Eserlere Göre Listeleme

ilaç prospektüsü

In the 1890s, the Danish lieutenant Ole Olufsen set out to lead two expeditions to Tsarist Central Asia. Exploring areas that were still blank on European and Russian maps, the participants spent more than a year traveling on horseback in the pamirs and adjacent valleys bordering Afghanistan, China, and British India.Esther Fihl offers an in-depth study of these Danish expeditions and presents the magnificent collection of objects brought back to the National Museum of Denmark. Drawing on diaries, reports, and published works and a scrutiny of the guiding principles for their collecting of objects, she demonstrates how these explorers portrayed the cultures encountered. This work is a treasure for anyone interested in Central Asia, early anthropological theory, material culture, or European travel literature.They lived with Kyrgyz nomads who carved out an existence for themselves above the tree line with their sheep, goats, and yaks. Traveling along the river Pandsh, they were the first Europeans to collect ethnographical information on the transhumant pastoralists in the elevated valleys bordering Afghanistan. On the steppes of the western lowlands, the Danish expeditions stopped in Samarkand, Khiva, and Bukhara, commercial hubs on the old Silk Road. As official guests of both the emir of Bukhara and the khan of Khiva, they studied the handicrafts of the bazaars and the irrigation agriculture practiced by the Tajiks and Uzbeks. On visits to Merv they also spent time with Turkmen nomadic tribes who had only recently been fighting the Russian colonial power.

This thesis applied NLP techniques to analyze and summarize bilingual student feedback collected via end-of-semester surveys. The dataset, which contained open-ended responses in both English and Turkish, required a model adept at preserving linguistic nuances across languages. The Llama 2-7b-hf model, which had been trained explicitly for text generation, was selected for its capability to produce coherent and contextually relevant summaries. Data preprocessing involved organizing metadata such as department, semester, course name, and section number, segregating comments by word count, and removing personal identifiers to ensure privacy. Shorter comments (fewer than ten words) were grouped and summarized using a pipeline from the Transformers library, while longer comments were fine-tuned with metadataspecific prompts for detailed summarization. To further enhance analysis, sentiment classification was performed using the “cardiffnlp/twitter-robertabase-sentiment” model, categorizing feedback into negative, neutral, and positive sentiments. Evaluation metrics included expert reviews, contextual relevance, and logical consistency with the dataset’s sentiment distribution. Compared to previous models, the Llama 2 model demonstrated superior performance in generating complete, coherent summaries while preserving the overall intent and tone of the comments. Ultimately, this research highlighted the effectiveness of LLMs in processing multilingual educational data and their potential to provide actionable insights for improving course content and student experiences.