A Study on Cultural Translatability from the Perspective of Lefevere’s Constraints on Literary Translation—Take the Nickname Translation in Shuihuzhuan as Examples

Cultural translatability has long been a hot but mysterious topic in translation academia. Lefevere, as one of the major figures of “cultural turn” in translation studies, investigated translation from socio-cultural perspective and stressed the extra-textual constraints on cultural translation. However, most studies have focused on the major four manipulations and neglected the other specific elements that hinder cultural translation. Nickname in Shuihuzhuan, originated from historical figures, anecdotes, legends, weapons, dialects, and other cultural elements, has revealed its research value in literary translation and cultural transmission. Therefore, this paper intends to take nickname translation in Shuihuzhuan as examples, analyzes those specific cultural elements, and carries out some translation strategies and cultural strategies to deal with them. Though the paper is aimed at discussing the cultural translation by using texts in Shuihuzhuan, the conclusion is applicable to all literature works

Model-based analysis in survey: an application in analytic inference and a simulation in Small Area Estimation

This paper addresses model-based procedures in data analysis for national complex data and a simulation study in small area estimation. For National Agriculture Workers Survey data, we do variable selection and want to find one model based on Akaike information criterion (AIC). We use augmented model and weighting approach to deal with the survey weight, equal weight, and smoothed weight. Research result indicates that the survey weight plays an important role in model selection. We also show what variables are significant predictors for the number of years farm workers are employed in their current employer. For small area estimator, we conduct a simulation to compare two direct estimators and one sample mean estimator in the area-level model and one EBLUP in the unit-level model. We are interested in the mean squared error of the area-level and unit-level estimators. The research result shows that the unit-level estimator performs the best. For a larger number of sample size in each area, a regression estimator in an area-level model will be as efficient as the unit-level estimator

Lower Sodium Intake and Risk of Headaches: Results From the Trial of Nonpharmacologic Interventions in the Elderly.

ObjectivesTo determine the effect of sodium (Na) reduction on occurrence of headaches.MethodsIn the Trial of Nonpharmacologic Interventions in the Elderly, 975 men and woman (aged 60-80 years) with hypertension were randomized to a Na-reduction intervention or control group and were followed for up to 36 months. The study was conducted between 1992 and 1995 at 4 clinical centers (Johns Hopkins University, Wake Forest University School of Medicine, Robert Wood Johnson Medical School, and the University of Tennessee).ResultsMean difference in Na excretion between the Na-reduction intervention and control group was significant at each follow-up visit (P < .001) with an average difference of 38.8 millimoles per 24 hours. The occurrence of headaches was significantly lower in the Na-reduction intervention group (10.5%) compared with control (14.3%) with a hazard ratio of 0.59 (95% confidence interval = 0.40, 0.88; P = .009). The risk of headaches was significantly associated with average level of Na excretion during follow-up, independent of most recent blood pressure. The relationship appeared to be nonlinear with a spline relationship and a knot at 150 millimoles per 24 hours.ConclusionsReduced sodium intake, currently recommended for blood pressure control, may also reduce the occurrence of headaches in older persons with hypertension

A Gap Analysis of Biodiversity Research in Rocky Mountain National Park: A Pilot Study on Spiders

Research on biodiversity and the relationship between organisms is imperative to establish management practices for the conservation of protected areas. The E.O. Wilson Biodiversity Foundation (EOWBF) formed our team of four Duke University students as the first of many ATBI/BioBlitz SWAT teams to travel to protected areas and develop approaches to conduct biodiversity research that can inform their conservation. Our project consisted of two elements. First, our team assessed the current status of biodiversity research at Rocky Mountain National Park (RMNP) to determine major gaps in the understanding of biodiversity. We used available species lists from research conducted in the Park to ensure that the National Park species database, NPSpecies, contained the most up-to-date information. Our team then added 645 species of plants and fungi to the database through this process. One of the identified gaps was a lack of research on spiders in the park. The second element of our study was a pilot analysis of spider biodiversity, to identify as many species in the park as possible and to relate their occurrences to environmental variables. Over 300 spider specimens were collected, 157 of which were identified, representing 51 species. Specimens were collected from three non-wilderness sites in RMNP at three different times of day (morning, afternoon, and night), over a span of ten days (July 16 - 25, 2014). The three sites represent a range of elevations (2,398 - 2,923 meters) and habitats. Cost-effective methods were utilized and evaluated for future spider research. We propose a more thorough spider survey in RMNP that can better inform management of the Park by providing information about spider diversity, abundance, function, and how spiders can be used as ecological indicators

Ameliorative effects of olibanum essential oil on learning and memory in Aβ1-42-induced Alzheimer’s disease mouse model

Purpose: To study the effect of olibanum essential oil (OEO) on learning and memory in Alzheimer’s disease (AD) mouse.Methods: Mice were administered the 42-amino acid form of amyloid β-peptide (Aβ1-42) to induce AD and then treated with OEO at 150, 300, and 600 mg/kg, p.o. for two weeks. Following treatment, the AD mice were assessed by step-down test (SDT), dark avoidance test (DAT), and Morris water maze test (MWM). Blood and brain tissues were collected for biochemical assessments. Gas chromatographymass spectroscopy was used to analyze the main constituents of OEO.Results: The main constituents of OEO were limonene, α-pinene, and 4-terpineol. Treatment with OEO prolonged t latency in SDT and DAT, but decreased error times. Escape latency decreased and crossing times were rose in the MWM following OEO treatment (p < 0.5). Treatment with OEO also enhanced the acetylcholine levels and decreased the acetylcholinesterase levels in serum and brain tissue (p < 0.5). Additionally, OEO reduced amyloid plaques in the hippocampus and protected hippocampal neurons from damage. Furthermore, OEO decreased c-fos expression in  hippocampus tissues from AD mice (p < 0.5).Conclusion: OEO has significant ameliorative effect AD-induced deterioration in learning and memory in AD mouse induced by Aβ1-42. The mechanisms of these effects are related to increased acetylcholine contents, reduction of amyloid plaques, protection of hippocampal neurons, and downregulation of c-fos in brain tissues. The results justify the need for further investigation of candidate drugs derived from OEO for the  management of AD. Keywords: Olibanum, Essential oil, Learning, Memory, A

From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection

Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem. We first put forth a quick initial upper confidence bound (Quick-Init UCB) algorithm to solve this SMAB problem under the federated analytics (FA) framework. Then, we further propose a belief propagation-based UCB (BP-UCB) algorithm under the democratized analytics (DA) framework. Moreover, we derive two regret upper bounds for the proposed algorithms, which increase logarithmically over the time horizon. The numerical results demonstrate that the proposed algorithms achieve nearly optimal performance, with a gap of less than 1.44% and 3.12% under the FA and DA frameworks, respectively.Comment: This work was partly presented at IEEE ICC 202