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Although the thesis is an integral part of the study programme, the way the guidance for this work is supervised can highly differ between supervisors. Writing the thesis is a complicated process requiring tailored guidance, that is often not feasible due to increasing numbers of students and need for efficiency. One possible solution is working with thesis circles: small collaborative groups in which students, together with one or more supervisors, conduct research on the same subject or theme. Thesis circles offer opportunities for self-regulated learning, (peer) feedback and prevention of feelings of loneliness. In this study we construct a typology for thesis circles aimed at supervisors following an Educational Design Research (EDR) approach. Based on a literature review and interviews, we formulate the boundaries of a thesis circle and identify 23 design characteristics that determine an instantiation of a thesis circle. This is developed into a typology, that is evaluated in a focus group. Avenues for future research are presented. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a hybrid nanocomposite consisting of a conducting polymer and gold nanoparticles (AuNPs) is fabricated onto a screen-printed carbon electrode (SPCE). A thin layer of poly(3,4-ethylenedioxythiophene) (PEDOT) is coated electrochemically on a bare SPCE; then, the nano-sized AuNPs are embedded by electrochemical deposition. The resultant SPCE/PEDOT/AuNPs-modified electrode is characterized by electrochemical methods, field emission scanning electron microscopy (FE-SEM) and X-ray photoelectron spectroscopy (XPS). The SPCE/PEDOT/AuNPs-modified electrode possesses great catalytic activity for the oxidation of cysteine in various pH buffer solutions (pH 2.0–8.0). The selectivity of the method is demonstrated by the separation of the oxidation peaks at up to 240mV for cysteine and glutathione in pH 6.0 buffer solutions. The effects of the oxidizable interferences are also investigated. Flow-injection amperometry is performed for 0.5–200μM of cysteine in pH 4.0 buffer solutions, and a linear calibration plot with a slope of 0.115μA/μM is obtained. The detection limit (S/N =3) is 0.05μM. Additionally, the proposed methods obtain satisfactory results in the detection of cysteine-containing medicine samples. [Copyright &y& Elsevier]

The main purpose of this article is to demonstrate how halo effects may be detected and quantified using two independent ratings of the same person. A practical illustration is given to show how halo effects can be avoided. [ABSTRACT FROM PUBLISHER]

Epidemiology estimates how exposure to pollutants may impact human health. It often needs detailed determination of ambient concentrations to avoid exposure misclassification. However, it is unrealistic to collect pollutant data from each and every subject. Land-use regression (LUR) models have thus been used frequently to estimate individual levels of exposures to ambient air pollution. This paper used remote sensing and geographical information system (GIS) tools to develop ten regression models for PM2.5-bound compound concentration based on measurements of a six-year period including NH 4 + ,   SO 4 2 − ,   NO 3 − , OC, EC, Ba, Mn, Cu, Zn, and Sb. The explained variance (R2) of these LUR models ranging from 0.60 to 0.92 confirms that this study successfully estimated the fine spatial variability of PM2.5-bound compound concentrations in Taiwan where the distribution of traffic, industrial area, greenness, and culture-specific PM2.5 sources like temples collected from GIS and remote sensing data were main variables. In particular, while they were much less used, this study showcased the necessity of remote sensing data of greenness in future LUR studies for reducing the exposure bias. In terms of local residents' health outcome or health effect indicators, this study further offers much-needed support for future air epidemiological studies. The results provide important insights into expanding the application of GIS and remote sensing on exposure assessment for PM2.5-bound compounds. [ABSTRACT FROM AUTHOR]

It is likely that exposure surrogates from monitoring stations with various limitations are not sufficient for epidemiological studies covering large areas. Moreover, the spatiotemporal resolution of air pollution modelling approaches must be improved in order to achieve more accurate estimates. If not, the exposure assessments will not be applicable in future health risk assessments. To deal with this challenge, this study featured Land-Use Regression (LUR) models that use machine learning to assess the spatial-temporal variability of Nitrogen Dioxide (NO 2). Daily average NO 2 data was collected from 70 fixed air quality monitoring stations, belonging to the Taiwanese EPA, on the main island of Taiwan. Around 0.41 million observations from 2000 to 2016 were used for the analysis. Several datasets were employed to determine spatial predictor variables, including the EPA environmental resources dataset, the meteorological dataset, the land-use inventory, the landmark dataset, the digital road network map, the digital terrain model, MODIS Normalized Difference Vegetation Index database, and the power plant distribution dataset. Regarding analyses, conventional LUR and Hybrid Kriging-LUR were performed first to identify important predictor variables. A Deep Neural Network, Random Forest, and XGBoost algorithms were then used to fit the prediction model based on the variables selected by the LUR models. Lastly, data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were applied to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 65% and 78%, respectively, of NO 2 variation. When the XGBoost algorithm was further incorporated in LUR and hybrid-LUR, the explanatory power increased to 84% and 91%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed all other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm to estimate the spatial-temporal variability of NO 2 exposure. For practical application, the associations of specific land-use/land cover types selected in the final model can be applied in land-use management and in planning emission reduction strategies. [Display omitted] • Estimating long-term daily NO 2 concentration with machine learning models. • Land-use patterns were included in machine learning models by using land-use regression. • The most contributed predictors were identified by stepwise variable selection. • Explanatory power of daily NO 2 concentration was increased from 0.65 to 0.91. • XGboost outperformed RF and DNN machine learning algorithms. Capsule: The explanatory power of Hybrid Kriging-LUR coupled with XGBoost algorithm on daily NO 2 variations reached 91% and outperformed all other integrated methods. [ABSTRACT FROM AUTHOR]