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Background: Stabilization of unstable systems is an important problem in the field of nonlinear dynamics with rich applications in engineering and applied sciences. Purpose: In this article we propose algorithms for stabilizing a double pendulum with an elastic joint. The stability zones in the parameter space of the model are obtained within the analytical approach. Methods: Feedback principle, software stabilization, as well as the Routh-Hurwitz stability criterion. Results: The critical value of the elastic coupling coefficient between the arms of the double inverted pendulum is found. This value allows to achieve the stabilization of the considered system in a small neighborhood of an unstable equilibrium. Conclusion: The obtained results open up new perspectives for stabilizing unstable systems in the field of the applied problems.

In this work we investigate various oscillating systems under hysteretic action. For a simple harmonic oscillator under hysteretic force an unusual unbounded increasing amplitude phenomena (growing rate for the amplitude is proportional to square root of time) are obtained and discussed. For a system of coupled van der Pol oscillators (taking into account the hysteretic coupling) we investigate various dynamic regimes (both regular and chaotic) and show that the hysteresis in the link between two van der Pol oscillators may serve as an effective mechanism for chaos control. Also we consider the effect of hysteresis on synchronisation processes in this system.

History of loading is the result of interaction of the material microstructure evolution and the loading conditions. The present work aims at an investigation of the behavior in face-centered cubic (FCC) metals under loading with a strain rate jump at different stages. In order to describe the behavior of FCC metals under loading we propose a mathematical model. This model is based on a system of ordinary differential equations (ODEs) and realized as software.

Common ragweed (Ambrosia artemisiifolia) is an invasive alien species in Europe producing pollen that causes severe allergic disease in susceptible individuals. Ragweed plants could further invade European land with climate and land-use changes. However, airborne pollen evolution depends not only on plant invasion, but also on pollen production, release and atmospheric dispersion changes. To predict the effect of climate and land-use changes on airborne pollen concentrations, we used two comprehensive modelling frameworks accounting for all these factors under high-end and moderate climate and land-use change scenarios. We estimate that by 2050 airborne ragweed pollen concentrations will be about 4 times higher than they are now, with a range of uncertainty from 2 to 12 largely depending on the seed dispersal rate assumptions. About a third of the airborne pollen increase is due to on-going seed dispersal, irrespective of climate change. The remaining two-thirds are related to climate and land-use changes that will extend ragweed habitat suitability in northern and eastern Europe and increase pollen production in established ragweed areas owing to increasing CO2. Therefore, climate change and ragweed seed dispersal in current and future suitable areas will increase airborne pollen concentrations, which may consequently heighten the incidence and prevalence of ragweed allergy.

In this study, the phenomenon of creep of face-centered cubic (FCC) crystals have been studied by mathematical modeling. The effect of applied stress on creep curves of copper at different temperatures and stress were performed. We have found that steady-state creep rate is proportional to the applied stress and observed the Stages I-III of creep.

UK grasslands perform important environmental and economic functions, but their future productivity under climate change is uncertain. Spring hay yields from 1902 to 2016 at one site (the Park Grass Long Term Experiment) in southern England under four different fertilizer regimes were modelled in response to weather (seasonal temperature and rainfall). The modelling approach applied comprised: (1) a Bayesian model comparison to model parametrically the heteroskedasticity in a gamma likelihood function; (2) a Bayesian varying intercept multiple regression model with an autoregressive lag one process (to incorporate the effect of productivity in the previous year) of the response of hay yield to weather from 1902 to 2016. The model confirmed that warmer and drier years, specifically, autumn, winter and spring, in the twentieth and twenty-first centuries reduced yield. The model was applied to forecast future spring hay yields at Park Grass under different climate change scenarios (HadGEM2 and GISS RCP 4.5 and 8.5). This application indicated that yields are forecast to decline further between 2020 and 2080, by as much as 48–50%. These projections are specific to Park Grass, but implied a severe reduction in grassland productivity in southern England with climate change during the twenty-first century.

AbstractIn total, eight evaluations with 27 critical configurations spanning 11 series of assemblies conducted at the BFS-1 and BFS-2 facilities [BFS: Bol’shoy Fizicheskiy Stand (in transcription from Russian: Big Physical Facility)] are documented in the 2013 edition of the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook). The evaluations concern four scientific issues: physics of sodium-cooled, medium-sized fast reactors; physics of sodium-cooled, small-sized experimental reactors; physics of lead-cooled fast reactors; and integral benchmark data for the mixed oxide (MOX) fuel manufacturing process, particularly low-moderated MOX fissile media. Also, two evaluations with 11 critical configurations spanning two series of assemblies conducted at the BFS-1 facility where many physical parameters in addition to criticality were measured and are documented in the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook). These experiments were designed to obtain data that are applicable to a wide range of criticality safety operations involving fissile contaminated waste streams. This paper provides an overview of the BFS experiments presented in the IRPhEP and ICSBEP Handbooks.

