Spartan Daily, October 12, 1950

Volume 39, Issue 11https://scholarworks.sjsu.edu/spartandaily/11432/thumbnail.jp

Spartan Daily, October 8, 1984

Volume 83, Issue 27https://scholarworks.sjsu.edu/spartandaily/7213/thumbnail.jp

Fragility Curves for Assessing the Resilience of Electricity Networks Constructed from an Extensive Fault Database

Robust infrastructure networks are vital to ensure community resilience; their failure leads to severe societal disruption and they have important postdisaster functions. However, as these networks consist of interconnected, but geographically-distributed, components, system resilience is difficult to assess. In this paper the authors propose the use of an extension to the catastrophe (CAT) risk modeling approach, which is primarily used to perform risk assessments of independent assets, to be adopted for these interdependent systems. To help to achieve this, fragility curves, a crucial element of CAT models, are developed for overhead electrical lines using an empirical approach to ascribe likely failures due to wind storm hazard. To generate empirical fragility curves for electrical overhead lines, a dataset of over 12,000 electrical failures is coupled to a European reanalysis (ERA) wind storm model, ERA-Interim. The authors consider how the spatial resolution of the electrical fault data affects these curves, generating a fragility curve with low resolution fault data with a R2R2 value of 0.9271 and improving this to a R2R2 value of 0.9889 using higher spatial resolution data. Recommendations for deriving similar fragility curves for other infrastructure systems and/or hazards using the same methodological approach are also made. The authors argue that the developed fragility curves are applicable to other regions with similar electrical infrastructure and wind speeds, although some additional calibration may be required

Highly Mutable Linker Regions Regulate HIV-1 Rev Function and Stability.

HIV-1 Rev is an essential viral regulatory protein that facilitates the nuclear export of intron-containing viral mRNAs. It is organized into structured, functionally well-characterized motifs joined by less understood linker regions. Our recent competitive deep mutational scanning study confirmed many known constraints in Rev's established motifs, but also identified positions of mutational plasticity, most notably in surrounding linker regions. Here, we probe the mutational limits of these linkers by testing the activities of multiple truncation and mass substitution mutations. We find that these regions possess previously unknown structural, functional or regulatory roles, not apparent from systematic point mutational approaches. Specifically, the N- and C-termini of Rev contribute to protein stability; mutations in a turn that connects the two main helices of Rev have different effects in different contexts; and a linker region which connects the second helix of Rev to its nuclear export sequence has structural requirements for function. Thus, Rev function extends beyond its characterized motifs, and is tuned by determinants within seemingly plastic portions of its sequence. Additionally, Rev's ability to tolerate many of these massive truncations and substitutions illustrates the overall mutational and functional robustness inherent in this viral protein

Reducing the Vulnerability of Electric Power Infrastructure Against Natural Disasters by Promoting Distributed Generation

Natural disasters cause significant damage to the electrical power infrastructure every year. Therefore, there is a crucial need to reduce the vulnerability of the electric power grid against natural disasters. Distributed generation (DG) represents small-scale decentralized power generation that can help reduce the vulnerability of the grid, among many other benefits. Examples of DG include small-scale photo-voltaic (PV) systems. Accordingly, the goal of this paper is to investigate the benefits of DG in reducing the vulnerability of the electric power infrastructure by mitigating against the impact of natural disasters on transmission lines. This was achieved by developing a complex system-of-systems (SoS) framework using agent-based modeling (ABM) and optimal power flow (OPF). N-1 contingency analysis and optimization were performed under two approaches: The first approach determined the minimum DG needed at any single location on the electric grid to avoid blackouts. The second approach used a genetic algorithm (GA) to identify the minimum total allocation of DG distributed over the electric grid to mitigate against the failure of any transmission line. Accordingly, the model integrates ABM, OPF, and GA to optimize the allocation of DG and reduce the vulnerability of electric networks. The model was tested on a modified IEEE 6-bus system as a proof of concept. The outcomes of this research are intended to support the understanding of the benefits of DG in reducing the vulnerability of the electric power grid. The presented framework can guide future research concerning policies and incentives that can strategically influence consumer decision to install DG and reduce the vulnerability of the electric power infrastructure

Machine Learning-Based Seismic Damage Assessment Of Residential Buildings Considering Multiple Earthquake And Structure Uncertainties

Wood-frame structures are used in almost 90% of residential buildings in the United States. It is thus imperative to rapidly and accurately assess the damage of wood-frame structures in the wake of an earthquake event. This study aims to develop a machine-learning-based seismic classifier for a portfolio of 6,113 wood-frame structures near the New Madrid Seismic Zone (NMSZ) in which synthesized ground motions are adopted to characterize potential earthquakes. This seismic classifier, based on a multilayer perceptron (MLP), is compared with existing fragility curves developed for the same wood-frame buildings near the NMSZ. This comparative study indicates that the MLP seismic classifier and fragility curves perform equally well when predicting minor damage. However, the MLP classifier is more accurate than the fragility curves in prediction of moderate and severe damage. Compared with the existing fragility curves with earthquake intensity measures as inputs, machine-learning-based seismic classifiers can incorporate multiple parameters of earthquakes and structures as input features, thus providing a promising tool for accurate seismic damage assessment in a portfolio scale. Once trained, the MLP classifier can predict damage classes of the 6,113 structures within 0.07 s on a general-purpose computer