A Study on the English Translation of the Penal Code Section of Ta Tsing Leu Lee from the Perspective of Eco-Translatology: A Case Study of the English Translation by Sir George Thomas Staunton

Ta Tsing Leu Lee is an important milestone in China’s legal history. The well-established law code has rich content, including provisions on the basic principles of criminal law, the identification and punishment of various crimes, and judicial procedures. The promulgation and implementation of Ta Tsing Leu Lee promoted the transformation of China’s traditional legal system to a modern one and had a profound impact on later research on criminal law in China. The English version of this legal book translated by Sir George Thomas Staunton was the first attempt by Europeans to translate a whole Chinese legal book, which is of great significance for enriching the research on English translation of criminal law works. In this paper, an in-depth analysis of excerpts from the English translation of the penal code section of the book was conducted using the “three-dimensional transformation” theory of eco-translatology, including the linguistic, cultural adaptation, and communicative dimensions, in order to provide some insights for the English translation of contemporary legal books and their overseas spread

A Cross-Cultural Perspective on the Preference for Potential Effect: An Individual Participant Data (IPD) Meta-Analysis Approach

A recent paper [Tormala ZL, Jia JS, Norton MI (2012). The preference for potential. Journal of personality and social psychology, 103:567-583] demonstrated that persons often prefer potential rather than achievement when evaluating others, because information regarding potential evokes greater interest and processing, resulting in more favorable evaluations. This research aimed to expand on this finding by asking two questions: (a) Is the preference for potential effect replicable in other cultures? (b) Is there any other mechanism that accounts for this preference for potential? To answer these two questions, we replicated Tormala et al.'s study in multiple cities (17 studies with 1,128 participants) in China using an individual participant data (IPD) meta-analysis approach to test our hypothesis. Our results showed that the preference for potential effect found in the US is also robust in China. Moreover, we also found a pro-youth bias behind the preference for potential effect. To be specific, persons prefer a potential-oriented applicant rather than an achievement-oriented applicant, partially because they believe that the former is younger than the latter

Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification algorithms for light curves. Among them, periodic variable stars are frequently studied. Different categories of periodic variable stars have a high degree of class imbalance and pose a challenge to algorithms including deep learning methods. We design two kinds of architectures of neural networks for the classification of periodic variable stars in the Catalina Survey's Data Release 2: a multi-input recurrent neural network (RNN) and a compound network combing the RNN and the convolutional neural network (CNN). To deal with class imbalance, we apply Gaussian Process to generate synthetic light curves with artificial uncertainties for data augmentation. For better performance, we organize the augmentation and training process in a "bagging-like" ensemble learning scheme. The experimental results show that the better approach is the compound network combing RNN and CNN, which reaches the best result of 86.2% on the overall balanced accuracy and 0.75 on the macro F1 score. We develop the ensemble augmentation method to solve the data imbalance when classifying variable stars and prove the effectiveness of combining different representations of light curves in a single model. The proposed methods would help build better classification algorithms of periodic time series data for future sky surveys (e.g., LSST).Comment: 10 pages, 8 figures, accepte

Nanostructure-induced performance degradation of WO3·nH2O for energy conversion and storage devices

Although 2D layered nanomaterials have been intensively investigated towards their application in energy conversion and storage devices, their disadvantages have rarely been explored so far especially compared to their 3D counterparts. Herein, WO3 center dot nH(2)O (n = 0, 1, 2), as the most common and important electrochemical and electrochromic active nanomaterial, is synthesized in 3D and 2D structures through a facile hydrothermal method, and the disadvantages of the corresponding 2D structures are examined. The weakness of 2D WO3 center dot nH(2)O originates from its layered structure. X-ray diffraction and scanning electron microscopy analyses of as-grown WO3 center dot nH(2)O samples suggest a structural transition from 2D to 3D upon temperature increase. 2D WO3 center dot nH(2)O easily generates structural instabilities by 2D intercalation, resulting in a faster performance degradation, due to its weak interlayer van der Waals forces, even though it outranks the 3D network structure in terms of improved electronic properties. The structural transformation of 2D layered WO3 center dot nH(2)O into 3D nanostructures is observed via ex situ Raman measurements under electrochemical cycling experiments. The proposed degradation mechanism is confirmed by the morphology changes. The work provides strong evidence for and in-depth understanding of the weakness of 2D layered nanomaterials and paves the way for further interlayer reinforcement, especially for 2D layered transition metal oxides