Europe is the largest producer of wheat, the second most widely grown cereal crop after rice. The increased occurrence and magnitude of adverse and extreme agroclimatic events are considered a major threat for wheat production. We present an analysis that accounts for a range of adverse weather events that might significantly affect wheat yield in Europe. For this purpose we analysed changes in the frequency of the occurrence of 11 adverse weather events. Using climate scenarios based on the most recent ensemble of climate models and greenhouse gases emission estimates, we assessed the probability of single and multiple adverse events occurring within one season. We showed that the occurrence of adverse conditions for 14 sites representing the main European wheat-growing areas might substantially increase by 2060 compared to the present (1981–2010). This is likely to result in more frequent crop failure across Europe. This study provides essential information for developing adaptation strategies.

Abstract: Weather data generated for different parts of the UK under five climate change scenarios (baseline, 2020s low CO2 emissions, 2020s high emissions, 2050s low emissions, 2050s high emissions) were inputted into weather-based models for predicting oilseed rape yields and yield losses from the two most important diseases, phoma stem canker and light leaf spot. An economic analysis of the predictions made by the models was done to provide a basis to guide government and industry planning for adaptation to effects of climate change on crops to ensure future food security. Modelling predicted that yields of fungicide-treated oilseed rape would increase by the 2020s and continue to increase by the 2050s, particularly in Scotland and northern England. If stem canker and light leaf spot were effectively controlled, the value of the crop was predicted to increase above the baseline 1980s value by £13 M in England and £28 M in Scotland by the 2050s under a high CO2 emissions scenario. However, in contrast to predictions that phoma stem canker will increase in severity and range with climate change, modelling indicated that losses due to light leaf spot will decrease in both Scotland and England. Combined losses from both phoma stem canker and light leaf spot are predicted to increase, with yield losses of up to 40% in southern England and some regions of Scotland by the 2050s under the high emission scenarios. For this scenario, UK disease losses are predicted to increase by £50 M (by comparison with the baseline losses). However, the predicted increases in fungicide-treated (potential) yield and phoma stem canker/light leaf spot yield losses compensate for each other so that the net UK losses from climate change for untreated oilseed rape are small.

Effects of climate change on productivity of agricultural crops in relation to diseases that attack them are difficult to predict because they are complex and nonlinear. To investigate these crop–disease–climate interactions, UKCIP02 scenarios predicting UK temperature and rainfall under high- and low-CO2emission scenarios for the 2020s and 2050s were combined with a crop-simulation model predicting yield of fungicide-treated winter oilseed rape and with a weather-based regression model predicting severity of phoma stem canker epidemics. The combination of climate scenarios and crop model predicted that climate change will increase yield of fungicide-treated oilseed rape crops in Scotland by up to 0.5 t ha−1(15). In contrast, in southern England the combination of climate scenarios, crop, disease and yield loss models predicted that climate change will increase yield losses from phoma stem canker epidemics to up to 50 per cent (1.5 t ha−1) and greatly decrease yield of untreated winter oilseed rape. The size of losses is predicted to be greater for winter oilseed rape cultivars that are susceptible than for those that are resistant to the phoma stem canker pathogen Leptosphaeria maculans. Such predictions illustrate the unexpected, contrasting impacts of aspects of climate change on crop–disease interactions in agricultural systems in different regions.

The frequency and magnitude of extreme weather events are likely to increase with global warming. However, it is not clear how these events might affect agricultural crops and whether yield losses resulting from severe droughts or heat stress will increase in the future. The aim of this paper is to analyse changes in the magnitude and spatial patterns of two impact indices for wheat: the probability of heat stress around flowering and the severity of drought stress. To compute these indices, we used a wheat simulation model combined with high-resolution climate scenarios based on the output from the Hadley Centre regional climate model at 18 sites in England and Wales. Despite higher temperature and lower summer precipitation predicted in the UK for the 2050s, the impact of drought stress on simulated wheat yield is predicted to be smaller than that at present, because wheat will mature earlier in a warmer climate and avoid severe summer drought. However, the probability of heat stress around flowering that might result in considerable yield losses is predicted to increase significantly. Breeding strategies for the future climate might need to focus on wheat varieties tolerant to high temperature rather than to drought.

