A Fungal-Mediated Cryptic Selenium Cycle Linked to Manganese Biogeochemistry
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A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.
Here we demonstrate a three-node quantum network for C-band photon pairs using 2 pairs of 59 km of deployed fiber between Fermi and Argonne National Laboratories. The C-band pairs are directed to nodes using a standard telecommunication switch and the detection system is synchronized to picosecond scale timing resolution using a coexisting O-or L-band optical clock distribution system. We measure a reduction of coincidence to-accidental ratio (CAR) of the C-band pairs from 51 ± 2 to 5.3 ± 0.4 due to Raman scattering of the O-band clock pulses. Despite this reduction, the CAR is nevertheless suitable for quantum networks.
This paper presents the analysis and implementation of a predictive control method for dc-link regulation and voltage balance in a cascaded modular reduced dc-link solid-state transformer (SST). Passive components like bulky dc links limit the power density of power converters, especially medium-voltage (MV) SST. Reduced dc-link or low-inertia converters can dramatically reduce the size, cost, and weight by tolerating larger dc-link ripples and improve the reliability with electrolytic capacitor-less dc link. However, a small dc link leads to tight coupling between the input and the output stages, which is a challenge for control design. In stacked low-inertia converters (SLIC), the low-inertia converter modules are stacked for MV applications, resulting in coupling between the modules and making the control more challenging. A new model predictive control method which can achieve deadbeat regulation on the dc link without weighting factors has been proposed to address this novel problem. This paper focuses on analyzing the condition of the low-inertia dc link up to 80% ripple, the robustness of the control under parameter mismatches, high-order terms, and important implementation issues such as model-based sampling and computation delay compensation. Significantly, the high-order terms are introduced because of the large dc-link ripple. These high-order terms are unique to the SLIC and negligible in conventional high-inertia converters. A discrete-time large-signal model is built to capture the dc-link’s nonlinear dynamics, and the eigenvalues of a small-signal Jacobian matrix are analyzed with Floquet theory to evaluate stability, using the modular soft-switching solid-state transformer (M-S4T) as an example of the SLIC. Simulation and experimental results of an MVDC M-S4T verify the analysis and the predictive control method. Finally, the general application of the predictive control to low-inertia converters is compared against a conventional PI controller using a reduced dc-link active-front-end (AFE) rectifier as an example.
Cross-linked protein residues exist as enzyme cofactors to enable or enhance catalytic activities. Despite their importance in nature, the chemical identity of the cross-links is limited to certain amino acid combinations, whose function and the formation mechanism remain insufficiently understood due to the difficulty in isolating native enzymes without the cross-links. Herein, we report the formation and characterization of both His-Tyr and His-His cross-links under oxidative enzymatic turnover conditions in L29H/F33Y/F43H Mb, a structural and functional model of heme-copper oxidase (HCO). The connectivity of the cross-link was characterized as Ne ε2 (His29)-Cd δ2 (His43) by mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR). Interestingly, formation of the cross-link significantly enhances the oxygen reduction activity of the enzyme at neutral or basic pH with higher product specificity. X-ray crystallography has identified a novel Tyr-His cross-link through a Tyr-O-His linkage. Our mechanistic studies indicate the involvement of high-valent heme-iron and the neighboring tyrosine in an oxidative self-processing pathway to generate the cross-link. In conclusion, this work serves as a new example while providing insights into the enzyme cross-link formation, allowing the design of artificial biocatalysts containing these novel cross-links with higher activity and pH adaptability.
