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  • 1
    Publication Date: 2020-11-10
    Description: Goals and pathways to achieve sustainable urban development have multiple interlinkages with human health and wellbeing. However, these interlinkages have not been examined in depth in recent discussions on urban sustainability and global urban science. This paper fills that gap by elaborating in detail the multiple links between urban sustainability and human health and by mapping research gaps at the interface of health and urban sustainability sciences. As researchers from a broad range of disciplines, we aimed to: 1) define the process of urbanization, highlighting distinctions from related concepts to support improved conceptual rigour in health research; 2) review the evidence linking health with urbanization, urbanicity, and cities and identify cross-cutting issues; and 3) highlight new research approaches needed to study complex urban systems and their links with health. This novel, comprehensive knowledge synthesis addresses issue of interest across multiple disciplines. Our review of concepts of urban development should be of particular value to researchers and practitioners in the health sciences, while our review of the links between urban environments and health should be of particular interest to those outside of public health. We identify specific actions to promote health through sustainable urban development that leaves no one behind, including: integrated planning; evidence-informed policy-making; and monitoring the implementation of policies. We also highlight the critical role of effective governance and equity-driven planning in progress towards sustainable, healthy, and just urban development.
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  • 2
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    In:  Essential Concepts of Global Environmental Governance
    Publication Date: 2020-12-21
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  • 3
    Publication Date: 2020-12-21
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  • 4
    Publication Date: 2021-01-07
    Description: There is an increasing interest to study the interactions between atmospheric electrical parameters and living organisms at multiple scales. So far, relatively few studies have been published that focus on possible biological effects of atmospheric electric and magnetic fields. To foster future work in this area of multidisciplinary research, here we present a glossary of relevant terms. Its main purpose is to facilitate the process of learning and communication among the different scientific disciplines working on this topic. While some definitions come from existing sources, other concepts have been re-defined to better reflect the existing and emerging scientific needs of this multidisciplinary and transdisciplinary area of research.
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  • 5
    Publication Date: 2021-01-05
    Description: This study looks into the question to what extent long-term change patterns of observed temperature and rainfall over Europe can be attributed to dynamical causes, in other words: Are the observed changes due to a change in frequency of the patterns or have the patterns’ dynamical properties changed? By using a combination of daily meteorological data and a European weather-type classification, the long-term monthly mean temperature and precipitation were calculated for each weather-type. Subsequently, the observed weather-type sequences were used to construct analogue time series for temperature and precipitation which only include the dynamical component of the long-term variability since 1961. The results show that only a fraction of about 20% of the past temperature rise since 1990, which for example amounted to 1 °C at the Potsdam Climate Station can be explained by dynamical changes, i.e. most of the weather-types have become warmer. Concerning long-term changes of seasonal rainfall patterns, a fraction of more than 60% is considerably higher. Moreover, the results indicate that for rainfall compared with temperature, the decadal variability and trends of the dynamical component follow the observed ones much stronger. Consequently, most of the explained seasonal rainfall variances can be linked to changes in weather-type sequences in Potsdam and over Europe. The dynamical contribution to long-term changes in annual and seasonal rainfall patterns dominates due to the fact that the alternation of wet and dry weather-types (e.g. the types Trough or High pressure over Central Europe), their frequencies and duration has significantly changed in the last decades.
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  • 6
    Publication Date: 2021-01-05
    Description: Despite the large body of literature on ape conservation, much of the data needed for evidence‐based conservation decision‐making is still not readily accessible and standardized, rendering cross‐site comparison difficult. To support knowledge synthesis and to complement the IUCN SSC Ape Populations, Environments and Surveys database, we created the A.P.E.S. Wiki (https://apeswiki.eva.mpg.de), an open‐access platform providing site‐level information on ape conservation status and context. The aim of this Wiki is to provide information and data about geographical ape locations, to curate information on individuals and organizations active in ape research and conservation, and to act as a tool to support collaboration between conservation practitioners, scientists, and other stakeholders. To illustrate the process and benefits of knowledge synthesis, we used the momentum of the update of the conservation action plan for western chimpanzees (Pan troglodytes verus) and began with this critically endangered taxon. First, we gathered information on 59 sites in West Africa from scientific publications, reports, and online sources. Information was compiled in a standardized format and can thus be summarized using a web scraping approach. We then asked experts working at those sites to review and complement the information (20 sites have been reviewed to date). We demonstrate the utility of the information available through the Wiki, for example, for studying species distribution. Importantly, as an open‐access platform and based on the well‐known wiki layout, the A.P.E.S. Wiki can contribute to direct and interactive information sharing and promote the efforts invested by the ape research and conservation community. The Section on Great Apes and the Section on Small Apes of the IUCN SSC Primate Specialist Group will guide and support the expansion of the platform to all small and great ape taxa. Similar collaborative efforts can contribute to extending knowledge synthesis to all nonhuman primate species. Despite the large body of literature on ape conservation, much of the data needed for evidence‐based conservation decision‐making is still not readily accessible and standardized, rendering cross‐site comparison difficult. To support knowledge synthesis and to complement the IUCN SSC Ape Populations, Environments and Surveys database, we created the A.P.E.S. Wiki (https://apeswiki.eva.mpg.de), an open‐access platform providing site‐level information on ape conservation status and context. The aim of this Wiki is to provide information and data about geographical ape locations, to curate information on individuals and organizations active in ape research and conservation, and to act as a tool to support collaboration between conservation practitioners, scientists, and other stakeholders. To illustrate the process and benefits of knowledge synthesis, we used the momentum of the update of the conservation action plan for western chimpanzees (Pan troglodytes verus) and began with this critically endangered taxon. First, we gathered information on 59 sites in West Africa from scientific publications, reports, and online sources. Information was compiled in a standardized format and can thus be summarized using a web scraping approach. We then asked experts working at those sites to review and complement the information (20 sites have been reviewed to date). We demonstrate the utility of the information available through the Wiki, for example, for studying species distribution. Importantly, as an open‐access platform and based on the well‐known wiki layout, the A.P.E.S. Wiki can contribute to direct and interactive information sharing and promote the efforts invested by the ape research and conservation community. The Section on Great Apes and the Section on Small Apes of the IUCN SSC Primate Specialist Group will guide and support the expansion of the platform to all small and great ape taxa. Similar collaborative efforts can contribute to extending knowledge synthesis to all nonhuman primate species.
