House Price Dynamics and Granger Causality: An Analysis of Taipei New Dwelling Market

The primary purpose of this paper is to examine dynamic causal relationships between house price and its five determinants, including total household income, short-run interest rates, stock price index, construction costs, and housing completions, in Taipei new dwelling market. Granger causality tests, variance decomposition, impulse response functions based on the vector error-correction model are utilised. All five determinants Granger cause house prices, but only house prices and stock price index have a bilateral feedback effect. The variance decomposition results suggest that disturbances originating from current house prices inflict greatest variability (66 percent of variance) to future prices. The remaining 34 percent of the variance is explained by the five determinants. On the supply side, the construction costs and housing completions together explain about 10 percent of the house price variance. On the demand side, short-run interest rates, total household income and stock price index explain about 24 percent of the variance.Vector Error-correction Model, Granger Causality Test, Generalised Impulse Response Function

Common Fixed-Point Theorems in Complete Generalized Metric Spaces

We introduce the notions of the 𝒲 function and 𝒮 function, and then we prove two common fixed point theorems in complete generalized metric spaces under contractive conditions with these two functions. Our results generalize or improve many recent common fixed point results in the literature

Some New Fixed-Point Theorems for a ()-Pair Meir-Keeler-Type Set-Valued Contraction Map in Complete Metric Spaces

We obtain some new fixed point theorems for a (,)-pair Meir-Keeler-type set-valued contraction map in metric spaces. Our main results generalize and improve the results of Klim and Wardowski, (2007)

Adaptive cyclically dominating game on co-evolving networks: Numerical and analytic results

A co-evolving and adaptive Rock (R)-Paper (P)-Scissors (S) game (ARPS) in which an agent uses one of three cyclically dominating strategies is proposed and studied numerically and analytically. An agent takes adaptive actions to achieve a neighborhood to his advantage by rewiring a dissatisfying link with a probability $p$ or switching strategy with a probability $1-p$. Numerical results revealed two phases in the steady state. An active phase for $p<p_{\text{cri}}$ has one connected network of agents using different strategies who are continually interacting and taking adaptive actions. A frozen phase for $p>p_{\text{cri}}$ has three separate clusters of agents using only R, P, and S, respectively with terminated adaptive actions. A mean-field theory of link densities in co-evolving network is formulated in a general way that can be readily modified to other co-evolving network problems of multiple strategies. The analytic results agree with simulation results on ARPS well. We point out the different probabilities of winning, losing, and drawing a game among the agents as the origin of the small discrepancy between analytic and simulation results. As a result of the adaptive actions, agents of higher degrees are often those being taken advantage of. Agents with a smaller (larger) degree than the mean degree have a higher (smaller) probability of winning than losing. The results are useful in future attempts on formulating more accurate theories.Comment: 17 pages, 4 figure

In-process tool wear prediction system development in end milling operations

Three in-process tool wear monitoring systems have been developed in this research. They are: (1) the multiple linear regression based in-process tool wear prediction (MLR-ITWP) system; (2) the artificial neural networks based in-process tool wear prediction (ANN-ITWP) system; and (3) the statistics assisted fuzzy-nets based in-process tool wear prediction (S-FN-ITWP) system.;Before these above-mentioned systems were developed and evaluated, statistical approaches had been implemented to analyze and identify the most significant force signal for tool wearing monitoring system. This study demonstrates that the average peak cutting forces in the Y direction (the direction that is perpendicular to the table feed) is the most effective cutting force representation for tool wear monitoring.;Following with this discovery, the first system (MLR-ITWP system) was developed using a multiple linear regression model through 100 experimental data sets. Another nine data sets were used to test the system. The average tool wear prediction error of the MLR-ITWP system was +/-0.039 mm through the testing data. The second system (ANN-ITWM system) was developed using back-propagation artificial neural network through the same experimental data and tested with another nine data sets. The average tool wear prediction error of this ANN-ITWM system was +/-0.037 mm. The third system (S-FN-ITWM system) was developed using fuzzy-nets assisted statistically through the same experimental data and tested with another nine data sets. The average tool wear prediction error was +/-0.023 mm.;The scope of this research is to provide systems that can be integrated into smart computer numerical control (CNC) machine development in tool monitoring system. The success of this research provides the researcher better position in further related research