Combinatorial Identities on Multinomial Coefficients and Graph Theory

We study combinatorial identities on multinomial coefficients. In particular, we present several new ways to count the connected labeled graphs using multinomial coefficients

Feature Investigation for Stock Returns Prediction Using XGBoost and Deep Learning Sentiment Classification

This paper attempts to quantify predictive power of social media sentiment and financial data in stock prediction by utilizing a comprehensive set of stock-related fundamental and technical variables and social media sentiments. For conducting sentiment analysis, this study employs a pretrained finBERT model that provides three different sentiment classifications and respective softmax scores. Hence, the significance of these variables is evaluated with XGBoost regression and Shapley Additive exPlanations (SHAP) frameworks. Through investigating feature importance, this study finds that statistical properties of sentiment variables provide a stronger predictive power than a weighted sentiment score and that it is possible to quantify the impact features make on so-called “black box” models

Electricity Demand Profile of Australian Low Energy Houses

AbstractThe paper demonstrates the profiles of electricity consumption in the low energy housing sector using various time frames, and provides a solid basis for energy estimation by analysing actual 12 month electricity data from 60 comprehensively monitored low energy houses in Australia's leading sustainable green village (Lochiel Park), located in South Australia. The results highlight that although considerable electricity reduction is achieved in low energy houses, the outdoor ambient air temperature is still a highly influential factor that determines the total and peak demand in these houses. It also suggests that energy estimation should focus on residents’ basic life style and appliance usage behaviour. The results presented here can be used to refine end-use electricity demand modelling for low energy houses in South Australia, and can hence assist the design of electrical infrastructure requirements in new low energy housing developments

Critical flux-based membrane fouling control of forward osmosis: Behavior, sustainability, and reversibility

Membrane fouling is closely related to the concept of critical flux. Therefore, a fouling control strategy for forward osmosis (FO) membranes that is based on the critical flux is necessary. This study systematically investigated the critical flux behavior of FO membranes (CTA and PA-TFC) in the short-term using a stepping method (draw solution (DS) concentration stepping). In addition, to test the reliability of this method, long-term experiments were conducted to evaluate the influences of operational critical flux on the fouling behavior (sustainable operation and fouling reversibility/irreversibility), thereby determining the critical flux for reversibility. Our results showed that the DS concentration stepping could be applied for critical flux determination in FO. Both membranes exhibited higher critical flux values for alginate fouling compared to other single foulants such as colloidal silica or gypsum. The values were 15.9 LMH for a cellulose triacetate membrane (CTA) and 20.5 LMH for the polyamide thin-film composite (PA-TFC). Whilst these values should be adequate in FO applications they were determined for single foulants. The presence of multispecies of foulants caused a significant decline in the critical flux values. This study found 5.4 LMH for the CTA membrane and 8.3 LMH for the PA-TFC membrane for the combined foulants of alginate + gypsum. This indicates that the critical flux behavior in FO was dependent on the foulant type and membrane type. Importantly, the high restoration of water flux was achieved with the PA-TFC membrane at an operation either close to critical flux (92–98%) or below critical flux (98–100%) (i.e., with negligible irreversible fouling). The critical fluxes for reversibility obtained in this study will aid the efficient operation of practical FO processes

ACTIVE SUSPENSION CONTROL WITH DIRECT-DRIVE TUBULAR LINEAR BRUSHLESS PERMANENT-MAGNET MOTOR

Recently, active suspension has been applied to many commercial automobiles. To develop the control algorithm for active suspension, a quarter-car test bed was built by using a direct-drive tubular linear brushless permanent-magnet motor (LBPMM) as a force-generating component. Two accelerometers and a linear variable differential transformer (LVDT) are used in this quarter-car test bed. Three pulse-width-modulation (PWM) amplifiers supply the currents in three phases. Simulated road disturbance is generated by a rotating cam. Modified lead-lag control, linear-quadratic (LQ) servo control with a Kalman filter, and the fuzzy control methodologies were implemented for active-suspension control. In the case of fuzzy control, asymmetric membership functions were introduced. This controller could attenuate road disturbance by up to 78%. Additionally, a sliding-mode controller (SMC) is developed with a different approach from the other three control methodologies. While SMC is developed for the position control, the other three controllers are developed for the velocity control. SMC showed inferior performance due to the drawback of the implemented chattering-proof method. Both simulation and experimental results are presented to demonstrate the effectiveness of these four control methodologies

Three Essays on Current International Financial Markets

This dissertation consists of three essays that address recent developments in international financial markets that have been of concern for scholars, policymakers, and practitioners. The first essay examines how cultural factors can influence individual investors’ trading behavior in response to risk in nine Eurozone countries. The markets studied were particularly affected by the global financial crisis, the subsequent European banking crisis, and the European sovereign debt crisis. Using mutual fund flows as proxy of investors’ trading behavior, our evidence indicates that a country culture variable significantly affects investors’ trading responsiveness to risk. Specifically, the impact of risk on fund flows is significantly positive and is larger in scale in countries with individualist cultures. The second essay attempts to investigate the effects of negative interest rate policies (NIRP) on foreign exchange and equity markets of eight European countries and Japan. To see the impacts of these policies, event studies and regime-switching vector autoregressive regression analyses are conducted for the nine countries that implement NIRP. The results provide valid evidence that the announcement of NIRP has a transitory effect on currency depreciation; long term effects are less evident. On the day of NIRP implementation, both currency and equity market returns reacted in response to the event efficiently and negatively, especially in Switzerland’s case. These outcomes suggest that simulative monetary policy by lowering interest rates below zero might have counter-effects from those observed when interest rates are lowered, but to rates that remain positive. Additionally, findings from the long term analyses explain that interest rate term structure and cointegration level of local and the U.S. equity index may be related to effectiveness of NIRP in currency and equity markets, respectively. The last essay examines the determinants of the price of the leading cryptocurrency, Bitcoin. The analyses identify a number of factors that significantly affect the returns to investments in Bitcoin including: trading volume, high-low price spread, and extreme price change in the previous period. The latter result supports the assertion that recent severe price fluctuations in Bitcoin markets are primarily due to speculative investment activities. Furthermore, evidences suggested in this study explain possibility of market compromise and inefficiency of the cryptocurrency market, implying pivotal risks for Bitcoin market participants