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  • 7
    Publication Date: 2021-01-05
    Description: This introductory chapter, written by the editors, provides an overview of their conceptual approach, the book’s line of argumentation, and an insight into the different chapters of the book “Sustainable Land Management in a European Context—a co-design approach”. The synopsis highlights the various approaches and possible applications of a co-design approach.
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  • 8
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    In:  Sustainable Land Management in a European Context: Human-Environment Interactions | Human-Environment Interactions
    Publication Date: 2021-01-05
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  • 9
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    In:  Human-Environment Interactions
    Publication Date: 2021-01-05
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  • 10
    Publication Date: 2021-01-06
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  • 11
    Publication Date: 2021-01-06
    Description: Climate change is threatening the livelihood of tea farmers in Nepal. Simultaneously,the production of tea is becoming an increasingly important economic sector for thecountry. This study aimed to reveal the adaptation behavior towards climate changeamong smallholder tea farmers, particularly which demographic, institutional, andinformation source factors are likely to influence the degree of adaptation. We collectedquantitative data in the district of Ilam via 91 farmers through a questionnaire surveyand applied descriptive statistics, multiple regression, and binary logistic regressionmodels to analyze the collected data. Findings revealed that information sources (peerexchange, internet, and training attendance), as well as institutional factors(cooperative membership and credit access), positively influenced the degree ofclimate change adaptation among the respondents. Easier credit access and joiningcooperatives could enhance the adaptative capacity of smallholder tea farmers.Improving the interaction between the Nepalese government and stakeholdersinvolved in the domestic tea value chain could also increase economic success
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  • 12
    Publication Date: 2021-01-15
    Description: Stalagmites are an extraordinarily powerful resource for the reconstruction of climatological palaeoseasonality. Here, we provide a review of different types of seasonality preserved by stalagmites and methods for extracting this information. A new drip classification scheme is introduced, which facilitates the identification of stalagmites fed by seasonally responsive drips and which highlights the wide variability in drip types feeding stalagmites. This hydrological variability, combined with seasonality in Earth atmospheric processes, meteoric precipitation, biological processes within the soil, and cave atmosphere composition means that every stalagmite retains a different and distinct (but correct) record of environmental conditions. Replication of a record is extremely useful but should not be expected unless comparing stalagmites affected by the same processes in the same proportion. A short overview of common microanalytical techniques is presented, and suggested best practice discussed. In addition to geochemical methods, a new modelling technique for extracting meteoric precipitation and temperature palaeoseasonality from stalagmite δ18O data is discussed and tested with both synthetic and real-world datasets. Finally, world maps of temperature, meteoric precipitation amount, and meteoric precipitation oxygen isotope ratio seasonality are presented and discussed, with an aim of helping to identify regions most sensitive to shifts in seasonality.
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  • 13
    Publication Date: 2021-01-14
    Description: Renewable energy resources, which depend on climate, may be susceptible to future climate change. Here we use climate and integrated assessment models to estimate this effect on key renewables. Future potential and costs are quantified across two warming scenarios for eight technologies: utility-scale and rooftop photovoltaic, concentrated solar power, onshore and offshore wind energy, first-generation and lignocellulosic bioenergy, and hydropower. The generated cost–supply curves are then used to estimate energy system impacts. In a baseline warming scenario, the largest impact is increased availability of bioenergy, though this depends on the strength of CO2 fertilization. Impacts on hydropower and wind energy are uncertain, with declines in some regions and increases in others, and impacts on solar power are minor. In a future mitigation scenario, these impacts are smaller, but the energy system response is similar to that in the baseline scenario given a larger reliance of the mitigation scenario on renewables.
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  • 14
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    In:  Journal of Physics: Complexity
    Publication Date: 2021-01-12
    Description: Complex systems can, to a first approximation, be characterized by the fact that their dynamics emerging at the macroscopic level cannot be easily explained from the microscopic dynamics of the individual constituents of the system. This property of complex systems can be identified in virtually all natural systems surrounding us, but also in many social, economic, and technological systems. The defining characteristics of complex systems imply that their dynamics can often only be captured from the analysis of simulated or observed data. Here, we summarize recent advances in nonlinear data analysis of both simulated and real-world complex systems, with a focus on recurrence analysis for the investigation of individual or small sets of time series, and complex networks for the analysis of possibly very large, spatiotemporal datasets. We review and explain the recent success of these two key concepts of complexity science with an emphasis on applications for the analysis of geoscientific and in particular (palaeo-) climate data. In particular, we present several prominent examples where challenging problems in Earth system and climate science have been successfully addressed using recurrence analysis and complex networks. We outline several open questions for future lines of research in the direction of data-based complex system analysis, again with a focus on applications in the Earth sciences, and suggest possible combinations with suitable machine learning approaches. Beyond Earth system analysis, these methods have proven valuable also in many other scientific disciplines, such as neuroscience, physiology, epidemics, or engineering.
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  • 15
    Publication Date: 2021-01-12
    Description: Cap-and-trade programs such as the European Union's Emissions Trading System (EU ETS) expose firms to considerable risks, to which the firms can respond with hedging. We develop an intertemporal stochastic equilibrium model to analyze the implications of hedging by risk-averse firms. We show that the resulting time-varying risk premium depends on the size of the permit bank. Applying the model to the EU ETS, we find that hedging can lead to a U-shaped price path, because prices initially fall due to negative risk premiums and then rise as the hedging demand declines. The Market Stability Reserve (MSR) reduces the permit bank and thus, increases the hedging value of the permits. This offers an explanation for the recent price hike, but also implies that prices may decline in the future due to more negative risk premiums. In addition, we find higher permit cancellations through the MSR than previous analyses, which do not account for hedging.