We study the patterns of multipartite entanglement in Chern-Simons theory with compact simple gauge group 𝐺 and level 𝑘 for states defined by the path integral on “link complements,” i.e., compact manifolds whose boundaries consist of 𝑛 topologically linked tori. We focus on link complements which can be described topologically as fibrations over a Seifert surface. We show that the entanglement structure of such fibered link complement states is controlled by a topological invariant, the monodromy of the fibration. Thus, the entanglement structure of a Chern-Simons link state is not simply a function of the link, but also of the background manifold in which the link is embedded. In particular, we show that any link possesses an embedding into some background that leads to Greenberger–Horne–Zeilinger state (GHZ)-like entanglement. Furthermore, we demonstrate that all fibered links with periodic monodromy have GHZ-like entanglement, i.e., a partial trace on any link component produces a separable state. These results generalize to any three dimensional topological field theory with a dual chiral rational conformal field theory.
Coherent optical links are becoming increasingly attractive for intra-data center applications as data rates scale. Realizing the era of high-volume short-reach coherent links will require substantial improvements in transceiver cost and power efficiency, necessitating a reassessment of conventional architectures best-suited for longer-reach links and a review of assumptions for shorter-reach implementations. In this work, we analyze the impact of integrated semiconductor optical amplifiers (SOAs) on link performance and power consumption, and describe the optimal design spaces for low-cost and energy-efficient coherent links. Placing SOAs after the modulator provide the most energy-efficient link budget improvement, up to 6 pJ/bit for large link budgets, despite any penalties from nonlinear impairments. Increased robustness to SOA nonlinearities makes QPSK-based coherent links especially attractive, and larger supported link budgets enable the inclusion of optical switches, which could revolutionize data center networks and improve overall energy efficiency.
The Department of Energy (DOE) has shown strong interest in detecting energy inefficiencies in regional road networks, so as to derive energy consumed at a high spatial temporal resolution. We have developed a workflow to automate the estimation of time-resolved vehicular energy consumption over each link in a road network of interest. The road network used in the current work is centered around the city of Chattanooga, Tennessee and its bordering regions. Utilizing the most mature road network for the Chattanooga, TN region, vehicle speed & count data from TomTom in conjunction with machine learning methods, we have developed an automated pipeline to estimate energy consumption for every link in the network. The first step in the pipeline is ingesting vehicle probe counts and speed estimates from TomTom API. In the next step, the probe counts, speed profiles and other exogenous data (i.e. road types, weather data, ground-truth volume counts and more) were used as input to a supervised learning algorithm to estimate the number of vehicles throughout the entire region for each road segment. These volume estimates were then mapped to a unified road network that contained additional important information such as percentage change in gradient across a link, number of lanes and link lengths that are features in pre-trained single vehicle energy-consumption models available with the RouteE software developed at NREL. The per vehicle energy consumption on each road link predicted using appropriate RouteE vehicular models were multiplied by the volume estimate for the corresponding link over a given time period to predict energy consumed per link for the time interval of interest. Currently, work is underway to improve both the RouteE per vehicle energy estimate and the volume estimates derived from TomTom probe counts. We have also explored the correlation of the link-wise energy estimates with the features of the pre-trained RouteE machine learning model in order to gain insight into what factors contribute most to the link-wise energy consumption.
Probability of loss of assured safety (PLOAS) is modeled for weak link (WL)/strong link (SL) systems in which one or more WLs or SLs could potentially degrade into a precursor condition to link failure that will be followed by an actual link failure after some amount of elapsed time. The descriptor loss of assured safety (LOAS) is used because failure of the WL system places the entire system in an inoperable configuration while failure of the SL system before failure of the WL system, although undesirable, does not necessarily result in an unintended operation of the entire system. Thus, safety is “assured” by failure of the WL system before failure of the SL system. Here, the following topics are considered: (i) Definition of precursor occurrence time cumulative distribution functions (CDFs) for individual WLs and SLs, (ii) Formal representation, approximation and illustration of PLOAS with (a) constant delay times, (b) aleatory uncertainty in delay times, and (c) delay times defined by functions of link properties at occurrence times for link failure precursors, and (iii) Procedures for the verification of PLOAS calculations for the three indicated definitions of delayed link failure.
