Alcohol use in the first three years of bereavement: a national representative survey

BACKGROUND:Earlier results concerning alcohol consumption of bereaved persons are contradictory. The aim of the present study was to analyze the relationship between bereavement and alcohol consumption accounting for time and gender differences on a nationally representative sample from Hungary ("Hungarostudy Epidemiological Panel Survey", N = 4457)METHODS:Drinking characteristics of mourning persons (alcohol consumption, dependence symptoms, and harmful consequences of alcohol use) in the first three years of grief were examined among persons between 18-75 years using the Alcohol Use Disorders Identification Test (AUDIT).RESULTS:Men bereaved for one year scored higher on two dimensions of AUDIT (dependence symptoms and harmful alcohol use), while men bereaved for two years scored higher on all three dimensions of AUDIT compared to the non-bereaved. The rate of men clinically at-risk concerning alcohol consumption among the non-bereaved is 12.9%, and among men bereaved for one year is 18.4% (a non-significant difference), while 29.8% (p<0.001, OR=2,781) among men bereaved for two years. However, men bereaved for three years did not differ from the non-bereaved in their drinking habits. In case of bereaved women, again no difference was found with respect to alcohol use compared to the non-bereaved.CONCLUSION:Among bereaved men, the risk of alcohol related problems tends to be higher, which can be shown both among men bereaved for one year as well as men bereaved for two years. Considering the higher morbidity and mortality rates of bereaved men, alcohol consumption might play a mediator role. These facts draw attention to the importance of prevention, early recognition, and effective therapy of hazardous drinking in bereaved men

A Method to Construct an Extension of Fuzzy Information Granularity Based on Fuzzy Distance

In fuzzy granular computing, a fuzzy granular structure is the collection of fuzzy information granules and fuzzy information granularity is used to measure the granulation degree of a fuzzy granular structure. In general, the fuzzy information granularity characterizes discernibility ability among fuzzy information granules in a fuzzy granular structure. In recent years, researchers have proposed some concepts of fuzzy information granularity based on partial order relations. However, the existing forms of fuzzy information granularity have some limitations when evaluating the fineness/coarseness between two fuzzy granular structures. In this paper, we propose an extension of fuzzy information granularity based on a fuzzy distance measure. We prove theoretically and experimentally that the proposed fuzzy information granularity is the best one to distinguish fuzzy granular structures. ACM Computing Classification System (1998): I.5.2, I.2.6

Some NP-Complete Problems for Attribute Reduction in Consistent Decision Tables

Over recent years, the research of attribute reduction for general decision systems and, in particular, for consistent decision tables has attracted great attention from the computer science community due to the emerge of big data. It has been known that, for a consistent decision table, we can derive a polynomial time complexity algorithm for finding a reduct. In addition, finding redundant properties can also be done in polynomial time. However, finding all reduct sets in a consistent decision table is a problem with exponential time complexity. In this paper, we study complexity of the problem for finding a certain class of reduct sets. In particular, we make use of a new concept of relative reduct in the consistent decision table. We present two NP-complete problems related to the proposed concept. These problems are related to the cardinality constraint and the relative reduct set. On the basis of this result, we show that finding a reduct with the smallest cardinality cannot be done by an algorithm with polynomial time complexity

An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database

Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation

On the Time Complexity of the Problem Related to Reducts of Consistent Decision Tables

In recent years, rough set approach computing issues concerning reducts of decision tables have attracted the attention of many researchers. In this paper, we present the time complexity of an algorithm computing reducts of decision tables by relational database approach. Let DS = (U, C ∪ {d}) be a consistent decision table, we say that A ⊆ C is a relative reduct of DS if A contains a reduct of DS. Let s = be a relation schema on the attribute set C ∪ {d}, we say that A ⊆ C is a relative minimal set of the attribute d if A contains a minimal set of d. Let Qd be the family of all relative reducts of DS, and Pd be the family of all relative minimal sets of the attribute d on s. We prove that the problem whether Qd ⊆ Pd is co-NP-complete. However, the problem whether Pd ⊆ Qd is in P