Gathering 250 classical Chinese poems translated by four major American poets (Pound, Williams, Rexroth, Snyder) and one poet-translator-scholar (Hinton) in his New Directions anthology Eliot Weinberger opens up the possibility of reading classical Chinese poetry through American poetry, or vice versa. While the impact of classical Chinese poetry on modern poetics has been amply acknowledged literary scholars have seldom examined the resulting texts beyond the paradigm of the renewal and transformation of American poetry and poetics. This oblique sense of indebtedness also strikes a curious contrast with the marginalization of Chinese literature in current discussions of world literature. An attempt at a world history of classical Chinese poetry, therefore, must not be concerned merely with the cataloguing of classical Chinese poetry in translation and the identification of its influences but with the formulation of new frameworks and strategies of reading that might resist or counteract what Shu-mei Shih has described as the Western canon's "technologies of recognition" (2004). This chapter will start by tracing the main trajectory of the "worlding" of classical Chinese poetry along the Pound-Rexroth-Snyder axis, but branching out to less-studied poets like Charles Bukowski and James Wright, and extending the line to contemporary poets such as Roo Borson and Kim Maltman (Canada), Diana Bridge and Nina Powles (New Zealand), and Peter Larkin and Sarah Howe (UK; UK-Hong Kong), all of whom create and inhabit their own world of Chinese poetry. Through this expanded mapping of the "worlds" of classical Chinese poetry, in both geopolitical and demographic terms, I seek to uncover a diverse range of resonances and echoes through translation, rewriting, rereading, and critique. Reframing such works as part of a Chinese-inflected world literature, I argue that the impact of classical Chinese poetry in contemporary writing has gone beyond the migration and transformation of aesthetic concepts and forms, but of personal histories, identities, and the very notions of cultural tradition and literary lineage.
Abstract Reported models have disadvantages such as poor prediction accuracy and time‐consuming. And they can not reflect the impact of chemical reactions on CO 2 solubility. To compensate for these deficiencies, parameters representing operational parameters, physical properties, chemical properties, and molecular properties are introduced as input variables. A series of models are constructed by three algorithms: back propagation neural network, radial basis function neural network, and random forest. The model with the best prediction performance is level OPCM (RBFNN), with the AARE of only 1.52%. By ranking the importance of the features using the RF algorithm, P CO2 , was found to be the key parameter affecting the CO 2 loadings, with M being the least important. Using the screened key parameters to model the model, as well as optimizing the structure, can further improve the predictive performance of the model. The full process development and optimization model framework constructed in this article can provide practical guidance for the development of machine learning models.
Abstract Background The etiology of chronic prostatitis remains unclear; consequently, this disease is associated with recurrence and ineffective clinical therapy. Therefore, there is an urgent need to investigate the underlying pathogenesis of chronic prostatitis in order to develop more efficacious treatments. Objective The previous study found that knocking out of PEBP4 leads to chronic prostatitis in the male mice. This research aimed to identify the role of PEBP4 in prostatitis, determine the molecular pathogenic mechanisms associated with chronic prostatitis, and provide guidelines for the development of new treatment strategies for chronic prostatitis. Materials and methods A PEBP4 exon knockout strain ( PEBP4 −/− ) was established in C57BL/6 mice via the Cre‐loxP system. Hematoxylin‐eosin (H&E) staining was used to investigate histological changes. RNA‐sequencing was used to investigate the gene expression signature of the prostate and the levels of inflammatory cytokines were determined by real‐time polymerase chain reaction (RT‐PCR). The expression of PEBP4 protein in prostate tissue was determined by immunohistochemistry in specimens from patients with BPH and BPH combined with chronic prostatitis. Finally, we used a CRISPR‐Cas9 plasmid to knockout PEBP4 in RWPE‐1 cells; western blotting was subsequently used to measure the level of activation in the NF‐κB signaling pathway after activating with TNF‐α. Results Hemorrhage and inflammatory cell infiltration were incidentally observed in the seminal vesicles and prostate glands of PEBP4 −/− mice after being fed with a normal diet for 1 year. In addition, we found significantly lower ( p < 0.001) expression levels of PEBP4 protein in prostate tissues from patients with benign prostate hyperplasia (BPH) and chronic and non‐bacterial prostatitis (CNP) when compared to those with BPH only. The reduced expression of PEBP4 led to a higher risk of prostatitis recurrence in patients after 2 years of follow‐up. Increased levels of NF‐κB and IκB phosphorylation were observed in PEBP4 ‐knockout RWPE‐1 cells and prostate glands from PEBP4 −/− mice. Conclusion The knockout of PEBP4 in experimental mice led to chronic prostatitis and the reduced expression of PEBP4 in patients with higher risk of chronic and non‐bacterial prostatitis suggested that PEBP4 might act as a protective factor against chronic prostatitis. The knockout of PEBP4 in RWPE‐1 cells led to the increased activation of NF‐κB and IκB, thus indicating that inhibition of PEBP4 faciliated the NF‐κB signaling cascade. Our findings provide a new etiology and therapeutic target for chronic prostatitis.
The air-conditioning equipments occupied the most space of a metro station's electronic and mechanical equipment room.After analyzed some representative engineering,several skills of arranging air-conditioning and ventilation equipment were drawn out,such as sharing space accurately,using trashy space skillfully,arranging pipeline reasonably,etc.The above skills can be used flexibly for architecture and air-conditioning ventilation designing.
We investigate the ground-state phase diagram of a binary mixture of Bose-Einstein condensates (BECs) with competing interspecies s-and p-wave interactions.Exploiting a pseudopotential model for the l = 1 partial wave, we derive an extended Gross-Pitaevskii (GP) equation for the BEC mixture that incorporates both s-and p-wave interactions.Based on it, we study the miscible-immiscible transition of a binary BEC mixture in the presence of interspecies p-wave interaction, by combining numerical solution of the GP equation and Gaussian variational analysis.Our study uncovers a dual effect-either enhance or reduce miscibility-of positive interspecies p-wave interaction, which can be precisely controlled by adjusting relevant experimental parameters.By complete characterizing the miscibility phase diagram, we establish a promising avenue towards experimental control of the miscibility of binary BEC mixtures via high partial-wave interactions.
A control system with a novel speed estimation approach based on model reference adaptive control (MRAC) is presented for low cost brushless dc motor drives with low-resolution hall sensors. The back EMF is usually used to estimate speed. But the estimation result is not accurate enough at low speeds because of the divided voltage of stator resistors and too small back EMF. Moreover, the stator resistor is always varying with the motor's temperature. A speed estimation algorithm based on MRAC was proposed to correct the speed error estimated by using back EMF. The proposed algorithm's most innovative feature is its adaptability to the entire speed range including low speeds and high speeds and temperature and different motors do not affect the accuracy of the estimation result. The effectiveness of the algorithm was verified through simulations and experiments.
In this article, an optimal strategy is proposed for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings to fairly ensure the occupants’ comfort, while participating in frequency regulation. With the nonlinear relationship between temperature setpoint and the fan power, the differences between indoor temperatures and temperature setpoints are considered as the objective to fairly ensure the occupants’ comfort. By the proposed strategy, the task for frequency regulation is optimally distributed according to the capacity of HVAC systems in a load aggregator. Therefore, performing frequency regulation and fairly ensuring occupants’ comfort are achieved simultaneously. Simulations on a two-area interconnected power system show that the proposed optimal strategy can improve the quality of frequency, reduce traditional generator regulation, and ensure the occupants’ comfort fairly.
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