Exploiting the Oil-GDP Effect to Support Renewables Deployment

The empirical evidence from a growing body of academic literature clearly suggests that oil price increases and volatility dampen macroeconomic growth by raising inflation and unemployment and by depressing the value of financial and other assets. Surprisingly, this issue seems to have received little attention from energy policy makers. In percentage terms, the Oil-GDP effect is relatively small, producing losses in the order of 0.5% of GDP for a 10% oil price increase. In absolute terms however, even a 10% oil price rise. and oil has risen at least 50% in the last year alone. produces GDP losses that, could they have been averted, would significantly offset the cost of increased RE deployment. While we focus on renewables, the GDP offset applies equally to energy efficiency, DSM and nuclear and other non-fossil technologies. This paper draws on the empirical Oil-GDP literature, which we summarize, to show that by displacing gas and oil, renewable energy investments can help nations avoid costly macroeconomic losses produced by the Oil-GDP effect. We show that a 10% increase in RE share avoids GDP losses in the range of $29.$53 billion in the US and the EU ($49.$90 billion for OECD). These avoided losses offset one-fifth of the RE investment needs projected by the EREC and half the OECD investment projected by a G-8 Task Force. For the US, the figures further suggest that each additional kW of renewables, on average, avoids $250.$450 in GDP losses, a figure that varies across technologies as a function of annual capacity factors. We approximate that the offset is worth $200/kW for wind and solar and$800/kW for geothermal and biomass (and probably nuclear). The societal valuation of non-fossil alternatives needs to reflect the avoided GDP losses, whose benefit is not fully captured by private investors. This said, we fully recognize that wealth created in this manner does not directly form a pool of public funds that is easily earmarked for renewables support. Finally, the Oil-GDP relationship has important implications for correctly estimating direct electricity generating cost for conventional and renewable alternatives and for developing more useful energy security and diversity concepts. We also address these issues.Oil price shocks, oil price volatility, Oil-GDP effects, renewable energy, RES-E targets, financial beta risk, funding renewables

A Simple Deterministic Distributed MST Algorithm, with Near-Optimal Time and Message Complexities

Distributed minimum spanning tree (MST) problem is one of the most central and fundamental problems in distributed graph algorithms. Garay et al. \cite{GKP98,KP98} devised an algorithm with running time $O(D + \sqrt{n} \cdot \log^* n)$, where $D$ is the hop-diameter of the input $n$-vertex $m$-edge graph, and with message complexity $O(m + n^{3/2})$. Peleg and Rubinovich \cite{PR99} showed that the running time of the algorithm of \cite{KP98} is essentially tight, and asked if one can achieve near-optimal running time **together with near-optimal message complexity**. In a recent breakthrough, Pandurangan et al. \cite{PRS16} answered this question in the affirmative, and devised a **randomized** algorithm with time $\tilde{O}(D+ \sqrt{n})$ and message complexity $\tilde{O}(m)$. They asked if such a simultaneous time- and message-optimality can be achieved by a **deterministic** algorithm. In this paper, building upon the work of \cite{PRS16}, we answer this question in the affirmative, and devise a **deterministic** algorithm that computes MST in time $O((D + \sqrt{n}) \cdot \log n)$, using $O(m \cdot \log n + n \log n \cdot \log^* n)$ messages. The polylogarithmic factors in the time and message complexities of our algorithm are significantly smaller than the respective factors in the result of \cite{PRS16}. Also, our algorithm and its analysis are very **simple** and self-contained, as opposed to rather complicated previous sublinear-time algorithms \cite{GKP98,KP98,E04b,PRS16}

Notched graphite polymimide composites at room and notched graphite polymide composites at room and elevated temperatures

The fracture behavior in graphite/polyimide (Gr/PI) Celion 6000/PMR-15 composites was characterized. Emphasis was placed on the correlation between the observed failure modes and the deformation characteristics of center-notched Gr/Pl laminates. Crack tip damage growth, fracture strength and notch sensitivity, and the associated characterization methods were also examined. Special attention was given to nondestructive evaluation of internal damage and damage growth, techniques such as acoustic emission, X-ray radiography, and ultrasonic C-scan. Microstructural studies using scanning electron microscopy, photomicrography, and the pulsed nuclear magnetic resonance technique were employed as well. All experimental procedures and techniques are described and a summary of representative results for Gr/Pl laminates is given

Disease dynamics across political borders : the case of rabies in Israel and the surrounding countries

