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Improving the Calibration of Surface Time Domain Reflectometry Sensors for Moisture Evaluation of Building Materials Using the Analysis of Covariance Method

100%

Futa A. , Jastrzębska M. , Paśnikowska-Łukaszuk M. , Wośko E. , Suchorab Z.

tom Vol. 17, no 5

326--336

The paper presents the models for moisture evaluation using a set of the reflectometric sensors in some types of building materials. The readouts reveal the relationship between the building material moisture, being assessed gravimetrically and the apparent permittivity values obtained by the TDR (Time Domain Reflectometry) method and surface sensors. Based on the readouts, equations describing this relationship were derived. These types of equations function as calibration equations and are used to calibrate the sensors. Most of the equations used to describe the examined relationships are linear regression. These equations very often refer to specific materials and cannot be applied to others that differ in density or chemical composition, which is the cause of many incorrect measurements. In this article, we propose the use of the analysis of covariance method (ANCOVA) for the analysis of reflectometric data. Using this method, it will be possible to determine the moisture content of materials, regardless of their type and construction of the sensor, which can significantly improve moisture measurements using the reflectometric method. For comparative aims data achieved in conducted research were analyzed using both traditional linear regression models and using the analysis of covariance method (ANCOVA). Both types of fitting models are discussed and their quality was compared in terms of accuracy expressed by the Residual Standard Error (RSE), the Root Mean Square Error (RMSE) and the determination coefficient (R2) values. The paper showed that the use of the ANCOVA method allows for improvement the fit of the model in terms of the determination coefficient by 0.0174. Moreover, the average RSE and RMSE value in the ANCOVA models are smaller about 1.24 vol.% and 1.25 vol.% than the ones in the regression model, respectively, which means that the models obtained using ANCOVA more accurately describe the examined relationship.

Improving Diesel Engine Reliability Using an Optimal Prognostic Model to Predict Diesel Engine Emissions and Performance Using Pure Diesel and Hydrogenated Vegetable Oil

86%

Žvirblis T. , Hunicz J. , Matijošius J. , Rimkus A. , Kilikevičius A. , Gęca M.

2023

tom Vol. 25, no. 4

art. no. 174358

The reliability of internal combustion engines becomes an important aspect when traditional fuels with biofuels. Therefore, the development of prognostic models becomes very important for evaluating and predicting the replacement of traditional fuels with biofuels in internal combustion engines. The models have been made to model AVL 5402 engine emission, vibration, and sound pressure parameters using a three-stage statistical regression models. The fifteen parameters might be accurately predicted by a single statistic presented here. Both fuel type (diesel fuel and HVO) and engine parameters that can be adjusted were considered, since this analysis followed the symmetry of the methods. The data analysis process included three distinct steps and symmetric statistical regression testing was performed. The algorithm examined the effectiveness of various engine settings. Finally, the optimal fixed engine parameter and the optimal statistic were used to construct an ANCOVA model. The ANCOVA model improved the accuracy of prediction for all fifteen missing parameters.

Considerations regarding relationship between tourists satisfaction and destination loyalty

81%

CIPRIANA MUNTEAN A. , BOGDAN DOBRA I.

tom 4

433-454

This study analyse the relationship between tourists satisfaction with destination loyalty in Alba County. The research is based on data collected from a market research contract in the tourism sector in Alba County and the period submitted for analysis is 2013 – 2015, when there have been identified 365 tourists. Regarding the methodology, one can notice that into Equation Estimation, Least Squares Options, we selected “White” for Coefficient covariance matrix. Also, we pointed out Standard deviation for Type weights options, and for Weighted series we selected Log(Expenditure). According to log-log regression model estimation output, 18 predictors determined a decrease (i.e. almost 55% from total predictors) and 15 independent variables determined an increase in the tourists’ number of visits. In terms of the dichotomous variables related to tourist satisfaction, it was highlighted that ten of exogenous cause a positive reaction in Number of Visits and ten of them a negative one. The elasticity of Number of Visits with respect to Log(Expenditure) is about -0.0959, suggesting that if the level of expenditure goes up by 10 percent, on average, the tourists’ number of visits goes down by about 1 percent. Thus, Number of Visits is very responsive to changes both variables related to tourist satisfaction and in personal tourist’s expenditure.

Niniejsze badanie analizuje związek między satysfakcją turystów a ich lojalnością wobec kierunku podróży w okręgu Alba. Badania opierają się na danych zebranych w ramach umowy o badaniach rynku w sektorze turystyki w okręgu Alba. Analiza obejmuje lata 2013–2015 i 365 turystów. Badania pokazały, że 18 predyktorów (tj. prawie 55% wszystkich predyktorów) wpłynęło na spadek, a 15 niezależnych zmiennych wpłynęło na wzrost liczby odwiedzin turystów. Jeśli chodzi o zmienne dychotomiczne związane z satysfakcją turystów, dziesięć zmiennych egzogennych wywołało pozytywną reakcję w liczbie odwiedzin, a dziesięć z nich negatywną. Elastyczność liczby wizyt w odniesieniu do Log(wydatki) wynosi około -0,0959, co sugeruje, że jeśli poziom wydatków wzrośnie średnio o 10 procent, liczba odwiedzin turystów zmniejszy się o około 1 procent. Tak więc liczba wizyt jest bardzo wrażliwa na zmiany zarówno zmiennych związanych z satysfakcją turystyczną, jak i wydatkami turystów indywidualnych.