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With the full implementation of the policy of “Chinese Culture Going Global”, more attention has been paid to the English translation of Chinese tourism publicity materials. One of the major challenges that translators face is the English translation of a large number of Chinese culture-specific items (CSIs) contained in the publicity materials. There has been some discussion about the Chinese-English translation of CSIs in tourism publicity materials. However the previous studies are largely inadequate and unsystematic. In this paper, a category-based functional approach is taken to address the issue more systematically. It features a tripartite categorization of the Chinese CSIs in the tourism publicity materials of Nanjing Confucius Temple and a detailed analysis of their specific textual functions. Two general principles and three concrete methods are proposed to improve further the quality of translating the CSIs in tourism publicity materials.

Corporate social responsibility was proposed by Bowen in 1953, but Chinese companies began to attach importance to it in the 21st century. China is a big exporter of clothing and textiles in the world, but there is little research on the social responsibility of Chinese clothing companies. Environmental problems have always been hot and arduous. The process of clothing production will cause a lot of negative impacts on the environment, so environmental protection is an important social responsibility of textile and clothing enterprises. Through the case study of Anta Group, this study discusses how Chinese clothing companies can enhance their sense of social responsibility and become environment-friendly companies. The significance and specific feasible methods of the clothing companies to carry out green activities are mentioned in this paper. This study provides some ideas for other companies on how to fulfill their social responsibilities through a successful case of a large company. We hope that through case analysis, more enterprises will pay attention to environmental responsibility and more research will focus on corporate social responsibility in China.

This paper ranks and selects suppliers based on their importance while providing the most economical ordering solution and the least lossy forwarding solution under meeting the enterprise capacity requirements. In this paper, four indicators are selected to measure the supplier's supply characteristics, and the entropy weight method is used to determine the weight of each indicator. The evaluation model is constructed using the TOPSIS algorithm to determine the importance of the supplier by calculating the distance from the sample to the ideal solution and the negative ideal solution. Then this paper uses 0-1 planning to establish a linear objective function with whether to choose one supplier as the decision variable and solves with a genetic algorithm to derive the need for at least 162 suppliers. Finally, the most economical ordering solution planning model is established, and the cost function is built and solved to find the ordering solution at its minimum value. Based on the most economical ordering solution, the least loss transshipment solution is developed, and the least loss transshipment solution planning model is developed to obtain the total loss function of raw materials and solve for the minimum value of the transshipment solution with the decision variable of whether to choose this transshipment provider. The final ordering solution obtained using the model in this paper saved approximately 9.1% of production costs, and the designed transportation solution reduced losses by 4.5%.

Data science can accelerate materials discovery by learning composition-processing-performance models from pre-existing data sets, which then feed into active learning cycles in the laboratory. Thermoset polymer waste is a pressing environmental challenge that may be addressed by the accelerated discovery of new deconstructable variants; however, the combinatorial space of possible monomers, crosslinkers, additives, and manufacturing conditions is vast and Edisonian experimentation may struggle to find optimal designs. Moreover, data-driven strategies are limited for complex (co)polymers like thermosets because the training data is scarce and sourced from heterogeneous experimental approaches, resulting in overfit transferable models. Here, we introduce a novel closed-loop approach to the predictive design of chemically deconstructable thermosets that leverages experimental synthesis and characterization, machine learning, and virtual screening. Our computational model learns to map the molecular features of bifunctional silyl ether (BSE)-based cleavable comonomers to the thermal properties of the industrial thermoset polydicyclopentadiene (pDCPD). We address the challenges of limited data and overfitting by relying on both structural and information-rich domain-specific molecular features as inputs and by thoroughly quantifying model uncertainty. By training an ensemble of predictive models mixing multiple model architectures and parametrizations, our approach achieves predictions of a key thermoset parameter—the glass transition temperature—within less than 15 °C error over a wide temperature range with only 101 data points. The trained models were used to screen new possible BSE comonomer compositions and synthesis conditions, with promising combinations successfully validated experimentally. This work offers a closed-loop design process that we expect to be widely applicable to the discovery of deconstructable polymeric materials.

