Neural network-based intrinsic structure relationship of TC20 titanium alloy for medical applications

Isothermal constant strain rate compression experiments were carried out on TC20 titanium alloy using a Gleeble- 1500 thermal simulation tester to investigate its high temperature flow behaviour at deformation temperatures of 750 - 900 °C and strain rates of 0,001 - 1 s-1. The results show that the flow stress basically decreases with increasing deformation temperature and increases with increasing strain rate. The correlation coefficients and mean relative errors were 0,998 and 5,06 % respectively, proving that the BP neural network-based intrinsic structure model is effective in predicting the flow stress of the alloy

Quantum Logic between Remote Quantum Registers

We analyze two approaches to quantum state transfer in solid-state spin systems. First, we consider unpolarized spin-chains and extend previous analysis to various experimentally relevant imperfections, including quenched disorder, dynamical decoherence, and uncompensated long range coupling. In finite-length chains, the interplay between disorder-induced localization and decoherence yields a natural optimal channel fidelity, which we calculate. Long-range dipolar couplings induce a finite intrinsic lifetime for the mediating eigenmode; extensive numerical simulations of dipolar chains of lengths up to L=12 show remarkably high fidelity despite these decay processes. We further consider the extension of the protocol to bosonic systems of coupled oscillators. Second, we introduce a quantum mirror based architecture for universal quantum computing which exploits all of the spins in the system as potential qubits. While this dramatically increases the number of qubits available, the composite operations required to manipulate "dark" spin qubits significantly raise the error threshold for robust operation. Finally, as an example, we demonstrate that eigenmode-mediated state transfer can enable robust long-range logic between spatially separated Nitrogen-Vacancy registers in diamond; numerical simulations confirm that high fidelity gates are achievable even in the presence of moderate disorder.Comment: 15 pages, 10 figure

Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

© 2020, Springer Nature Switzerland AG. Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment

Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Increasingly, organizations are adopting ontologies to describe their large catalogues of items. These ontologies need to evolve regularly in response to changes in the domain and the emergence of new requirements. An important step of this process is the selection of candidate concepts to include in the new version of the ontology. This operation needs to take into account a variety of factors and in particular reconcile user requirements and application performance. Current ontology evolution methods focus either on ranking concepts according to their relevance or on preserving compatibility with existing applications. However, they do not take in consideration the impact of the ontology evolution process on the performance of computational tasks – e.g., in this work we focus on instance tagging, similarity computation, generation of recommendations, and data clustering. In this paper, we propose the Pragmatic Ontology Evolution (POE) framework, a novel approach for selecting from a group of candidates a set of concepts able to produce a new version of a given ontology that i) is consistent with the a set of user requirements (e.g., max number of concepts in the ontology), ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. Our approach also supports users in navigating the space of possible solutions by showing how certain choices, such as limiting the number of concepts or privileging trendy concepts rather than historical ones, would reflect on the application performance. An evaluation of POE on the real-world scenario of the evolving Springer Nature taxonomy for editorial classification yielded excellent results, demonstrating a significant improvement over alternative approaches

Transformation of microbially-induced protodolomite to dolomite proceeds under dry-heating conditions

