Transient damage spreading and anomalous scaling in mortar crack surfaces

The scaling properties of a post-mortem mortar crack surface are investigated. The root mean square of the height fluctuations is found to obey anomalous scaling properties, but with three exponents, two of them characterizing the local roughness (zeta~=0.79 and zetae~=0.41) and the third one driving the global roughness (zetag~=1.60). The critical exponent zeta~=0.79 is conjectured to reflect damage screening occurring for length scales smaller than the process zone size, while the exponent zetae~=0.41 characterizes roughness at larger length scales, i.e., at length scales where the material can be considered as linear elastic. Finally, we argue that the global roughness exponent could be material dependent contrary to both local roughness exponents (zeta~=0.8 and zetae~=0.4) which can be considered as universal

Retrieving Decadal Climate Change from Satellite Radiance Observations-A 100-year CO2 Doubling OSSE Demonstration.

Preparing for climate change depends on the observation and prediction of decadal trends of the environmental variables, which have a direct impact on the sustainability of resources affecting the quality of life on our planet. The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is proposed to provide climate quality benchmark spectral radiance observations for the purpose of determining the decadal trends of climate variables, and validating and improving the long-range climate model forecasts needed to prepare for the changing climate of the Earth. The CLARREO will serve as an in-orbit, absolute, radiometric standard for the cross-calibration of hyperspectral radiance spectra observed by the international system of polar operational satellite sounding sensors. Here, we demonstrate that the resulting accurately cross-calibrated polar satellite global infrared spectral radiance trends (e.g., from the Metop IASI instrument considered here) can be interpreted in terms of temperature and water vapor profile trends. This demonstration is performed using atmospheric state data generated for a 100-year period from 2000-2099, produced by a numerical climate model prediction that was forced by the doubling of the global average atmospheric CO2 over the 100-year period. The vertical profiles and spatial distribution of temperature decadal trends were successfully diagnosed by applying a linear regression profile retrieval algorithm to the simulated hyperspectral radiance spectra for the 100-year period. These results indicate that it is possible to detect decadal trends in atmospheric climate variables from high accuracy all-sky satellite infrared radiance spectra using the linear regression retrieval technique

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period

Why Do People Demand Rent Control?

We conduct a representative survey experiment in Germany to understand why people support inefficient policies. In particular, we measure beliefs about and preferences for rent control – a policy that is widely regarded as harmful by experts. To tease out causal mechanisms, we provide randomly selected subsets of participants with empirical estimates about the effects of rent control on rent prices and housing supply and with information about the consensus among economists against rent control. We find that people update their beliefs and that this leads to lower demand for rent control. Left-wingers update their beliefs more strongly, which reduces the ideological gap in support for rent control by about one-third. Providing information about economists’ rejection of this policy leads to the largest reduction in support. However, the main drivers of support for rent control are fairness considerations and profit motives. Our study also highlights the importance of trust in expert advice since treatment effects are consistently larger among those who indicate trust in expert advice. Finally, an obfuscated follow-up survey conducted three weeks later reveals that the effects, both on support for rent control and on beliefs, persist only for those who trust

Deep learning-based automated detection and clustering of potholes using variational autoencoder for efficient road maintenance

In this study we explore the possibility of using unsupervised learning for pothole detection. We will use images of roads from Brazil where both potholes and cracks can be present in the images, as well as clean images with no damage on the road. This will be done using a variational autoencoder (VAE) and clustering. The study will also explore a supervised method, support vector machine (SVM), to compare the performance of supervised model vs. unsupervised model. The goal for this study is to correctly cluster images containing potholes from images that do not contain potholes

A characterization of graphs with regular distance-2 graphs

For non-negative integers~$k$, we consider graphs in which every vertex has exactly $k$ vertices at distance~$2$, i.e., graphs whose distance-$2$ graphs are $k$-regular. We call such graphs $k$-metamour-regular motivated by the terminology in polyamory. While constructing $k$-metamour-regular graphs is relatively easy -- we provide a generic construction for arbitrary~$k$ -- finding all such graphs is much more challenging. We show that only $k$-metamour-regular graphs with a certain property cannot be built with this construction. Moreover, we derive a complete characterization of $k$-metamour-regular graphs for each $k=0$, $k=1$ and $k=2$. In particular, a connected graph with~$n$ vertices is $2$-metamour-regular if and only if $n\ge5$ and the graph is a join of complements of cycles (equivalently every vertex has degree~$n-3$), a cycle, or one of $17$ exceptional graphs with $n\le8$. Moreover, a characterization of graphs in which every vertex has at most one metamour is acquired. Each characterization is accompanied by an investigation of the corresponding counting sequence of unlabeled graphs