Component Outage Estimation based on Support Vector Machine

Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events

Autocatalytic Biochemical Networks and Their Fundamental Limits

In the present work, we study autocatalytic pathways which contain reactions that need the use of one of their own productions. These pathways are common in biology; one of the simplest and widely studied autocatalytic pathways is Glycolysis. This pathway produces energy by breaking down Glucose. It is shown that this pathway can be simplified as a network of three biochemical reactions. We first revisit some conditions on the underlying structure of the autocatalytic network, which guarantee the existence of fundamental limits on the output energy of such networks. Then we focus on autocatalytic pathways with several biochemical reactions. Our aim is to characterize the zero-dynamics for a class of autocatalytic networks and then study the fundamental limitations of feedback control laws, using their associated zero-dynamics. For this aim, it is shown that the zero-dynamics of autocatalytic networks play an important role in studying the fundamental limits on performance. Zero-dynamics is defined as the dynamics of a system restricted to the control input and initial conditions such that the output of the system remains zero for all future time instances. We characterize the zero-dynamics for a class of unperturbed autocatalytic networks based on the structure of the original network. It is well-known that by knowing the zero-dynamics of a specific class of systems, one can obtain lower bounds on the best achievable performance (L2-norm of the output) for the system. For a specific class of autocatalytic networks, we characterize their zero-dynamics in terms of the state-space matrices of the underlying network. This can be utilized to quantify inherent fundamental limits on performance (the level of disturbance attenuation) for this class of network. In general, one should apply numerical algorithms to obtain such fundamental limits. We explain our method using a simple but illustrative example

Stark localization as a resource for weak-field sensing with super-Heisenberg precision

Gradient fields can effectively suppress particle tunneling in a lattice and localize the wave function at all energy scales, a phenomenon known as Stark localization. Here, we show that Stark systems can be used as a probe for the precise measurement of gradient fields, particularly in the weak-field regime where most sensors do not operate optimally. In the extended phase, Stark probes achieve super-Heisenberg precision, which is well beyond most of the known quantum sensing schemes. In the localized phase, the precision drops in a universal way showing fast convergence to the thermodynamic limit. For single-particle probes, we show that quantum-enhanced sensitivity, with super-Heisenberg precision, can be achieved through a simple position measurement for all the eigenstates across the entire spectrum. For such probes, we have identified several critical exponents of the Stark localization transition and established their relationship. Thermal fluctuations, whose universal behavior is identified, reduce the precision from super-Heisenberg to Heisenberg, still outperforming classical sensors. Multiparticle interacting probes also achieve super-Heisenberg scaling in their extended phase, which shows even further enhancement near the transition point. Quantum-enhanced sensitivity is still achievable even when state preparation time is included in resource analysis.Comment: 10 pages, 9 figures, (Comments are welcome!

Self-Assembling Peptides and Carbon Nanomaterials Join Forces for Innovative Biomedical Applications

Self-assembling peptides and carbon nanomaterials have attracted great interest for their respective potential to bring innovation in the biomedical field. Combination of these two types of building blocks is not trivial in light of their very different physico-chemical properties, yet great progress has been made over the years at the interface between these two research areas. This concise review will analyze the latest developments at the forefront of research that combines self-assembling peptides with carbon nanostructures for biological use. Applications span from tissue regeneration, to biosensing and imaging, and bioelectronics