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    Data-Driven Discovery of Stochastic Differential Equations
    (Beijing : Engineering Sciences Press, 2022) Wang, Yasen; Fang, Huazhen; Jin, Junyang; Ma, Guijun; He, Xin; Dai, Xing; Yue, Zuogong; Cheng, Cheng; Zhang, Hai-Tao; Pu, Donglin; Wu, Dongrui; Yuan, Ye; Gonçalves, Jorge; Kurths, Jürgen; Ding, Han
    Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system's dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.
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    Wildlife-vehicle collisions in hurungwe safari area, northern zimbabwe
    (Amsterdam [u.a.] : Elsevier, 2020) Gandiwa, Edson; Mashapa, Clayton; Muboko, Never; Chemura, Abel; Kuvaoga, Phillip; Mabika, Cheryl T.
    This study is the first to assess wildlife-vehicle collisions (WVC) in Zimbabwe. The study analysed the impact and factors that influence vehicle collisions with large wild mammals along the Harare-Chirundu road section in the protected Hurungwe Safari Area, northern Zimbabwe. Data were retrieved from the Hurungwe Safari Area records and covered the period between 2006 and 2013. Descriptive statistics were used to analyse the recorded variables across the sampled area and to show trends of the prevalence of large wild mammals roadkill over time. Using STATISTICA version 10 for Windows, a two-tailed Mann-Whitney U test was used to determine differences between the number of wild mammal animal roadkills and seasons. A total of 47 large wild mammal animals were killed between 2006 and 2013. The large wild mammal animals that died as a result of vehicle collisions constituted a total of 11 species, with the African buffalo and spotted hyena being the most hit and killed animal species. Most WVC involved heavy haulage trucks and passenger buses. There was no significance difference (P = 0.936) between number of large wild mammal animals killed from WVC between dry and wet seasons. The large wild mammal animals were mostly killed in areas near water sources. We recommend for the inclusion of wildlife protection safeguards in road infrastructure network design and development, particularly on roads that traverse across protected areas in Zimbabwe and beyond. © 2020 The Author(s)
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    Climate change and specialty coffee potential in Ethiopia
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Chemura, Abel; Mudereri, Bester Tawona; Yalew, Amsalu Woldie; Gornott, Christoph
    Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience.
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    Yield trends, variability and stagnation analysis of major crops in France over more than a century
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2018) Schauberger, Bernhard; Ben-Ari, Tamara; Makowski, David; Kato, Tomomichi; Kato, Hiromi; Ciais, Philippe
    France is a major crop producer, with a production share of approx. 20% within the European Union. Yet, a discussion has recently started whether French yields are stagnating. While for wheat previous results are unanimously pointing to recent stagnation, there is contradictory evidence for maize and few to no results for other crops. Here we analyse a data set with more than 120,000 yield observations from 1900 to 2016 for ten crops (barley, durum and soft wheat, maize, oats, potatoes, rapeseed, sugar beet, sunflower and wine) in the 96 mainland French départements (NUTS3 administrative division). We dissect the evolution of yield trends over time and space, analyse yield variation and evaluate whether growth of yields has stalled in recent years. Yields have, on average across crops, multiplied four-fold over the course of the 20th century. While absolute yield variability has increased, the variation relative to the mean has halved – mean yields have increased faster than their variability. But growth of yields has stagnated since the 1990’s for winter wheat, barley, oats, durum wheat, sunflower and wine on at least 25% of their areas. Reaching yield potentials is unlikely as a cause for stagnation. Maize, in contrast, shows no evidence for stagnation.
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    Network-based identification and characterization of teleconnections on different scales
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Agarwal, Ankit; Caesar, Levke; Marwan, Norbert; Maheswaran, Rathinasamy; Merz, Bruno; Kurths, Jürgen
    Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
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    Robustly forecasting maize yields in Tanzania based on climatic predictors
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Laudien, Rahel; Schauberger, Bernhard; Makowski, David; Gornott, Christoph
    Seasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill.
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    Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Kurths, Juergen; Feigin, Alexander
    Currently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation.
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    Statistical Properties and Predictability of Extreme Epileptic Events
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Frolov, Nikita S.; Grubov, Vadim V.; Maksimenko, Vladimir A.; Lüttjohann, Annika; Makarov, Vladimir V.; Pavlov, Alexey N.; Sitnikova, Evgenia; Pisarchik, Alexander N.; Kurths, Jürgen; Hramov, Alexander E.
    The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. The latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (EEG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. Our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. The return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of EEG statistical properties.
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    Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Forkel, Matthias; Drüke, Markus; Thurner, Martin; Dorigo, Wouter; Schaphoff, Sibyll; Thonicke, Kirsten; von Bloh, Werner; Carvalhais, Nuno
    The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations.
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    Comparing socioeconomic inequalities between early neonatal mortality and facility delivery: Cross-sectional data from 72 low- and middle-income countries
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Lohela, Terhi J.; Nesbitt, Robin C.; Pekkanen, Juha; Gabrysch, Sabine
    Facility delivery should reduce early neonatal mortality. We used the Slope Index of Inequality and logistic regression to quantify absolute and relative socioeconomic inequalities in early neonatal mortality (0 to 6 days) and facility delivery among 679,818 live births from 72 countries with Demographic and Health Surveys. The inequalities in early neonatal mortality were compared with inequalities in postneonatal infant mortality (28 days to 1 year), which is not related to childbirth. Newborns of the richest mothers had a small survival advantage over the poorest in unadjusted analyses (−2.9 deaths/1,000; OR 0.86) and the most educated had a small survival advantage over the least educated (−3.9 deaths/1,000; OR 0.77), while inequalities in postneonatal infant mortality were more than double that in absolute terms. The proportion of births in health facilities was an absolute 43% higher among the richest and 37% higher among the most educated compared to the poorest and least educated mothers. A higher proportion of facility delivery in the sampling cluster (e.g. village) was only associated with a small  decrease in early neonatal mortality. In conclusion, while socioeconomically advantaged mothers had much higher use of a health facility at birth, this did not appear to convey a comparable survival advantage.