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While lee-wave generation has been argued to be a major sink for the 1-TW wind work on the ocean’s circulation, microstructure measurements in the Antarctic Circumpolar Currents find dissipation rates as much as an order of magnitude weaker than linear lee-wave generation predictions in bottom-intensified currents. Wave action conservation suggests that a substantial fraction of lee-wave radiation can be reabsorbed into bottom-intensified flows. Numerical simulations are conducted here to investigate generation, reabsorption, and dissipation of internal lee waves in a bottom-intensified, laterally confined jet that resembles a localized abyssal current over bottom topography. For the case of monochromatic topography with |kU₀| ≈ 0.9N, wherekis the along-stream topographic wavenumber, |U₀| is the near-bottom flow speed, andNis the buoyancy frequency; Reynolds-decomposed energy conservation is consistent with linear wave action conservation predictions that only 14% of lee-wave generation is dissipated, with the bulk of lee-wave energy flux reabsorbed by the bottom-intensified flow. Thus, water column reabsorption needs to be taken into account as a possible mechanism for reducing the lee-wave dissipative sink for balanced circulation.

Executing quantum algorithms on error-corrected logical qubits is a critical step for scalable quantum computing, but the requisite numbers of qubits and physical error rates are demanding for current experimental hardware. Recently, the development of error correcting codes tailored to particular physical noise models has helped relax these requirements. In this work, we propose a qubit encoding and gate protocol for¹⁷¹Yb neutral atom qubits that converts the dominant physical errors into erasures, that is, errors in known locations. The key idea is to encode qubits in a metastable electronic level, such that gate errors predominantly result in transitions to disjoint subspaces whose populations can be continuously monitored via fluorescence. We estimate that 98% of errors can be converted into erasures. We quantify the benefit of this approach via circuit-level simulations of the surface code, finding a threshold increase from 0.937% to 4.15%. We also observe a larger code distance near the threshold, leading to a faster decrease in the logical error rate for the same number of physical qubits, which is important for near-term implementations. Erasure conversion should benefit any error correcting code, and may also be applied to design new gates and encodings in other qubit platforms.

Adhesives typically fall into two categories: those that have high but irreversible adhesion strength due to the formation of covalent bonds at the interface and are slow to deploy, and others that are fast to deploy and the adhesion is reversible but weak in strength due to formation of noncovalent bonds. Synergizing the advantages from both categories remains challenging but pivotal for the development of the next generation of wound dressing adhesives. Here, we report a fast and reversible adhesive consisting of dynamic boronic ester covalent bonds, formed between poly(vinyl alcohol) (PVA) and boric acid (BA) for potential use as a wound dressing adhesive. Mechanical testing shows that the adhesive film has strength in shear of 61 N/cm 2 and transcutaneous adhesive strength of 511 N/cm 2 , generated within 2 min of application. Yet the film can be effortlessly debonded when exposed to excess water. The mechanical properties of PVA/BA adhesives are tunable by varying the cross-linking density. Within seconds of activation by water, the surface boronic ester bonds in the PVA/BA film undergo fast debonding and instant softening, leading to conformal contact with the adherends and reformation of the boronic ester bonds at the interface. Meanwhile, the bulkmore »film remains dehydrated to offer efficient load transmission, which is important to achieve strong adhesion without delamination at the interface. Whether the substrate surface is smooth (e.g., glass) or rough (e.g., hairy mouse skin), PVA/BA adhesives demonstrate superior adhesion compared to the most widely used topical skin adhesive in clinical medicine, Dermabond.« less

Abstract A classifier is a software component, often based on Deep Learning, that categorizes each input provided to it into one of a fixed set of classes. An IDK classifier may additionally output “I Don’t Know” (IDK) for certain inputs. Multiple distinct IDK classifiers may be available for the same classification problem, offering different trade-offs between effectiveness, i.e. the probability of successful classification, and efficiency, i.e. execution time. Optimal offline algorithms are proposed for sequentially ordering IDK classifiers such that the expected duration to successfully classify an input is minimized, optionally subject to a hard deadline on the maximum time permitted for classification. Solutions are provided considering independent and dependent relationships between pairs of classifiers, as well as a mix of the two.

System biology relies on holistic biomolecule measurements, and untangling biochemical networks requires time-series metabolomics profiling. With current metabolomic approaches, time-series measurements can be taken for hundreds of metabolic features, which decode underlying metabolic regulation. Such a metabolomic dataset is untargeted with most features unannotated and inaccessible to statistical analysis and computational modeling. The high dimensionality of the metabolic space also causes mechanistic modeling to be rather cumbersome computationally. We implemented a faster exploratory workflow to visualize and extract chemical and biochemical dependencies. Time-series metabolic features (about 300 for each dataset) were extracted by Ridge Tracking-based Extract (RTExtract) on measurements from continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) in Neurospora crassa under different conditions. The metabolic profiles were then smoothed and projected into lower dimensions, enabling a comparison of metabolic trends in the cultures. Next, we expanded incomplete metabolite annotation using a correlation network. Lastly, we uncovered meaningful metabolic clusters by estimating dependencies between smoothed metabolic profiles. We thus sidestepped the processes of time-consuming mechanistic modeling, difficult global optimization, and labor-intensive annotation. Multiple clusters guided insights into central energy metabolism and membrane synthesis. Dense connections with glucose 1-phosphate indicated its central position in metabolism in N . crassa .more »Our approach was benchmarked on simulated random network dynamics and provides a novel exploratory approach to analyzing high-dimensional metabolic dynamics.« less