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  • 16
    Publication Date: 2021-01-12
    Description: Food system innovations will be instrumental to achieving multiple Sustainable Development Goals (SDGs). However, major innovation breakthroughs can trigger profound and disruptive changes, leading to simultaneous and interlinked reconfigurations of multiple parts of the global food system. The emergence of new technologies or social solutions, therefore, have very different impact profiles, with favourable consequences for some SDGs and unintended adverse side-effects for others. Stand-alone innovations seldom achieve positive outcomes over multiple sustainability dimensions. Instead, they should be embedded as part of systemic changes that facilitate the implementation of the SDGs. Emerging trade-offs need to be intentionally addressed to achieve true sustainability, particularly those involving social aspects like inequality in its many forms, social justice, and strong institutions, which remain challenging. Trade-offs with undesirable consequences are manageable through the development of well planned transition pathways, careful monitoring of key indicators, and through the implementation of transparent science targets at the local level.
    Language: English
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  • 17
    Publication Date: 2021-01-12
    Description: Pine Island Glacier in West Antarctica is among the fastest changing glaciers worldwide. Over the last two decades, the glacier has lost in excess of a trillion tons of ice, or the equivalent of 3 mm of sea level rise. The ongoing changes are commonly attributed to ocean-induced thinning of its floating ice shelf and the associated reduction in buttressing forces. However, other drivers of change such as large-scale calving, changes in ice rheology and basal slipperiness could play a vital, yet unquantified, role in controlling the ongoing and future evolution of the glacier. In addition, recent studies have shown that mechanical properties of the bed are key to explaining the observed speed-up. Here we used a combination of the latest remote sensing datasets between 1996 and 2016, data assimilation tools and numerical perturbation experiments to quantify the relative importance of all processes in driving the recent changes in Pine Island Glacier dynamics. We show that (1) calving and ice shelf thinning have caused a comparable reduction in ice-shelf buttressing over the past two decades, that (2) simulated changes in ice flow over a viscously deforming bed are only compatible with observations if large and widespread changes in ice viscosity and/or basal slipperiness are taken into account, and that (3) a spatially varying, predominantly plastic bed rheology can closely reproduce observed changes in flow without marked variations in ice-internal and basal properties. Our results demonstrate that in addition to its evolving ice thickness, calving processes and a heterogeneous bed rheology play a key role in the contemporary evolution of Pine Island Glacier.
    Language: English
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  • 18
    Publication Date: 2021-01-12
    Description: Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of 9 crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1 to -19%) than for CMIP5 (+5 to -13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.
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  • 19
    Publication Date: 2021-01-11
    Description: This paper studies the delay-dependent consensus problem of heterogeneous multi-agent systems over directed topology. The heterogeneous dynamics consisting of both first-order and second-order agents with random time delay are considered. New distributed control protocols based on the probability distribution of time delay are proposed for the leader-following and leaderless systems. By adopting matrix theory, Lyapunov-Krasovskii function and stochastic analysis, some less conservative conditions for the mean-square consensus are established over directed fixed topology and switching topologies. Moreover, the larger upper bounds of time delay are obtained. Finally, several simulations are presented to illustrate the obtained results.
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  • 20
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    In:  International Migration Review
    Publication Date: 2021-01-11
    Description: The COVID-19 pandemic has disrupted global human mobility dynamics. This IMR Dispatch examines the historical, bidirectional links between pandemics and mobility and provides an early analysis of how they unfolded during the first nine months of the COVID-19 emergency. Results show, first, that international travel restrictions to combat the spread of the coronavirus are not a panacea in and of themselves. Second, our analysis demonstrates that the pandemic, government responses, and resulting economic impacts can lead to the involuntary immobility of at-risk populations, such as aspiring asylum-seekers or survival migrants. In a similar fashion, stay-at-home measures have posed dire challenges for those workers who lack options to work from home, as well as for migrants living in precarious, crowded circumstances. Moreover, global economic contraction has increased involuntary immobility by reducing both people’s resources to move and the demand for labor. Third, we show that people’s attempts to protect themselves from the virus can result in shifting patterns of mobility, such as increases in cross-border return migration and urban-to-rural movements. Drawing on international guidance for measures to combat pandemics and relevant frameworks on mobility, we propose approaches to alleviate the burden of travel restrictions on migrants and people aspiring to move, while still addressing the need to contain the pandemic and lessen its repercussions.
    Language: English
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  • 21
    Publication Date: 2021-01-20
    Description: Terrestrial water storage (TWS) modulates the hydrological cycle and is a key determinant of water availability and an indicator of drought. While historical TWS variations have been increasingly studied, future changes in TWS and the linkages to droughts remain unexamined. Here, using ensemble hydrological simulations, we show that climate change could reduce TWS in many regions, especially those in the Southern Hemisphere. Strong inter-ensemble agreement indicates high confidence in the projected changes that are driven primarily by climate forcing rather than land and water management activities. Declines in TWS translate to increases in future droughts. By the late twenty-first century, the global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976–2005 to 7% and 8%, respectively. Our findings highlight the importance of climate change mitigation to avoid adverse TWS impacts and increased droughts, and the need for improved water resource management and adaptation.
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  • 22
    Publication Date: 2021-01-20
    Description: Amplified climate warming has led to permafrost degradation and a shortening of the winter season, both impacting cost-effective overland travel across the Arctic. Here we use, for the first time, four state-of-the art Land Surface Models that explicitly consider ground freezing states, forced by a subset of bias-adjusted CMIP5 General Circulation Models to estimate the impact of different global warming scenarios (RCP2.6, 6.0, 8.5) on two modes of winter travel: Overland Travel Days (OTDs) and Ice Road Construction Days (IRCDs). We show that OTDs decrease by on average -13% in the near future (2021-2050) and between -15% (RCP2.6) and -40% (RCP8.5) in the far future (2070-2099) compared to the reference period (1971-2000) when 173 days year-1 are simulated across the Pan-Arctic. Regionally, we identified Eastern Siberia (Sakha (Yakutia), Khabarovsk Krai, Magadan Oblast) to be most resilient to climate change, while Alaska (USA), the Northwestern Russian regions (Yamalo, Arkhangelsk Oblast, Nenets, Komi, Khanty-Mansiy), Northern Europe and Chukotka are highly vulnerable. The change in OTDs is most pronounced during the shoulder season, particularly in autumn. The IRCDs reduce on average twice as much as the OTDs under all climate scenarios resulting in shorter operational duration. The results of the low-end global warming scenario (RCP2.6) emphasize that stringent climate mitigation policies have the potential to reduce the impact of climate change on winter mobility in the second half of the 21st century. Nevertheless, even under RCP2.6, our results suggest substantially reduced winter overland travel implying a severe threat to livelihoods of remote communities and increasing costs for resource exploration and transport across the Arctic.
