Indication for Light Sneutrinos and Gauginos from Precision Electroweak Data

The present Standard Model fit of precision data has a low confidence level, and is characterized by a few inconsistencies. We look for supersymmetric effects that could improve the agreement among the electroweak precision measurements and with the direct lower bound on the Higgs mass. We find that this is the case particularly if the 3.6 sigma discrepancy between sin^2 theta_eff from leptonic and hadronic asymmetries is finally settled more on the side of the leptonic ones. After the inclusion of all experimental constraints, our analysis selects light sneutrinos, with masses in the range 55-80 GeV, and charged sleptons with masses just above their experimental limit, possibly with additional effects from light gauginos. The phenomenological implications of this scenario are discussed.Comment: 17 pages LaTex, 9 figures, uses epsfi

An entropy approach for evaluating the maximum information content achievable by an urban rainfall network

Hydrological models are the basis of operational flood-forecasting systems. The accuracy of these models is strongly dependent on the quality and quantity of the input information represented by rainfall height. Finer space-time rainfall resolution results in more accurate hazard forecasting. In this framework, an optimum raingauge network is essential in predicting flood events. This paper develops an entropy-based approach to evaluate the maximum information content achievable by a rainfall network for different sampling time intervals. The procedure is based on the determination of the coefficients of transferred and nontransferred information and on the relative isoinformation contours. The nontransferred information value achieved by the whole network is strictly dependent on the sampling time intervals considered. An empirical curve is defined, to assess the objective of the research: the nontransferred information value is plotted versus the associated sampling time on a semi-log scale. The curve has a linear trend. In this paper, the methodology is applied to the high-density raingauge network of the urban area of Rome

Propagation of crises in the virtual water trade network

The international trade of agricultural goods is associated to the displacement of the water used to produce such goods and embedded in trade as a factor of production. Water virtually exchanged from producing to consuming countries, named virtual water, defines flows across an international network of “virtual water trade” which enable the assessment of environmental forcings and implications of trade, such as global water savings or country dependencies on foreign water resources. Given the recent expansion of commodity (and virtual water) trade, in both displaced volumes and network structure, concerns have been raised about the exposure to crises of individuals and societies. In fact, if one country had to markedly decrease its export following a socio-economical or environmental crisis, such as a war or a drought, many -if not all- countries would be affected due to a cascade effect within the trade network. The present contribution proposes a mechanistic model describing the propagation of a local crisis into the virtual water trade network, accounting for the network structure and the virtual water balance of all countries. The model, built on data-based assumptions, is tested on the real case study of the Argentinean crisis in 2008-09, when the internal agricultural production (measured as virtual water volume) decreased by 26% and the virtual water export of Argentina dropped accordingly. Crisis propagation and effects on the virtual water trade are correctly captured, showing the way forward to investigations of crises impact and country vulnerability based on the results of the model propose

Measuring economic water scarcity in agriculture: a cross-country empirical investigation

High water availability enhances agricultural performance and food security. However, many countries where water is abundant according to hydrological indicators face difficulties in the utilization of water in agriculture, being in a situation of economic water scarcity (EWS), due to lack of institutional and material means for water management and governance. EWS faces a stronger challenge of measurability, if compared to physical water scarcity. Since the Sustainable Development Goal Indicator on Integrated management of domestic and transboundary water resources (IWRM) is a unique attempt to quantify information on water management at a national level, we explore whether it can represent a valid metric for EWS measurement. We first show that a high level of water management is neither necessarily associated to high economic power of the country nor to low physical water availability. Then, we analyze whether the indicator can predict typical EWS situations such as low agricultural productivity and inefficient water use. Although the importance of water institutions for agriculture is well known through case studies at the local level, we make the first attempt to quantify the strengths of this relation at a global scale for different crops in climatic diverse countries. We detect a positive and significant association between IWRM level and yield, and consequently a negative and equally significant association between the IWRM level and the crop water footprint. Statistical significance holds also when potentially confounding variables are included in a multiple regression analysis. We infer from this analysis that good water management, as detectable through the IWRM indicator, improves land productivity and water saving, in turn mitigating EWS. Our findings pave the way toward the use of the IWRM indicator as a valuable tool for measuring EWS in agriculture, bridging the measurability gap of economic water scarcity, with straightforward policy implications in favour of investments in water management as a lever for enhancing food security and development

Rainfall threshold definition using an entropy decision approach and radar data

Flash flood events are floods characterised by a very rapid response of basins to storms, often resulting in loss of life and property damage. Due to the specific space-time scale of this type of flood, the lead time available for triggering civil protection measures is typically short. Rainfall threshold values specify the amount of precipitation for a given duration that generates a critical discharge in a given river cross section. If the threshold values are exceeded, it can produce a critical situation in river sites exposed to alluvial risk. It is therefore possible to directly compare the observed or forecasted precipitation with critical reference values, without running online real-time forecasting systems. The focus of this study is the Mignone River basin, located in Central Italy. The critical rainfall threshold values are evaluated by minimising a utility function based on the informative entropy concept and by using a simulation approach based on radar data. The study concludes with a system performance analysis, in terms of correctly issued warnings, false alarms and missed alarms

