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AbstractThe present study investigated the occurrence and characteristic frequency of nappe oscillations on weirs by reanalyzing data from 52 experimental configurations. The considered configurati...

Enibedding fast valving (FV) and automatic voltage regulation (AVIt) in the dynamic equal-area ciiterion (DEEAC) is considered to be a great asset to the neth hod, in paiticular for its on-line use. In this paper we consider FV and AV11 curves to be furnished by a time-domain numerical piograrn, then embed theni in tlie orie-iriachine-infinite-bus equivalent via appropriate transformations. Tlle way of obtaining these curves is not tlie main concern of this pragmatic approach. Rather, what matters here is to exploie whether the approach has potential, i.e. wlietlier the DEEAC thus augmented is still able to preserve accuracy and coniputing speed similar to those of the basic DEEAC. The first siniulation iesults reported in tliis paper show the feasibility of the approach and anticipate good perfoiinances.

Septoria leaf blotch (SLB) is among the most damaging foliar diseases of wheat worldwide. In this study, data for seven cropping seasons (2003–2009) at four representative wheat-growing sites in the Grand-Duchy of Luxembourg (GDL) were used to assess SLB risk on the three upper leaves (L3 to L1, L1 being the flag leaf) based on the combination of conducive weather conditions, simulated potential daily infection events by Zymoseptoria tritici, and SLB severity on lower leaves between stem elongation and mid-flowering. Results indicated that the variability in SLB severity on L3 to L1 at soft dough was significantly (p < 0.05) influenced by the disease severity on the lower leaf L5 at L3 emergence and the sum of daily mean air temperature between stem elongation and mid-flowering. Moreover, analyzing the predictive power of these variables through multiple linear regression indicated that the disease severity on L5 at L3 emergence and mild weather conditions between stem elongation and mid-flowering critically influenced the progress of SLB later in the season. Such results can help fine tune weather-based SLB risk models to guide optimal timing of fungicide application in winter wheat fields and ensure economic and ecological benefits.

Agricultural sectors play an important role in the process of economic development of a country, especially in developing ones. Vietnam is known as an emerging market, which depends directly on agriculture-related activities for their livelihood, in which the issue of rural credit access still remains a confounding problem. The paper focuses on the characteristics of rural credit markets, the determinants of farmer access to the markets, the socio-economic impacts of credit access in Vietnam and briefly comparing with those of some developing countries. This question is addressed by reviewing existing literature and empirical evidence, followed by a comprehensive case study in Vietnam. Comprehensive literature review with secondary data collection and key informant interviews are methods that are applied in this research. The results of this analysis indicate the features of Vietnam markets as participated constraints, government intervention, and segmentation. Other results reveal the significant determinants of credit accessibility. Impacts of credit access on output production, household income, and poverty reduction are highlighted in this paper. Some managerial implications are recommended for households through participation in lending networks; for financial institutions relating to expand target clients as well as capital allocation; and, for policy-makers via ensuring market competitiveness and sustainable development in the long run.

Abstract In state estimation of electric power systems, anomalous measurement diagnosis is essential for guaranteeing a reliable database. More specifically, the identification of anomalous data is a very important but also very difficult task, especially in the case of multiple interacting ones. This work focuses primarily on the latter problem. The proposed solution is based on the classical hypothesis testing of decision theory and its application to the random vector of measurement error estimates; the latter are obtained via a perfect observations estimator which is proven to be optimal. Further, strategies able to treat any type of bad measurements are considered; the identification strategy under an upper bounded β risk is chosen and extensively exploited. The developments concern reliability aspects as well as on-line computational requirements for which many algorithmic improvements and useful formulae are derived. A practical example, coming from actual implementation experience, illustrates the overall methodology and shows the consistently good performances of the method.

Grassland models often yield more uncertain outputs than arable crop models due to more complex interactions and the largely undocumented sensitivity of grassland models to environmental factors. The aim of the present study was to assess the impact of single-factor changes in temperature, precipitation, and atmospheric [CO2] on simulated soil water content (SWC), actual evapotranspiration (ET), gross primary production (GPP) and yield biomass, and also to link the sensitivity analysis with experimental results. We employed an unprecedented multi-model framework consisting of seven grassland models at nine sites with different environmental characteristics in Europe and Israel, with two management options at three sites. For warming/cooling and wetting/ drying, models showed general consistency in the direction of SWC and ET changes, but less agreement regarding GPP and biomass changes. The simulated responses consistently revealed an overall positive effect of CO2 enrichment on GPP and biomass, while the direction of change differed for SWC and ET. Comparing with singlefactor experimental manipulations, SWC simulations slightly underestimated the observed effect of warming, while the overall mean model sensitivity for biomass (+7.5%) closely matched the mean response observed with 1-2 degrees C warming (+6.6%). The models exhibited lower sensitivity of SWC to wetting or drying compared to the experiments. The overall mean sensitivity of biomass to drying was -4.3%, contrasting with the mean experimental effect size of -9.6%, which proved to be more realistic than the mean wetting effect (+3.2%, against +38.9% in the field trials). The simulated sensitivity of SWC to CO2 enrichment was markedly underestimated, while the biomass response (+12.0%) closely matched the observations (+17.5%). Although the multi-model averaging did not manifestly improve the realism of the simulations, it ensured a realistic response in the direction of change to varying conditions. The results suggest a paradigm shift in grassland modelling meaning that the usual practice of model optimisation/validation needs to be complemented by a sensitivity analysis following the approach presented. The results also highlight the importance of model improvements, especially in terms of soil hydrology representation, a key environmental driver of grassland functioning.

SUMMARYWhisky (25–50 ml) increased plasma levels of immunoreactive calcitonin (iCT) in seventeen of nineteen patients with chronic renal failure. The effect was greater in patients with high levels of iCT than in those with normal levels. Changes in plasma iCT were not related to changes in calcium, phosphate or immunoreactive gastrin, but were inhibited by the prior administration of propranolol.

Abstract The building sector has become a major source of worldwide carbon emissions and energy consumption because of rapid population growth and a continuous environmental strain caused by humanity. A lack of consistent data on life-cycle carbon emissions and energy demand at the neighbourhood level has made it difficult to understand the origins of climate change at this scale. A sensitivity analysis brought clarity concerning the extent of environmental impacts on future climate evolution. From this perspective, the authors aimed to evaluate, analyse, compare, and provides recommendations to reduce carbon emissions, as well as the energy required by three types of neighbourhoods (urban, rural, and sustainable) located in and adapted to all countries worldwide. The most important parameters affecting carbon emission and energy consumption were analysed, including the energy mix of countries, local building materials and climate, technological solutions utilised, daily mobility, and occupied spaces. The results indicated that the highest levels of carbon dioxide emissions were produced by countries with prosperous economies, such as China, the United States, India, Germany, and Poland, because of high concentrations of coal in their energy mixes. Modernising cities through the construction of new eco-districts and increasing the use of new techniques for substantial renovations of outdated buildings worldwide could mitigate the amount of greenhouse gases emitted by neighbourhoods 53–97 % by 2050. Moreover, by combining substantial building renovations with the installation of photovoltaic panels on roofs, the objective of ‘zero carbon’ at the neighbourhood level could be achievable by 2050 in rural neighbourhoods. Radical changes in the judicious choice of construction materials and use of green energy production represent targeted opportunities to resolve the future climate dilemma.