Investitionsplanung unter Unsicherheit – Ein agentenbasierter Ansatz für liberalisierte Strommärkte

In liberalisierten Strommärkten bilden umfassende Wirtschaftlichkeitsbewertungen die Basis für Investitionsentscheidungen. Unter anderem aufgrund der Kapitalintensität sowie langfristiger Investitionshorizonte verhalten sich Investoren dabei in hohem Maße risikoavers. In diesem Beitrag wird das agentenbasierte Strommarktmodell PowerACE um die Berücksichtigung von Unsicherheiten und dem damit verbundenen Risiko für die Ausbauplanung erweitert. Für die Generierung der Szenarios werden verschiedene Wetterjahre derart kombiniert, dass das Ausmaß der Volatilität der Residuallast repräsentativ über den gesamten Investitionshorizont abgebildet wird. Mithilfe der Szenarios wird eine Verteilung der Profitabilität abgeleitet, auf deren Basis für die Bewertung von Investitionsoptionen neben der erwarteten Profitabilität auch der Conditional Value-at-Risk in einem multikriteriellen Entscheidungskalkül berücksichtigt wird. Die Ergebnisse werden in Bezug auf die Entwicklung der europäischen Kraftwerkskapazitäten, der Day-Ahead Marktpreise sowie der Versorgungssicherheit ausgewertet. Bei einer Investitionsplanung unter Risikoaversion ergibt sich gegenüber dem risikoneutralen Fall länderübergreifend ein etwas niedrigeres Kapazitätsniveau. Dies wiederum führt zu negativen Auswirkungen auf die Versorgungssicherheit in Form häufigerer Knappheitssituationen sowie generell erhöhten Day-Ahead Marktpreisen. Diese Ergebnisse verdeutlichen die Relevanz einer geeigneten Abbildung der Risikoaversion von Investoren im Kontext der Diskussion um ein angemessenes Marktdesign für sehr hohe Anteile erneuerbarer Energien

Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs) which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF), which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF).</p> <p>Results</p> <p>SURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm.</p> <p>Conclusion</p> <p>Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be used instead of ReliefF to filter a dataset before an exhaustive MDR analysis. This change increases the ability of a study to detect gene-gene interactions. The SURF algorithm is implemented in the open source Multifactor Dimensionality Reduction (MDR) software package available from <url>http://www.epistasis.org</url>.</p

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Improving the error behavior of DRAM by exploiting its Z-channel property

In this paper, we present a new communication theoretic channel model for Dynamic Random Access Memory (DRAM) retention errors, that relies on the fully asymmetric retention error behavior of DRAM cells. This new model shows that the traditional approach is over pessimistic and we confirm this with real measurements of DDR3 and DDR4 DRAM devices. Together with an exploitation of the vendor specific true- and anti-cell structure, a low complexity bit-flipping approach is presented, that can largely increase DRAM's reliability with minimum overhead

Using run-time reverse-engineering to optimize DRAM refresh

The overhead of DRAM refresh is increasing with each density generation. To help offset some of this overhead, JEDEC designed the modern Auto-Refresh command with a highly optimized architecture internal to the DRAM-an architecture that violates the timing rules external controllers must observe and obey during normal operation. Numerous refresh-reduction schemes manually refresh the DRAM row-by-row, eliminating unnecessary refreshes to improve both energy and performance of the DRAM. However, it has been shown that modern Auto-Refresh is incompatible with these schemes, that their manual refreshing of specified rows through explicit Activate and Precharge precludes them from exploiting the architectural optimizations available internally for Auto-Refresh operations. This paper shows that various DRAM timing parameters, which should be followed during normal DRAM operations can be reduced for performing Refresh operation, and by reverse engineering those internal timing parameters at system-init time an external memory controller can use them in conjunction with individual Activate and Precharge commands, thereby reducing the performance overhead afforded Auto-Refresh, while imultaneously supporting row-by-row refresh reduction schemes. Through physical experiments and measurement, we find that our optimized scheme reduces tRFC by up to 45% compared to the already highly-optimized Auto-Refresh mechanism. It is also 10% more energy-efficient and 50% more performance-efficient than the non-optimized row-by-row refresh. Further evaluations done by simulating future 16 Gb DDR4 devices show how the reduction in tRFC improves the application performance and energy efficiency. The proposed technique enhances all of the existing refresh-optimization schemes that use row-by-row refresh, and it does so without requiring any modification to the DRAM or DRAM protocol

Fecal microbiota transplant for infection in older adults

Background: The objective of this study was to describe the safety of fecal microbiota transplant (FMT) for Clostridium difficile infection (CDI) among older adults. Methods: We performed a case review of all FMT recipients aged 65 or older treated at Emory University Hospital, a tertiary care and referral center for Georgia and surrounding states. Results: CDI resolved in 27 (87%) of 31 respondents, including three individuals who received multiple FMTs. Among four whose CDI was not resolved at follow up, three respondents did well initially before CDI recurred, and one individual never eradicated his CDI despite repeating FMT. During the study, five deaths and eight serious adverse events requiring hospitalization were reported within the study group during the follow-up period. Fecal transplant was not a causative factor in these events. The most common adverse event reported in 4 (13%) of 31 respondents was subjective worsening of arthritis. Conclusion: FMT is a generally safe and effective treatment option for older adults with CDI