF = R2: 67.5%, adjusted R2: 63.4%, P < .001). The coefficient of determination of time domain variables in the developed multiple regression models was shown to be very high (adjusted R2: 69.8%–92.1%, P < .001), but the coefficient of determination of frequency domain variables was moderate (adjusted R2: 40.3%–72.6%, P < .001). In addition to the equipment used for measuring HRV in clinical trials, this study confirmed that simple physiological variables could predict HRV.

scholarly journals Growth curve Nopalea cochenillifera in fractionation function of cladodes using power regression model

2018 ◽  
Vol 36 (3) ◽  
pp. 578
Author(s):  
Leandro Ricardo Rodrigues de LUCENA ◽  
Juliana De Souza PEREIRA ◽  
Maurício Luiz de Mello Vieira LEITE
Keyword(s):  
Plant Growth ◽  
Regression Model ◽  
Pest Control ◽  
Growth Curve ◽  
Regression Models ◽  
Growth Period ◽  
Coefficient Of Determination ◽  
Planting Method ◽  
Nopalea Cochenillifera

In this work we evaluate the growth length of bud of Nopalea cochenillifera using five different forms of crops through power regression model. The adjusted models showed very similar estimates of lengths observed independent using of planting method. The power regression models showed coefficient of determination of model high 99.65% (treatment 1), 99.82% (treatment 2), 99.26% (treatment 3), 99.93% (treatment 4) and 99.34% (treatment 5). The power regression model proved effective to model the growth length of Nopalea cochenillifera of bud can generate strategies and plans for future plantings, as well useful information as: appropriate crop management, increased plant growth period and pest control.

The quantitative comparison of experimental and simulated diffraction contrast images

1995 ◽  
Vol 53 ◽  
pp. 102-103
Author(s):  
Stuart McKernan
Keyword(s):  
Image Processing ◽  
Personal Computer ◽  
Close Agreement ◽  
Quantitative Comparison ◽  
Image Simulation ◽  
Computing Power ◽  
Diffraction Contrast ◽  
Simulated Images ◽  
Made In

For many years the concept of quantitative diffraction contrast experiments might have consisted of the determination of dislocation Burgers vectors using a g.b = 0 criterion from several different 2-beam images. Since the advent of the personal computer revolution, the available computing power for performing image-processing and image-simulation calculations is enormous and ubiquitous. Several programs now exist to perform simulations of diffraction contrast images using various approximations. The most common approximations are the use of only 2-beams or a single systematic row to calculate the image contrast, or calculating the image using a column approximation. The increasing amount of literature showing comparisons of experimental and simulated images shows that it is possible to obtain very close agreement between the two images; although the choice of parameters used, and the assumptions made, in performing the calculation must be properly dealt with. The simulation of the images of defects in materials has, in many cases, therefore become a tractable problem.

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