Dislocations are line defects in crystals that multiply and self-organize into a complex network during strain hardening. The length of dislocation links, connecting neighboring nodes within this network, contains crucial information about the evolving dislocation microstructure. By analyzing data from Discrete Dislocation Dynamics (DDD) simulations in face-centered cubic (fcc) Cu, we characterize the statistical distribution of link lengths of dislocation networks during strain hardening on individual slip systems. Here, our analysis reveals that link lengths on active slip systems follow a double-exponential distribution, while those on inactive slip systems conform to a single-exponential distribution. The distinctive long tail observed in the double-exponential distribution is attributed to the stress-induced bowing out of long links on active slip systems, a feature that disappears upon removal of the applied stress. We further demonstrate that both observed link length distributions can be explained by extending a one-dimensional Poisson process to include different growth functions. Specifically, the double-exponential distribution emerges when the growth rate for links exceeding a critical length becomes super-linear, which aligns with the physical phenomenon of long links bowing out under stress. This work advances our understanding of dislocation microstructure evolution during strain hardening and elucidates the underlying physical mechanisms governing its formation.
Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.
This article proposes a fast predictive control method and a small DC-link inductor to minimize the DC-link current in current-source converter (CSC)-based solid-state transformer. The DC-link current minimization can significantly reduce power loss and improve efficiency. The challenge of this problem is on improving both steady-state and dynamic performance. PI control methods and large DC-link inductors are conventionally used in the CSC but have limited dynamic performance. A model predictive control (MPC) method is proposed to achieve switching-cycle-level settling time, and the DC-link inductor is sized for 40% ripple to enable fast current change. Importantly, this article also proposes to minimize the DC-link current by varying the current even within a line cycle under single-phase load to improve the steady-state performance, in contrast with the reduction to a constant value in the literature. The proposed MPC features a constant switching frequency without weighting factors. The MPC does not have a high computational burden and is implemented in a regular digital controller for a prototype of soft-switching solid-state transformer (S4T) with reduced conduction loss. The effectiveness of the proposed method has been experimentally verified on the SiC S4T prototype during steady-state and dynamics under different multiport power flow conditions up to 2 kV peak. Here, the DC-link current in the experiments is close to the minimum current with a short zero-vector duration, which further verifies the performance of the proposed method.
Comprehensive study of the environmental impacts associated with demand for an energy resource or carrier in any one sector requires a full consideration of the direct and indirect impacts on the rest of the regional and global energy system. It is important to consider the energy that is consumed in producing primary energy resources and energy consumed in transporting these energy resources internationally and domestically in order to produce a complete picture of these impacts. Biofuels are especially complex since they have feedbacks not just to the energy system but also to regional and global crop markets. Different modeling and analysis strategies have been applied to consider these impacts, which occur “upstream” of final energy consumption, with some success. Traditional life cycle models allow for rich technological detail in linking upstream impacts of energy demand with downstream final consumption; however, they cannot by themselves represent dynamic economic feedbacks across multiple sectors and regions over time. Computable general equilibrium (CGE) modeling does account for economic feedback among all sectors and regions; however, CGE modeling does not lend itself to physical representation of technological detail. Partial equilibrium (PE) modeling is a heterogeneous category describing economic models that focus on a subset of the economy, and they differ in coverage and ease in incorporating more sectors and economic links. In this study, we present a strategy for dynamically including the direct and indirect impacts of energy demand with physical and technology detail by explicitly adding these upstream energy and transportation links to GCAM, a PE model of global energy, land use, and emissions. We incorporate the following inter-sectoral linkages: energy inputs to crop production, energy inputs to fossil resource production, and freight transport requirements of energy and agricultural commodities. We assess the implications of explicitly including these links by measuring the global impacts of increased corn ethanol demand in the United States with and without these links included. Although the net global impact of the upstream links on energy and emissions are relatively modest in the scenarios we studied, the inclusion of these links illustrates interesting trade-offs in energy and transportation demand among fossil fuel and agriculture sectors. These sectoral interactions suggest that this level of modeling detail could be important in evaluating future analytical questions.
