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 & Context (language use). The organization has 5658 authors who have published 11562 publications receiving 295257 citations.

Risk management practices of German firms

Abstract: Identifies some gaps in corporate risk management research and presents a study of risk management practices in large, non‐financial German firms. Compares the perceived relevance of different types of risk with the intensity of their management and reports that no respondents admitted major difficulty in developing a risk management system. Finds that firm survival is rated as the top goal of risk management, that respondents are closer to risk‐neutral than risk‐averse for financial risks, that around half centralize treasury management and 88 per cent use derivatives. Ranks the types of derivatives used and the importance of associated problems; shows how foreign exchange risk, US $ exposure and interest rate risk are managed; and assesses attitudes towards foreign exchange and interest rate risk management. Considers consistency with other research and calls for more.

New strategies against Stenotrophomonas maltophilia: a serious worldwide intrinsically drug-resistant opportunistic pathogen

Abstract: Stenotrophomonas maltophilia is a worldwide human opportunistic pathogen associated with serious infections in humans, and is most often recovered from respiratory tract infections. In addition to its intrinsic drug resistance, this organism may acquire resistance via multiple molecular mechanisms. New antimicrobial strategies are needed to combat S. maltophilia infections, particularly in immunocompromised patients, cystic fibrosis patients with polymicrobial infections of the lung, and in patients with chronic infections. This editorial reports on newer drugs and antimicrobial strategies and their potential for use in treatment of S. maltophilia infections, the development of new technologies to detect this organism, and identifies strategies currently in use to reduce transmission of this pathogen.

Image Theory, Social Identity, and Social Dominance: Structural Characteristics and Individual Motives Underlying International Images

Abstract: The present study provides an empirical test of international relations image theory and extends the theory by emphasizing that individuals’ social identity and social dominance motives contribute to such images. One hundred forty-five Lebanese participants completed a survey that assessed their perceptions of U.S.-Lebanese relations, the images they have of the United States, their social identities, and their social dominance orientations. Participants were more likely to hold the barbarian image of the United States than the enemy, imperialist, or ally images. Participants also tended to perceive the United States as having relatively superior power, inferior cultural status, and goals that are incompatible with those of Lebanon. Consistent with image theory predictions, this constellation of structural perceptions was associated with stronger endorsement of the barbarian image. Furthermore, participants were more likely to endorse the barbarian image of the United States the more they identified with Arabs and Palestinians, the less they identified with Christians and the Western world, and the lower their social dominance orientation. Results highlight the importance of considering both structural characteristics and individual motives underlying international images and demonstrate the need for scholars to move beyond the enemy image of nations when describing international relations.

Improving the Effectiveness of Collaborative Filtering on Anonymous Web Usage Data

Abstract: Recommender systems based on collaborative filtering usually require real-time comparison of users’ ratings on objects In the context of Web personalization, particularly at the early stages of a visitor’s interaction with the site (ie, before registration or authentication), recommender systems must rely on anonymous clickstream data The lack of explicit user ratings and the shear amount of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance Offline clustering of users transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy In this paper we study the impact of various preprocessing techniques applied to clickstream data, such as clustering, normalization, and significance filtering, on collaborative filtering Our experimental results, performed on real usage data, indicate that with proper data preparation, the clustering-based approach to collaborative filtering can achieve dramatic improvements in terms of recommendation effectiveness, while maintaining the computational advantage over the direct approaches such as the k-NearestNeighbor technique