Objectives Long-term isoflurane anesthesia exposure could result in postoperative cognitive dysfunction (POCD). Preoperative stress is also reported to be a risk factor of POCD. However, it is unknown whether acute stress could impair memory after long-term isoflurane anesthesia. Methods In this study, we categorized the mice with acute stress into mild (30 min restraint stress), moderate (60 min restraint stress), and severe (120 min restraint stress) stress groups and then we used Open-Field Test (OFT) to detect whether different scales of acute restraint stress successfully induced acute stress in mice. The memory performance of mice was measured using contextual and cued memory test, and the brain-derived neurotrophic factor protein levels of hippocampus was detected by Western blot. Results We verified that mild stress has pro-cognitive effect, but severe stress has amnestic effect. Moreover, we found that mild and moderate other than severe acute stress could partially attenuate the memory impairment induced by long-term isoflurane anesthesia. Conclusion Mild and moderate acute stress could partially attenuate the memory impairment induced by long-term isoflurane anesthesia.

Triptolide (TP), the main active ingredient of Tripterygium wilfordii Hook.f., displays potent anti-inflammatory, antioxidant, and antiproliferative activities. In the present study, the effect of TP on acute pancreatitis and the underlying mechanisms of the disease were investigated using a caerulein-induced animal model of acute pancreatitis (AP) and an in vitro cell model. In vivo, pretreatment with TP notably ameliorated pancreatic damage, shown as the improvement in serum amylase and lipase levels and pancreatic morphology. Meanwhile, TP modulated the infiltration of neutrophils and macrophages (Ly6G staining and CD68 staining) and decreased the levels of proinflammatory factors (TNF-α and IL-6) through inhibiting the transactivation of nuclear factor-κB (NF-κB) in caerulein-treated mice. Furthermore, TP reverted changes in oxidative stress markers, including pancreatic glutathione (GSH), superoxide dismutase (SOD), and malondialdehyde (MDA), in acute pancreatitis mice. Additionally, TP pretreatment inhibited intracellular reactive oxygen species (ROS) levels via upregulated nuclear factor erythroid 2-related factor 2 (Nrf2) expression and Nrf2-regulated redox genes expression (HO-1, SOD1, GPx1 and NQO1) in vitro. Taken together, our data suggest that TP exert protection against pancreatic inflammation and tissue damage by inhibiting NF-κB transactivation, modulating immune cell responses and activating the Nrf2-mediated antioxidative system, thereby alleviating acute pancreatitis.

The outbreak of the COVID-19 pneumonia in 2019 has caused great damage to the world economy. With the continuous growth of the amount of data, using machine learning algorithm to analyze and predict the economic development of different countries and regions is a hot topic in recent years. In this paper, three machine learning algorithms (XGBoost, AdaBoost and random forest algorithms) are coupled together, and a new algorithm is proposed. Combined with data preprocessing and fine feature engineering processing, GDP values of different countries and regions are predicted. Experimental results show that our coupled method has better performance than each single machine learning algorithm used in this paper. Specifically, the MSE metrics of proposed model is 1.64%, 3.69% and 8.95% lower than XGBoost, AdaBoost and Random Forest algorithm, respectively. In addition, we also study the correlation coefficient between features and get some constructive guidance to improve the accuracy of the algorithm and restrain the further development of the epidemic situation.

Polymer nanostructures have drawn tremendous attention due to their wide applications in nanotechnology. However, the morphology of the polymer nanostructures is fragile under harsh conditions such as high-power irradiation and organic-solution environments during the fabrication or the measurement processes, significantly limiting their potential applications. In this work, we propose and demonstrate a simple approach to improve the stability of polymer nanostructures by coating a conformal ultrathin oxide film via atomic-layer deposition. Due to the refractory and dense coating of the oxide layer, the stability of polymer structures is enhanced by the prohibition of deformation occurrences from thermally induced reflow and organic solution. As a proof of concept, poly(methyl methacrylate) (PMMA) nanostructures coated with a sub-10-nm TiO2 layer are demonstrated, and the structures exhibit high temperature stability at 180 °C and good resistance to soluble damage from organic solutions. Subsequently, the mechanism of the improved thermal stability is analyzed via mechanical simulations. Such an effective approach is proposed to significantly broaden the application of polymer nanostructures as functional elements for optical structures/devices that require excellent thermal and chemical stability.

