Optimal Exchange Rate Policy in a Growing Semi-Open Economy

This paper considers an alternative perspective to China's exchange rate policy. It studies a semi-open economy where the private sector has no access to international capital markets but the central bank has full access. Moreover, it assumes limited financial development generating a large demand for saving instruments by the private sector. The paper analyzes the optimal exchange rate policy by modeling the central bank as a Ramsey planner. Its main result is that in a growth acceleration episode it is optimal to have an initial real depreciation of the currency combined with an accumulation of reserves, which is consistent with the Chinese experience. This depreciation is followed by an appreciation in the long run. The paper also shows that the optimal exchange rate path is close to the one that would result in an economy with full capital mobility and no central bank intervention.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Rockfall detection from LIDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution is fundamental to understand and model the main factors that predispose to failure. In our study we analysed LiDAR point clouds aiming to: (1) detect and characterise single rockfalls; (2) investigate their spatial distribution. To this end, different cluster algorithms were applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination with the Expectation?Maximization (EM) in order to separate feature points from clutter; 1b) a density based algorithm (DBSCAN) was applied to isolate the single clusters (i.e. the rockfall events); 2) finally we computed the Ripley's K-function to investigate the global spatial pattern of the extracted rockfalls. The method allowed proper identification and characterization of more than 600 rockfalls occurred on a cliff located in Puigcercos (Catalonia, Spain) during a time span of six months. The spatial distribution of these events proved that rockfall were clustered distributed at a welldefined distance-range. Computations were carried out using R free software for statistical computing and graphics. The understanding of the spatial distribution of precursory rockfalls may shed light on the forecasting of future failures.

Nutritional behaviour and beliefs of ski-mountaineers: a semi-quantitative and qualitative study.

BACKGROUND: Endurance athletes are advised to optimize nutrition prior to races. Little is known about actual athletes' beliefs, knowledge and nutritional behaviour. We monitored nutritional behaviour of amateur ski-mountaineering athletes during 4 days prior to a major competition to compare it with official recommendations and with the athletes' beliefs. METHODS: Participants to the two routes of the 'Patrouille des Glaciers' were recruited (A, 26 km, ascent 1881 m, descent 2341 m, max altitude 3160 m; Z, 53 km, ascent 3994 m, descent 4090 m, max altitude 3650 m). Dietary intake diaries of 40 athletes (21 A, 19 Z) were analysed for energy, carbohydrate, fat, protein and liquid; ten were interviewed about their pre-race nutritional beliefs and behaviour. RESULTS: Despite belief that pre-race carbohydrate, energy and fluid intake should be increased, energy consumption was 2416 ± 696 (mean ± SD) kcal · day(-1), 83 ± 17 % of recommended intake, carbohydrate intake was only 46 ± 13 % of minimal recommended (10 g · kg(-1) · day(-1)) and fluid intake only 2.7 ± 1.0 l · day(-1). CONCLUSIONS: Our sample of endurance athletes did not comply with pre-race nutritional recommendations despite elementary knowledge and belief to be compliant. In these athletes a clear and reflective nutritional strategy was lacking. This suggests a potential for improving knowledge and compliance with recommendations. Alternatively, some recommendations may be unrealistic.

An automated microreactor for semi-continuous biosensor measurements.

Living bacteria or yeast cells are frequently used as bioreporters for the detection of specific chemical analytes or conditions of sample toxicity. In particular, bacteria or yeast equipped with synthetic gene circuitry that allows the production of a reliable non-cognate signal (e.g., fluorescent protein or bioluminescence) in response to a defined target make robust and flexible analytical platforms. We report here how bacterial cells expressing a fluorescence reporter ("bactosensors"), which are mostly used for batch sample analysis, can be deployed for automated semi-continuous target analysis in a single concise biochip. Escherichia coli-based bactosensor cells were continuously grown in a 13 or 50 nanoliter-volume reactor on a two-layered polydimethylsiloxane-on-glass microfluidic chip. Physiologically active cells were directed from the nl-reactor to a dedicated sample exposure area, where they were concentrated and reacted in 40 minutes with the target chemical by localized emission of the fluorescent reporter signal. We demonstrate the functioning of the bactosensor-chip by the automated detection of 50 μgarsenite-As l(-1) in water on consecutive days and after a one-week constant operation. Best induction of the bactosensors of 6-9-fold to 50 μg l(-1) was found at an apparent dilution rate of 0.12 h(-1) in the 50 nl microreactor. The bactosensor chip principle could be widely applicable to construct automated monitoring devices for a variety of targets in different environments.