Assessment of Anger and Aggression in Male Offenders With Developmental Disabilities.

Systematic assessment of anger among people with developmental disabilities has been lacking, especially for hospital inpatients. Reliability and validity of anger self-report psychometric scales were investigated with 129 male patients, mostly forensic. Anger prevalence and its relationship to demographic, cognitive, and personality variables and to hospital assaultive behavior were examined. High internal and intermeasure consistency, and some concurrent validity with staff ratings, were found. Retrospective validity regarding physically assaultive behavior in the hospital was obtained. Hierarchical regressions revealed that patient-reported anger was a significant predictor of assaults postadmission, controlling for age, length of stay, IQ, violence offense history, and personality variables. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.     

CMV colitis masquerading as colon cancer--an unusual presentation of acquired immunodeficiency syndrome

To determine whether there is any correlation between sudden decrease in barometric pressure and onset of labor, a non-experimental, retrospective study at a 948-bed tertiary care hospital was done. Pregnant patients of 36 weeks gestation or more who presented with spontaneous onset of labor during the 48 hours surrounding the 12 occurrences of significant drop in barometric pressure in 1992 were included in the study. Significantly more occurrences of onset of labor were identified in the 24 hours after a drop in barometric pressure than were identified in the 24 hours prior to the drop in barometric pressure (P < 0.05). Therefore, the overall number of labor onsets increased in the 24 hours following a significant drop in barometric pressure.     

Fuzzy versus quantitative association rules: a fair data-driven comparison.

As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.     

A Diffusion-Compensation Relation for Normal Grain Growth and Grain-Boundary Diffusion of Oxygen in Oxides

Values of D₀/δ and Q for grain-boundary diffusion, derived from published studies of normal grain growth in Al₂O₃, BeO, CaO, MgO, SiO₂, and CaSiO₃, are fit by the linear compensation equation log D₀/δ=0.03170Q -7.6792 (r²=0.9384). Comparison of grain-boundary diffusion coefficients derived from grain growth in oxides with those obtained by direct experimental measurement suggests that the kinetics of normal grain growth are controlled by grain-boundary diffusion of oxygen.     

Logical foundations of nonmonotonic reasoning

In this paper we shall review the main appraches to nonmonotonic reasoning which we classify from the perspective of their underlying logical settings as classical, intuitionistic, three-valued/partial models, and conditional. We shall be placing special emphasis on some of the prominent approaches. We shall also give hints on potential future directions and emphasize that more theoretical work is still needed before a move to application is made.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced