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Background Scientifically sound and valid information concerning course and prediction of mental health problems in children and adolescents in the general population is scarce, although needed for public mental health issues and daily clinical practice. Objectives The psychopathological profiles of children and adolescents were analysed using the parent version of the Strengths and Difficulties Questionnaire (SDQ-P) in a longitudinal setting, also investigating the predictive value of the SDQ-scores. Methods SDQ’s total psychopathological difficulties, emotional symptoms and hyperactivity-inattention scores of n  = 630 children and adolescents (age 6–18;11 years) were examined along four assessment measurement points (T 0 –T 3 ) over 6 years, using data from the BELLA study. According to the English normative data, the participants were categorized as “normal”, “borderline” or “abnormal” based on their SDQ-scores. Groups remaining within categories were descriptively determined by means of frequency analysis, a subsequent graphical evaluation displayed the transitions from T 0 to T 3 concerning the different categorical classifications. Finally, ordered probit regression was used to examine whether age, gender, socio-economic status (SES) and baseline impact-score (IS) correspond to the SDQ-predicted classification. Results As expected, low SES and high SDQ-IS were associated with significantly increased scores on all examined SDQ-scales. Regarding the long-term aspect of SDQ-scores it could be shown that most children and adolescents remained “normal” over a measurement period of 6 years, while only a small number of children and adolescents steadily remained “abnormal” or newly developed mental health problems, respectively. For example, on the “hyperactivity-inattention”-scale, only 1 % of the children and adolescents changed from “normal” to “abnormal” (T 0 –T 3 ), whereas on the “emotional symptoms”-scale, 7 % changed from “normal” to “abnormal” (T 0 –T 3 ). In general, the SDQ-category “borderline” and specifically the subscale “emotional symptoms” change in both directions. Abnormal SDQ-scores at baseline, SES, gender and IS were related to the prediction of the SDQ-sores at T3. Conclusion An SDQ-screening of children and adolescents may help for early detection, prediction and treatment planning. Also, these results may contribute to a better understanding of the course of mental health problems in childhood and concurrently may allow a better psychoeducation and prevention.

Hidden Markov models (HMMs) are flexible time series models in which the distribution of the observations depends on unobserved serially correlated states. The state-dependent distributions in HMMs are usually taken from some class of parametrically specified distributions. The choice of this class can be difficult, and an unfortunate choice can have serious consequences for example on state estimates, and more generally on the resulting model complexity and interpretation. We demonstrate these practical issues in a real data application concerned with vertical speeds of a diving beaked whale, where we demonstrate that parametric approaches can easily lead to overly complex state processes, impeding meaningful biological inference. In contrast, for the dive data, HMMs with nonparametrically estimated state-dependent distributions are much more parsimonious in terms of the number of states and easier to interpret, while fitting the data equally well. Our nonparametric estimation approach is based on the idea of representing the densities of the state-dependent distributions as linear combinations of a large number of standardized B-spline basis functions, imposing a penalty term on non-smoothness in order to maintain a good balance between goodness-of-fit and smoothness.

We propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of different functional effect types such as nonlinear effects, spatial effects, random coefficients, interaction surfaces or other (possibly nonstandard) basis function representations. To enforce specific properties of the functional effects such as smoothness, informative multivariate Gaussian priors are assigned to the basis function coefficients. Inference can then be based on computationally efficient Markov chain Monte Carlo simulation techniques where a generic procedure makes use of distribution-specific iteratively weighted least squares approximations to the full conditionals. The framework of distributional regression encompasses many special cases relevant for treating non-standard response structures such as highly skewed nonnegative responses, overdispersed and zero-inflated counts or shares including the possibility for zero- and one-inflation. We discuss distributional regression along a study on determinants of labour incomes for full-time working males in Germany with a particular focus on regional differences after the German reunification. Controlling for age, education, work experience and local disparities, we estimate full conditional income distributions allowing us to study various distributional quantities such as moments, quantiles or inequality measures in a consistent manner in one joint model. Detailed guidance on practical aspects of model choice including the selection of several competing distributions for labour incomes and the consideration of different covariate effects on the income distribution complete the distributional regression analysis. We find that next to a lower expected income, full-time working men in East Germany also face a more unequal income distribution than men in the West, ceteris paribus.

Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or Student-t-distributed returns, given the volatility, has however been questioned. In this manuscript, we introduce a novel maximum penalized likelihood approach for estimating the conditional distribution in an SV model in a nonparametric way, thus avoiding any potentially critical assumptions on the shape. The considered framework exploits the strengths both of the hidden Markov model machinery and of penalized B-splines, and constitutes a powerful alternative to recently developed Bayesian approaches to semiparametric SV modelling. We demonstrate the feasibility of the approach in a simulation study before outlining its potential in applications to three series of returns on stocks and one series of stock index returns.