Climate change affects plants in natural and agricultural ecosystems throughout the world but little work has been done on the effects of climate change on plant disease epidemics. To illustrate such effects, a weather-based disease forecasting model was combined with a climate change model predicting UK temperature and rainfall under high- and low-carbon emissions for the 2020s and 2050s. Multi-site data collected over a 15-year period were used to develop and validate a weather-based model forecasting severity of phoma stem canker epidemics on oilseed rape across the UK. This was combined with climate change scenarios to predict that epidemics will not only increase in severity but also spread northwards by the 2020s. These results provide a stimulus to develop models to predict the effects of climate change on other plant diseases, especially in delicately balanced agricultural or natural ecosystems. Such predictions can be used to guide policy and practice in adapting to effects of climate change on food security and wildlife.

The yield and quality of food crops is central to the well being of humans and is directly affected by climate and weather. Initial studies of climate change on crops focussed on effects of increased carbon dioxide (CO2) level and/or global mean temperature and/or rainfall and nutrition on crop production. However, crops can respond nonlinearly to changes in their growing conditions, exhibit threshold responses and are subject to combinations of stress factors that affect their growth, development and yield. Thus, climate variability and changes in the frequency of extreme events are important for yield, its stability and quality. In this context, threshold temperatures for crop processes are found not to differ greatly for different crops and are important to define for the major food crops, to assist climate modellers predict the occurrence of crop critical temperatures and their temporal resolution.This paper demonstrates the impacts of climate variability for crop production in a number of crops. Increasing temperature and precipitation variability increases the risks to yield, as shown via computer simulation and experimental studies. The issue of food quality has not been given sufficient importance when assessing the impact of climate change for food and this is addressed. Using simulation models of wheat, the concentration of grain protein is shown to respond to changes in the mean and variability of temperature and precipitation events. The paper concludes with discussion of adaptation possibilities for crops in response to drought and argues that characters that enable better exploration of the soil and slower leaf canopy expansion could lead to crop higher transpiration efficiency.

Climate change effects on UK winter wheat grain yield are complex: warmer temperature, negative; greater carbon dioxide (CO2) concentration, positive; but other environmental variables and their timing also affect yield. In the absence of long-term experiments where temperature and CO2concentration were manipulated separately, we applied the crop simulation model Sirius with long-term daily meteorological data (1892–2016) for Rothamsted, Hertfordshire, UK (2007–2016 mean growing season temperature 1.03°C warmer than 1892–1991), and CO2concentration over this period, to investigate the separate effects of historic CO2and weather on simulated grain yield in three wheat cultivars of the modern era. We show a slight decline in simulated yield over the period 1892–2016 from the effect of weather (daily temperature, rainfall and sunshine hours) at fixed CO2(294.50 ppm, 1892 reference value), but a maximum 9.4% increase when accounting for increasing atmospheric CO2(from 294.50 to 404.21 ppm), differing slightly among cultivars. Notwithstanding considerable inter-annual variation, the slight yield decline at 294.50 ppm CO2over this 125-year period from the historic weather simulations for Rothamsted agrees with the expected decline from temperature increase alone, but the positive yield trend with actual CO2values does not match the recent stagnation in UK wheat yield.

Improving yield potential and closing the yield gap are important to achieve global food security. Europe is the largest wheat producer, delivering about 35% of wheat globally, but European wheat's yield potential from genetic improvements is as yet unknown. We estimated wheat ‘genetic yield potential’, i.e. the yield of optimal or ideal genotypes in a target environment, across major wheat growing regions in Europe by designing in silicoideotypes. These ideotypes were optimised for current climatic conditions and based on optimal physiology, constrained by available genetic variation in target traits. A ‘genetic yield gap’ in a location was estimated as the difference between the yield potential of the optimal ideotype compared with a current, well-adapted cultivar. A large mean genetic yield potential (11–13 t ha−1) and genetic yield gap (3.5–5.2 t ha−1) were estimated under rainfed conditions in Europe. In other words, despite intensive wheat breeding efforts, current local cultivars were found to be far from their optimum, meaning that a large genetic yield gap still exists in European wheat. Heat and drought tolerance around flowering, optimal canopy structure and phenology, improved root water uptake and reduced leaf senescence under drought were identified as key traits for improvement. Closing this unexploited genetic yield gap in Europe through crop improvements and genetic adaptations could contribute towards global food security.

A series of critical assemblies at the BFS facility was created for the validation of the neutron data for Pb and Bi in fast reactor spectrum. It is planned to continue the experiments in the nearest future. The analysis of the experiments has shown that the use of neutron data from files of various national libraries results in significant differences in the calculated values of basic neutronic characteristics.

