Measuring change in subjective wellbeing: Methods to quantify recall bias and recalibration response shift

We propose to use subjective well-being (SWB) measures to determine patient-relevant treatment benefit. Benefit can be measured either prospectively (pre-post) or retrospectively, but both approaches can be biased: Prospective evaluation may be subject to response shift; retrospective evaluation may be subject to recall bias. As prospective and retrospective evaluations often differ in effect size and since there is no gold standard to compare against, the extent of the two biases needs to be determined. Response shift includes reprioritization, reconceptualization, and recalibration. We argue that in SWB measures only recalibration, but not reprioritization and reconceptualization are validity threats. We review approaches to quantify recall bias, response shift, or both in the measurement of health-related quality of life. We discuss which of these approaches are most suitable for application to SWB measurement, where only recall bias and recalibration are to be quantified, ignoring the other two response shift types. Some approaches of bias detection will not be applicable to SWB measurement, because they do not distinguish between recalibration and other types of response shift, or quantify reprioritization and/or reconceptualization alone. For other approaches, it is unclear whether underlying assumptions apply to SWB measurement. Anchor recalibration, structural equation modelling, and ROSALI are most suitable, the latter two with some limitations. Anchor recalibration was considered by its developers to be too difficult for participants to understand in its current form. Refining the anchor recalibration method may provide the most promising way to quantify both scale recalibration and recall bias

Numerical Simulation of the Fouling on Structured Heat Transfer Surfaces (Fouling)

The objective of this work is to make a contribution to a good and fast prediction of the crystal growth on flat and structured heat transfer surfaces. For the numerical simulation the CFD code Fluent is used. The simulation enables an unsteady calculation of the fouling process and a realistic description of the temporal modification of the flow and temperature fields due to the continuous crystal growth. The numerical simulation of the crystal growth is based on models for the calculation of the deposition (Krause, 1993) and removal (Bohnet, 1990) mass rates. Based on experimental results of Hirsch (Bohnet et. al., 1999), a model was developed which enables the calculation of the density of the fouling layer not only as a function of the local position within the fouling layer, but also as a function of the time-dependent total thickness of the fouling layer. In addition a model was developed, that enables a realistic distribution of the heat flux along the heat transfer surface during the simulation. All models are implemented into the simulation with the help of the programming user interface of the CFD code. During the experimental and numerical investigations the operating parameters like flow rate, surface temperature, concentration of the salt solution and geometry of the flow channel are varied. The induction period and the effects of aging which occur with almost all fouling processes are not considered. Result of the numerical simulation is the prediction of the fouling resistance as function of time. In view of the complexity of the fouling process during the incrustation of heat transfer surfaces and the fact that not all influences from the used models could be considered the agreement between calculated and experimentally obtained data is satisfactory

Crystallization Fouling Of The Aqueous Two-Component System CaSO\u3csub\u3e4\u3c/sub\u3e/CaCO\u3csub\u3e3\u3c/sub\u3e

Solutions, which cause fouling problems, consist mostly of more than one single component. Up to now only few studies concerning the fouling phenomena in such multicomponent systems exist. Therefore batch and continuous experiments with the aqueous two-component system CaSO4/CaCO3 were carried out, investigating especially the influence of pH-value on the fouling behaviour. As measure for the crystalline deposit the fouling resistance Rf was used. The composition of the obtained fouling layers were analysed by x-ray diffraction and scanning electron microscopy (SEM). Further the strength of the crystalline deposits were determined in abrasion experiments. The measured abrasion was correlated with crushing strength values. In the fouling experiments a strong effect of the pH-value on crystallization fouling was observed. Lowest fouling tendency was seen for experiments at pH 7.0. At different pH-values the crystalline layers showed big differences in their macroscopic as well as in their microscopic structure. As it could be seen with the SEM the crystals differed in their size but also in their shape. Below pH 6.0 only calcium sulphate was detected by x-ray diffraction, which agrees with the saturation theory. At higher pH values besides calcium sulphate also calcium carbonate was found in different modifications. The different layer composition leads to different strength of the layers. Highest strength values in the crystalline upper and middle layer were measured for crystalline scales grown at pH 7.0, followed by layers at pH 6.5. At the moment it is difficult to correlate the fouling behaviour clearly to the different experimental conditions

Analyzing and Explaining Image Classifiers via Diffusion Guidance

While deep learning has led to huge progress in complex image classification tasks like ImageNet, unexpected failure modes, e.g. via spurious features, call into question how reliably these classifiers work in the wild. Furthermore, for safety-critical tasks the black-box nature of their decisions is problematic, and explanations or at least methods which make decisions plausible are needed urgently. In this paper, we address these problems by generating images that optimize a classifier-derived objective using a framework for guided image generation. We analyze the behavior and decisions of image classifiers by visual counterfactual explanations (VCEs), detection of systematic mistakes by analyzing images where classifiers maximally disagree, and visualization of neurons to verify potential spurious features. In this way, we validate existing observations, e.g. the shape bias of adversarially robust models, as well as novel failure modes, e.g. systematic errors of zero-shot CLIP classifiers, or identify harmful spurious features. Moreover, our VCEs outperform previous work while being more versatile

Using imprecise continuous time Markov chains for assessing the reliability of power networks with common cause failure and non-immediate repair.

