Application of statistical and neural network model for oil palm yield study

This thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. This study began by exploring the commonly used statistical models for plant growth such as nonlinear growth model, multiple linear regression models and robust M regression model. Data used were OP yield growth data, foliar composition data and fertiliser treatments data, collected from seven stations in the inland and coastal areas provided by Malaysian Palm Oil Board (MPOB). Twelve nonlinear growth models were used. Initial study shows that logistic growth model gave the best fit for modelling OP yield. This study then explores the causality relationship between OP yield and foliar composition and the effect of nutrient balance ratio to OP yield. In improving the model, this study explores the use of neural network. The architecture of the neural network such as the combination activation functions, the learning rate, the number of hidden nodes, the momentum terms, the number of runs and outliers data on the neural network’s performance were also studied. Comparative studies between various models were carried out. The response surface analysis was used to determine the optimum combination of fertiliser in order to maximise OP yield. Saddle points occurred in the analysis and ridge analysis technique was used to overcome the saddle point problem with several alternative combinations fertiliser levels considered. Finally, profit analysis was performed to select and identify the fertiliser combination that may generate maximum yiel

Preferred vs. Actual Working Hours - A Ten Years Paneleconometric Analysis for Professions, Entrepreneurs and Employees in Germany

Labour market dynamics according the individual working hour tension (preferred working hours minus actual working hours) of active people with focus on the self-employed, as professions and entrepreneurs, and employees are investigated in our study. The individual longitudinal analysis based on panel data allows us to follow the individual process of working time preferences and actual outcomes in its individual convergence/divergence balancing process in the course of time. Our microanalytic and paneleconometric results (with pooled, one and two factor fixed and random effects models) quantify the working hour tension developments and its determinants in a decade from the mid 80s to the mid 90s. Microdata base is the German Socio-Economic Panel with ten waves from 1985 to 1994. Finally, we discuss impacts of our results for labour market strategies and a targeted economic and social policy

Quantum limits of cold damping with optomechanical coupling

Thermal noise of a mirror can be reduced by cold damping. The displacement is measured with a high-finesse cavity and controlled with the radiation pressure of a modulated light beam. We establish the general quantum limits of noise in cold damping mechanisms and we show that the optomechanical system allows to reach these limits. Displacement noise can be arbitrarily reduced in a narrow frequency band. In a wide-band analysis we show that thermal fluctuations are reduced as with classical damping whereas quantum zero-point fluctuations are left unchanged. The only limit of cold damping is then due to zero-point energy of the mirrorComment: 10 pages, 3 figures, RevTe