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Laminin-111 (Ln1) is an essential part of the extracellular matrix in epithelia, muscle and neural systems. We have previously demonstrated that the microstructure of Ln1 alters the way that it signals to cells, possibly because Ln1 assembly into networks exposes different adhesive domains. In this paper, we describe three methods to generate polymerized Ln1.
The Global Positioning System (GPS) is critical to the real-time synchronized sampling of phasor measurement units (PMUs). Unfortunately, GPS signals are occasionally unstable due to several factors such as weak satellite signal and GPS spoofing, thereby leaving the PMU with a degraded sampling performance. In this letter, a novel Pulsar-calibrated Timing Source (PTS) is proposed as the alternative timing source for synchronized sampling. Further, the PTS can generate the timing signal with a 1 μs drift error within 91 holdover minutes to ensure continuity of sampling accuracy. Experimental tests are conducted and the results reveal the reliability and accuracy of the PTS for PMU synchronization.
We analysed infrasound waves associated with the Gyeongju earthquake (ML 5.8) that occurred on 2016 September 12, in the southeastern Korean Peninsula. For infrasound wave detection, the Progressive Multichannel Correlation method was applied to the infrasound data set recorded at seven arrays operating in South Korea at epicentral distances ranging from 178 to 472 km. Based on the back-projection method constrained by array-dependent celerity and azimuth deviation models, the source regions were identified in both the epicentral and non-epicentral regions. Remarkably, the non-epicentral secondary sources of this earthquake were located in regions with shallow water depths: (i) the western coastal area in the Yellow Sea and (ii) the shallow ocean basin and bank in the East Sea. The location results obtained from the earthquake could be corroborated through its foreshock (ML 5.1), yielding location results consistent with those of the main shock. The generation of infrasound waves over shallow water depths was fortuitously validated by direct recordings of dominant single-frequency (~0.3 Hz) infrasound waves at close range via temporary sensors near the ocean basin and bank. We interpreted that low-frequency infrasound signals could be generated from interactions among the ocean floor, shallow seawater and atmosphere. We performed numerical simulations of seismoacoustic fields to predict ground motions on the seafloor and acoustic transmission efficiency between the water and air interface. The simulations quantified the energy transfer through different media and clarified our observational results. We found that because this solid Earth‒water‒atmosphere coupled air wave has a relatively low frequency (~0.3 Hz), it can survive propagation over long distances compared with high-frequency infrasound waves generated in inland and mountain regions. In this study, we extend our understanding of water‒atmosphere coupling and the monitoring framework for earthquake-associated non-epicentral infrasound waves, encompassing not only inland ground shaking but also shallow sea regions located far from the epicentre.
A system for measuring gas flow generally including a passive acoustic wave generator disposed in a gas flow stream to passively generate an audio signal through vortex shedding, a sound capturing instrument disposed outside the gas stream to produce an electrical signal representative of the acoustic signal, a temperature sensor to obtain temperature measurements indicative of the temperature of the gas flow stream and a control system for determining the gas flow, such as velocity or flow rate, as a function of the acquired acoustic and temperature measurements. The acoustic wave generator includes a corrugated flow channel whose geometric design is so tuned to generate an acoustic emission whose frequency signature varies as a function of the gas flow velocity. The control system may acquires time-domain acoustic data, and process that data to obtain a frequency-domain representation from which gas velocity or gas flow rate can be determined.
The time required to trip a pressurized water reactor (PWR) by inserting malicious signals into its steam generator (SG) control system has been studied using the Generic PWR (GPWR) Simulator. A semi-analytical model is developed to approximately reproduce the simulator response and understand the dynamics of the control unit. A series of two proportional-integral controllers determines control action according to preset constants, the readings from the feedwater level sensor, and those from feedwater and steam flowrate transmitters. It is observed that the most important factor that determines whether a trip will occur is how much additional water is added to or withheld from the SG over time compared to normal operating conditions. In order to determine the effects of control action on the SG, changes in mass inventory are considered. This approach models the SG water level as a function of mass inventory and has a backward temporal memory. A Python interface is developed for the GPWR framework to automatically simulate different spoofing scenarios and post-process the related data. We observe that the trip times predominantly depend on flow mismatch and/or level errors. Controller parameters, including the integral time and gain constants, either speed up or slow down the rate of progression to a trip setpoint but do not cause a trip by themselves. The reactor can trip on a high-level signal when the reading crosses above 78%, increased from its reference level of 57%, or a low-level reading when it is below 25%. The present results show roughly how long the operators would have to respond to an attack, given a specific set of spoofing signals within the issue space analyzed. Furthermore, we have generated a simple surface by fitting a combination of exponential functions to the data obtained from the GPWR Simulator. In general, trips on a low level have been observed to occur faster than those on a high level.
Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.
Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.
The magneto-optical Kerr effect (MOKE), the differential reflection of oppositely circularly polarized light, has traditionally been associated with relativistic spin-orbit coupling (SOC), which links a particle’s spin with its orbital motion. In ferromagnets, large MOKE signals arise from the combination of magnetization and SOC, while in certain coplanar antiferromagnets, SOC-induced Berry curvature enables MOKE despite zero net magnetization. Theoretically, large MOKE can also arise in a broader class of magnetic materials with compensated spins, without relying on SOC - for example, in systems exhibiting real-space scalar spin chirality. The experimental verification has remained elusive. Here, we demonstrate such a SOC- and magnetization-free MOKE in the noncoplanar antiferromagnet Co 1/3 TaS 2 . Using a Sagnac interferometer microscope, we image domains of scalar spin chirality and their reversal. Our findings establish experimentally a new mechanism for generating large MOKE signals and position chiral spin textures in compensated magnets as a compelling platform for ultrafast, stray-field-immune opto-spintronic applications.
The management of end-use energy resources, e.g., commercial buildings, has been investigated as a promising source of services for the electric power grid. Lighting consumes about 17% electricity of U.S. commercial buildings; however, it may contribute significantly to services that improve the reliability and resilience of the grid due to its rapid speed of response. Connected lighting systems (CLS), which build upon solid-state lamp technology, can change their power demand more quickly than most other building electricity end uses. But the potential of CLS to provide grid services has not been considerably investigated. In this paper, we describe initial research to evaluate the potential of CLS for the grid service of frequency regulation. Frequency regulation is a reliability product that corrects in a matter of seconds for short-term changes in the balance between supply and demand in the balancing area that might affect the stability of the power system. Frequency regulation signals for a medium office building are generated from the normalized test signals for the PJM Interconnection Reg-A and Reg-D regulations services. Control of the CLS locally to follow the Reg-A or Reg-D signal is used to provide contributions to the corresponding frequency regulation service. The performance of CLS for frequency regulation is evaluated using the PJM 40-Minute Performance Score Template. The performance scores obtained for all CLS categories for both Reg-A and Reg-D signals far exceed the minimum score for the qualification, a very promising result for CLS to provide frequency regulation service.
Significance The capabilities of modern X-ray sources have substantially increased the temporal and spectral resolutions for the observation of elementary molecular events. Theoretically, it has been shown that these unique features can be exploited to probe vibronic coherences that emerge when molecules pass through conical intersections. Here, we demonstrate this technique for the photorelaxation of the RNA-nucleobase uracil after UV excitation. The generated coherence maps reveal the path of the nuclear wavepacket on the excited-state surface in the vicinity of the conical intersection. This information is directly accessible from the spectroscopically recorded signal. Being transferrable to other molecules, these coherence maps will provide detailed visualizations of other nonadiabatic passages in the future.
High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.
A pulsed sinusoidal acoustic signal transmitted through a subsurface volume of a wellbore may be detected. A time-reversed acoustic signal of the pulsed sinusoidal acoustic signal may be transmitted through the subsurface volume of the wellbore. Transmission of the time-reversed acoustic signal through the subsurface volume of the wellbore may result in generation of focused acoustic signal in the subsurface volume of the wellbore. The focused acoustic signal in the subsurface volume of the wellbore may be detected, and the integrity of the wellbore may be determined based on the focused acoustic signal in the subsurface volume of the wellbore.
High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.