An eco-historical analysis facilitated the identification of the socio-political, demographical and environmental changes that have affected the distribution and abundance of vertebrates living in Israeli and Palestinian territories, their pathogens and the extent of human -animal contacts, all contributing to the risk of rabies, leading to three deaths in the late 90's. There are indications that the implementation of uncoordinated control strategies with a lack of an ecological perspective on one side of the border, such as the destruction of the main reservoirs, led to the emergence of a more potent reservoir coming from the other side, and the creation of an additional one yet to be identified. We analyze the lessons of historical mistakes, aiming at future regional control of the disease

RoBuSt: A Crash-Failure-Resistant Distributed Storage System

In this work we present the first distributed storage system that is provably robust against crash failures issued by an adaptive adversary, i.e., for each batch of requests the adversary can decide based on the entire system state which servers will be unavailable for that batch of requests. Despite up to $\gamma n^{1/\log\log n}$ crashed servers, with $\gamma>0$ constant and $n$ denoting the number of servers, our system can correctly process any batch of lookup and write requests (with at most a polylogarithmic number of requests issued at each non-crashed server) in at most a polylogarithmic number of communication rounds, with at most polylogarithmic time and work at each server and only a logarithmic storage overhead. Our system is based on previous work by Eikel and Scheideler (SPAA 2013), who presented IRIS, a distributed information system that is provably robust against the same kind of crash failures. However, IRIS is only able to serve lookup requests. Handling both lookup and write requests has turned out to require major changes in the design of IRIS.Comment: Revised full versio

Parallel Batch-Dynamic Graph Connectivity

In this paper, we study batch parallel algorithms for the dynamic connectivity problem, a fundamental problem that has received considerable attention in the sequential setting. The most well known sequential algorithm for dynamic connectivity is the elegant level-set algorithm of Holm, de Lichtenberg and Thorup (HDT), which achieves $O(\log^2 n)$ amortized time per edge insertion or deletion, and $O(\log n / \log\log n)$ time per query. We design a parallel batch-dynamic connectivity algorithm that is work-efficient with respect to the HDT algorithm for small batch sizes, and is asymptotically faster when the average batch size is sufficiently large. Given a sequence of batched updates, where $\Delta$ is the average batch size of all deletions, our algorithm achieves $O(\log n \log(1 + n / \Delta))$ expected amortized work per edge insertion and deletion and $O(\log^3 n)$ depth w.h.p. Our algorithm answers a batch of $k$ connectivity queries in $O(k \log(1 + n/k))$ expected work and $O(\log n)$ depth w.h.p. To the best of our knowledge, our algorithm is the first parallel batch-dynamic algorithm for connectivity.Comment: This is the full version of the paper appearing in the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 201

Tradeoffs in worst-case equilibria

AbstractWe investigate the problem of routing traffic through a congested network in an environment of non-cooperative users. We use the worst-case coordination ratio suggested by Koutsoupias and Papadimitriou to measure the performance degradation due to the lack of a centralized traffic regulating authority. We provide a full characterization of the worst-case coordination ratio in the restricted assignment and unrelated parallel links model. In particular, we quantify the tradeoff between the “negligibility” of the traffic controlled by each user and the worst-case coordination ratio. We analyze both pure and mixed strategies systems and identify the range where their performance is similar

Adaptive Regret Minimization in Bounded-Memory Games

Online learning algorithms that minimize regret provide strong guarantees in situations that involve repeatedly making decisions in an uncertain environment, e.g. a driver deciding what route to drive to work every day. While regret minimization has been extensively studied in repeated games, we study regret minimization for a richer class of games called bounded memory games. In each round of a two-player bounded memory-m game, both players simultaneously play an action, observe an outcome and receive a reward. The reward may depend on the last m outcomes as well as the actions of the players in the current round. The standard notion of regret for repeated games is no longer suitable because actions and rewards can depend on the history of play. To account for this generality, we introduce the notion of k-adaptive regret, which compares the reward obtained by playing actions prescribed by the algorithm against a hypothetical k-adaptive adversary with the reward obtained by the best expert in hindsight against the same adversary. Roughly, a hypothetical k-adaptive adversary adapts her strategy to the defender's actions exactly as the real adversary would within each window of k rounds. Our definition is parametrized by a set of experts, which can include both fixed and adaptive defender strategies. We investigate the inherent complexity of and design algorithms for adaptive regret minimization in bounded memory games of perfect and imperfect information. We prove a hardness result showing that, with imperfect information, any k-adaptive regret minimizing algorithm (with fixed strategies as experts) must be inefficient unless NP=RP even when playing against an oblivious adversary. In contrast, for bounded memory games of perfect and imperfect information we present approximate 0-adaptive regret minimization algorithms against an oblivious adversary running in time n^{O(1)}.Comment: Full Version. GameSec 2013 (Invited Paper