Social Investment Policies in Times of Permanent Austerity

The objective of this paper is to identify the political conditions that are most likely to be conducive to the development of social investment policies. It starts from the view put forward by theorists of welfare retrenchment that in the current context of permanent austerity, policy is likely to be dominated by retrenchment and implemented in a way that allows governments to minimise the risk of electoral punishment (blame avoidance). It is argued that this view is inconsistent with developments observed in several European countries, were some welfare state expansion has taken place mostly in the fields of childcare and active labour market policy. An alternative model is put forward, that emphasises the notion of "affordable credit claiming". It is argued that even under strong budgetary pressures, governments maintain a preference for policies that allow them to claim credit for their actions. Since the traditional redistributive policies tend to be off the menu for cost reasons, governments have tended to favour investments in childcare and active labour market policy as credit claiming tools. Policies developed in this way while they have a social investment flavour, tend to be rather limited in the extent to which they genuinely improve prospects of disadvantaged people by investing in their human capital. A more ambitious strategy of social investment sees unlikely to develop on the basis of affordable credit claiming. The paper starts by presenting the theoretical argument, which is then illustrated with examples taken from European countries both in the pre-crisis and in the post-crisis years.

Permanent.Plot.ch - a database for Swiss permanent vegetation plots

The aim of the Permanent.Plot.ch project is the conservation of historical data about permanent plots in Switzerland and the monitoring of vegetation in a context of environmental changes (mainly climate and land use). Permanent plots are currently being recognized as valuable tools to monitor long-term effects of environmental changes on vegetation. Often used in short studies (3 to 5 years), they are generally abandoned at the end of projects. However, their full potential might only be revealed after 10 or more years, once the location is lost. For instance, some of the oldest permanent plots in Switzerland (first half of the 20th century) were nearly lost, although they are now very valuable data. The Permanent.Plot.ch national database (GIVD ID EU-CH-001), by storing historical and recent data, will allow to ensuring future access to data from permanent vegetation plots. As the database contains some private data, it is not directly available on internet but an overview of the data can be downloaded from internet (http://www.unil.ch/ppch) and precise data are available on request.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Whole-body vibration training: metabolic cost of synchronous, side-alternating or no vibrations.

Whole-body vibration training improves strength and can increase maximal oxygen consumption ([·V]O(2max)). No study has compared the metabolic demand of synchronous and side-alternating whole-body vibration. We measured [·V]O₂ and heart rate during a typical synchronous or side-alternating whole-body vibration session in 10 young female sedentary participants. The 20-min session consisted of three sets of six 45-s exercises, with 15 s recovery between exercises. Three conditions were randomly tested on separate days: synchronous at 35 Hz and 4 mm amplitude, side-alternating at 26 Hz and 7.5 mm amplitude (peak acceleration matched at 20 g in both vibration conditions), and no vibrations. Mean [·V]O₂ (expressed as %[·V]O(2max)) did not differ between conditions: 29.7 ± 4.2%, 32.4 ± 6.5%, and 28.7 ± 6.7% for synchronous, side-alternating, and no vibrations respectively (P = 0.103). Mean heart rate (% maximal heart rate) was 65.6 ± 7.3%, 69.8 ± 7.9%, and 64.7 ± 5.6% for synchronous, side-alternating, and no vibrations respectively, with the side-alternating vibrations being significantly higher (P = 0.019). When analysing changes over exercise sessions, mean [·V]O₂ was higher for side-alternating (P < 0.001) than for synchronous and no vibrations. In conclusion, side-alternating whole-body vibration elicits higher heart rate responses than synchronous or no vibrations, and could elevate [·V]O₂, provided the session lasts more than 20 min.

Treatment of ruptured abdominal aortic aneurysm, a permanent challenge or a waste of resources? Prediction of outcome using a multi-organ-dysfunction score.

OBJECTIVES: in a retrospective study, attempts have been made to identify individual organ-dysfunction risk profiles influencing the outcome after surgery for ruptured abdominal aortic aneurysms. METHODS: out of 235 patients undergoing graft replacement for abdominal aortic aneurysms, 57 (53 men, four women, mean age 72 years [s.d. 8.8]) were treated for ruptured aneurysms in a 3-year period. Forty-eight preoperative, 13 intraoperative and 34 postoperative variables were evaluated statistically. A simple multi-organ dysfunction (MOD) score was adopted. RESULTS: the perioperative mortality was 32%. Three patients died intraoperatively, four within 48 h and 11 died later. A significant influence for pre-existing risk factors was identified only for cardiovascular diseases. Multiple linear-regression analysis indicated that a haemoglobin <90 g/l, systolic blood pressure <80 mmHg and ECG signs of ischaemia at admission were highly significant risk factors. The cause of death for patients, who died more than 48 h postoperatively, was mainly MOD. All patients with a MOD score >/=4 died (n=7). These patients required 27% of the intensive-care unit (ICU) days of all patients and 72% of the ICU days of the non-survivors. CONCLUSION: patients with ruptured aortic aneurysms from treatment should not be excluded. However, a physiological scoring system after 48 h appears justifiable in order to decide on the appropriateness of continual ICU support.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.

A synchronous, middle early Aptian age for the demise of the Helvetic Urgonian platform related to the unfolding oceanic anoxic event 1a ("Selli event")

Un âge synchrone (partie moyenne de l'Aptien inférieur) de l'ennoiement de la plate-forme Urgonienne helvétique en relation avec l'événement océanique anoxique 1a ("événement Selli"). - La fin de la plate-forme urgonienne, calibrée par analyse des isotopes stables du carbone sur roche totale et par biostratigraphie basée sur les ammonites, est datée du milieu de l'Aptien inférieur (Près de la limite des zones weissi et deshayesi). Cet arrêt, synchrone dans des coupes représentatives du domaine helvétique alpin, est un événement environemental majeur renregistré en France, en Espagne, au Protugal, en Oman, au Mexique et dans le domaine Pacifique. En tenant compte des limites de résolution de la biostatrigraphie et des autres techniques de datation, cet épisode semble également être synchrone à l'échelle globale. Pour beaucoup d'auteurs, la disparition de récifs de coraux et de rudistes corrélée à la fin de la sédimentation urgonienne correspond à la mise en place de conditions anoxiques à l'Aptien inférieur. Celles-ci caractérisent un événement d'importance global: l'événement anoxique OAE 1a.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

High quantitative and no molecular differentiation of a freshwater snail (Galba truncatula) between temporary and permanent water habitats.

We investigate the variation in quantitative and molecular traits in the freshwater snail Galba truncatula, from permanent and temporary water habitats. Using a common garden experiment, we measured 20 quantitative traits and molecular variation using seven microsatellites in 17 populations belonging to these two habitats. We estimated trait means in each habitat. We also estimated the distributions of overall genetic quantitative variation (QST), and of molecular variation (FST), within and between habitats. Overall, we observed a lack of association between molecular and quantitative variance. Among habitats, we found QST>FST, an indication of selection for different optima. Individuals from temporary water habitat matured older, at a larger size and were less fecund than individuals from permanent water habitat. We discuss these findings in the light of several theories for life-history traits evolution.