Quantum measurement of hyperfine interaction in nitrogen-vacancy center

We propose an efficient quantum measurement protocol for the hyperfine interaction between the electron spin and the $^{15}$N nuclear spin of a diamond nitrogen-vacancy center. In this protocol, a sequence of quantum operations of successively increasing duration is utilized to estimate the hyperfine interaction with successively higher precision approaching the quantum metrology limit. This protocol does not need the preparation of the nuclear spin state. In the presence of realistic operation errors and electron spin decoherence, the overall precision of our protocol still surpasses the standard quantum limit

Embedded Solitons in Lagrangian and Semi-Lagrangian Systems

We develop the technique of the variational approximation for solitons in two directions. First, one may have a physical model which does not admit the usual Lagrangian representation, as some terms can be discarded for various reasons. For instance, the second-harmonic-generation (SHG) model considered here, which includes the Kerr nonlinearity, lacks the usual Lagrangian representation if one ignores the Kerr nonlinearity of the second harmonic, as compared to that of the fundamental. However, we show that, with a natural modification, one may still apply the variational approximation (VA) to those seemingly flawed systems as efficiently as it applies to their fully Lagrangian counterparts. We call such models, that do not admit the usual Lagrangian representation, \textit{semi-Lagrangian} systems. Second, we show that, upon adding an infinitesimal tail that does not vanish at infinity, to a usual soliton ansatz, one can obtain an analytical criterion which (within the framework of VA) gives a condition for finding \textit{embedded solitons}, i.e., isolated truly localized solutions existing inside the continuous spectrum of the radiation modes. The criterion takes a form of orthogonality of the radiation mode in the infinite tail to the soliton core. To test the criterion, we have applied it to both the semi-Lagrangian truncated version of the SHG model and to the same model in its full form. In the former case, the criterion (combined with VA for the soliton proper) yields an \emph{exact} solution for the embedded soliton. In the latter case, the criterion selects the embedded soliton with a relative error $\approx 1$.Comment: 10 pages, 1 figur

EFFECTS OF AL(2)O(3) NANOPARTICLES DEPOSITION ON CRITICAL HEAT FLUX OF R-123 IN FLOW BOILING HEAT TRANSFER

In this study, R-123 flow boiling experiments were carried out to investigate the effects of nanoparticle deposition on heater surfaces on flow critical heat flux (CHF) and boiling heat transfer. It is known that CHF enhancement by nanoparticles results from porous structures that are very similar to layers of Chalk River unidentified deposit formed on nuclear fuel rod surfaces during the reactor operation period. Although previous studies have investigated the surface effects through surface modifications, most studies are limited to pool boiling conditions, and therefore, the effects of porous surfaces on flow boiling heat transfer are still unclear. In addition, there have been only few reports on suppression of wetting for decoupled approaches of reasoning. In this study, bare and Al2O3 nanoparticle-coated surfaces were prepared for the study experiments. The CHF of each surface was measured with different mass fluxes of 1,600 kg/m(2)s, 1,800 kg/m(2)s, 2,100 kg/m(2)s, 2,400 kg/m(2)s, and 2,600 kg/m(2)s. The nanoparticle-coated tube showed CHF enhancement up to 17% at a mass flux of 2,400 kg/m(2)s compared with the bare tube. The factors for CHF enhancement are related to the enhanced rewetting process derived from capillary action through porous structures built-up by nanoparticles while suppressing relative wettability effects between two sample surfaces as a highly wettable R-123 refrigerant was used as a working fluid. Copyright (C) 2015, Published by Elsevier Korea LLC on behalf of Korean Nuclear Societyclose0

Identification technique of misalignment-rubbing coupling fault in dual-disk rotor system supported by rolling bearing

For the diagnosis of misalignment-rubbing coupling fault of rotor-rolling bearing system caused by misalignment fault, the mechanical model and finite element model of dual-disc rotor system with misalignment-rubbing coupling fault were established based on the nonlinear finite element method, rolling bearing force, equivalent misalignment torque and contact theory in this paper. And then its accuracy was validated by related experiment. According to research on dynamic characteristics of the rotor system with different rubbing stiffness, misalignment angles and rotation rates, it was found that the misalignment-rubbing coupling fault is often characterized by rubbing fault, and that double frequency appeared early, and that peak value increased rapidly. It could be used as a theoretical basis for diagnosing misalignment-rubbing coupling fault of rotor-rolling bearing system

Customizing General-Purpose Foundation Models for Medical Report Generation

Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given medical images. However, the scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks capable of harnessing the potential artificial general intelligence power like large language models (LLMs). In this work, we propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs), in computer vision and natural language processing with a specific focus on medical report generation. Specifically, following BLIP-2, a state-of-the-art vision-language pre-training approach, we introduce our encoder-decoder-based MRG model. This model utilizes a lightweight query Transformer to connect two FMs: the giant vision Transformer EVA-ViT-g and a bilingual LLM trained to align with human intentions (referred to as ChatGLM-6B). Furthermore, we conduct ablative experiments on the trainable components of the model to identify the crucial factors for effective transfer learning. Our findings demonstrate that unfreezing EVA-ViT-g to learn medical image representations, followed by parameter-efficient training of ChatGLM-6B to capture the writing styles of medical reports, is essential for achieving optimal results. Our best attempt (PCLmed Team) achieved the 4th and the 2nd, respectively, out of 13 participating teams, based on the BERTScore and ROUGE-1 metrics, in the ImageCLEFmedical Caption 2023 Caption Prediction Task competition.Comment: 14 pages, 3 figure