The silver linings of lottery play: motivation and subjective well-being of British lottery participants

Although certain researchers have attributed widespread lottery play to irrational beliefs that people hold regarding the chances of winning the lottery, another explanation for the popularity of lottery gambling is that lottery players may experience positive emotions before the draw. Therefore, in this study, we examine the relationship between lottery participation and happiness. Using data from the British Gambling Prevalence Survey 2010 and utilizing propensity score matching methods, we find a small positive effect of lottery participation on happiness for individuals who engage in lottery play for recreational purposes

Unsuccessful Subjective Well-Being Assimilation Among Immigrants: The Role of Faltering Perceptions of the Host Society

Immigrants in developed countries typically fail to assimilate in terms of their subjective well-being, meaning that their happiness and life satisfaction do not substantially increase with their length of stay or across generations, and therefore their subjective well-being remains lower than that of natives. This finding contrasts with migrants’ own expectations and the predictions of straight-line assimilation theory, along with the general improvement of immigrants’ objective living conditions with their length of stay. Using European Social Survey data, we show that the gradual development of less positive perceptions of the host country’s economic, political, and social conditions is associated with less positive subjective well-being trajectories among first generation immigrants and across migrant generations in developed European countries. This negative association is particularly strong for immigrants whose societal conditions strongly improved by migration and immigrants who arrived after childhood. However, compared with natives, the more positive societal perceptions of first-generation immigrants are associated with a subjective well-being advantage. We attribute these findings to immigrants’ growing aspirations and expectations that follow from their habituation to better conditions in their host country and fewer (more) comparisons to the inferior (better) conditions of the people in their home (host) country. Our findings suggest that delaying or decelerating the process of immigrants’ faltering societal perceptions is a promising pathway to improved subjective well-being assimilation and reduced frustration about their perceived lack of progress

Steering towards happiness: An experience sampling study on the determinants of happiness of truck drivers

The relatively low levels of employee well-being reported among truck drivers directly relate to some of the key challenges faced in the transportation industry, including high turnover of staff and difficulties attracting people to the profession. Drawing on the job demands-resources model, this study addresses this problem by examining how various state-like and trait-like job demands and resources relate to truck drivers’ momentary happiness at work. Using an experience sampling study comprising 82 Dutch truck drivers, truck drivers were found to be happier during off-job activities and non-work-related job activities, such as breaks, than during work-related job activities. Furthermore, this study shows that road congestion aggravates the inverse relationship between work-related job activities and momentary happiness. Social support of colleagues and flexible work hours alleviate this relationship. These findings provide valuable information to the industry about the road to happiness for truck drivers

Quadruple junction polymer solar cells with four complementary absorber layers

A monolithic two‐terminal solution‐processed quadruple junction polymer solar cell in an n–i–p (inverted) configuration with four complementary polymer:fullerene active bulk‐heterojunction layers is presented. The subcells possess different optical bandgaps ranging from 1.90 to 1.13 eV. Optical modeling using the transfer matrix formalism enables prediction of the fraction of absorbed photons from sunlight in each subcell and determine the optimal combination of layer thicknesses. The quadruple junction cell features an open‐circuit voltage of 2.45 V and has a power conversion efficiency of 7.6%, only slightly less than the modeled value of 8.2%. The external quantum efficiency spectrum, determined with appropriate light and voltage bias conditions, exhibits in general an excellent agreement with modeled spectrum. The device performance is presently limited by bimolecular recombination, which prevents using thick photoactive layers that could absorb light more efficiently

Why are Locals Happier than Internal Migrants? The Role of Daily Life

Several survey studies have found that internal migrants report lower levels of happiness than locals, even after accounting for socio-economic factors. Traditional global self-ratings reveal that the migrant–local happiness-gap is also present in the data we present. The reasons for the migrant–local happiness-gap are as yet unclear. This paper aims to open this ‘black box’ by exploring the role of daily activities among a population that has generally been overlooked despite their high migration frequency: young adults. An innovative smartphone application is used that combines two techniques for multiple moment assessment: the experience sampling method and the day reconstruction method. Based on the application data, we examine whether internal migrants spend their time differently than locals and in which situations they feel noticeably less happy than locals. The data reveal that internal migrants distribute less time to happiness-producing activities such as active leisure, social drinking/parties, and activities outside home/work/transit. Internal migrants feel less happy than locals when spending time with friends and while eating. Possible explanations focusing on the role of social capital are discussed. Further analyses reveal that daily life experiences greatly enhance the explanation of the migrant–local happiness-gap. This paper demonstrates the potential value of real-time data and phone applications in solving happiness puzzles

Estimating particulate matter health impact related to the combustion of different fossil fuels

