Identifying sources of global contention in constraint satisfaction search

Much work has been done on learning from failure in search to boost solving of combinatorial problems, such as clause-learning and clause-weighting in boolean satisfiability (SAT), nogood and explanation-based learning, and constraint weighting in constraint satisfaction problems (CSPs). Many of the top solvers in SAT use clause learning to good effect. A similar approach (nogood learning) has not had as large an impact in CSPs. Constraint weighting is a less fine-grained approach where the information learnt gives an approximation as to which variables may be the sources of greatest contention. In this work we present two methods for learning from search using restarts, in order to identify these critical variables prior to solving. Both methods are based on the conflict-directed heuristic (weighted-degree heuristic) introduced by Boussemart et al. and are aimed at producing a better-informed version of the heuristic by gathering information through restarting and probing of the search space prior to solving, while minimizing the overhead of these restarts. We further examine the impact of different sampling strategies and different measurements of contention, and assess different restarting strategies for the heuristic. Finally, two applications for constraint weighting are considered in detail: dynamic constraint satisfaction problems and unary resource scheduling problems.

Caring and anxiety among registered nurses in an acute hospital setting

Background: Even though caring remains the essence of nursing it is still an ambiguous concept as the lens through which each nurse perceives caring differs. The differences are due to multiple factors including the setting in which the nurse works. Nurses experience high levels of anxiety when caring for patients in acute settings. Despite an abundance of published studies on caring there is a dearth of research available that focuses on the relationship between caring and anxiety. Aim: The aim of this research study was to investigate caring and anxiety in a sample of registered nurses working in an acute hospital and to determine the relationship between these and other variables. Method: A quantitative descriptive study using a correlational design was employed, with a sample of 280 registered nurses. The Caring Behaviours Inventory-24 was used to measure caring and the State Trait Anxiety Inventory to measure Anxiety. The study was guided by the Theory of Human Caring (Watson 2008). Findings: Nurses reported high levels of caring and low levels of anxiety. A statistical significant relationship was found between caring and anxiety and between caring and supportive work environment and job satisfaction. A statistical significant relationship was found between anxiety and work environment, job satisfaction gender, age, relationship status and education. Conclusion: This is the first study to investigate the relationship between caring and anxiety in an acute hospital setting. This research contributes to advancing nursing knowledge by providing evidence of the relationship between caring and anxiety among nurses in an acute hospital setting. Despite nurses reporting high levels of caring and low levels of anxiety, it is important to further enhance caring and reduce anxiety levels among all nurses. Thus, educators and managers need to explore strategies for the alleviation of anxiety among nurses, practising in acute care settings.