We use observed transmission line outage data to make a Markovian influence graph that describes the probabilities of transitions between generations of cascading line outages. Each generation of a cascade consists of a single line outage or multiple line outages. The new influence graph defines a Markov chain and generalizes previous influence graphs by including multiple line outages as Markov chain states. The generalized influence graph can reproduce the distribution of cascade size in the utility data. In particular, it can estimate the probabilities of small, medium and large cascades. The influence graph has the key advantage of allowing the effect of mitigations to be analyzed and readily tested, which is not available from the observed data. We exploit the asymptotic properties of the Markov chain to find the lines most involved in large cascades and show how upgrades to these critical lines can reduce the probability of large cascades.

We transform historically observed line outages in a power transmission network into an influence graph that statistically describes how cascades propagate in the power grid. The influence graph can predict the critical lines that are historically most involved in cascading propagation. After upgrading these critical lines, simulating the influence graph suggests that these upgrades could mitigate large blackouts by reducing the probability of large cascades. Disciplines

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Behavioral assessment, such as systematic scoring or biomechanical measurement, is often used to evaluate the extent of the damage and the degree of recovery after spinal cord injury. However, the use of these methods in standardized evaluation is limited because they are subjective and require complex test systems to implement. Here, we report a novel, flexible, microstructure-based pressure sensor and demonstrate its superior sensitivity (235.12 kPa−1 for 5.5~135 Pa and 2.24 kPa−1 for 0.6~25 kPa), good waterproofness, fast response and recovery times (response time: 8 ms, recovery time: 12 ms), stable response over 8000 loading/unloading cycles, and wide sensing range. These features readily allow the sensor to be comfortably attached to the hindlimbs of mice for full-range, real-time detection of their behavior, such as crawling and swimming, helping to realize quantitative evaluation of animal motor function recovery after spinal cord injury.

Transmission line outage rates are fundamental to power system reliability analysis. Line outages are infrequent, occurring only about once a year, so outage data are limited. We propose a Bayesian hierarchical model that leverages line dependencies to better estimate outage rates of individual transmission lines from limited outage data. The Bayesian estimates have a lower standard deviation than estimating the outage rates simply by dividing the number of outages by the number of years of data, especially when the number of outages is small. The Bayesian model produces more accurate individual line outage rates, as well as estimates of the uncertainty of these rates. Better estimates of line outage rates can improve system risk assessment, outage prediction, and maintenance scheduling.

We describe some bulk statistics of historical initial line outages and the implications for forming contingency lists and understanding which initial outages are likely to lead to further cascading. We use historical outage data to estimate the effect of weather on cascading via cause codes and via NOAA storm data. Bad weather significantly increases outage rates and interacts with cascading effects, and should be accounted for in cascading models and simulations. We suggest how weather effects can be incorporated into the OPA cascading simulation and validated. There are very good prospects for improving data processing and models for the bulk statistics of historical outage data so that cascading can be better understood and quantified.

With the need for fast and low-power radiation-hardened processors, advanced technology process is applied to obtain both high performance as well as high reliability. However, scaling down of the size of the transistor makes the transistor sensitive to outside disturbances, such as soft error introduced by the strikes of the cosmic neutron beams. Besides aerospace applications, such reliability should also be taken into consideration for the sub-100[Formula: see text]nm CMOS designs to ensure the robustness of the circuit. In such circumstances, several radiation-hardened flip-flops are designed and simulated under SMIC 40[Formula: see text]nm process. Simulation results show that with five aspects (performance, power, area, PVT variation and reliability) taken into consideration, TSPC-based DICE and TMR combined architecture has the best soft-error robustness in comparison with other radiation-hardened flip-flops, and the critical charge of such architecture is 490[Formula: see text]fC, which is 12.5X higher than the traditional unhardened flip-flop.

Spinal cord injury (SCI) is a major disability that results in motor and sensory impairment and extensive complications for the affected individuals which not only affect the quality of life of the patients but also result in a heavy burden for their families and the health care system. Although there are few clinically effective treatments for SCI, research over the past few decades has resulted in several novel treatment strategies which are related to neuromodulation. Neuromodulation—the use of neuromodulators, electrical stimulation or optogenetics to modulate neuronal activity—can substantially promote the recovery of sensorimotor function after SCI. Recent studies have shown that neuromodulation, in combination with other technologies, can allow paralyzed patients to carry out intentional, controlled movement, and promote sensory recovery. Although such treatments hold promise for completely overcoming SCI, the mechanisms by which neuromodulation has this effect have been difficult to determine. Here we review recent progress relative to electrical neuromodulation and optogenetics neuromodulation. We also examine potential mechanisms by which these methods may restore sensorimotor function. We then highlight the strengths of these approaches and remaining challenges with respect to its application.