Quantitative Analysis of Knowledge Graph of Domestic Interpretation Research -- Taking the Periodical Literature of CNKI from 2001 to 2022 as an Example

With the help of CiteSpace, this paper sorts out all the articles on interpretation research collected by China National Knowledge Infrastructure from 2000 to 2022, and analyzes the number of articles published in this field, the status quo of research methods, research hotspots and research trends. The results show that China's interpretation research has formed a clear research framework, which mainly includes four subject categories and research hotspots: theoretical research, skill research, teaching research and other research. The most influential and core keywords include “empirical research”, “training methods”, “pre-translation preparation” and "corpus", etc. These keywords have strong co-occurrence ability with other keywords, which are easy to form a specific research topic and are also the core keywords to promote the structural changes of interpretation research. From 2011 to 2015, the number of published articles reached its peak, and interpretation in China developed professionally. At this stage, interpretation research showed a trend of objectification and standardization

Smoothing and mean-covariance estimation of functional data with a Bayesian hierarchical model

Functional data, with basic observational units being functions (e.g., curves, surfaces) varying over a continuum, are frequently encountered in various applications. While many statistical tools have been developed for functional data analysis, the issue of smoothing all functional observations simultaneously is less studied. Existing methods often focus on smoothing each individual function separately, at the risk of removing important systematic patterns common across functions. We propose a nonparametric Bayesian approach to smooth all functional observations simultaneously and nonparametrically. In the proposed approach, we assume that the functional observations are independent Gaussian processes subject to a common level of measurement errors, enabling the borrowing of strength across all observations. Unlike most Gaussian process regression models that rely on pre-specified structures for the covariance kernel, we adopt a hierarchical framework by assuming a Gaussian process prior for the mean function and an Inverse-Wishart process prior for the covariance function. These prior assumptions induce an automatic mean-covariance estimation in the posterior inference in addition to the simultaneous smoothing of all observations. Such a hierarchical framework is flexible enough to incorporate functional data with different characteristics, including data measured on either common or uncommon grids, and data with either stationary or nonstationary covariance structures. Simulations and real data analysis demonstrate that, in comparison with alternative methods, the proposed Bayesian approach achieves better smoothing accuracy and comparable mean-covariance estimation results. Furthermore, it can successfully retain the systematic patterns in the functional observations that are usually neglected by the existing functional data analyses based on individual-curve smoothing.Comment: Submitted to Bayesian Analysi

Ordering policies for a dual sourcing supply chain with disruption risks

Purpose: The main purpose of this article is to explore the trade-off between ordering policies and disruption risks in a dual-sourcing network under specific (or not) service level constraints, assuming that both supply channels are susceptible to disruption risks. Design/methodology/approach: Stochastic newsvendor models are presented under both the unconstrained and fill rate constraint cases. The models can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Findings: Through the model, we obtain some important managerial insights and evaluate the value of contingency strategies in managing uncertain supply chains. Originality/value: This paper attempts to combine explicitly disruption management with risk aversion issues for a two-stage supply chain with two unreliable suppliers.Peer Reviewe

The 1α,25(OH)2D3 Analogs ZK159222 and ZK191784 Show Anti-Inflammatory Properties in Macrophage-Induced Preadipocytes via Modulating the NF-κB and MAPK Signaling.

Purpose:Key research findings suggest that attenuating metaflammation in adipose tissue might be a strategic step to prevent the metabolic syndrome and its associated disease outcomes. The anti-inflammatory effects of 1α,25(OH)2D3 have been confirmed in our previous studies, but adverse effects induced at high concentrations restrict its potential clinical translation. Two synthetic 1α,25(OH)2D3 analogs ZK159222 and ZK191784 have manifested promising tissue-specific immunomodulatory actions, but limited data are available on adipose tissue. Hence, in this study, we investigated whether ZK159222 and ZK191784 act on preadipocytes or macrophages to attenuate metaflammatory responses via modulating inflammatory and metabolic signaling in macrophage-induced preadipocytes. Methods:Preadipocyte-specific effects of ZK159222 and ZK191784 on macrophage-induced preadipocytes were tested by pre-incubating and incubating preadipocytes with the analogs and MacCM. Separately, macrophage-specific effects of both analogs on macrophage-induced preadipocytes were tested by incubating preadipocytes with analog-MacCM or MacCM. The effects of 1α,25(OH)2D3 were also examined and set as the positive control. Metaflammatory responses were determined as the concentrations and gene expression of major pro-inflammatory cytokines including IL-1β, IL-6, IL-8, MCP-1 and RANTES, measured using ELISA and qPCR. Inflammatory and metabolic signaling including NF-κB and MAPK were probed using Western blotting. Results:ZK159222 and ZK191784 act on preadipocytes and macrophages to decrease the secretion and gene expression of the major pro-inflammatory cytokines in macrophage-induced preadipocytes. The anti-inflammatory effects were at least as potent as 1α,25(OH)2D3, and no preadipocyte apoptosis was induced at high concentrations. In addition, mostly at high concentrations, both analogs moderately decreased the phosphorylation of relA, p44/42 and p38 MAPK in macrophage-induced preadipocytes. Conclusion:ZK159222 and ZK191784 act on macrophages and preadipocytes to attenuate metaflammatory responses in macrophage-induced preadipocytes, by decreasing phosphorylation of relA/NF-κB, p44/42 and p38 MAPK

STSyn: Speeding Up Local SGD with Straggler-Tolerant Synchronization

Synchronous local stochastic gradient descent (local SGD) suffers from some workers being idle and random delays due to slow and straggling workers, as it waits for the workers to complete the same amount of local updates. In this paper, to mitigate stragglers and improve communication efficiency, a novel local SGD strategy, named STSyn, is developed. The key point is to wait for the $K$ fastest workers, while keeping all the workers computing continually at each synchronization round, and making full use of any effective (completed) local update of each worker regardless of stragglers. An analysis of the average wall-clock time, average number of local updates and average number of uploading workers per round is provided to gauge the performance of STSyn. The convergence of STSyn is also rigorously established even when the objective function is nonconvex. Experimental results show the superiority of the proposed STSyn against state-of-the-art schemes through utilization of the straggler-tolerant technique and additional effective local updates at each worker, and the influence of system parameters is studied. By waiting for faster workers and allowing heterogeneous synchronization with different numbers of local updates across workers, STSyn provides substantial improvements both in time and communication efficiency.Comment: 12 pages, 10 figures, submitted for transaction publicatio