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Showing 3 results for Jing
Volume 5, Issue 4 (12-2019)
Abstract
In this study thirteen species of subfamily Eumeninae are recorded from the Sistan-o Baluchestan province (South East of Iran). Among the studied material, five species including Cyrtolabulus karachiensis Gusenleitner, 2006; Cyrtolabulus syriacus (Giordani Soika, 1968); Stenancistrocerus biblicus (Giordani Soika, 1952); Stenodynerus trotzinai (Morawitz, 1895) and Tachyancistrocerus quabosi Giordani Soika, 1979 are recorded for the first time from Iran. Stenancistrocerus biblicus also represents a new generic record for the faun of Iran.
Volume 15, Issue 3 (July & August (Articles in English & French) 2024)
Abstract
This is a systematic review of Translator Translation Styles from 2013 to 2023, based on 16 highly relevant articles, which are included after searching a total of 193 articles in three databases using specific keywords. This review aims to investigate the current trends and development of the four core research elements, namely, the research topics, research approaches/tools, research objects, and research trends of Translator Translation Styles. Corpus-based research and computing technology have become the core research trends in this field. By taking the translated texts as the research objects and using a variety of research approaches to analyze and compile statistics on a large number of texts, researchers can accurately identify and compare the different emphases of their respective studies on Translator Translation Styles. At present, it seems that the research on Translator Translation Styles has established a system framework. However, it still needs to be optimized and improved in some aspects (e.g., the research objects are too limited and lack innovation in types), and future research can further explore those aspects. The present paper ends with some suggestions and recommendations for future researchers in academic contexts.
Volume 27, Issue 3 (3-2025)
Abstract
This study applies Artificial Neural Networks (ANNs) to assess the impact of climate factors on the collaborative development of agriculture and logistics in Zhejiang, China. The ANN model investigates how average temperature and rainfall from 2017-2022 influence crop yield, water usage, energy demand, logistics efficiency, and economic growth at yearly and seasonal scales. By training the neural network using temperature and rainfall data obtained from ten weather stations, alongside output indicators sourced from statistical yearbooks, the ANN demonstrates exceptional precision, yielding an average R2 value of 0.9725 when compared to real-world outputs through linear regression analysis. Notably, the study reveals climate-induced variations in outputs, with peaks observed in crop yield, water consumption, energy usage, and economic growth during warmer summers that surpass historical norms by 1-2°C. Furthermore, the presence of subpar rainfall ranging from 20-30 mm also exerts an influence on these patterns. Seasonal forecasts underscore discernible reactions to climatic factors, especially during the spring and summer seasons. The findings underscore the intricate relationship between environmental and economic factors, indicating progress in agricultural practices, with vulnerability to short-term climate fluctuations. The study emphasizes the necessity of adapting supply management to address increased water demands and transitioning to clean energy sources due to rising energy consumption. Moreover, optimizing logistics requires strategic seasonal infrastructure planning.