Efficient accumulation and on-demand intracellular drug release in the desired site are a crucial issue in developing ideal drug delivery systems (DDSs). Glutathione (GSH)/pH dual-bioresponsive degradable Nanosponges were developed based on β-CD-appended hyper-cross-linked polymer by one-pot polymerization of acryloyl-6- ethylenediamine-6-deoxy-β-Cyclodextrin (β-CD-NH-ACy), acrylic acid (AA) and N,N-bis(acryloyl)- cystamine (BACy) as cross-linker to deliver doxorubcin (DOX) and investigated for GSH/pH triggered DOX release, in which the massive carboxyl and amino groups, and disulfide bonds were used as pH and GSH bioresponsive fragments, respectively. In the proposed DDSs, DOX was readily incorporated into the three dimensional networks of the Nanosponges either as inclusion complexes or as non-inclusion complexes, with a high drug loading capacity of 22.6%. In vitro release studies suggested that the Nanosponges exhibited GSH/pH triggered disintegration and drug release performance, in which DOX release was significantly accelerated in acidic (pH5.0) and cytosolic reduction (10 mM GSH) conditions, with ~77.0% of DOX release. The morphology changes of DOX@Nanosponges in releasing media (pH5.0, 10 mM GSH) were further studied by TEM. Confocal microscopy observation demonstrated that DOX was delivered and released into cytoplasm and nucleus of A549 cells in 7 h incubation with DOX@Nanosponges. MTT assays manifested that the Nanosponges exhibited low cytotoxicity up to a concentration of 1000 μg/mL and DOX@Nanosponges had high anti-tumor activity. These findings demonstrated that the dual-bioresponsive Nanosponges may function as a promising platform for targeted delivery and intracellular drug controlled release in tumor therapy.

In order to solve the problems such as poor diversity and poor convergence ability of the offspring population of the NSGA-II Algorithm in the vehicle production scheduling problem, an improved shop scheduling algorithm based on NSGA-II is proposed. The improved NSGA-ii Algorithm mainly focuses on the crossover and mutation of the traditional NSGA-II Algorithm, and proposes a new improved self-adaptive Crossover and mutation operator. By comparing the individual crowding degree with the average crowding degree of the population, and combining the iterative evolution process of the population, in order to avoid blind orientation and to improve the convergence speed of the population, the genetic probability is correlated with the individuals and the evolution iteration times of the population, and a new uniform evolution strategy is proposed to select the individuals of the population through adaptive hierarchical selection, in order to improve the quality of the solution, the problem of the poor diversity of the offspring population was solved. The improved NSGA-II Algorithm is used to carry out the experimental simulation analysis. The effectiveness of the proposed algorithm is verified by comparing the optimization results before and after the improvement.

As a natural enemy of the European honeybee, the Chinese great steller’s wasp can have a serious impact on local bee populations, so they are considered an agricultural pest. After the emergence of these pests, countries must decide how to prioritize their limited resources for follow-up investigations. In this paper, a model was established to predict the emergence of wild silkworm, which could be used to provide strong support for pest management. when predicting how pests spread over time, we built a GM model to predict the spread of Vespa mandarinia from the spatial and temporal distribution. When predicting the geographic location of Vespa mandarinia, we set up a two-dimensional GM model using longitude and latitude as two variables to predict the longitude and latitude of the next Vespa mandarinia. At the same time, when predicting the occurrence time of Vespa mandarinia, we formed a sequence of the annual 12-month report times of previous years, and still used GM model for data processing and prediction.