We explore how imprecise continuous time Markov chains can improve traditional reliability models based on precise continuous time Markov chains. Specifically, we analyse the reliability of power networks under very weak statistical assumptions, explicitly accounting for non-stationary failure and repair rates and the limited accuracy by which common cause failure rates can be estimated. Bounds on typical quantities of interest are derived, namely the expected time spent in system failure state, as well as the expected number of transitions to that state. A worked numerical example demonstrates the theoretical techniques described. Interestingly, the number of iterations required for convergence is observed to be much lower than current theoretical bounds

Mobile Telephony Access and Usage in Africa

This paper uses data from nationally representative household surveys conducted in 17 African countries to analyse mobile adoption and usage. The paper shows that countries differ in their levels of ICT adoption and usage and also in factors that influence adoption and usage. Income and education vastly enhance mobile adoption but gender, age and membership of social networks have little impact. Income is the main explanatory variable for usage. In terms of mobile expenditure the study also finds linkages to fixed-line, work and public phone usages. These linkages need, however, to be explored in more detail in future. Mobile expenditure is inelastic with respect to income, ie the proportion of mobile expenditure to individual income increases less than1% for each1% increase in income. This indicates that people with higher income spend a smaller proportion of their income on mobile expenditure compared to those with less income. The study provides tools to identify policy intervention to improve ICT take-up and usage and defines universal service obligations based on income and monthly usage costs. It helps to put a number to what can be expected from lower access and usage costs in terms of market volume and number of new subscribers. Linking this to other economic data such as national household income and expenditure surveys and GDP calculation would allow forecast of the economic and social impact of policy interventions. Key policy interventions would be regulatory measures to decrease access and usage costs, rural electrification and policies to increase ICT skills of pupils and teachers

Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet

Benchmark performance of deep learning classifiers alone is not a reliable predictor for the performance of a deployed model. In particular, if the image classifier has picked up spurious features in the training data, its predictions can fail in unexpected ways. In this paper, we develop a framework that allows us to systematically identify spurious features in large datasets like ImageNet. It is based on our neural PCA components and their visualization. Previous work on spurious features of image classifiers often operates in toy settings or requires costly pixel-wise annotations. In contrast, we validate our results by checking that presence of the harmful spurious feature of a class is sufficient to trigger the prediction of that class. We introduce a novel dataset "Spurious ImageNet" and check how much existing classifiers rely on spurious features

Microplastic fibers affect dynamics and intensity of CO2 and N2O fluxes from soil differently

Microplastics may affect soil ecosystem functioning in critical ways, with previously documented effects including changes in soil structure and water dynamics; this suggests that microbial populations and the processes they mediate could also be affected. Given the importance for global carbon and nitrogen cycle and greenhouse warming potential, we here experimentally examined potential effects of plastic microfiber additions on CO2 and N2O greenhouse gas fluxes. We carried out a fully factorial laboratory experiment with the factors presence of microplastic fibers (0.4% w/w) and addition of urea fertilizer (100 mg N kg− 1) using one target soil. The conditions in an intensively N-fertilized arable soil were simulated by adding biogas digestate at the beginning of the incubation to all samples. We continuously monitored CO2 and N2O emissions from soil before and after urea application using a custom-built flow-through steady-state system, and we assessed soil properties, including soil structure. Microplastics affected soil properties, notably increasing soil aggregate water-stability and pneumatic conductivity, and caused changes in the dynamics and overall level of emission of both gases, but in opposite directions: overall fluxes of CO2 were increased by microplastic presence, whereas N2O emission were decreased, a pattern that was intensified following urea addition. This divergent response is explained by effects of microplastic on soil structure, with the increased air permeability likely improving O2 supply: this will have stimulated CO2 production, since mineralization benefits from better aeration. Increased O2 would at the same time have inhibited denitrification, a process contributing to N2O emissions, thus likely explaining the decrease in the latter. Our results clearly suggest that microplastic consequences for greenhouse gas emissions should become an integral part of future impact assessments, and that to understand such responses, soil structure should be assessed

A robust Bayesian analysis of the impact of policy decisions on crop rotations.

We analyse the impact of a policy decision on crop rotations, using the imprecise land use model that was developed by the authors in earlier work. A specific challenge in crop rotation models is that farmer’s crop choices are driven by both policy changes and external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary stochastic process, where crop transition probabilities are multinomial logistic functions of such external factors. We use a robust Bayesian approach to estimate the parameters of our model, and validate it by comparing the model response with a non-parametric estimate, as well as by cross validation. Finally, we use the resulting predictions to solve a hypothetical yet realistic policy problem