International audienceExposure to particulate matter (PM) in ambient air leads to adverse health effects. To design cost effective mitigation strategies, a thorough understanding of the sources of particulate matter is crucial. We have successfully generated a web map service that allows to access information on fuel dependent health effects due to particulate matter. For this purpose, the LOTOS-EUROS air pollution model was equipped with a source apportionment module that tracks the origin of the modelled particulate matter distributions throughout a simulation. Combined with a dedicated emission inventory PM2.5 maps specified by fuel type were generated for 2007-2009. These maps were combined with a health impact calculation to estimate Lost of Life Expectancy for each fuel categories. An user friendly web client was generated to access the results and use the web mapping service in an easy manner

Curriculum vitae of the LOTOS-EUROS (v2.0) chemistry transport model

The development and application of chemistry transport models has a long tradition. Within the Netherlands the LOTOS–EUROS model has been developed by a consortium of institutes, after combining its independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the curriculum vitae of the model system, describing the model's history, model philosophy, basic features and a validation with EMEP stations for the new benchmark year 2012, and presents cases with the model's most recent and key developments. By setting the model developments in context and providing an outlook for directions for further development, the paper goes beyond the common model description. With an origin in ozone and sulfur modelling for the models LOTOS and EUROS, the application areas were gradually extended with persistent organic pollutants, reactive nitrogen, and primary and secondary particulate matter. After the combination of the models to LOTOS–EUROS in 2005, the model was further developed to include new source parametrizations (e.g. road resuspension, desert dust, wildfires), applied for operational smog forecasts in the Netherlands and Europe, and has been used for emission scenarios, source apportionment, and long-term hindcast and climate change scenarios. LOTOS–EUROS has been a front-runner in data assimilation of ground-based and satellite observations and has participated in many model intercomparison studies. The model is no longer confined to applications over Europe but is also applied to other regions of the world, e.g. China. The increasing interaction with emission experts has also contributed to the improvement of the model's performance. The philosophy for model development has always been to use knowledge that is state of the art and proven, to keep a good balance in the level of detail of process description and accuracy of input and output, and to keep a good record on the effect of model changes using benchmarking and validation. The performance of v2.0 with respect to EMEP observations is good, with spatial correlations around 0.8 or higher for concentrations and wet deposition. Temporal correlations are around 0.5 or higher. Recent innovative applications include source apportionment and data assimilation, particle number modelling, and energy transition scenarios including corresponding land use changes as well as Saharan dust forecasting. Future developments would enable more flexibility with respect to model horizontal and vertical resolution and further detailing of model input data. This includes the use of different sources of land use characterization (roughness length and vegetation), detailing of emissions in space and time, and efficient coupling to meteorology from different meteorological models

On the feasibility of using TCR sequencing to follow a vaccination response – lessons learned

T cells recognize pathogens by their highly specific T-cell receptor (TCR), which can bind small fragments of an antigen presented on the Major Histocompatibility Complex (MHC). Antigens that are provided through vaccination cause specific T cells to respond by expanding and forming specific memory to combat a future infection. Quantification of this T-cell response could improve vaccine monitoring or identify individuals with a reduced ability to respond to a vaccination. In this proof-of-concept study we use longitudinal sequencing of the TCRβ repertoire to quantify the response in the CD4+ memory T-cell pool upon pneumococcal conjugate vaccination. This comes with several challenges owing to the enormous size and diversity of the T-cell pool, the limited frequency of vaccine-specific TCRs in the total repertoire, and the variation in sample size and quality. We defined quantitative requirements to classify T-cell expansions and identified critical parameters that aid in reliable analysis of the data. In the context of pneumococcal conjugate vaccination, we were able to detect robust T-cell expansions in a minority of the donors, which suggests that the T-cell response against the conjugate in the pneumococcal vaccine is small and/or very broad. These results indicate that there is still a long way to go before TCR sequencing can be reliably used as a personal biomarker for vaccine-induced protection. Nevertheless, this study highlights the importance of having multiple samples containing sufficient T-cell numbers, which will support future studies that characterize T-cell responses using longitudinal TCR sequencing

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

AimTo identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient-centred care. MethodsWe conducted a retrospective study in two cohorts, using routinely collected individual patient data from primary care practices obtained from two large Dutch diabetes patient registries. Participants included adult patients newly diagnosed with type 2 diabetes between January 2006 and December 2014 (development cohort, n=10528; validation cohort, n=3777). Latent growth mixture modelling identified distinct glycaemic 5-year trajectories. Machine learning models were built to predict the trajectories using easily obtainable patient characteristics in daily clinical practice. ResultsThree different glycaemic trajectories were identified: (1) stable, adequate glycaemic control (76.5% of patients); (2) improved glycaemic control (21.3% of patients); and (3) deteriorated glycaemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. Body mass index and glycated haemoglobin and triglyceride levels were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver-operating characteristic area under the curve of 0.96 in the validation cohort, indicating excellent accuracy. ConclusionsThe developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care