Superradiance clouds of kinetically mixed dark photons around spinning black holes can produce observable multimessenger electromagnetic and gravitational wave signals. The cloud generates electric fields of up to a teravolt-per-meter, which leads to a cascade production of charged particles, yielding a turbulent quasiequilibrium plasma around the black hole, and resulting in electromagnetic fluxes ranging from supernova to pulsar-like luminosities. For stellar mass black holes, such systems resemble millisecond pulsars and are expected to emit pulsating radio waves and continuous gravitational waves (CWs) within the LIGO-Virgo-KAGRA (LVK) sensitivity band. We select 44 sources with approximately coincident frequencies or positive frequency drifts from existing pulsar catalogs as potential candidates of long-lasting superradiance clouds around old Galactic black holes. For a subset of 34 sources that are well measured and have not been previously targeted, we perform the first search for CW emission in LVK data from the third observing run. We find no evidence of a CW signal and place 95% confidence level upper limits on the emitted strain amplitude. We interpret these results, together with limits from previous searches, in terms of the underlying dark photon theory by performing an analysis of the expected signals from superradiance clouds from Galactic black holes. Finally, we find that, even for moderately spinning black holes, the absence of an observed CW signal disfavors a discrete set of dark photon masses between about 10 −13 and 10 −12 eV/c 2 and kinetic mixing couplings in the range of 10 −9 –10 −7 , subject to assumptions about the properties of the black hole population and the cloud’s electromagnetic emission.
Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. Here, in this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O 2 A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.
We perform a new, detailed calculation of the flux and energy spectrum of Earth-emerging τ -leptons generated from the interactions of tau neutrinos and antineutrinos in the Earth. A layered model of the Earth is used to describe the variable density profile of the Earth. Different assumptions regarding the neutrino charged- and neutral-current cross sections as well as the τ -lepton energy loss models are used to quantify their contributions to the systematic uncertainty. A baseline simulation is then used to generate the optical Cherenkov signal from upward-moving extensive air showers generated by the τ -lepton decay in the atmosphere, applicable to a range of space-based instruments. Here, we use this simulation to determine the neutrino sensitivity for E ν ≳10 PeV for a space-based experiment with performance similar to that for the Probe of Extreme MultiMessenger Astrophysics (POEMMA) mission currently under study.
Understanding the atomic structure of quantum emitters, often originating from point defects or impuritie, is essential for designing and optimizing materials for quantum technologies such as quantum computing, communication, and sensing. Despite the availability of atomic-resolution scanning transmission electron microscopy and nanoscale cathodoluminescence microscopy, experimentally determining the atomic structure of individual emitters is challenging due to the conflicting needs for thick samples to generate strong cathodoluminescence signals and thin samples for structural analysis. To overcome this challenge, significantly enhanced cathodoluminescence at twisted interfaces is leveraged to achieve sub-nanometer localization precision for the first time in mapping individual quantum emitters in carbon-implanted hexagonal boron nitride. This unprecedent spatial sensitivity, together with correlative electron energy loss spectroscopy quantitative scanning transmission electron microscopy imaging, and first principles density functional theory calculations, enables the identification of the atomic structure of the 440 nm blue emitter in hexagonal boron nitride as a substituted vertical carbon dimer. Building on the atomic structure insights, nanoscale spatially precise creation of blue emitters is demonstrated by electron beam irradiation of carbon-coated hexagonal boron nitride. This advancement in correlating atomic structures with optical properties lays the foundation for a deeper understanding and precise engineering of quantum emitters, significantly advancing the development of cutting-edge quantum information technologies.
The progression of electrification in the building and transportation sectors brings new opportunities for energy decarbonization. With higher dependence on the grid power supply, the variation of the grid carbon emission intensity can be utilized to reduce the carbon emissions from the two sectors. Existing coordinated control methods for buildings with distributed energy resources (DERs) either consider electricity price or renewable energy generation as the input signal, or adopt optimization in the decision-making, which is difficult to implement in the real-world environment. This paper aims to propose and validate an easy-to-deploy rule-based carbon responsive control framework that facilitates coordination between all-electric buildings and electric vehicles (EVs). The signals of the grid carbon emission intensity and the local photovoltaics (PV) generation are used for shifting the controllable loads. Extensive simulations were conducted using a model of an all-electric mixed-use community in a cold climate to validate the control performance with metrics such as emissions, energy consumption, peak demand, and EV end-of-day state-of-charge (SOC). Our study identifies that 4.5% to 27.1% of annual emission reduction can be achieved with limited impact on energy costs, peak demand, and thermal comfort. Additionally, up to 32.7% of EV emission reduction can be obtained if the EV owners reduce the target SOC by less than 21.2%.