Full Validation of A New Formula Estimating The Smoking Economic Burden By Morbidity

Introduction: In the Health Economy research context usually researches are assuming that the probability of the morbidity attributable to smoking is equal to the probability of the health spend attributable to smoking. Also is attributed the whole health spend because of active smokers to smoking. To solve this limitation, the authors suggested a new rate to measure the smoking economic burden by morbidity. Objective: To evaluate the feasibility from a new rate to measure the smoking economic burden by morbidity. Methods: Was made a bibliographic research to describe the estimation process from the smoking economic burden by morbidity. Were utilized as theoretical method the analysis and synthesis, the comparative and the systematization. As empiric methods were used the Principle of Multiplication and the bibliographic research. Results: The rate designed solves the limitation identified during the research about the estimation of the smoking economic burden by morbidity. Conclusion: The new rate will provide a better estimation from the smoking economic burden by morbidity. It application will may identify the role of active smokers and passive smokers in the formation of the smoking economic burden. However, it application must be agree to the supposes identified.

Rainfall Trend And Seasonality Estimation For Marathwada Region

The present research work conducts a seasonality and trend analysis of rainfall over the 8 districts of the Marathwada region India. The study is carried out for the last 39 years ranging from 1980 to 2018. The rainfall data analysed pertains to pre-monsoon season, monsoon season (Kharif), and annual. The trend has been estimated using Sen’s slope estimation process along with Mann-Kendal test. It was observed that the all the Eight districts of the region show a negative trend in the annual rainfall received. Nanded district showed the largest negative trend in the annual rainfall. Out of eight districts seven districts of the region show a decline in rainfall during the monsoon season. The district of Nanded showed largest decline in the rainfall received during monsoon season. The present research work concludes with discussion on possible causes of such estimated trends.

Range Restriction affects Factor Analysis: Normality, Estimation, Fit, Loadings, and Reliability

A sample suffers range restriction (RR) when its variance is reduced comparing to its population variance and, in turn, it fails representing such population. If the RR occurs over the latent factor, not directly over the observed variable, the researcher deals with an indirect RR, common when using convenience samples. This work explores how this problem affects different outputs of the factor analysis: multivariate normality (MVN), estimation process, goodness-of-fit, recovery of factor loadings, and reliability. In doing so, a Monte Carlo study was conducted. Data were generated following the linear selective sampling model, simulating tests varying their sample size (N = 200 and 500 cases), test size (J = 6, 12, 18, 24 items), loading size (L = .50, .70, and .90) and restriction size (from R = 1, .90, .80, and so on till .10 selection ratio). Our results systematically suggest that an interaction between decreasing the loading size and increasing the restriction size affects the MVN assessment, obstructs the estimation process, and leads to an underestimation of the factor loadings and reliability. However, most of the MVN tests and most of the fit indices employed were nonsensitive to the RR problem. We provide some recommendations to applied researchers.

Kalman Filter Adaptation to Disturbances of the Observer’s Parameters

Currently, one of the most effective algorithms for state estimation of stochastic systems is a Kalman filter. This filter provides an optimal root-mean-square error in state vector estimation only when the parameters of the dynamic system and its observer are precisely known. In real conditions, the observer’s parameters are often inaccurately known; moreover, they change randomly over time. This in turn leads to the divergence of the Kalman estimation process. The problem is currently being solved in a variety of ways. They include the use of interval observers, the use of an extended Kalman filter, the introduction of an additional evaluating observer by nonlinear programming methods, robust scaling of the observer’s transmission coefficient, etc. At the same time, it should be borne in mind that, firstly, all of the above ways are focused on application in specific technical systems and complexes, and secondly, they fundamentally do not allow estimating errors in determining the parameters of the observer themselves in order to compensate them for further improving the accuracy and stability of the filtration process of the state vector. To solve this problem, this paper proposes the use of accurate observations that are irregularly received in a complex measuring system (for example, navigation) for adaptive evaluation of the observer’s true parameters of the stochastic system state vector. The development of the proposed algorithm is based on the analytical dependence of the Kalman estimate variation on the observer’s parameters disturbances obtained using the mathematical apparatus for the study of perturbed multidimensional dynamical systems. The developed algorithm for observer’s parameters adaptive estimation makes it possible to significantly increase the accuracy and stability of the stochastic estimation process as a whole in the time intervals between accurate observations, which is illustrated by the corresponding numerical example.

Impact of Quadrature Booster on Power-System State Estimation in Polar Coordinates

The paper concerns the estimation of the state of a power system in which there is a phase shifter called a quadrature booster. The aim of the paper is a comparative analysis of two different cases including the quadrature booster in the state estimation. In the first case, the quadrature booster is represented by a model consisting of two real voltage sources, one in series with a power line and the other in a shunt branch. In the second case, in the power system model, the real branch with the quadrature booster is represented as off at the end where the considered quadrature booster is actually installed. The state estimation is assumed to be carried out in the polar coordinate system. The properties of the state estimation are characterized by: the number of iterations in the calculation process, the index of conditioning of the matrix of coefficients in the equations to be solved (cond(G)), and ratio Je/Jm, which is a measure of the accuracy of the estimation. Using IEEE 14-bus test system, investigations are carried out in such a way as to cover the entire state space of the power system as possible. In the investigations, Monte Carlo experiments are carried out for each of the considered cases of the state estimation. Each of these cases is also analyzed from the point of view of the assumed definition of the state estimation. Investigations show that in the first of the previously described cases, the state estimation is more accurate, but there are more iterations in the calculations and worse conditioning of the estimation process. The comparative analysis also shows that, the accuracy of the results obtained in each of the considered cases is practically independent of the coordinate system in which the estimation calculations are performed. Taking into account the number of iterations in the estimation process and index cond(G), it can be concluded that the implementation of each of the above-mentioned estimation cases in the rectangular coordinate system is more reasonable.

Digitalization of Decommissioning Cost Estimation Process for Upstream Assets

The paper provides an insight on how has addressed PETRONAS has addressed its pain points on limited resources, simplified work processes with reliable auditable tool for decision making through digitalization. PETRONAS is currently performing its annual budgetary assessment for all Malaysia assets which consist of more than 300 platforms with close to 600 pipelines and other assets eg. terminals, subsea systems & floating structure. With limited timeline and resources to establish decommissioning cost, the consistency and quality is vital for estimating work to improvise process efficiency and cost effective via digitalization. The process improvement requirements are pooled and possible digitalization takeovers are studied in detail via stakeholder engagements, technical workshops and lessons learned analysis. The method is solely based on digitalization of bottoms-up cost estimation process which has been embedded in a single tool to fix and standardize all technical and commercial basis. The tool has been developed with taking into all technical and commercial aspects in decommissioning offshore assets. Twelve base options which include reefing options, cutting methodologies, cost sharing execution strategies have been embedded in the tool. Based on the digital approach, it has been proven that cost estimation process duration has been optimized up to 60% for all Class V- and Class IV decommissioning cost estimates which is equivalent to 3,600 manhours for 1000 facilities. Furthermore, consistency in cost estimation approach and robustness in developing cost estimates for multiple options for decision making has been guaranteed with the centralization cost estimating approach via digital platform. Centralized digital depository of the technical inputs, basis and assumptions are also crucial to ensure this essential data could be retrieved in the future as most decommissioning projects would only be executed during the tail end of a facility’s production life.