Mental health and educational attainment: How developmental stage matters

Developmental science suggests that the consequences of mental health problems for life-course outcomes may depend on the timing of their onset. This study investigated the extent to which mental health predicted educational attainment at ages 17, 20, and 25 and whether gender moderated the links between mental health and educational attainment. It used data from Next Steps, a nationally representative panel survey of individuals born in 1989/1990 in England (N = 15,594, 48% female, 33% ethnic minority). The findings suggest that differences in mental health were more consequential for educational attainment during adolescence than in young adulthood. On average, girls attained higher levels of education than boys, but gender did not moderate the role that mental health played for educational attainment. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Would you switch? Understanding intra-peak demand shifting among rail commuters

This study examines predictors of railway commuters' changes to departure time choice. Specifically, we sought to understand the impact of pre-departure information about in-carriage crowding on train choice behavior. We present the results of an online experiment, multiple-choice task, and a survey of UK rail commuters who regularly travel on crowded trains. Our findings show that most respondents are highly sensitive to crowding on trains. That notwithstanding, we identify a group of commuters who are free from constraints but do not use their flexibility to switch. This finding leads us to suggest further research into the decision-making processes of this specific sub-group of passengers to maximize the potential of personalized real-time and predictive provision of crowdedness information. Our study contributes insights relevant to practitioners grappling with innovative information provision to encourage operationally desirable behavior change among regular commuters

Adaptive Finite Element Method for Simulation of Optical Nano Structures

We discuss realization, properties and performance of the adaptive finite element approach to the design of nano-photonic components. Central issues are the construction of vectorial finite elements and the embedding of bounded components into the unbounded and possibly heterogeneous exterior. We apply the finite element method to the optimization of the design of a hollow core photonic crystal fiber. Thereby we look at the convergence of the method and discuss automatic and adaptive grid refinement and the performance of higher order elements

Application of statistical pattern recognition and deep learning for morphological classification in radio astronomy

Thesis (MSc)--Stellenbosch University, 2022.ENGLISH ABSTRACT: The morphological classification of radio sources is important to gain a full under standing of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citi zen scientists and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is Convolutional Neural Networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. Firstly, a proper analysis to identify whether overfitting occurs when training CNNs to perform radio galaxy morphological clas sification is needed. Secondly, a comparative study regarding the practical appli cability of the CNN architectures in literature is required. Both of these short comings are addressed in this thesis. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, compu tational complexity and mean per class accuracy. A ranking system based upon recognition and computational performance is proposed. MCRGNet, ATLAS and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.AFRIKAANSE OPSOMMING: Die morfologiese klassifikasie van radiobronne is belangrik om ’n volledige begrip van die evolusieprosesse binnein sterrestelsels te ontwikkel, asook die rol wat hul plaaslike omgewings hierin speel. As gevolg van die ingewikkelde aard van die probleem, asook die aantrekkingskrag daarvan vir “burgerwetenskaplikes” en die groot hoeveelhede data wat deur bestaande en opkomende radioteleskope gege nereer word, maak die morfologiese klassifikasie van radiobronne ’n ideale proef gebied vir die toepassing van masjienleertegnieke. ’n Benadering wat belowend lyk, is Konvolusionele Neurale Netwerke (KNNe). Literatuur ontbreek egter twee belangrike dinge as dit kom by KNNe en die morfologiese klassifikasie van radio sterrestelsels. Eerstens is daar ’n analise nodig rondom die identifikasie van oor passing wanneer KNNe afgerig word om radio sterrestelsels volgens morfologie te klassifiseer. Tweedens word ’n vergelykende studie oor die praktiese toepaslik heid van die KNN-argitekture in literatuur benodig. Albei hierdie tekortkominge word in hierdie tesis aagespreek. Veelvuldige prestasiemetings word vir laasgenoemde vergelykende studie gebruik, soos inferensietyd, modelkompleksiteit, berekeningkompleksiteit en gemiddelde akkuraatheid per klas. ’n Rangorde skema word voorgestel gebaseer op herkenning en berekeningsprestasie. MCRGNet, AT LAS en ConvXpress (nuwe bydrae) is die argitekture wat berekeningsvereistes en herkenningsprestasie die beste balanseer.Master

Antireflective nanotextures for monolithic perovskite silicon tandem solar cells

Recently, we studied the effect of hexagonal sinusoidal textures on the reflective properties of perovskite silicon tandem solar cells using the finite element method FEM . We saw that such nanotextures, applied to the perovskite top cell, can strongly increase the current density utilization from 91 for the optimized planar reference to 98 for the best nanotextured device period 500 nm and peak to valley height 500 nm , where 100 refers to the Tiedje Yablonovitch limit. [D. Chen et al., J. Photonics Energy 8, 022601, 2018 , doi 10.1117 1.JPE.8.022601] In this manuscript we elaborate on some numerical details of that work we validate an assumption based on the Tiedje Yablonovitch limit, we present a convergence study for simulations with the finite element method, and we compare different configurations for sinusoidal nanotexture

Parameter identification for soil simulation based on the discrete element method and application to small scale shallow penetration tests

The Discrete Element Method (DEM) is well-established and widely used in soil-tool interaction related applications. As for all simulation tools, a proper calibration of the model parameters is crucial. In this contribution, we present the parametrization procedure of the DEM software GRAnular Physics Engine (GRAPE), developed and implemented at Fraunhofer ITWM, and attempt to use two parametrized soil samples for the simulation of small scale shallow penetration tests. The results are compared to laboratory measurements

Feature Guided Training and Rotational Standardisation for the Morphological Classification of Radio Galaxies

State-of-the-art radio observatories produce large amounts of data which can be used to study the properties of radio galaxies. However, with this rapid increase in data volume, it has become unrealistic to manually process all of the incoming data, which in turn led to the development of automated approaches for data processing tasks, such as morphological classification. Deep learning plays a crucial role in this automation process and it has been shown that convolutional neural networks (CNNs) can deliver good performance in the morphological classification of radio galaxies. This paper investigates two adaptations to the application of these CNNs for radio galaxy classification. The first adaptation consists of using principal component analysis (PCA) during preprocessing to align the galaxies' principal components with the axes of the coordinate system, which will normalize the orientation of the galaxies. This adaptation led to a significant improvement in the classification accuracy of the CNNs and decreased the average time required to train the models. The second adaptation consists of guiding the CNN to look for specific features within the samples in an attempt to utilize domain knowledge to improve the training process. It was found that this adaptation generally leads to a stabler training process and in certain instances reduced overfitting within the network, as well as the number of epochs required for training.Comment: 20 pages, 17 figures, this is a pre-copyedited, author-produced PDF of an article accepted for publication in the Monthly Notices of the Royal Astronomical Societ

Increased fluorescence of PbS quantum dots in photonic crystals by excitation enhancement

We report on the enhanced fluorescence of lead sulfide quantum dots interacting with leaky modes of slab type silicon photonic crystals. The photonic crystal slabs were fabricated, supporting leaky modes in the near infrared wavelength range. Lead sulfite quantum dots which are resonant in the same spectral range were prepared in a thin layer above the slab. We selectively excited the leaky modes by tuning the wavelength and angle of incidence of the laser source and measured distinct resonances of enhanced fluorescence. By an appropriate experiment design, we ruled out directional light extraction effects and determined the impact of enhanced excitation. Three dimensional numerical simulations consistently explain the experimental findings by strong near field enhancements in the vicinity of the photonic crystal surface. Our study provides a basis for systematic tailoring of photonic crystals used in biological applications such as biosensing and single molecule detection, as well as quantum dot solar cells and spectral conversion application