Time resolved characterization of a free-burning argon arc after ignition by optical emission spectroscopy

Time resolvedproperties of a free-burning argon arc after ignition have been characterized using optical spectroscopic method. After ignition, when the arc current keeps constant, the plasma temperature decreases with time at any position of the arc. The decrease of the plasma temperature is associated with the increase of the arc cathode surface temperature. It is suggested that the variation of the cathode surface temperature, which changes the current density distribution over the cathode surface, leads to the decrease of the plasma temperature in the free-burning arc after ignition

Modified Fowler–Milne method for the spectroscopic determination of thermal plasma temperature without the measurement of continuum radiation

A technique based on the Fowler-Milne method for the spectroscopic determination of thermal plasma temperatures without measuring continuum radiation is presented. This technique avoids the influence of continuum radiation with the combined line and continuum emission coefficients to derive the plasma temperatures. The amount of continuum emission coefficient is estimated by using an expression related to the Biberman factors. Parameters that affect the accuracy of the proposed technique and errors in the measured plasma temperatures are analyzed. It is shown that, by using the Ar I 696.5 nm line with a bandwidth of 3.27 nm without taking into account the continuum radiation, the plasma temperature measured will be lower on the order of up to 1000-3000 K for temperatures from 20,000 to 24,000 K. The theoretically predicted temperature errors are in good agreement with the experimental results, indicating that the proposed technique is reliable for plasma temperature measurement

Modeling Shopping Cart Decisions

The most recent consumer propensity study by SAP indicates that online shopping cart abandonment is high and the associated reasons are complex. In order to examine this phenomenon, we construct online SCA decision as a discrete choice model (DCM) and capture consumer segments by a latent class model (LCM) in this research-in-progress (RIP) paper, grounded on the theories of product involvement, word of mouth, and consumer heterogeneity. We will apply the clickstream dataset from 78,746 consumers at a large Chinese online platform to verify the proposed models in future study. The objective of this research project is to scrutinize the heterogeneous impacts of product involvement and online reviews on shopping cart decision-making in view of individual-level sequential behavior and the associated products in the form of stock-keeping-unit items. We conclude this RIP paper with the discussion of potential theoretical contributions and managerial implications

Magnetic band representations, Fu-Kane-like symmetry indicators and magnetic topological materials

To realize novel topological phases and to pursue potential applications in low-energy consumption spintronics, the study of magnetic topological materials is of great interest. Starting from the theory of nonmagnetic topological quantum chemistry [Bradlyn et al., Nature 547, 298 (2017)], we have obtained irreducible (co)representations and compatibility relations (CRs) in momentum space, and we constructed a complete list of magnetic band (co)representations (MBRs) in real space for other MSGs with anti-unitary symmetries (i.e. type-III and type-IV MSGs). The results are consistent with the magnetic topological quantum chemistry [Elcoro et al., Nat. Comm. 12, 5965 (2021)]. Using the CRs and MBRs, we reproduce the symmetry-based classifications for MSGs, and we obtain a set of Fu-Kane-like formulas of symmetry indicators (SIs) in both spinless (bosonic) and spinful (fermionic) systems, which are implemented in an automatic code - TopMat - to diagnose topological magnetic materials. The magnetic topological materials, whose occupied states can not be decomposed into a sum of MBRs, are consistent with nonzero SIs. Lastly, using our online code, we have performed spin-polarized calculations for magnetic compounds in the materials database and find many magnetic topological candidates.Comment: 6 pages, 3128 pages for the Appendice

ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising

While various deep learning methods have been proposed for low-dose computed tomography (CT) denoising, most of them leverage the normal-dose CT images as the ground-truth to supervise the denoising process. These methods typically ignore the inherent correlation within a single CT image, especially the anatomical semantics of human tissues, and lack the interpretability on the denoising process. In this paper, we propose a novel Anatomy-aware Supervised CONtrastive learning framework, termed ASCON, which can explore the anatomical semantics for low-dose CT denoising while providing anatomical interpretability. The proposed ASCON consists of two novel designs: an efficient self-attention-based U-Net (ESAU-Net) and a multi-scale anatomical contrastive network (MAC-Net). First, to better capture global-local interactions and adapt to the high-resolution input, an efficient ESAU-Net is introduced by using a channel-wise self-attention mechanism. Second, MAC-Net incorporates a patch-wise non-contrastive module to capture inherent anatomical information and a pixel-wise contrastive module to maintain intrinsic anatomical consistency. Extensive experimental results on two public low-dose CT denoising datasets demonstrate superior performance of ASCON over state-of-the-art models. Remarkably, our ASCON provides anatomical interpretability for low-dose CT denoising for the first time. Source code is available at https://github.com/hao1635/ASCON.Comment: MICCAI 202

CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization

Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models to address the over-smoothness and training instability encountered by previous deep-learning-based denoising models. However, diffusion models suffer from long inference times due to the large number of sampling steps involved. Very recently, cold diffusion model generalizes classical diffusion models and has greater flexibility. Inspired by the cold diffusion, this paper presents a novel COntextual eRror-modulated gEneralized Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff. First, CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs a novel mean-preserving degradation operator to mimic the physical process of CT degradation, significantly reducing sampling steps thanks to the informative LDCT images as the starting point of the sampling process. Second, to alleviate the error accumulation problem caused by the imperfect restoration operator in the sampling process, we propose a novel ContextuaL Error-modulAted Restoration Network (CLEAR-Net), which can leverage contextual information to constrain the sampling process from structural distortion and modulate time step embedding features for better alignment with the input at the next time step. Third, to rapidly generalize to a new, unseen dose level with as few resources as possible, we devise a one-shot learning framework to make CoreDiff generalize faster and better using only a single LDCT image (un)paired with NDCT. Extensive experimental results on two datasets demonstrate that our CoreDiff outperforms competing methods in denoising and generalization performance, with a clinically acceptable inference time. Source code is made available at https://github.com/qgao21/CoreDiff.Comment: IEEE Transactions on Medical Imaging, 202

Researching Dynamic Brand Competitiveness Based on Consumer Clicking Behavior

Analyzing brand dynamic competition relationship by using consumer sequential online click data, which was collected from JD.com. It is found that the competition intensity of the products across categories is quite different. Owing to the purchasing time of durable-like goods is more flexible, that is, the purchasing probability of such products changes more obviously over time. Therefore, we use the Local Polynomial Regression Model to analyze the relationship between the brand competition of durable-like goods and the purchasing probability of the specific brand. Finding that when brands increase at a half of the total market share for consumers cognition preference, the brands’ competitiveness is peak and makes no significant different from one hundred percent for consumer to complete a transaction. The findings contribute to brand competitiveness for setting up marketing strategy from the dynamic and online consumer behavior’s perspective