Nature Plants3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

This corrects the article DOI: 10.1038/nplants.2017.102

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Calibration of cultivar parameters of a crop simulation model can represent a considerable challenge when observed data for a single cultivar is available for multiple environments. Calibration can be considered as a search of the optimal set of parameters in a multidimensional parameter space. An evolutionary algorithm with self-adaptation has been developed and applied to calibrate parameters of the Sirius crop simulation model for several experimental datasets.

Improving crop performance to satisfy an increasing demand for agricultural products is a constant challenge to plant scientists. The recent improvements of process-based simulation models offer new avenues to explore variations of genetic traits on crop performance. In this study, global sensitivity analyses were performed using the Morris and Sobol’ methods sequentially to identify influential parameters of the SiriusQuality1 wheat simulation model. Twenty three influential parameters were identified. The response of the model outputs to these parameters was analyzed. A genetic algorithm with self-adaptation was then developed to optimize these parameters for 16 genotypes of wheat and to define the minimum set of parameters needed to simulate genetic differences in grain yield under diverse environments.

A method for mapping a two-dimensional color image of the microstructure of the material to a complex network is proposed. Each image elements is assigned to node network. A weighted combination of distance metrics - the Euclidean distance and the Manhattan distance - defines whether there is or not an edge between corresponding nodes. The first metric is used to calculate the spatial distance between the picture elements (pixels), the second metric takes into account the contrast between the brightness of pixels in the gray scale. On the basis of the topological properties of the constructed network the edge pixels were detected that allows us to identify the border areas in the microstructure of materials. The proposed method can be used in automated systems of materialographic analysis.

Ways of increasing the production of wheat, the most widely grown cereal crop, will need to be found to meet the increasing demand caused by human population growth in the coming decades. This increase must occur despite the decrease in yield gains now being reported in some regions, increased price volatility and the expected increase in the frequency of adverse weather events that can reduce yields. However, if and how the frequency of adverse weather events will change over Europe, the most important wheat-growing area, has not yet been analysed. Here, we show that the accumulated probability of 11 adverse weather events with the potential to significantly reduce yield will increase markedly across all of Europe. We found that by the end of the century, the exposure of the key European wheat-growing areas, where most wheat production is currently concentrated, may increase more than twofold. However, if we consider the entire arable land area of Europe, a greater than threefold increase in risk was predicted. Therefore, shifting wheat production to new producing regions to reduce the risk might not be possible as the risk of adverse events beyond the key wheat-growing areas increases even more. Furthermore, we found a marked increase in wheat exposure to high temperatures, severe droughts and field inaccessibility compared with other types of adverse events. Our results also showed the limitations of some of the presently debated adaptation options and demonstrated the need for development of region-specific strategies. Other regions of the world could be affected by adverse weather events in the future in a way different from that considered here for Europe. This observation emphasizes the importance of conducting similar analyses for other major wheat regions.

This study proposes the stochastic weather generator (WG)‐based bootstrap approach to provide the probabilistic climate change information on mean precipitation as well as extremes, which applies a WG (i.e., LARS‐WG) to daily precipitation under the present‐day and future climate conditions derived from dynamical and statistical downscaling models. Additionally, the study intercompares the precipitation change scenarios derived from the multimodel ensemble for Japan focusing on five precipitation indices (mean precipitation, MEA; number of wet days, FRE; mean precipitation amount per wet day, INT; maximum number of consecutive dry days, CDD; and 90th percentile value of daily precipitation amount in wet days, Q90). Three regional climate models (RCMs: NHRCM, NRAMS and TWRF) are nested into the high‐resolution atmosphere‐ocean coupled general circulation model (MIROC3.2HI AOGCM) for A1B emission scenario. LARS‐WG is validated and used to generate 2000 years of daily precipitation from sets of grid‐specific parameters derived from the 20‐year simulations from the RCMs and statistical downscaling model (SDM: CDFDM). Then 100 samples of the 20‐year of continuous precipitation series are resampled, and mean values of precipitation indices are computed, which represents the randomness inherent in daily precipitation data. Based on these samples, the probabilities of change in the indices and the joint occurrence probability of extremes (CDD and Q90) are computed. High probabilities are found for the increases in heavy precipitation amount in spring and summer and elongated consecutive dry days in winter over Japan in the period 2081–2100, relative to 1981–2000. The joint probability increases in most areas throughout the year, suggesting higher potential risk of droughts and excess water‐related disasters (e.g., floods) in a 20 year period in the future. The proposed approach offers more flexible way in estimating probabilities of multiple types of precipitation extremes including their joint probability compared to conventional approaches. A proposition of new method to provide probabilistic climate informationAn intercomparison of regional precipitation change scenarios in JapanA presentation of an application of multi‐downscaling model ensemble dataset