Pengaruh Penerapan Konsep Good Corporate Governance Terhadap Kinerja Non-Keuangan Di Kantor Pusat PT Asuransi Jasa Indonesia

Good corporate governance defines the correlation among corporate\u27s elements which can determine the performance of the corporation. By implementing GCG within the corporates, it is expected to increase corporate performance improvement in both financial and non-financial sector. The purposes of this research is to analyze how the implementation of GCG and the performance of PT Asuransi Jasa Indonesia and how the GCG\u27s influence towards corporate performance. The analytical method used in this research is multiple linear regression analysis. The analysis result shows that the GCG implementation was not significantly effect PT Asuransi Jasa Indonesia performance. Accountability has significantly effect to PT Asuransi Jasa Indonesia performance. The coefficient determination (R2) was 0,187 (18,7%) showed that GCG implementation was still has small contribution to PT Asuransi Jasa Indonesia performance

Urban growth assessment and its impact on deforestation in Bauchi metropolis, Nigeria using remote sensing and GIS techniques

Urban areas are rapidly expanding due to population growth and development, leading to deforestation and land degradation. This study employed remote sensing and GIS techniques to assess urban growth and its impact on deforestation in Bauchi metropolis, Nigeria within the last three decades (1986-2016). The study made use of Land sat images of four epochs; Thematic Mapper (TM) of 1986 and 1996, Enhanced Thematic Mapper of 2006, and Operational Land Imager (OLI) of 2016. Color compositions were made after which the images were geometrically and radio metrically restituted. The images were classified using maximum likelihood algorithm and the accuracy of the classification was assessed by cross-validation using confusion matrices and ground truthing by the use of a hand-held Global Positioning System (GPS). The classified images with their kappa indexes were TM of 1986 (0.83%) and 1996 (0.87%), ETM+ of 2006 (0.90%) and OLI of 2016 (0.92%), respectively. Post-classification comparisons and analyses were performed and the results revealed that changes have taken place in bare surface (+32.43%), built-up area (+565.24%), farm land (+66.42%), forest (-91.80%) and rock outcrop (-49.21%) in the metropolis between 1986 and 2016. The land cover features of the metropolis were reclassified into forest and non-forest for cross-tabulation analysis and the result of the analysis indicates a change-over of 14965.97Ha (39.68%) form forest to non-forest (deforestation) and that of 467.69Ha (1.24%) form non-forest to forest (afforestation) between 1986 and 2016. This shows a rapid increase in built-up area (urban growth) and rapid decrease in forest (deforestation), which may be attributed to lack of improper environmental protection strategy in place in the metropolis. The study demonstrates the potentialities of remote sensing and GIS in assessing urban growth and its impacts on deforestation. The outcome of the study can serve as input into a relationship model for predicting the impact of urban growth on deforestation

EXPLORING THE INAR MODEL ON HEAVY TAILED TIME SERIES DATA WITH OUTLIERS

Count data are intrinsically measures of event frequency; it is clear that there is an intrinsic relationship with recurring time to event. Events are typically tallied within time intervals for practical and convenient reasons. The existence of outliers is one issue that prevents count data from being stationary in time series analysis; this has an impact on the effectiveness of fitting several common stationary models to the count data collected over time. Thus, the purpose of this study was to examine how well the Integer Valued Autoregressive (INAR) model performed while modeling count data that included outliers. While this model has been studied for count time series data, it has not been studied for varying degrees of outliers. A monte-carlo simulation was carried out to select the best INAR(p), where p=1,2,3 and 4 on data with 10%, 20% and 30% outliers at different sample sizes. The INAR (4) has the best fit across the sample sizes at the larger percentages of outliers while INAR (3) at the lowest percentage with smallest information criteria of assessment and they are therefore recommended for such modeling.

Optimised intake stroke analysis for flat and dome head pistons

This research exerts are suitable for the automobile industry in understanding the performance characteristics optioned between flat head and dome head pistons in engine design. This study was carried out to analyze the optimization parameters for effective and efficient flow characteristics of air-fuel mixture at the intake port of the combustion chamber of an internal combustion engine. A unique and industrial standard CFD software, STAR-CCM V8, was used to model both geometry for flat head and dome head pistons which was developed with precise dimensions of a 1.8L gasoline engine. A planar 3-D model approach was adopted for simplified static CAD modeling and also to reduce the solver processing time. The piston models were meshed using tetrahedral mesh of base size 0.001m. The boundary and physics conditions were applied to simulate the actual intake stroke process for normal operating conditions and initial conditions. The extracted results were validate and comparisons developed to analyze the various optimization parameters for performance characteristics of the two pistons.Keywords: optimized intake stroke, CFD analysis, flat and dome head pistons, internal combustion engine, CAD modellin

Trajectory Optimization of Quadrotor-UAV Drone Using Genetic Algorithm

Unmanned Aerial Vehicles (UAVs) Technology recently attracts attention of many researchers; this is due toits numerous potentialities in civil application. One of the key areas of interest by researches is how to achievea total talent of “Sense and Avoid” in the UAV which will enhance safe and efficient trajectory of the vehicle.This is why this paper is going to use an optimization technique to optimize trajectory path of the UAV flight.The chosen optimization algorithm is Genetic algorithm (GA) which is going to be use to optimize the trajectoryof UAV by determine the shortest path of flight as well as obstacle-free path in order to save energy and timeduring flight. MATLAB and Simulink are used to simulate as well as evaluate the algorithm. In the result fromthe experiment, it appeared that an optimized trajectory path is tremendously better than path from the firstrandomly generated population in term of distance covered as well as time taken before triumph the target pointfrom the initial point