The textile industry is the largest sector globally. However, it is also the most polluted sector due to the large amount of textile waste and wastewater. The Circular Economy (CE) can play a crucial role in reducing waste. Developed and developing economies are implementing and developing CE practices for waste management. The current study aims to explore CE practices that could be implemented globally. To achieve the study’s aim, a Systematic Literature Review (SLR) was employed. Through SLR and based on 54 selected articles, 35 CE practices were identified and categorised into seven major groups. The study’s findings show that using recycled materials in the production of textile products is the most popular strategy, as it was primarily cited. It also highlights the need for integrated collaboration among supply chain stakeholders, including the government, manufacturers, consumers, and recyclers. The government could implement policies that encourage manufacturers to adopt sustainable practices, such as eco-designing products. Manufacturers can utilise sustainable materials in their production processes. Recyclers could adopt efficient sorting, segregation, and recycling technologies to enhance their operations. The current study offers valuable insights for practitioners and policymakers, providing a range of strategies and policy recommendations.

NMT for Assamese language remains challenging because of its morphological rich language and lack of parallel resources. The paper aims to study the effect of word embeddings, different neural architectures and bidirectional pre-training for enhancing bidirectional English–Assamese translation. Three embedding methdos (Word2Vec, GloVe and FastText) were evaluated within RNNs using GRU and LSTM architecture. FastText consistently yielded the highest BLEU scores among recurrent models, achieving 24.99 for Assamese–English and 24.65 for English–Assamese, owing to its ability to capture subword level information and handle rare word forms. To establish stronger baselines, we fine tuned Transformer-based models (MarianMT and mBART) on a 175,000-sentence parallel corpus and further enhanced mBART through a Bidirectional Training (BiT) phase to promote cross-lingual parameter sharing. The BiT-pretrained variant achieved the best performance, with BLEU scores of 28.93 (As–En) and 28.57 (En–As). Finally, we compared these systems against a zero-shot GPT baseline, which underperformed relative to the fine-tuned models, reaffirming the effectiveness of specialized training for low-resource translation. Human evaluation of fluency and adequacy supported these results, confirming that Transformer-based architectures with bidirectional pre-training remain the most effective solution for English–Assamese NMT.

Translation is a medium for learning and understanding languages apart from the native language. Translation also helps to communicate with the speakers of non-native languages, thereby facilitating cross-cultural interactions and enhancing mutual understanding. In the present era of artificial intelligence (AI), machine translation and computer-assisted translation have revolutionised the translation field. Currently, people are using artificial intelligence tools to translate content from one language to another. However, using artificial intelligence tools for translation presents numerous benefits, such as increased efficiency and accuracy, along with several challenges, including issues related to context and cultural nuances, which can lead to misunderstandings or misinterpretations in the translated content. The authors of this study used a qualitative approach and interviewed twenty-one academics of Gujarati and Hindi languages working in universities in Gujarat, India. This study aims to qualitatively explore and thematically interpret the perspectives of Hindi and Gujarati language academics on the benefits and challenges of AI-assisted translation from English to Hindi and Gujarati languages. To analyse the data NVivo 15 was used. The results show two main themes: the benefits and challenges of AI translation. Participants highlighted several advantages of AI translation tools, including time efficiency, cost effectiveness, and their usefulness in supporting language learning and accessing academic resources written in non-native languages. In contrast, the participants mentioned concerns about the linguistic limitations of AI systems, particularly in relation to syntactic differences, script variations, semantic ambiguity, and errors in Hindi and Gujarati translations. The study enhances current discourse around the role of AI in multilingual translations.

Introduction: Media is considered the most powerful platform to create awareness in human society. It plays an important role in spreading public awareness and disseminating information about pandemic diseases such as coronavirus. A survey through a questionnaire was used and the descriptive analysis of questions as asked to the people on COVID 19 published by the leading newspapers of Rajasthan, western India was done to analyze the role print media played in spreading awareness about Coronavirus Disease (COVID-19). Materials and Methods: The objectives used both quantitative and qualitative methodology. In the quantitative method, a self-structured questionnaire was circulated among the various professionals. A descriptive method of secondary sources was also used to measure the effect of articles and advisements on the people published in the newspapers. Results: A total of 106 various professionals participated in the study. The research reveals that 56 percent of males and 43 percent of females are part of this research; more than 80 percent of respondents belong to the age group of fewer than 40 years. Among the active respondents, 91 percent are said to read the newspaper regularly. The study also shows that 96 percent of respondents are read articles, news, columns related to COVID-19 and particular about Coronavirus. Nearly 91 percent of respondents are in favor that media coverage of COVID-19 has improved their knowledge of Coronavirus. Around 93 percent of respondents agree that media spreading awareness among the people on Coronavirus through newspaper published articles, news, and advisements. The study exhibits that 95 percent of respondents expressed their views that print media has played an active role in disseminating the government directives on Coronavirus and almost 97 percent of the respondents are opined that print media will play a more effective role in spreading awareness on coronavirus. Conclusion: The respondents expressed that print media has played a vital role in spreading awareness on the pandemic of coronavirus disease. The newspaper spread awareness among the people on coronavirus through newspaper published articles, news and advisements. The newspaper also published a variety of queries and doubts as asked by the people to Doctors on coronvirus.

This study examines the transition from print to digital documentation at BITS Pilani, Manipal University Jaipur, and Banasthali Vidyapith in Rajasthan. The research aims to determine how faculty members view the digitization of work processes in terms of ease and efficacy, and how this transformation affects faculty productivity in their academic roles. The mixed method study collects faculty experiences with digital documentation using quantitative questionnaires and qualitative insights. The poll compares digital tools' ease of use and efficacy to older ones and investigates how digitization affects productivity. The findings aim to provide a complete understanding of the benefits and drawbacks of digital transformation across academic institutions to help other universities with sustainability initiatives.