The genesis of sedimentary dolomite remains an unresolved issue. Protodolomite has been considered as a metastable precursor for some sedimentary dolomites. Through laboratory experiments, much has been learnt about the transformation of protodolomite into dolomite under hydrothermal conditions mimicking those in open diagenetic systems. However, it is still unclear whether such mineral transformation could proceed in closed diagenetic systems, in which the supply of externally-derived fluids is often limited. Here through dry-heating experiments we demonstrated that low-temperature protodolomite converts into dolomite in the absence of external fluid. The starting materials for the recrystallization reactions included two types of protodolomite: biotic protodolomite and its abiotic counterpart. Biotic protodolomite was synthesized by means of a halophilic bacterium at 30 °C. Since the synthesis of abiotic protodolomite normally requires higher temperatures than biotic ones, the abiotic protodolomite samples used herein were prepared at 60 °C and 100 °C. These protodolomites were spherical in shape and composed of nano-globular subunits. Our protodolomite samples contained considerable structural water in the range of 1.4-7 wt%. The water content of protodolomites was linearly correlated with their synthesis temperature, that is, biotic protodolomite had a higher amount of water than its abiotic counterparts. The protodolomite samples were then dry-annealed at temperatures of 100 to 300 °C for two months. The results indicated that the rate of protodolomite-to-dolomite transformation was higher in the reactors using biotic protodolomite than those using abiotic protodolomites. This conversion was likely triggered by the dehydration of structural water within protodolomite. The resulting dolomite mostly retained spherical morphology, whereas its nanosized subunits tended to become rhombohedral. Calcite neoformation was also found to accompany the dolomite formation. Our findings suggest that structural water within protodolomite is an overlooked internal fluid and it might have an impact on the genesis of sedimentary dolomite during burial diagenesis

ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.

Chromatin immunoprecipitation with massively parallel DNA sequencing (ChIP-seq) has greatly improved the reliability with which transcription factor binding sites (TFBSs) can be identified from genome-wide profiling studies. Many computational tools are developed to detect binding events or peaks, however the robust detection of weak binding events remains a challenge for current peak calling tools. We have developed a novel Bayesian approach (ChIP-BIT) to reliably detect TFBSs and their target genes by jointly modeling binding signal intensities and binding locations of TFBSs. Specifically, a Gaussian mixture model is used to capture both binding and background signals in sample data. As a unique feature of ChIP-BIT, background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. Extensive simulation studies showed a significantly improved performance of ChIP-BIT in target gene prediction, particularly for detecting weak binding signals at gene promoter regions. We applied ChIP-BIT to find target genes from NOTCH3 and PBX1 ChIP-seq data acquired from MCF-7 breast cancer cells. TF knockdown experiments have initially validated about 30% of co-regulated target genes identified by ChIP-BIT as being differentially expressed in MCF-7 cells. Functional analysis on these genes further revealed the existence of crosstalk between Notch and Wnt signaling pathways

Two-dimensional Transport Induced Linear Magneto-Resistance in Topological Insulator Bi2_2Se3_3 Nanoribbons

We report the study of a novel linear magneto-resistance (MR) under perpendicular magnetic fields in Bi2Se3 nanoribbons. Through angular dependence magneto-transport experiments, we show that this linear MR is purely due to two-dimensional (2D) transport, in agreement with the recently discovered linear MR from 2D topological surface state in bulk Bi2Te3, and the linear MR of other gapless semiconductors and graphene. We further show that the linear MR of Bi2Se3 nanoribbons persists to room temperature, underscoring the potential of exploiting topological insulator nanomaterials for room temperature magneto-electronic applications.Comment: ACS Nano, in pres

Van der Waals epitaxy of Bi2Se3 on Si(111) vicinal surface: An approach to prepare high-quality thin films of topological insulator

Epitaxial growth of topological insulator Bi2Se3 thin films on nominally flat and vicinal Si(111) substrates is studied. In order to achieve planner growth front and better quality epifilms, a two-step growth method is adopted for the van der Waal epitaxy of Bi2Se3 to proceed. By employing vicinal Si(111) substrate surfaces, the in-pane growth rate anisotropy of Bi2Se3 is explored to achieve single crystalline Bi2Se3 epifilms, in which threading defects and twins are effectively suppressed. Optimization of the growth parameters has resulted in vicinal Bi2Se3 films showing a carrier mobility of ~ 2000 cm2V-1s-1 and the background doping of ~ 3 x 1018 cm-3 of the as-grown layers. Such samples not only show relatively high magnetoresistance but also a linear dependence on magnetic field.Comment: 18 pages, 4 figure