Does trade cause inequality

textThe relationship between international trade and income distribution of countries becomes a hot topic in economics research. This paper use random forest method and stepwise regression method to complete variables selection work from a big panel data set with many economic variables. Analysis of an unbalanced panel of country level data reveals that the trade will reduce income inequality in most situations. The coefficients for trade variables are significant in both two types of models, i.e., with and without considering about country effects. But when we split data set into two groups, the coefficients are significant for developed countries but not significant for developing countries.Statistic

Wind tunnel project for teaching and researching purposes

Wind tunnels are test devices used in aerodynamic experiments to understand the effects of air moving through solid objects. Wind tunnels are often used to provide a steady and controlled flow of wind to replace natural winds and to test new models of spacecraft, aircraft and vehicles, and some wind tunnels have enough room to inspect full-size cars. A small wind tunnel system has been acquired and requires re-design and testing. These works follow on the ENG470 work by Mr Ibrahim Noor Izham, who undertook the initial study that analysed the functionality and limitations of the existing wind tunnel. However, the conclusion of the works shows the current wind tunnel does not have the capability to meet the requirements of Murdoch University. Therefore, this project aims to design and construct a new open return wind tunnel system for Murdoch University, which has been designed to achieve 20m / s in the test section with expected low turbulence intensity level. Making it available for Murdoch University research and education purposes, for example, research particular emission from biochar-amended soil, low-speed aerodynamics experiments (ENG 339: Wind and Hydro Power Systems: testing aerofoil and wind turbine models). In order to implement these goals, a very detail design was carried on using theoretical modelling and CFD simulations. Moreover, flow stabilization and control are also performed by using a honeycomb and screens; all of these are optimized to produce low turbulence levels in the test section. Furthermore, the axial fan with a VFD for this wind tunnel project was delivered at the end of March 2019, to assist the subsequent wind tunnel project, an experiment was conducted on 25th May 2019 to study the relationship between wind flow speed generated by the axial fan with different fan speed in RPM. The report has addressed the dimension of the new wind tunnel design by theoretical modelling method. Base on this dimension of the wind tunnel design, an AutoCAD modelling and a CFD simulation is conducted to investigate the turbulence intensity level in the test section. The results show that when the wind tunnel is operated at a wind speed of 20 m / s, the turbulence intensity of the test section is less than 3%. The fan test results show that the maximum wind speed produced by the axial fan is 12.97 m / s, which is much lower than the expected speed of the fan section of 15.405 m / s. The reason is that the vibrations of the fan test structure affect the wind speed measurement of the Pitot tube. In addition, after completing the construction of the entire wind tunnel, the contraction cone will accelerate the flow through the wind tunnel to meet the expected speed

IS THE BLACK–SCHOLES MODEL GOOD ENOUGH FOR RETAIL INVESTORS IN CHINA?

This study answers a simple question for Chinese investors, especially Chinese retail investors: Is the Black–Scholes model good enough for them to make investment decisions? Using the absolute out-of-sample error as a measure of model efficiency, I find that the volume-weighted mean absolute out-of-sample error is 12.03% of the option premium and investors have to tolerate more than 1% absolute error in almost all subsample groups. The significant out-of-sample error indicates that using the Black–Scholes model solely in the decision-making process may have a negative impact on the investments’ performance

Automated Car Guiding System Using Reinforcement Learning

The major objective of this project is to design and implement a car guiding system in a desk-size area, with a remote-controlled toy car. The software, including the calculation, image processing, and movement control, was coded with Python and C++. Q-learning algorithm was selected to be the core of the calculation part, and OpenCV library was used for image processing.The hardware consists of a webcam, a laptop, and a toy car with Bluetooth connection. Some wood boards were also used to build a frame for restraining the area for running the car.The system is able to track the car, detect the obstacles, calculate the optimal path, and send signals to the car in order to control the movement

Design of New Oscillograph Based on FPGA

AbstractOscillograph is one of the necessary measurement instruments in modern electronic design field. A new type of Oscillograph based on FPGA is proposed and designed in this paper. It consists of oscillograph, logic analyzer and signal generator. The resolution of the oscillograph is 8 bit and the maximum value can reach 200Mbps with support of software based on windows operation system. That of the logic analyzer is 100 Msps with 16 channels. The resolution of signal generator is 140Msps with 10-bit

Drug prescription support in dental clinics through drug corpus mining

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients

Drug prescription support in dental clinics through drug corpus mining

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients

Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring

While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.Comment: Published in the ACL 202

Efficient Estimation for Longitudinal Network via Adaptive Merging

Longitudinal network consists of a sequence of temporal edges among multiple nodes, where the temporal edges are observed in real time. It has become ubiquitous with the rise of online social platform and e-commerce, but largely under-investigated in literature. In this paper, we propose an efficient estimation framework for longitudinal network, leveraging strengths of adaptive network merging, tensor decomposition and point process. It merges neighboring sparse networks so as to enlarge the number of observed edges and reduce estimation variance, whereas the estimation bias introduced by network merging is controlled by exploiting local temporal structures for adaptive network neighborhood. A projected gradient descent algorithm is proposed to facilitate estimation, where the upper bound of the estimation error in each iteration is established. A thorough analysis is conducted to quantify the asymptotic behavior of the proposed method, which shows that it can significantly reduce the estimation error and also provides guideline for network merging under various scenarios. We further demonstrate the advantage of the proposed method through extensive numerical experiments on synthetic datasets and a militarized interstate dispute dataset.Comment: 26 pages and 2 figures; appendix including technical proof will be uploaded late