Changes in some health and lifestyle behaviours are significantly associated with changes in gambling behaviours: Findings from a longitudinal New Zealand population study.

Although a large number of studies have investigated associations between risky gambling behaviours and health, lifestyle and social factors, research has not focused on changes in these factors and associations with changes in gambling risk level. This study utilised existing data from the four waves of the longitudinal New Zealand National Gambling Study to examine associations between changes in substance use, mental and physical health, and quality of life and deprivation with changes in gambling risk level over time. A Markov chain transition model was used to perform these analyses using data from participants who had completed all four waves (11,080 data transitions).

Bias-Corrected CMIP5 Projections for Climate Change and Assessments of Impact on Malaria in Senegal under the VECTRI Model.

On the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. Extreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. This study aims to understand the impact of future climate change on malaria transmission using, for the first time in Senegal, the ICTP's community-based vector-borne disease model, TRIeste (VECTRI).

Coherent combination of micropulse tapered amplifiers at 828 nm for direct-detection LIDAR applications.

We report on the design of a compact master oscillator power amplifier (MOPA) diode laser architecture at 828 nm suitable for direct-detection LIDARs, specifically applied to water vapor differential absorption LIDARs. Coherent beam combination of two pulsed high-brightness tapered amplifiers (1 s, 10 kHz), seeded by a DBR laser diode, is demonstrated. The phase dynamics during the pulses have been thoroughly investigated.

Predicting veal-calf trading events in France.

Global trade has been ranked as one of the top five drivers of infectious disease threat events. More specifically, livestock trade is known to increase the speed at which infectious diseases circulate and to facilitate their dissemination over large distances Therefore, predicting animal movements arising from trade is crucial for assessing epidemic risk and the impact of preventive measures. In this study, we developed a statistical framework for predicting trading events using predictors accessible from routinely collected data.