Quantum phases can be classified by topological invariants, which take on discrete values capturing global information about the quantum state. Over the past decades, these invariants have come to play a central role in describing matter, providing the foundation for understanding superfluids, magnets, the quantum Hall effect, topological insulators, Weyl semimetals and other phenomena. Here we report an unusual linking-number (knot theory) invariant associated with loops of electronic band crossings in a mirror-symmetric ferromagnet. Using state-of-the-art spectroscopic methods, we directly observe three intertwined degeneracy loops in the material's three-torus, T 3 , bulk Brillouin zone. We find that each loop links each other loop twice. Through systematic spectroscopic investigation of this linked-loop quantum state, we explicitly draw its link diagram and conclude, in analogy with knot theory, that it exhibits the linking number (2, 2, 2), providing a direct determination of the invariant structure from the experimental data. Here we further predict and observe, on the surface of our samples, Seifert boundary states protected by the bulk linked loops, suggestive of a remarkable Seifert bulk-boundary correspondence. Our observation of a quantum loop link motivates the application of knot theory to the exploration of magnetic and superconducting quantum matter.
Flow control credit management is provided when converting traffic from a first parallel link width on a first link to a second parallel link width on a second link A current value is calculated for a variable flow control credit exchange rate (R) associated with the first and second links. A first flow control credit indicator is received on the second link, and a credit amount calculated based on the first flow control credit indicator and R. A second flow control credit indicator for the credit amount is then transmitted on the first link.
This paper presents a comprehensive study on the magnetohydrodynamic (MHD) flow in a slotted channel with a cylindrical anchor link, which is an imperative component of the toroidally symmetric lead-lithium (TSLL) blanket concept. Following the validation of the MHD solver implemented in COMSOL and a proposed subtraction approach to compute the anchor link pressure drop, the effects of computational domain size on the pressure drop and velocity distribution are examined. The results show that the pressure drop associated with the anchor link and the maximum flow velocity follow an asymptotic trend as the domain width increases, with wall-induced pressure drop being more dominant than that of the anchor link. The velocity distribution analysis revealed the formation of an internal boundary layer extending along the magnetic field direction, which is a unique feature of the investigated MHD flow. An estimation of the total MHD pressure drop associated with the array of anchor links under the TSLL blanket conditions suggests ~0.3 MPa, which is significantly lower than the recommended maximum allowable blanket pressure drop of 2 MPa. In conclusion, this work offers valuable insights into the anchor link–associated MHD phenomena and serves as a foundation for further development of the TSLL blanket concept for future fusion reactors.
Programmable self-assembly of dyes using DNA templates to promote exciton delocalization in dye aggregates is gaining considerable interest. New methods to improve the rigidity of the DNA scaffold and thus the stability of the molecular dye aggregates to encourage exciton delocalization are desired. In these dye–DNA constructs, one potential way to increase the stability of the aggregates is to create an additional covalent bond via photo-cross-linking reactions between thymines in the DNA scaffold. Specifically, we report an approach to increase the yield of photo-cross-linking reaction between thymines in the core of a DNA Holliday junction while limiting the damage from UV irradiation to DNA. We investigated the effect of the distance between thymines on the photo-cross-linking reaction yields by using linkers with different lengths to tether the dyes to the DNA templates. By comprehensively evaluating the photo-cross-linking reaction yields of dye–DNA aggregates using linkers with different lengths, we conclude that interstrand thymines tend to photo-cross-link more efficiently with short linkers. A higher cross-linking yield was achieved due to the shorter intermolecular distance between thymines influenced by strong dye–dye interactions. Furthermore, our method establishes the possibility of improving the stability of DNA-scaffolded dye aggregates, thereby expanding their use in exciton-based applications such as light harvesting, nanoscale computing, quantum computing, and optoelectronics.