Optimization of structures on the basis of fracture mechanics and reliability criteria

Systematic summary of factors which are involved in optimization of given structural configuration is part of report resulting from study of analysis of objective function. Predicted reliability of performance of finished structure is sharply dependent upon results of coupon tests. Optimization analysis developed by study also involves expected cost of proof testing

Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones

Optimum pressure vessel design based on fracture mechanics and reliability criteria

Optimization design methods for spacecraft structural systems and subsystem

Endogenous Correlation

We model endogenous correlation in asset returns via the role of heterogeneous expectations in investor types, and the dynamic impact of imitative learning by investors. Learning is driven by relative performance. In addition, we allow a cautious slow learning pace to reflect institutional conditions. Imitative learning shapes the market ecology that influences price formation. Using the model of non-imitative agents as a benchmark, our results show that the dynamics of imitative learning endogenously induce a significant degree of asset dependency and patterns of non-constant correlation. The asymmetric learning effect on correlation, however, implies a self-reinforcing process, where a bearish condition amplifies the effect that further exacerbates asset dependency. We conclude that imitative learning, even when rational, can to a certain extent account for the phenomena of market crashes. Our results have implications for transparency in regulation issues

Antiferromagnetic Alignment and Relaxation Rate of Gd Spins in the High Temperature Superconductor GdBa_2Cu_3O_(7-delta)

The complex surface impedance of a number of GdBa$_2$Cu$_3$O$_{7-\delta}$ single crystals has been measured at 10, 15 and 21 GHz using a cavity perturbation technique. At low temperatures a marked increase in the effective penetration depth and surface resistance is observed associated with the paramagnetic and antiferromagnetic alignment of the Gd spins. The effective penetration depth has a sharp change in slope at the N\'eel temperature, $T_N$, and the surface resistance peaks at a frequency dependent temperature below 3K. The observed temperature and frequency dependence can be described by a model which assumes a negligibly small interaction between the Gd spins and the electrons in the superconducting state, with a frequency dependent magnetic susceptibility and a Gd spin relaxation time $\tau_s$ being a strong function of temperature. Above $T_N$, $\tau_s$ has a component varying as $1 / (T - T_N)$, while below $T_N$ it increases $\sim T^{-5}$.Comment: 4 Pages, 4 Figures. Submitted to Phys. Rev.

Accuracy of the domain method for the material derivative approach to shape design sensitivities

Numerical accuracy for the boundary and domain methods of the material derivative approach to shape design sensitivities is investigated through the use of mesh refinement. The results show that the domain method is generally more accurate than the boundary method, using the finite element technique. It is also shown that the domain method is equivalent, under certain assumptions, to the implicit differentiation approach not only theoretically but also numerically

Scalable Methods for Adaptively Seeding a Social Network

In recent years, social networking platforms have developed into extraordinary channels for spreading and consuming information. Along with the rise of such infrastructure, there is continuous progress on techniques for spreading information effectively through influential users. In many applications, one is restricted to select influencers from a set of users who engaged with the topic being promoted, and due to the structure of social networks, these users often rank low in terms of their influence potential. An alternative approach one can consider is an adaptive method which selects users in a manner which targets their influential neighbors. The advantage of such an approach is that it leverages the friendship paradox in social networks: while users are often not influential, they often know someone who is. Despite the various complexities in such optimization problems, we show that scalable adaptive seeding is achievable. In particular, we develop algorithms for linear influence models with provable approximation guarantees that can be gracefully parallelized. To show the effectiveness of our methods we collected data from various verticals social network users follow. For each vertical, we collected data on the users who responded to a certain post as well as their neighbors, and applied our methods on this data. Our experiments show that adaptive seeding is scalable, and importantly, that it obtains dramatic improvements over standard approaches of information dissemination.Comment: Full version of the paper appearing in WWW 201