New Isomers in the Full Seniority Scheme of Neutron-Rich Lead Isotopes: The Role of Effective Three-Body Forces

The neutron-rich lead isotopes, up to Pb-216, have been studied for the first time, exploiting the fragmentation of a primary uranium beam at the FRS-RISING setup at GSI. The observed isomeric states exhibit electromagnetic transition strengths which deviate from state-of-the-art shell-model calculations. It is shown that their complete description demands the introduction of effective three-body interactions and two-body transition operators in the conventional neutron valence space beyond Pb-208.

Amphetamine, cocaine and cannabinoids use among truck drivers on the roads in the State of Sao Paulo, Brazil

Drugs are important risk factors for traffic accidents. In Brazil, truck drivers report using amphetamines to maintain their extensive work schedule and stay awake. These drugs can be obtained without prescription easily on Brazilian roads. The use of these stimulants can result in health problems and can be associated with traffic accidents. There are Brazilian studies that show that drivers use drugs. However, these studies are questionnaire-based and do not always reflect real-life situations. The purpose of this study was to demonstrate the prevalence of drug use by truck drivers on the roads of Sao Paulo State, Brazil, during 2009. Drivers of large trucks were randomly stopped by police officers on the interstate roads during morning hours. After being informed of the goals of the study, the drivers gave written informed consent before providing a urine sample. In addition, a questionnaire concerning sociodemographic characteristics and health information was administered. Urine samples were screened for amphetamines, cocaine, and cannabinoids by immunoassay and the confirmation was performed using gas chromatography-mass spectrometry (GC-MS). Of the 488 drivers stopped, 456 (93.4%) provided urine samples, and 9.3% of them (n = 42) tested positive for drugs. Amphetamines were the most commonly found (n = 26) drug, representing 61.9% of the positive samples. Ten cases tested positive for cocaine (23.8%), and five for cannabinoids (11.9%). All drivers were male with a mean age of 40 +/- 10.8 years, and 29.3% of them reported some health problem (diabetes, high blood pressure and/or stress). A high incidence of truck drivers who tested positive for drug use was found, among other reported health problems. Thus, there is an evident need to promote a healthier lifestyle among professional drivers and a need for preventive measures aimed at controlling the use of drugs by truck drivers in Brazil. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.