DePaul University

About: DePaul University is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 5658 authors who have published 11562 publications receiving 295257 citations.

Dividend Initiations and Asymmetric Information: A Hazard Model

Abstract: This paper investigates the dynamics of dividend policy using a hazard model. Specifically, the paper examines dividend initiations for a sample of firms that went public between 1990 and 1997. These dividend initiations are examined in the context of an alternative explanation based on the pecking order theory. The results indicate that the probability or the hazard rate of a dividend initiation is negatively related to both the level of asymmetric information and growth opportunities and positively related to the level of cash flow. These results are consistent with a pecking order explanation but inconsistent with a signaling explanation.

Black Markets: The Supply and Demand of Body Parts

Abstract: 1. Introduction Part I: 2. Institutional supply and demand 3. Nuances, judicial authority, and legal limits of altruism 4. Equal opportunity rationing: racial and economic disparities Part II. Legal Frameworks and Alternatives: 5. The legal process of procurement and allocation: regulatory frame 6. Presumed consent 7. Commodification Part III: 8. Tissue sales: an African American predicament?: critiquing the slavery and black body market comparison 9. The private and public financial transaction in tissue transplantation 10. African Americans and organ sales 11. Conclusion.

Wavelet-based texture classification of tissues in computed tomography

Abstract: The research presented in this article is aimed at developing an automated imaging system for classification of tissues in medical images. The article focuses on using texture analysis for the classification of tissues from CT scans. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest in the CT medical images and creation of a classifier that will automatically identify the various tissues. A comparative study of wavelets-based texture descriptors from three families of wavelets (Haar, Daubechies, Coiflets), coupled with the implementation of a decision tree classifier based on the Classification and Regression Tree (C&RT) approach is carried on. Preliminary results for a 3D data set from normal chest and abdomen CT scans are presented.

Towards automated requirements prioritization and triage

Abstract: Time-to-market deadlines and budgetary restrictions require stakeholders to carefully prioritize requirements and determine which ones to implement in a given product release. Unfortunately, existing prioritization techniques do not provide sufficient automation for large projects with hundreds of stakeholders and thousands of potentially conflicting requests and requirements. This paper therefore describes a new approach for automating a significant part of the prioritization process. The proposed method utilizes data-mining and machine learning techniques to prioritize requirements according to stakeholders’ interests, business goals, and cross-cutting concerns such as security or performance requirements. The effectiveness of the approach is illustrated and evaluated through two case studies based on the requirements of the Ice Breaker System, and also on a set of stakeholders’ raw feature requests mined from the discussion forum of an open source product named SugarCRM.