Compliance with EAT–Lancet dietary guidelines would reduce global water footprint but increase it for 40% of the world population

The EAT–Lancet Commission has proposed a global benchmark diet to guide the shift towards healthy and sustainable dietary patterns. Yet it is unclear whether consumers’ choices are convergent with those guidelines. Applying an advanced statistical analysis, we mapped the diet gap of 15 essential foods in 172 countries from 1961 to 2018. We found that countries at the highest level of development have an above-optimal consumption of animal products, fats and sugars but a sub-optimal consumption of legumes, nuts and fruits. Countries suffering from limited socio-economic progress primarily rely on carbohydrates and starchy roots. Globally, a gradual change towards healthy and sustainable dietary targets can be observed for seafood, milk products, poultry and vegetable oils. We show that if all countries adopted the EAT–Lancet diet, the water footprint would fall by 12% at a global level but increase for nearly 40% of the world’s population

Numerical modelling of hot polymer-coated steel pipeline joints in bending

A numerical method to analyse the effect of the application of polymer coatings on the bending resistance of steel pipeline joints is presented. Experiments were conducted to investigate the influence of the thickness of polymer field joint coatings and the cooldown time provided after applying the coatings on the behaviour of pipeline joints when being bent during reeling operations. Temperature readings were obtained from thermocouples inside the polymer field joint coating during the application process, and pipeline ovality measurements were taken during mechanical testing. Thermal modelling of the coating application procedure was developed using COMSOL Multiphysics modelling software; this model is validated against the thermocouple readings, while a mechanical model simulating the pipe being bent to a reel developed in Abaqus finite element modelling software is described. The temperature outputs, areas of stress concentration and pipe ovalities obtained from the experiments are shown to be predicted accurately by the numerical models. After successful validation of the numerical models, a parametric study assessing the influence of field joint coating thickness and cooldown times is described, whose results are used to find an optimal solution to reduce the cooldown time required prior to bending the pipe without buckling

Charting out the future agricultural trade and its impact on water resources

International agricultural trade triggers inter-dependency among distant countries, not only in economic terms but also under an environmental perspective. Agricultural trade has been shown to drive environmental threats pertaining to biodiversity loss and depletion and pollution of freshwater resources. Meanwhile, trade can also encourage production where it is most efficient, hence minimizing the use of natural resources required by agriculture. In this study, we provide a country-level assessment of the future international trade for 6 primary crops and 3 animal products composing 70% of the human diet caloric content. We set up four variegate socio-economic scenarios with different level of economic developments, diets habits, population growth dynamics, and levels of market liberalization. Results show that the demand of agricultural goods and the correspondent trade flow will increase with respect to current levels by 10–50% and 74–178% by 2050, respectively. The largest increase in the amount of traded goods is expected under the Economic Optimism scenario that will see an average trade flow of 2830 kcal/cap/day (i.e., nearly doubling the current per-capita flow). Most of the increase will be driven by the trade of crops for animal feeding, particularly maize will be the most traded crop. The trade networks architecture in 2050 and 2080 will be very different from the one we actually know, with a clear shift of the trade pole from the Western toward the Eastern economies. The dramatic changes of global food-sources and trade patterns will jeopardize the water resources of new regions while exacerbating the pressure in those areas that will continue serving food also in the future. In spite of this, trade may annually save around 40–60 m3 of water per person, compared to a situation where countries are self-sufficient

Artificial Neural Networks and Entropy-based Methods to Determine Pressure Distribution in Water Distribution Systems

AbstractPressure determination in water distribution systems (WDS) is important because it generally drives the operational actions for leakage and failure management, backwater intrusion and demand control. This determination would ideally be done through pressure monitoring at every junction in the distribution system. However, due to limited resources, it is only possible to monitor at a limited number of nodes. To this end, this work explores the use of an Artificial Neural Network (ANN) to estimate pressure distributions in a WDS using the available data at the monitoring nodes as inputs. The optimal subset of monitoring nodes are chosen through an entropy-based method. Finally, pressure values are compared to synthetic pressure measures estimated through a hydraulic model

International corporations trading Brazilian soy are keystone actors for water stewardship

Transnational corporations play a major, but poorly constrained, role in reallocating global water resources. Here, we couple high-resolution, company-specific trade data with hydrological and crop models to estimate the virtual water trade of the top 9 transnational corporations that trade Brazilian soy. We identify 4429 virtual water flows connecting 1620 Brazilian municipalities with the top-10 soy importing countries and find that the total virtual water flow increased from 43 billion m3 to 100 billion m3 between 2004 and 2018. We find that the largest soy traders displace on average twice as much virtual water as top-importing countries, excluding China. For example, in 2018 one transnational corporation exported 15 Gm3, almost tripling the Netherlands’s virtual water import (the second largest importer at about 5 Gm3). Our findings highlight the importance of transnational corporations for achieving water stewardship and sustainable supply chains to support water resource security at municipal and international scales