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  • 23
    Publication Date: 2021-01-20
    Description: Adaptive networks change their connectivity with time, depending on their dynamical state. While synchronization in structurally static networks has been studied extensively, this problem is much more challenging for adaptive networks. In this Letter, we develop the master stability approach for a large class of adaptive networks. This approach allows for reducing the synchronization problem for adaptive networks to a low-dimensional system, by decoupling topological and dynamical properties. We show how the interplay between adaptivity and network structure gives rise to the formation of stability islands. Moreover, we report a desynchronization transition and the emergence of complex partial synchronization patterns induced by an increasing overall coupling strength. We illustrate our findings using adaptive networks of coupled phase oscillators and FitzHugh-Nagumo neurons with synaptic plasticity.
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  • 24
    Publication Date: 2021-01-20
    Description: Limiting global warming to “well below 2°C” as stated in the Paris Agreement requires ambitious emissions reductions from all sectors. Rapid technology cost declines in the energy sector are changing energy investment and emissions, even with the weak climate policies currently in place. We assess how energy supply costs and carbon dioxide removal (CDR) availability affect mitigation by performing a sensitivity analysis with the energy-economy-climate model REMIND. We use new scenarios with carbon price paths that aim to reduce the frequently seen temperature overshoot. Further, we measure the sensitivities of mitigation indicators to the costs of technologies across economic sectors. We assess the sensitivity to nine techno-economic parameters: the costs of wind, solar, biomass, gas, coal, oil, nuclear, and electric/hydrogen vehicles, as well as the injection rate of Carbon Capture and Storage (CCS). While technology costs play a role in shaping optimal pathways, we find that transport sector costs affect the economics of deep decarbonization, whereas costs of renewables are more important for scenarios under weak climate policies. This further highlights the value of renewable energy deployment as a no-regrets option in climate policy. In terms of the sensitivity of model outputs, economic indicators become more sensitive to costs than emissions, with increasing policy stringency.
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  • 25
    Publication Date: 2021-01-20
    Description: Assessing vulnerability to climate change and extremes is the first step towards guiding climate change adaptation. It provides the basis to decide 'what' adaptation measures are needed 'where'. Vulnerability which is defined as a function of exposure, sensitivity, and adaptive capacity, differs spatially and evolves temporally. Therefore, it is imperative to understand the dynamics of vulnerability at sub-national scales to be prepared for and respond to current and future climatic risks. This paper focuses on Ethiopia where a sub-national understanding of vulnerability dynamics in smallholder agriculture systems is missing to date. The paper assesses the vulnerability of crop-based smallholder systems in Ethiopia for the past (1996-2005), current (2006-2015), and two future (2036-2045 and 2066-2075) climate scenarios using an indicator-based approach. The future scenarios are based on two Representative Concentration Pathways (RCPs) RCP 2.6 and RCP 6.0 from four General Circulation Models (GCMs). Results show the emergence of highly vulnerable zones that were missing in the past scenario. With Paris agreement pathway, keeping global warming under 2 °C (RCP 2.6), reduction in vulnerability of 10% of the zones is noted in far future (2066-75) as compared to RCP 6.0 where the exposure increases, making 30% of the zones highly vulnerable. The projected increase in exposure to climatic hazards will worsen the vulnerability of smallholder agricultural systems in future unless the current adaptation deficit is sufficiently addressed. This study maps the temporal dynamics of vulnerability unlike the prevailing snapshot assessments at subnational-level for Ethiopia. The study seeks to assist the decision-making process to build resilience to climate change in Ethiopia and other low-income countries with similar geophysical and socio-economic conditions.
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  • 26
  • 27
    Publication Date: 2020-07-14
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    Publication Date: 2020-07-14
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  • 29
    Publication Date: 2020-07-14
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  • 31
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    In:  Communications in Nonlinear Science and Numerical Simulation
    Publication Date: 2020-07-14
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  • 32
    Publication Date: 2020-07-14
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  • 33
    Publication Date: 2020-07-14
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  • 34
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    In:  Migration Policy Practice Journal
    Publication Date: 2020-07-14
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  • 35
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    In:  Proceedings of the Royal Society A : Mathematical, Physical and Engineering Sciences
    Publication Date: 2020-07-14
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  • 36
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    In:  Frontiers in Sustainable Food Systems
    Publication Date: 2020-07-14
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  • 37
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    Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
    In:  Climate Risk Profiles for Sub-Saharan Africa Series
    Publication Date: 2020-07-14
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  • 38
    Publication Date: 2020-07-14
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  • 39
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    Unknown
    In:  Frontiers of Information Technology and Electronic Engineering
    Publication Date: 2020-07-14
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  • 40
    Publication Date: 2020-07-14
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  • 41
    Publication Date: 2020-07-14
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  • 42
    Publication Date: 2020-07-14
    Language: English
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  • 43
    Publication Date: 2020-07-14
    Description: In this study, we investigate how specific micro-interaction structures (motifs) affect the occurrence of tipping cascades on networks of stylized tipping elements. We compare the properties of cascades in Erdős–Rényi networks and an exemplary moisture recycling network of the Amazon rainforest. Within these networks, decisive small-scale motifs are the feed forward loop, the secondary feed forward loop, the zero loop, and the neighboring loop. Of all motifs, the feed forward loop motif stands out in tipping cascades since it decreases the critical coupling strength necessary to initiate a cascade more than the other motifs. We find that for this motif, the reduction of critical coupling strength is 11% less than the critical coupling of a pair of tipping elements. For highly connected networks, our analysis reveals that coupled feed forward loops coincide with a strong 90% decrease in the critical coupling strength. For the highly clustered moisture recycling network in the Amazon, we observe regions of a very high motif occurrence for each of the four investigated motifs, suggesting that these regions are more vulnerable. The occurrence of motifs is found to be one order of magnitude higher than in a random Erdős–Rényi network. This emphasizes the importance of local interaction structures for the emergence of global cascades and the stability of the network as a whole. Tipping elements are nonlinear systems, where a small perturbation can be sufficient to induce a qualitative change in the whole system as soon as a critical threshold (tipping point) is crossed. Coupled tipping elements exist, for instance, in connected lake systems, in the Earth’s climate system, or in social systems. Here, we investigate networks of interacting tipping elements, where each node consists of a stylized tipping element and explore important interaction structures on the microscale of the network, the so-called motifs. Such motifs in complex networks have been found in multiple systems such as cell metabolism, food webs, or neural networks and are known to be significantly overexpressed in real-world compared to random networks. However, motifs have not yet been studied extensively in complex networks, where nodes have their own dynamics. In our study, we find that tipping cascades occur more often at locations with a high motif frequency revealing locations (nodes) of decreased robustness.
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  • 44
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    In:  Zukunftsdiskurse – Nachhaltiges Wirtschaften zwischen Gesellschaft, Ökonomie und Bildung
    Publication Date: 2020-07-14
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  • 45
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    Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
    In:  Climate Risk Profiles for Sub-Saharan Africa Series
    Publication Date: 2020-07-14
    Language: English
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  • 46
    Publication Date: 2020-07-14
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  • 47
    Publication Date: 2020-07-14
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  • 48
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    In:  Health of People, Health of Planet and Our Responsibility
    Publication Date: 2020-07-14
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  • 49
    Publication Date: 2020-07-14
    Description: Climate change presents a global environmental threat to all economic sectors and particularly to the agricultural sector. Kenya is one of the countries negatively affected by climate change due to its high exposure to extreme events and the low adaptive capacity of smallholder farmers. Farmers are facing water scarcity, unpredictable weather patterns, dry spells, droughts, and rising temperatures. The effects of high temperatures, drought, and dry spells lead to serious losses in vegetable yields. Smallholder farmers involved in vegetable production are most at risk due to the sensitivities of vegetable production and their high vulnerability. Drought and water stress have been identified as important limiting factors in vegetable production. This paper examines the climate change adaptation strategies of farmers of African indigenous vegetable (AIV) in three agro-climatic zones (ACZs) in Kenya. Data from 18 focus group discussions (FGDs) and 269 interviews with farmers were analyzed. This study showed that AIV farmers have responded to climate change with a wide range of farm-level adaptation measures and all of the respondents use a combination of these strategies. Farm production practices, such as the application of manure, frequent weeding, and watering of vegetables, were most widespread, while migration to urban areas and buying insurance were the strategies adopted least across all zones. The results revealed a significant association between particular adaptation strategies and ACZs, particularly in soil and water management practices and land-use adjustments. This study offers policy recommendations for accelerating AIV farmers’ resilience by supporting opportunities for livelihood diversification.
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  • 50
    Publication Date: 2020-07-14
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  • 51
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    Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft WSL
    In:  Envidat
    Publication Date: 2020-07-14
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  • 52
    Publication Date: 2020-07-14
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  • 53
  • 54
    Publication Date: 2020-07-14
    Description: Tipping points occur in diverse systems in various disciplines such as ecology, climate science, economy, and engineering. Tipping points are critical thresholds in system parameters or state variables at which a tiny perturbation can lead to a qualitative change of the system. Many systems with tipping points can be modeled as networks of coupled multistable subsystems, e.g., coupled patches of vegetation, connected lakes, interacting climate tipping elements, and multiscale infrastructure systems. In such networks, tipping events in one subsystem are able to induce tipping cascades via domino effects. Here, we investigate the effects of network topology on the occurrence of such cascades. Numerical cascade simulations with a conceptual dynamical model for tipping points are conducted on Erdős-Rényi, Watts-Strogatz, and Barabási-Albert networks. Additionally, we generate more realistic networks using data from moisture-recycling simulations of the Amazon rainforest and compare the results to those obtained for the model networks. We furthermore use a directed configuration model and a stochastic block model which preserve certain topological properties of the Amazon network to understand which of these properties are responsible for its increased vulnerability. We find that clustering and spatial organization increase the vulnerability of networks and can lead to tipping of the whole network. These results could be useful to evaluate which systems are vulnerable or robust due to their network topology and might help us to design or manage systems accordingly.
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  • 55
    Publication Date: 2020-07-14
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  • 56
    Publication Date: 2020-07-14
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  • 57
    Publication Date: 2020-07-14
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  • 58
    Publication Date: 2020-07-14
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  • 59
    Publication Date: 2020-07-14
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  • 60
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    In:  International Journal of Pattern Recognition and Artificial Intelligence
    Publication Date: 2020-07-14
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  • 61
    Publication Date: 2020-07-14
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  • 62
    Publication Date: 2020-07-14
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  • 63
    Publication Date: 2020-07-15
    Description: The Arctic Ocean is an early warning system for indicators and effects of climate change. We use a novel combination of experimental and time-series data on effects of ocean warming and acidification on the commercially important Northeast Arctic cod (Gadus morhua) to incorporate these physiological processes into the recruitment model of the fish population. By running an ecological-economic optimization model, we investigate how the interaction of ocean warming, acidification and fishing pressure affects the sustainability of the fishery in terms of ecological, economic, social and consumer-related indicators, ranging from present day conditions up to future climate change scenarios. We find that near-term climate change will benefit the fishery, but under likely future warming and acidification this large fishery is at risk of collapse by the end of the century, even with the best adaptation effort in terms of reduced fishing pressure.
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  • 64
    Publication Date: 2020-07-15
    Description: Gas turbine combustors are susceptible to thermoacoustic instability, which manifests as large amplitude periodic oscillations in acoustic pressure and heat release rate. The transition from a stable operation characterized by combustion noise to thermoacoustic instability in turbulent combustors has been described as an emergence of order (periodicity) from chaos in the temporal dynamics. This emergence of order in the acoustic pressure oscillations corresponds to a loss of multifractality in the pressure signal. In this study, we investigate the spatiotemporal dynamics of a turbulent flame in a bluff-body stabilized combustor during the transition from combustion noise to thermoacoustic instability. During the occurrence of combustion noise, the flame wrinkles due to the presence of small-scale vortices in the turbulent flow. On the other hand, during thermoacoustic instability, large-scale coherent structures emerge periodically. These large-scale coherent structures roll up the wrinkled flame surface further and introduce additional complexity in the flame topology. We perform multifractal analysis on the flame contours detected from high-speed planar Mie scattering images of the reactive flow seeded with non-reactive tracer particles. We find that multifractality exists in the flame topology for all the dynamical states during the transition to thermoacoustic instability. We discuss the variation of multifractal parameters for the different states. We find that the multifractal spectrum oscillates periodically during the occurrence of thermoacoustic instability at the time scale of the acoustic pressure oscillations. The loss of multifractality in the temporal dynamics and the oscillation of the multifractal spectrum of the spatial dynamics go hand in hand.
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  • 65
    Publication Date: 2020-07-16
    Description: Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data.
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  • 66
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    In:  IEEE Transactions on Neural Networks and Learning Systems
    Publication Date: 2020-07-16
    Description: In this paper, the output feedback set stabilization problem for Boolean control networks (BCNs) is investigated with the help of the semi-tensor product (STP) tool. The concept of output feedback control invariant (OFCI) subset is introduced, and novel methods are developed to obtain the OFCI subsets. Based on the OFCI subsets, a technique, named spanning tree method, is further introduced to calculate all possible output feedback set stabilizers. An example concerning lac operon for the bacterium Escherichia coli is given to illustrate the effectiveness of the proposed method. This technique can also be used to solve the state feedback (set) stabilization problem for BCNs. Compared with the existing results, our method can dramatically reduce the computational cost when designing all possible state feedback stabilizers for BCNs.
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  • 67
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    In:  New Journal of Physics
    Publication Date: 2020-07-16
    Description: Many high-dimensional complex systems exhibit an enormously complex landscape of possible asymptotic states. Here, we present a numerical approach geared towards analyzing such systems. It is situated between the classical analysis with macroscopic order parameters and a more thorough, detailed bifurcation analysis. With our machine learning method, based on random sampling and clustering methods, we are able to characterize the different asymptotic states or classes thereof and even their basins of attraction. In order to do this, suitable, easy to compute, statistics of trajectories with randomly generated initial conditions and parameters are clustered by an algorithm such as DBSCAN. Due to its modular and flexible nature, our method has a wide range of possible applications in many disciplines. While typical applications are oscillator networks, it is not limited only to ordinary differential equation systems, every complex system yielding trajectories, such as maps or agent-based models, can be analyzed, as we show by applying it the Dodds–Watts model, a generalized SIRS-model, modeling social and biological contagion. A second order Kuramoto model, used, e.g. to investigate power grid dynamics, and a Stuart–Landau oscillator network, each exhibiting a complex multistable regime, are shown as well. The method is available to use as a package for the Julia language.
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  • 68
    Publication Date: 2020-07-16
    Description: A system consisting of correlated noises and a channel is analyzed. Via the Fick-Jacobs equation for the system’s current evolution, the validity are discussed under three kinds of correlated noise. i) The first case is two Gaussian white noises with a white correlation. We found that in contrast to the single white noise, the white correlation between these two noises breaks the system’s symmetry and causes a directed current and the larger the correlation degree, the smaller the current. However, the interaction between the correlation degree and a sinusoidal potential may produce an increasing steady current. ii) The second one is two Gaussian white noises with an exponential correlation. And our results perform that the correlation time between them contributes to a decrease of the steady current. iii) Finally, the case that two Gaussian colored noises with an exponential correlation is investigated. Unlike the former two cases, whether the correlation time comes from the noise itself or the correlation between the two noises, its increase here can always cause an increasing current.
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  • 69
    Publication Date: 2020-07-16
    Description: The probability density function of stochastic differential equations is governed by the Fokker-Planck (FP) equation. A novel machine learning method is developed to solve the general FP equations based on deep neural networks. The proposed algorithm does not require any interpolation and coordinate transformation, which is different from the traditional numerical methods. The main novelty of this paper is that penalty factors are introduced to overcome the local optimization for the deep learning approach, and the corresponding setting rules are given. Meanwhile, we consider a normalization condition as a supervision condition to effectively avoid that the trial solution is zero. Several numerical examples are presented to illustrate performances of the proposed algorithm, including one-, two-, and three-dimensional systems. All the results suggest that the deep learning is quite feasible and effective to calculate the FP equation. Furthermore, influences of the number of hidden layers, the penalty factors, and the optimization algorithm are discussed in detail. These results indicate that the performances of the machine learning technique can be improved through constructing the neural networks appropriately.
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  • 70
    Publication Date: 2020-07-16
    Description: We study scaling features in the reactions of cereral blood vessel network to sudden “jumps” in peripheral arterial pressure in rats. Using laser speckle contrast imaging (LSCI) to measure the relative velocity of cerebral blood flow (CBF) and detrended fluctuation analysis (DFA) for processing experimental data, we investigate distinctions in the responses of veins and capillaries. To quantify short-term reactions associated with transients, we propose an extension of the conventional DFA approach, which estimates an additional scaling exponent reflecting the effect of nonstationarity. We also consider the ability of characterizing vascular dynamics with multifractal DFA in terms of the degree of multiscality. Based on statistical analysis, we report significant distinctions in the responses of small network of microcerebral blood vessels compared to veins such as the sagittal sinus, which are quite insensitive to variations in peripheral blood circulation.
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  • 71
    Publication Date: 2020-07-16
    Description: The threat of climate catastrophes has been shown to radically change optimal climate policy and prospects for international climate agreements. We characterize the strategic behavior in emissions mitigation and agreement participation with a potential climate catastrophe happening at a temperature threshold. Players are heterogeneous in a conceptual and two numerical models. We confirm that thresholds can induce large, stable coalitions. The relationship between the location of the threshold and the potential for cooperation is non-linear, with the highest potential for cooperation at intermediate temperature thresholds located between 2.5 and 3 degrees of global warming. We find that some regions such as Europe, the USA and China are often pivotal to keeping the threshold because the rest of the world abandons ambitious mitigation and the threshold is crossed without their participation. As a result, their incentives to cooperate can be amplified at the threshold. This behavior critically depends on the characteristics of the threshold as well as the numerical model structure. Conversely, non-pivotal regions are more likely to free-ride as the threshold inverts the strategic response of the remaining coalition. Moreover, we find that our results depend on which equilibrium concepts is applied to analyze coalition formation as well as the introduction of uncertainty about the threshold.
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  • 72
    Publication Date: 2020-07-17
    Description: Understanding spatiotemporal patterns of climate extremes has gained considerable relevance in the context of ongoing climate change. With enhanced computational capacity, data driven methods such as functional climate networks have been proposed and have already contributed to significant advances in understanding and predicting extreme events, as well as identifying interrelations between the occurrences of various climatic phenomena. While the (in its basic setting) parameter free event synchronization (ES) method has been widely applied to construct functional climate networks from extreme event series, its original definition has been realized to exhibit problems in handling events occurring at subsequent time steps, which need to be accounted for. Along with the study of this conceptual limitation of the original ES approach, event coincidence analysis (ECA) has been suggested as an alternative approach that incorporates an additional parameter for selecting certain time scales of event synchrony. In this work, we compare selected features of functional climate network representations of South American heavy precipitation events obtained using ES and ECA without and with the correction for temporal event clustering. We find that both measures exhibit different types of biases, which have profound impacts on the resulting network structures. By combining the complementary information captured by ES and ECA, we revisit the spatiotemporal organization of extreme events during the South American Monsoon season. While the corrected version of ES captures multiple time scales of heavy rainfall cascades at once, ECA allows disentangling those scales and thereby tracing the spatiotemporal propagation more explicitly.
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  • 73
    Publication Date: 2020-07-17
    Description: Power flow studies in traditional power systems aim to uncover the stationary relationship between voltage amplitude and phase and active and reactive powers; they are important for both stationary and dynamic power system analysis. With the increasing penetration of large-scale power electronics devices including renewable generations interfaced with converters, the power systems become gradually power-electronics-dominant and correspondingly their dynamical behavior changes substantially. Due to the fast dynamics of converters, such as AC current controller, the quasi-stationary state approximation, which has been widely used in power systems, is no longer appropriate and should be reexamined. In this paper, for a better description of network characteristics, we develop a novel concept of dynamic power flow and uncover an explicit dynamic relation between the instantaneous powers and the voltage vectors. This mathematical relation has been well verified by simulations on transient analysis of a small power-electronics-based power system, and a small-signal frequency-domain stability analysis of a voltage source converter connected to an infinitely strong bus. These results demonstrate the applicability of the proposed method and shed an improved light on our understanding of power-electronics-dominant power systems, whose dynamical nature remains obscure.
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  • 74
    Publication Date: 2020-07-17
    Description: One of the most common causes of failures in complex systems in nature or engineering is an abrupt transition from a stable to an alternate stable state. Such transitions cause failures in the dynamic power systems. We focus on this transition from a stable to an unstable manifold for a rate-dependent mechanical power input via a numerical investigation in a theoretical power system model. Our studies uncover early transitions that depend on the rate of variation of mechanical input. Furthermore, we determine the dependency of a critical rate on initial conditions of the system. Accordingly, this knowledge of the critical rate can be used in devising an effective control strategy based on artificial intelligence (AI). Blackout is the short-term loss of the ability of a power grid to deliver reliable electric power supply. The global trend toward urbanization leads to an increase in demand for electric power that magnifies the challenge of controlling blackout. There are many causes for the power system failures, which can lead to a blackout. Often, our electric power system operates close to the stability margin, which significantly magnifies the chance of cascading failures in the presence of disturbances. The stability analysis conducted hitherto in the power system confined to either linear or nonlinear stability analysis by considering the power system as an autonomous system. However, the power system is a non-autonomous system in which parameters are time-dependent. In this paper, we study the rate-dependent variation of the control parameter and rate-induced transition in a mathematical model of the power system. As the transition will result in power system instability, leading to interruption of the supply and a massive loss of revenue, studying factors leading to transition is highly essential
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  • 75
    Publication Date: 2020-07-17
    Description: In wireless sensor networks, the dynamic network topology and the limitation of communication resources may lead to degradation of the estimation performance of distributed algorithms. To solve this problem, we propose an event-triggered adaptive partial diffusion least mean-square algorithm (ET-APDLMS). On the one hand, the adaptive partial diffusion strategy adapts to the dynamic topology of the network while ensuring the estimation performance. On the other hand, the event-triggered mechanism can effectively reduce the data redundancy and save the communication resources of the network. The communication cost analysis of the ET-APDLMS algorithm is given in the performance analysis. The theoretical results prove that the algorithm is asymptotically unbiased, and it converges in the mean sense and the mean-square sense. In the simulation, we compare the mean-square deviation performance of the ET-APDLMS algorithm and other different diffusion algorithms. The simulation results are consistent with the performance analysis, which verifies the effectiveness of the proposed algorithm. In distributed algorithms, nodes cooperate to complete parameter estimation, which plays a vital role in signal processing. The existing traditional distributed algorithms have high estimation performance but often ignore the problem of high communication costs. Therefore, in recent years, there have been many studies on communication cost reduction algorithms (such as partial, data-selective). In this article, we propose the event-triggered adaptive partial diffusion least mean-square (ET-APDLMS) algorithm to reduce the communication costs of the network. Besides, different from the traditional communication cost reduction algorithm, the ET-APDLMS algorithm considers that the actual environment (such as the ocean and atmosphere) on the impact of network topology structure, makes the algorithm adapt to different application environments. The theoretical results prove that the algorithm is asymptotically unbiased, and it converges in the mean sense and the mean-square sense. In the simulation, we compared the mean-square deviation (MSD) performance of the ET-APDLMS algorithm and other different diffusion algorithms
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  • 76
    Publication Date: 2020-08-06
    Description: The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain–computer interface applications. Even though some well-known features of motor-related electroencephalograms have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here, we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of μ-rhythm in the sensorimotor cortex, i.e., tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of μ -band neuronal activity. This is due to the lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case, we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this, we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and, therefore, are able to classify the laterality of executed movements. The detection of the motor-related brain activity for noninvasive electroencephalogram (EEG)-based brain–computer interfaces (BCIs) is an actively discussed topic in many areas of research. This is of special interest in the context of neurorehabilitation and non-muscular control of remote devices using BCI-based techniques. Traditionally used methods for motor-related feature extraction, such as spatial filtering and time-frequency analysis, allow one to associate motor actions with event-related desynchronization (ERD) of μ -band oscillations (8–13 Hz) over the sensorimotor cortex. However, these features, i.e., location of brain activity sources, amplitudes of spectral components, etc., are of strong inter- and intrasubject variability. Moreover, inherent nonstationarity and a poor signal-to-noise ratio of EEG signals strongly complicate the detection and classification of motor-related patterns in single trials. To find new features of the motor-related brain activity, we explore EEG signals from the viewpoint of signal complexity. In particular, we put forward the hypothesis that μ-band ERD causes the reduction of random fluctuations of neuronal activity, resulting in a more regular behavior of EEG signals during motor task accomplishments. With this goal in mind, we apply recurrence quantification analysis (RQA), a nonlinear method that describes the recurrence structure of a system by several quantifiers, in order to examine its complexity and uncover hidden underlying phenomena. Our findings show that certain RQA measures, namely, determinism and recurrence time entropy, allow one to reveal new features associated with neuronal activity complexity reduction. These measures are not only sensitive to the transitions from background to motor-related brain activity but also very useful for distinguishing different types of motor actions (left/right limbs motion), which is valuable in the context of potential BCI applications
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  • 77
    Publication Date: 2020-08-06
    Description: Humanity's transformation of the nitrogen cycle has major consequences for ecosystems, climate and human health, making it one of the key environmental issues of our time. Understanding how trends could evolve over the course of the 21st century is crucial for scientists and decision-makers from local to global scales. Scenario analysis is the primary tool for doing so, and has been applied across all major environmental issues, including nitrogen pollution. However, to date most scenario efforts addressing nitrogen flows have either taken a narrow approach, focusing on a singular impact or sector, or have not been integrated within a broader scenario framework – a missed opportunity given the multiple environmental and socio-economic impacts that nitrogen pollution exacerbates. Capitalizing on our expanding knowledge of nitrogen flows, this study introduces a framework for new nitrogen-focused narratives based on the widely used Shared Socioeconomic Pathways that include all the major nitrogen-polluting sectors (agriculture, industry, transport and wastewater). These new narratives are the first to integrate the influence of climate and other environmental pollution control policies, while also incorporating explicit nitrogen-control measures. The next step is for them to be used as model inputs to evaluate the impact of different nitrogen production, consumption and loss trajectories, and thus advance understanding of how to address environmental impacts while simultaneously meeting key development goals. This effort is an important step in assessing how humanity can return to the planetary boundary of this essential element over the coming century.
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  • 78
    Publication Date: 2020-08-06
    Description: Yield estimations are of great interest to support interventions from governmental policies and to increase global food security. This study presents a novel model to perform in‐season corn yield predictions at the US‐county level, providing robust results under different weather and yield levels. The objectives of this study were to: (i) evaluate the performance of a random forest classification to identify corn fields using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and weather variables (temperature, precipitation, and vapor pressure deficit, VPD); (ii) evaluate the contribution of weather variables when forecasting corn yield via remote sensing data, and perform a sensitivity analysis to explore the model performance in different dates; and (iii) develop a model pipeline for performing in‐season corn yield predictions at county‐scale. Main outcomes from this study were: (i) high accuracy (87% on average) for corn field classification achieved in late August, (ii) corn yield forecasts with a mean absolute error (MAE) of 0.89 Mg ha−1, (iii) weather variables (VPD and temperature) highly influenced the model performance, and (iv) model performance decreased when predictions were performed early in the season (mid‐July), with MAE increasing from 0.87–1.36 Mg ha−1 when forecast timing changed from day of year 232–192. This research portrays the benefits of integrating statistical techniques and remote sensing to field survey data in order to perform more reliable in‐season corn yield forecasts.
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  • 79
    Publication Date: 2020-08-06
    Description: Scientific meetings should be organized in the spirit of responsible consumption and production, including the prioritization of plant-based meals for reduced nitrogen loss. The Cercedilla Manifesto indicates how.
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 80
    Publication Date: 2020-08-06
    Type: info:eu-repo/semantics/article
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  • 81
    Publication Date: 2020-08-06
    Description: Global municipal waste production causes multiple environmental impacts, including greenhouse gas emissions, ocean plastic accumulation, and nitrogen pollution. However, estimates of both past and future development of waste and pollution are scarce. We apply compositional Bayesian regression to produce the first estimates of past and future (1965–2100) waste generation disaggregated by composition and treatment, along with resultant environmental impacts, for every country. We find that total wastes grow at declining speed with economic development, and that global waste generation has increased from 635 Mt in 1965 to 1999 Mt in 2015 and reaches 3539 Mt by 2050 (median values, middle-of-the-road scenario). From 2015 to 2050, the global share of organic waste declines from 47% to 39%, while all other waste type shares increase, especially paper. The share of waste treated in dumps declines from 28% to 18%, and more sustainable recycling, composting, and energy recovery treatments increase. Despite these increases, we estimate environmental loads to continue increasing in the future, although yearly plastic waste input into the oceans has reached a peak. Waste production does not appear to follow the environmental Kuznets curve, and current projections do not meet UN SDGs for waste reduction. Our study shows that a continuation of current trends and improvements is insufficient to reduce pressures on natural systems and achieve a circular economy. Relative to 2015, the amount of recycled waste would need to increase from 363 Mt to 740 Mt by 2030 to begin reducing unsustainable waste generation, compared to 519 Mt currently projected.