Optically-Nonactive Assorted Helices Array with Interchangeable Magnetic/Electric Resonance

We report here the designing of optically-nonactive metamaterial by assembling metallic helices with different chirality. With linearly polarized incident light, pure electric or magnetic resonance can be selectively realized, which leads to negative permittivity or negative permeability accordingly. Further, we show that pure electric or magnetic resonance can be interchanged at the same frequency band by merely changing the polarization of incident light for 90 degrees. This design demonstrates a unique approach to construct metamaterial.Comment: 15 pages, 4 figure

ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. The aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand

An Integrated Bus and Taxi Routes for a Mobile Trip Planning System

With the popular usage of Google Maps and smart phones, more and more people are using smart phones to surf and inquire about travel information. As a result, every major city plans to push the existing online public transportation trip planning system beyond traditional computer users to mobile phone users. The trip planning system is based on the starting and ending points that a user inputs, and guides the user to take a bus or metro through an electronic map interface. The system usually provides different kind of alternative travel routes with the estimated time of arrival. However, people who use the public transport system may encounter some uncertainties, such as long waiting times, long routes, long walking distances, etc. In each big city, the taxi is a universal transport vehicle which is available at almost anytime, anywhere. Taxis can save passengers’ walking distance and travel time with a deficit of high cost. Therefore, we design a trip planning system to unify the Taipei public transportation system with taxis. The users can inquire of a travel route through the mobile phones. This system uses Google Maps as a base map. The users assign an upper limit of fare which they are willing to pay. The system will balance between travel time and travel cost to obtain a route which may combine usage of the bus and taxi. Because of the high density of bus stations in Taipei city, the route search may consume a lot of system resources. We propose an improvement method to eliminate some intermediate bus stations in route search processing

Examining the online reading behavior and performance of fifth-graders: evidence from eye-movement data

Online reading is developing at an increasingly rapid rate, but the debate concerning whether learning is more effective when using hypertexts than when using traditional linear texts is still persistent. In addition, several researchers stated that online reading comprehension always starts with a question, but little empirical evidence has been gathered to investigate this claim. This study used eye-tracking technology and retrospective think aloud technique to examine online reading behaviors of fifth-graders (N = 50). The participants were asked to read four texts on the website. The present study employed a three-way mixed design: 2 (reading ability: high vs. low) 2 (reading goals: with vs. without) 2 (text types: hypertext vs. linear text). The dependent variables were eye-movement indices and the frequencies of using online reading strategy. The results show that fifth-graders, irrespective of their reading ability, found it difficult to navigate the nonlinear structure of hypertexts when searching for and integrating information. When they read with goals, they adjusted their reading speed and the focus of their attention. Their offline reading ability also influenced their online reading performance. These results suggest that online reading skills and strategies have to be taught in order to enhance the online reading abilities of elementary-school students

VaR and the cross-section of expected stock returns: an emerging market evidence

In this paper we investigate the explanatory power of the market beta, firm size, and the book-to-market ratio, as well as Value-at-Risk regarding the cross-sectional expected stock returns in a less developed stock market – Taiwan's stock market. The main purpose is to examine whether the Value-at-Risk factor has marginal explanatory power related to the Fama-French three-factor model. The empirical results show that Value-at-Risk can account for the average stock returns at both 1% and 5% significance levels based on cross-sectional regression analysis. Moreover, from the perspective of the time series regression, the Value-at-Risk factor can also demonstrate the variation of the stock market, especially for the larger companies in the Taiwan stock market

Data-driven Demand Control Ventilation Using Machine Learning CO2 Occupancy Detection Method

Heating, ventilation, and air-conditioning (HVAC) system accounts for approximately 40% of total building energy consumption in the United States. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a room excessively and result in a waste of energy. Previous studies show that CO2-based demand-controlled ventilation methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this manuscript, a data-driven control strategy was developed to optimize the energy consumption of supply fans by feed-forward neural network to predict real-time occupancy as an active constraint. As for the validation, the experiment was carried out in an auditorium located on a university campus. The result shows, after utilizing feed-forward neural network to enhance the occupancy estimation, the new primary fan schedule can reduce the daily ventilation energy by 75% when compared to the current on/off control