We explore the application of structured additive distributional regression for the analysis of conditional income distributions in Germany following the reunification using the German Socio Economic Panel (SOEP) database. This methodology allows us to explore both between and within income inequality at a highly disaggregated level. Using a bootstrapped version of the Kolmogorov-Smirnov test, we find that conditional personal income distributions can generally be modelled using a mixture distribution entailing the three parameter Dagum distribution. JEL Classification: C13, C21, D31, J31

A comprehensive field investigation was conducted at Potrero Hills Landfill (PHL) located in Suisun City, California to quantify emissions of twelve (hydro)chlorofluorocarbons (i.e. F-gases). The specific target constituents for this study included CFC-11, CFC-12, CFC-113, CFC-114, HCFC-21, HCFC-22, HCFC-141b, HCFC-142b, HCFC-151a, HFC-134a, HFC-152a, and HFC-245fa. The majority of the F-gas emission studies have been conducted outside of the United States and very limited field landfill emission data are available in the United States. Because of historical usage of blowing agents in insulation foams including CFC-11, HCFC-142b, HFC-134a, and HFC-245fa, models reported in literature predicted high F-gas emissions from a landfill environment, but very limited field data are available to verify such predictions.In this investigation, the surface flux of the twelve F-gases, methane, and carbon dioxide was quantified from various landfill cover systems and in areas with different waste ages, waste heights, and cover thicknesses at Potrero Hills Landfill. In addition, destruction efficiencies for the twelve F-gases were determined based on inlet and outlet concentrations of the onsite flare system. Lastly, the surface flux values were scaled up to a facility-wide emission value to estimate the total fugitive emissions from the landfill.The F-gas flux values for the daily covers were in the 10-8 to 10-1 g m-2 day -1 range and 10-7 to 10-2 g m-2 day-1 range for the wet and dry season, respectively. The F-gas flux values for the intermediate covers in the -10-6 to 10-4 g m-2 day-1 range and -10-6 to 10-4 g m-2 day-1 range for the wet and dry season, respectively. The F-gas flux values for the final covers were in the 10-7 to 10-5 g m-2 day-1 range and -10-7 to 10-6 g m-2 day-1range for the wet and dry season, respectively. F-gas fluxes for the final covers had the highest number of below detection limit cases as well as lower than R2 threshold cases. Thest F-gas fluxes were measured from daily cover system constructed with auto shredder residue (i.e. auto fluff) for the both the wet and dry seasons. The highest fluxes were measured for CFC-11, HCFC-21, and HCFC-141b in the wet season and for CFC-11, HCFC-141b, and HFC-134a in the dry season across the seven cover locations.Lower level of variation was observed for methane and carbon dioxide with flux values ranging over five orders of magnitude for the seven tested locations. The methane flux values for the daily covers were in the 10-2 to 10+1 g m-2 d-1 range and 1 to 10+1 g m-2 day-1 range for the wet and dry season, respectively. The carbon dioxide flux values for the daily covers were in the -10+1 to 10+2 g m-2 day-1 range and -10+1 to 10+1 g m-2 day-1 range for the wet and dry season, respectively. The methane flux values for the intermediate covers were in the -10-2 to 10+1 g m-2 d -1 range and -10-3 to 10+1 g m-2 day-1range for the wet and dry season, respectively.

Erratum to: Eur Child Adolesc PsychiatryDOI 10.1007/s00787‑014‑0640‑x Unfortunately, the names of two authors, Ulrike Ravens- Sieberer and Fionna Klasen, were omitted in the original publication of the article. Please find the correct author list below:Andreas Becker · Aribert Rothenberger · Alexander Sohn · Ulrike Ravens-Sieberer · Fionna Klasen · The BELLA study group

We propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of different functional effect types such as nonlinear effects, spatial effects, random coefficients, interaction surfaces or other (possibly nonstandard) basis function representations. To enforce specific properties of the functional effects such as smoothness, informative multivariate Gaussian priors are assigned to the basis function coefficients. Inference can then be based on computationally efficient Markov chain Monte Carlo simulation techniques where a generic procedure makes use of distribution-specific iteratively weighted least squares approximations to the full conditionals. The framework of distributional regression encompasses many special cases relevant for treating nonstandard response structures such as highly skewed nonnegative responses, overdispersed and zero-inflated counts or shares including the possibility for zero- and one-inflation. We discuss distributional regression along a study on determinants of labour incomes for full-time working males in Germany with a particular focus on regional differences after the German reunification. Controlling for age, education, work experience and local disparities, we estimate full conditional income distributions allowing us to study various distributional quantities such as moments, quantiles or inequality measures in a consistent manner in one joint model. Detailed guidance on practical aspects of model choice including the selection of several competing distributions for labour incomes and the consideration of different covariate effects on the income distribution complete the distributional regression analysis. We find that next to a lower expected income, full-time working men in East Germany also face a more unequal income distribution than men in the West, ceteris paribus.

Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch.