Slowdown of Circumpolar Deepwater flow during the Late Neogene: Evidence from a mudwave field at the Argentine continental slope

Geochemical evidence from boreholes suggests enhanced transport of Northern Component Water (NCW) to southern latitudes from about 6 Ma onwards. However, information on how this change in transport influenced the intensity and position of current systems is sparse. Here we use seismic reflection profiles interpreted together with bathymetric data to investigate current derived deposits at the central Argentine Margin. Upslope migrating mudwaves overlying a late Miocene erosional unconformity provide evidence that Circumpolar Deepwater (CDW) flow slowed down with the onset of NCW inflow. During the last ~3 Ma changes in dimensions and migration rates of the waves are small indicating continuous bottom current flow conditions similar to today with only minor variations in flow speed, suggesting that the Deep Western Boundary Current (DWBC) in the western south Atlantic as observed today, has been a pervasive feature of the global thermohaline circulation system during the Plio-/Pleistocene

Decarbonisation through digitalisation : Proposals for Transforming the Energy Sector

The successful and rapid achievement of sustainability and climate protection goals is becoming an ever-greater focus of political, economic, and societal action. Against this background, the energy industry contributes and will further contribute to decarbonisation in Germany and throughout Europe. Indeed, it already provides a significant contribution to the Paris Agreement and European Green Deal. In this light, the next transformation phase to a sustainable energy system is inevitably linked to the modernisation and especially to the digitalisation of the energy industry. The aim of this thesis paper is to intensify the discussion on the digitalisation of the energy industry and, in particular, to outline recommendations for flexible and proactive action by all stakeholders. The University of Bayreuth, the Fraunhofer FIT Project Group Business & Information Systems Engineering and the European transmission system operator TenneT are united by the vision of climate-neutral economic growth based on the innovative strength of the European economy. In 2021, decarbonisation is already shaping the digitalisation of the energy industry. Following on from the steps initiated in recent years to move the energy industry towards greater sustainability in the course of the energy transition, the main concern now is to accelerate sustainable growth while continuing to keep the energy supply secure and economical. A crucial building block in this development is the electrification of additional sectors. Accordingly, we discuss the role of grid expansion with respect to sector coupling and emphasise the digitalisation of end-to-end energy industry processes. In this context, we see decentralised digital identities as a promising way of bridging the current digital gap and addressing the need for digital certificates for thorough decarbonisation. In view of the urgency of climate policy action, we recommend an appropriate innovation policy to enable promising solutions to be tested in an agile way and findings to be drawn rapidly. Finally, we offer an overview of the monitoring of carbon emissions in grid expansion projects. This paper is aimed at political decision-makers, energy industry stakeholders, and all citizens interested in energy policy

Bootstrap Autoregressive Order Selection

In this paper we deal with the problem of fitting an autoregression of order p to given data coming from a stationary autoregressive process with infinite order. The paper is mainlyconcerned with the selection of an appropriate order of theautoregressive model. Based on the so-called final prediction error (FPE) a bootstrap order selection can be proposed, because it turns out that one relevant expression occuring in the FPE is ready for the application of the bootstrap principle. Some asymptotic properties of the bootstrap order selection are proved. To carry through the bootstrap procedure an autoregression with increasing but non-stochastic order is fitted to the given data. The paper is concluded by some simulations

Bootstrap of Kernel Smoothing in Nonlinear Time Series

Kernel smoothing in nonparametric autoregressive schemes offers a powerful tool in modelling time series. In this paper it is shown that the bootstrap can be used for estimating the distribution of kernel smoothers. This can be done by mimicking the stochastic nature of the whole process in the bootstrap resampling or by generating a simple regression model. Consistency of these bootstrap procedures will be shown

The Advocate

Headlines Include: Laurels For Feerick: An Alumnus To Remember; Crime at Fordham; Who\u27s Next?, Film at 11https://ir.lawnet.fordham.edu/student_the_advocate/1007/thumbnail.jp

Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

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 nine 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

A kilobit special number field sieve factorization

We describe how we reached a new factoring milestone by completing the first special number field sieve factorization of a number having more than 1024 bits, namely the Mersenne number 21039 -1. Although this factorization is orders of magnitude 'easier' than a factorization of a 1024-bit RSA modulus is believed to be, the methods we used to obtain our result shed new light on the feasibility of the latter computation. © International Association for Cryptology Research 2007