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Pepper Pike, OH, United States

For the English Catholic Sports College, based in Westgate, see Ursuline College, Westgate-on-Sea.Ursuline College is a small, Roman Catholic liberal arts women's college in Pepper Pike, Ohio, United States. It was founded in 1871 by the Ursuline Sisters of Cleveland and is one of the oldest institutions of higher education for women in the United States. Ursuline College offers a diverse spectrum of undergraduate and graduate studies within the Catholic tradition of education. The College offers 30 undergraduate, 11 graduate programs, and a Doctorate of Nursing Practice. Although Ursuline College is considered to be a college that focuses primarily on the liberal arts, the institution also offers courses such as nursing and business administration. The school is widely recognized for its Art Therapy program, Breen School of Nursing, and teaching certification program. The campus is situated approximately 10 miles outside of Cleveland and 30 miles outside of Akron. Ursuline's campus is quite spacious and meticulously landscaped, featuring 12 educational buildings such as the Matthew J. O'Brien Recreation Complex, and the newer Bishop Anthony M. Pilla Center. The Pilla Center is quintessentially the essence of the Ursuline College Campus, acting as the social catalyst for students to meet and exchange ideas in the confines of a spectacularly engineered building. The main gathering space has a delightful view of the lake, and is two stories high with crystalline glass windows on adjacent sides, and a luminescent stained glass window that faces the main quadrangle. The building provides a space for commuters on the go, as well as resident students and faculty, for a place to converse or grab some sustenance on the way to class. Additionally, the college's Florence O'Donnell Wasmer Gallery is host to changing display of both professional and student artwork exhibits, and remains open for public consumption Tuesday through Sunday in the afternoon. Wikipedia.

Wei L.-Y.,Yuanpei University | Chen T.-L.,Ursuline College | Ho T.-H.,Yuanpei University
Expert Systems with Applications | Year: 2011

In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle (1982) and Cheng, Chen, and Wei (2010). After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network- based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen's and Yu's models). © 2011 Elsevier Ltd. All rights reserved. © 2011 Elsevier Ltd. All rights reserved.

Lee H.-C.,Ursuline College | Chen T.-F.,National Chung Cheng University
Computers and Mathematics with Applications | Year: 2010

The paper concerns a nonlinear weighted least-squares finite element method for the solutions of the incompressible Stokes equations based on the application of the least-squares minimization principle to an equivalent first order velocity-pressure-stress system. Model problem considered is the flow in a planar channel. The least-squares functional involves the L 2-norms of the residuals of each equation multiplied by a nonlinear weighting function and mesh dependent weights. Using linear approximations for all variables, by properly adjusting the importance of the mass conservation equation and a carefully chosen nonlinear weighting function, the least-squares solutions exhibit optimal L 2-norm error convergence in all unknowns. Numerical solutions of the flow pass through a 4 to 1 contraction channel will also be considered. © 2009 Elsevier Ltd. All rights reserved.

Wu C.-F.,Ursuline College | Lin C.-J.,National Chin - Yi University of Technology
International Journal of Innovative Computing, Information and Control | Year: 2013

This study proposes a real-time video stabilization method to eliminate unwanted vibration, preserve the intended movement of camera, and improve the stability of the captured video sequence. The proposed method uses a functional neural fuzzy network to learn the characteristics of different vibrations and then choose the adequate compen-sation weight for two different methods to calculate the correction vector. Experimental results show that the proposed method has superior performance over other motion com-pensation methods. © 2013 ICIC International.

Cheng C.-H.,National Yunlin University of Science and Technology | Chen T.-L.,Ursuline College | Wei L.-Y.,Yuanpei University
Information Sciences | Year: 2010

In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized. In order to overcome these drawbacks, this paper proposes a hybrid forecasting model, using multi-technical indicators to predict stock price trends. Further, it includes four proposed procedures in the hybrid model to provide efficient rules for forecasting, which are evolved from the extracted rules with high support value, by using the toolset based on rough sets theory (RST): (1) select the essential technical indicators, which are highly related to the future stock price, from the popular indicators based on a correlation matrix; (2) use the cumulative probability distribution approach (CDPA) and minimize the entropy principle approach (MEPA) to partition technical indicator value and daily price fluctuation into linguistic values, based on the characteristics of the data distribution; (3) employ a RST algorithm to extract linguistic rules from the linguistic technical indicator dataset; and (4) utilize genetic algorithms (GAs) to refine the extracted rules to get better forecasting accuracy and stock return. The effectiveness of the proposed model is verified with two types of performance evaluations, accuracy and stock return, and by using a six-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) as the experiment dataset. The experimental results show that the proposed model is superior to the two listed forecasting models (RST and GAs) in terms of accuracy, and the stock return evaluations have revealed that the profits produced by the proposed model are higher than the three listed models (Buy-and-Hold, RST and GAs). © 2010 Elsevier Inc. All rights reserved.

Sharpnack P.A.,Ursuline College
Journal of holistic nursing : official journal of the American Holistic Nurses' Association | Year: 2011

Self-transcendence, the ability to expand one's relationship to others and the environment, has been found to provide hope which helps a person adapt and cope with illness. Spiritual well-being, the perception of health and wholeness, can boost self-confidence and self esteem. The purpose of this descriptive correlational study was to describe the relationship between self-transcendence and spiritual well-being in adult Amish. A random sample of Old Order Amish was surveyed by postal mail; there were 134 respondents. Two valid and reliable questionnaires were used to measure the key variables. The participants had high levels of self-transcendence and spiritual well-being and there was a statistically significant positive relationship between the two variables. The findings from this study will increase nurses' awareness of the holistic nature of the Amish beliefs and assist nurses in serving this population. Additional research is needed to develop further understanding of the study variables among the Amish.

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