NH3 sensor based on 3D hierarchical flower-shaped n-ZnO/p-NiO heterostructures yields outstanding sensing capabilities at ppb level

Hierarchical three-dimensional (3D) flower-like n-ZnO/p-NiO heterostructures with various ZnxNiy molar ratios (Zn5Ni1, Zn2Ni1, Zn1Ni1, Zn1Ni2 and Zn1Ni5) were synthesized by a facile hydrothermal method. Their crystal phase, surface morphology, elemental composition and chemical state were comprehensively investigated by XRD, SEM, EDS, TEM and XPS techniques. Gas sensing measurements were conducted on all the as-developed ZnxNiy-based sensors toward ammonia (NH3) detection under various working temperatures from 160 to 340 °C. In particular, the as-prepared Zn1Ni2 sensor exhibited superior NH3 sensing performance under optimum working temperature (280 °C) including high response (25 toward 100 ppm), fast response/recovery time (16 s/7 s), low detection limit (50 ppb), good selectivity and long-term stability. The enhanced NH3 sensing capabilities of Zn1Ni2 sensor could be attributed to both the specific hierarchical structure which facilitates the adsorption of NH3 molecules and produces much more contact sites, and the improved gas response characteristics of p-n heterojunctions. The obtained results clear demonstrated that the optimum n-ZnO/p-NiO heterostructure is indeed very promising sensing material toward NH3 detection for different applications

Mixed infection of Bartonella and Eperythrozoon in a dog - case report

ABSTRACT A March male Golden, weighing 7.6kg, presented with gradual weight loss, high body temperature, depression, poor appetite and thirst, and vomiting before consultation. The results showed that the erythrocytes, hematocrit, hemoglobin, and platelets were lower than the reference values. The diagnosis of mixed infection with haematocrit and eosinophilic bodies was confirmed by real-time fluorescence PCR of whole blood, which was positive for haematocrit and eosinophilic bodies. The dog was treated with doxycycline and ceftriaxone, and the dog fully recovered after 2 weeks with blood transfusion, symptomatic treatment, and supportive therapy. This indicates that the disease can be treated well by a comprehensive treatment approach

Transcriptomic diversification of granulosa cells during follicular development between White Leghorn and Silky Fowl hens

Egg production rate in chicken is related to the continuity of follicle development. In this study, we found that the numbers of white prehierarchical, dominant, and yellow preovulatory follicles in the high-yielding layer breed, White Leghorn (WL), were significantly higher than those in the low egg-yielding variety, Silky Fowl (SF). The proliferation and differentiation of granulosa cells (GCs) play an important role in follicle maturation. Histological observation revealed a large number of melanocytes in the outer granulosa layer of follicles in SF but not in WL. Finally, RNA-sequencing was used to analyze the gene expression profiles and pathways of the GC layer in the follicles in both WL and SF hens. Transcriptome analysis of prehierarchical GCs (phGCs) and preovulatory GCs (poGCs) between WL and SF showed that steroid hormone-, oxytocin synthesis-, tight junction-, and endocytosis-related genes were expressed at higher levels in WL phGCs than in SF phGCs, whereas the insulin signaling pathway- and vascular smooth muscle contraction-related genes were upregulated in SF phGCs. Fatty acid synthesis, calcium signaling, and Wnt signaling pathway-related genes were expressed at higher levels in WL poGCs than in SF poGCs; however, adrenergic signaling, cGMP-PKG, and melanogenesis-related genes were upregulated in SF poGCs. These results indicate that genes that promote GC proliferation and secretion of various sex hormones are more active in WL than in SF hens. The upregulated signaling pathways in SF help in providing energy to GCs and for angiogenesis and melanogenesis. In vitro experiments confirmed that both the proliferation of poGCs and synthesis of reproductive hormones were higher in WL than in SF hens

Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys

The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as EAZY for template-fitting approach and CATBOOST for machine learning. Then the created models are tested by the cross-matched samples of the DESI Legacy Imaging SurveysDR9 galaxy catalogue with LAMOST DR7, GAMA DR3 and WiggleZ galaxy catalogues. Moreover three machine learning methods (CATBOOST, Multi-Layer Perceptron and Random Forest) are compared, CATBOOST shows its superiority for our case. By feature selection and optimization of model parameters, CATBOOST can obtain higher accuracy with optical and infrared photometric information, the best performance ($MSE=0.0032$, $\sigma_{NMAD}=0.0156$ and $O=0.88$ per cent) with $g \le 24.0$, $r \le 23.4$ and $z \le 22.5$ is achieved. But EAZY can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redhisft range of training sample. Finally, we finish the redshift estimation of all DESI DR9 galaxies with CATBOOST and EAZY, which will contribute to the further study of galaxies and their properties.Comment: Accepted for publication in MNRAS. 14 pages, 9 figures, 11 table

GLM-130B: An Open Bilingual Pre-trained Model

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good as GPT-3 (davinci) and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we face numerous unexpected technical and engineering challenges, particularly on loss spikes and divergence. In this paper, we introduce the training process of GLM-130B including its design choices, training strategies for both efficiency and stability, and engineering efforts. The resultant GLM-130B model offers significant outperformance over GPT-3 175B (davinci) on a wide range of popular English benchmarks while the performance advantage is not observed in OPT-175B and BLOOM-176B. It also consistently and significantly outperforms ERNIE TITAN 3.0 260B -- the largest Chinese language model -- across related benchmarks. Finally, we leverage a unique scaling property of GLM-130B to reach INT4 quantization without post training, with almost no performance loss, making it the first among 100B-scale models and more importantly, allowing its effective inference on 4$\times$RTX 3090 (24G) or 8$\times$RTX 2080 Ti (11G) GPUs, the most affordable GPUs required for using 100B-scale models. The GLM-130B model weights are publicly accessible and its code, training logs, related toolkit, and lessons learned are open-sourced at \url{https://github.com/THUDM/GLM-130B/}.Comment: Accepted to ICLR 202

Chinese cross-cultural adaptation and validation of the Well-being Numerical Rating Scales

IntroductionWell-being is a multi-domain concept that involves measuring physical, psychological, social, and spiritual domains. However, there are currently few multi-domain and comprehensive well-being instruments available. In addition, measures that do exist customarily contain a vast number of items that may lead to boredom or fatigue in participants. The Well-being Numerical Rating Scales (WB-NRSs) offer a concise, multi-domain well-being scale. This study aimed to perform the translation, adaptation, and validation of the Chinese version of WB-NRSs (WBNRSs-CV).MethodsA total of 639 clinical participants and 542 community participants completed the WB-NRSs-CV, the Single-item Self-report Subjective Well-being Scale (SISRSWBS), the World Health Organization Five-item Well-Being Index (WHO-5), the 10-item Perceived Stress Scale (PSS-10), and the Kessler Psychological Distress Scale (K10).ResultsHigh internal consistency and test-retest reliability were obtained for both samples. Additionally, WB-NRSs-CV was positively associated with SISRSWBS and WHO-5 and negatively associated with PSS-10 and K10. In the item response theory analysis, the model fit was adequate with the discrimination parameters ranging from 2.73 to 3.56. The diffculty parameters ranged from −3.40 to 1.71 and were evenly spaced along the trait, attesting to the appropriateness of the response categories. The invariance tests demonstrated that there was no difference in WB-NRSs-CV across groups by gender or age.DiscussionThe WB-NRSs-CV was translated appropriately and cross-culturally adapted in China. It can be used as a rapid and relevant instrument to assess well-being in both clinical and non-clinical settings, with its utility for well-being measurement and management among the Chinese people