Alcohol, binge drinking and associated mental health problems in young urban Chileans

OBJECTIVE: To explore the link between alcohol use, binge drinking and mental health problems in a representative sample of adolescent and young adult Chileans. METHODS: Age and sex-adjusted Odds Ratios (OR) for four mental wellbeing measures were estimated with separate conditional logistic regression models for adolescents aged 15-20 years, and young adults aged 21-25 years, using population-based estimates of alcohol use prevalence rates from the Chilean National Health Survey 2010. RESULTS: Sixty five per cent of adolescents and 85% of young adults reported drinking alcohol in the last year and of those 83% per cent of adolescents and 86% of young adults reported binge drinking in the previous month. Adolescents who reported binging alcohol were also more likely, compared to young adults, to report being always or almost always depressed (OR 12.97 [95% CI, 1.86-19.54]) or to feel very anxious in the last month (OR 9.37 [1.77-19.54]). Adolescent females were more likely to report poor life satisfaction in the previous year than adolescent males (OR 8.50 [1.61-15.78]), feel always or almost always depressed (OR 3.41 [1.25-9.58]). Being female was also associated with a self-reported diagnosis of depression for both age groups (adolescents, OR 4.74 [1.49-15.08] and young adults, OR 4.08 [1.65-10.05]). CONCLUSION: Young people in Chile self-report a high prevalence of alcohol use, binge drinking and associated mental health problems. The harms associated with alcohol consumption need to be highlighted through evidence-based prevention programs. Health and education systems need to be strengthened to screen and support young people. Focussing on policy initiatives to limit beverage companies targeting alcohol to young people will also be needed

Functional regression control chart for monitoring ship CO2 emissions

On modern ships, the quick development in data acquisition technologies is producing data-rich environments where variable measurements are continuously streamed and stored during navigation and thus can be naturally modelled as functional data or profiles. Then, both the CO (Formula presented.) emissions (i.e. the quality characteristic of interest) and the variable profiles that have an impact on them (i.e. the covariates) are called to be explored in the light of the new worldwide and European regulations on the monitoring, reporting and verification of harmful emissions. In this paper, we show an application of the functional regression control chart (FRCC) with the ultimate goal of answering, at the end of each ship voyage, the question: given the value of the covariates, is the observed CO (Formula presented.) emission profile as expected? To this aim, the FRCC focuses on the monitoring of residuals obtained from a multivariate functional linear regression of the CO (Formula presented.) emission profiles on the functional covariates. The applicability of the FRCC is demonstrated through a real-case study of a Ro-Pax ship operating in the Mediterranean Sea. The proposed FRCC is also compared with other alternatives available in the literature and its advantages are discussed over some practical examples

Ship fuel consumption monitoring and fault detection via partial least squares and control charts of navigation data

New regulations in the shipping sector aim to give greater transparency to operations and public access to CO2 emissions data. EU regulation 2015/757 became mandatory in January 2018 and urges shipping companies to set up systems for daily monitoring, reporting and verification (MRV) of emissions for individual ships. Manual acquisition and handling of emissions data may be allowed (e.g. bunker fuel delivery note, bunker fuel tank monitoring), but is adversely affected by uncertainty due to human intervention and will eventually be unusable for monitoring purposes. However, the massive amounts of navigation data acquired by multi-sensor systems installed on-board of modern ships have great potential to aid compliance with regulations but their use is hampered by the lack of effective analytical methods in the maritime literature. This work demonstrates a statistical framework and automatic reporting system for fuel consumption monitoring that addresses the MRV requirements needed to comply with the regulations. The framework has been applied to the Grimaldi Group’s Ro-Ro Pax cruise ships and is shown, in addition, to be capable of supporting fault detection as well as verifying CO2 savings achieved after energy efficiency initiatives

Additive stacking for disaggregate electricity demand forecasting

Future grid management systems will coordinate distributed production and storage resources to manage, in a cost effective fashion, the increased load and variability brought by the electrification of transportation and by a higher share of weather dependent production. Electricity demand forecasts at a low level of aggregation will be key inputs for such systems. We focus on forecasting demand at the individual household level, which is more challenging than forecasting aggregate demand, due to the lower signal-to-noise ratio and to the heterogeneity of consumption patterns across households. We propose a new ensemble method for probabilistic forecasting which borrows strength across the households while accommodating their individual idiosyncrasies. In particular, we develop a set of models or “experts” which capture different demand dynamics, and we fit each of them to the data from each household. Then, we construct an aggregation of experts where the ensemble weights are estimated on the whole data set, the main innovation being that we let the weights vary with the covariates by adopting an additive model structure. In particular, the proposed aggregation method is an extension of regression stacking where themixture weights are modelled using linear combinations of parametric, smooth or random effects. The methods for building and fitting additive stacking models are implemented by the gamFactory R package, available at https://github.com/mfasiolo/gamFactory