Tiếng Việt
Chọn ngôn ngữ
Tiếng Việt
English
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 228.01/Q2)
In an era where technology and artificial intelligence (AI) are rapidly reshaping scientific disciplines, the fields of the Earth and environmental sciences, and geotechnical engineering are experiencing unprecedented advancements. This book, Advanced Technologies and Artificial Intelligence in the Earth and Environmental Sciences, marks the inaugural volume in the series Advances in the Earth, Mining and Environmental Sciences for Safe and Sustainable Development. It features a selection of high-quality research papers presented at the GeoAI2024 International Conference.
This publication represents the collaborative efforts of VNU University of Science, Vietnam National University, Hanoi, the Interdisciplinary Professor Council of the Earth and Mining Sciences, and a broad network of esteemed institutions and organizations, including the Vietnam Union of Geological Sciences, Vietnam Association of Engineering Geology and Environment, Institute of Geological Sciences - Vietnam Academy of Science and Technology, Norwegian Geotechnical Institute, Vietnam Atomic Energy Institute, Hanoi University of Mining and Geology, and Dong Nai Technology University.
The support and cooperation of these institutions have been instrumental in bringing this volume to fruition.
This book represents a focused exploration of the transformative role of technology and AI in addressing critical challenges in sustainability and environmental resilience. The content is organized into three thematic sections: Part 1: Earth Technology and Geotechnical Engineering This section explores innovative methods for addressing geotechnical and geological challenges with a focus on sustainability, resilience, and safety. The studies of this part demonstrate how modern technologies and engineering can advance infrastructural development while minimizing environmental risks.
Part 2: Environment and Recycled Materials This section addresses environmental challenges through creative approaches, including pollution control, resource recovery, and sustainable materials. The research in this part highlights innovative ways to restore and protect the environment, contributing to a more sustainable future.
Part 3: Digital Transformation and GeoAI The final section demonstrates the transformative power of AI, machine learning, and digital technologies in the Earth and environmental sciences. Contributions cover a wide spectrum of applications, including IoT-based environmental monitoring and AI-assisted disaster detection. This section exemplifies the role of GeoAI in reshaping our understanding of natural processes and improving decision-making for sustainable development.
We express our sincere gratitude to the authors, researchers, and practitioners whose works have shaped this volume. Special thanks go to the GeoAI2024 International Conference, which provided a vibrant platform for collaboration, innovation, and knowledge exchange. The collective efforts of contributors and organizers have ensured that this book captures the forefront of technological innovation in the Earth and environmental sciences.
This volume aims to inspire researchers, practitioners, and policymakers, encouraging them to explore and implement advanced technologies and AI in tackling some of the most pressing challenges of our time. As the inaugural volume in the book series, it sets the stage for future explorations into the intersections of the Earth and environmental sciences.
We hope the insights shared in this book will contribute to advancing global efforts toward safe and sustainable development.
Editorial Board
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 228.13/Q1)
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 229.13-Q2)
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 228.05/Q1)
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 229.011-Q2)
The book's content is organized into 7 chapters with the following contents:
- Chapter 1: Natural language processing.
- Chapter 2: Multilingualizing software based on automata.
- Chapter 3: N-grams and language recognition.
- Chapter 4: Vector space model and applications.
- Chapter 5: Dendrogram and applications.
- Chapter 6: UNL and multilingual automatic translation.
- Chapter 7: Deep learning and fake news detection.
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 228.04)
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 228.08/Q1)
Kỷ yếu Hội thảo đánh giá tiềm năng, cơ hội và thách thức trong quản lý nông nghiệp và quy trình sản xuất, chế biến, tiêu thụ nông sản tiêu biểu địa phương
(Thuộc đề tài nghiên cứu khoa học cấp tỉnh: Chuyển đổi số trong quản lý và phát triển nông sản chủ lực huyện Võ Nhai đến năm 2025, tầm nhìn 2030. Mã số: ĐT KTCN 05/2022)
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 228.10/Q1)
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 228.09/Q2)
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 228.01/Q1)
This report highlights the precarious working conditions and vulnerability of app-based motorbike drivers in Viet Nam and their linkage with an outdated labour law framework. It not only pinpoints the problems, but also considers solutions to enhancing legal protection for platform drivers, taking into consideration regulatory innovations around the world and the unique context of Viet Nam. Data collection was undertaken between 2019 and 2023. This consisted of a quantitative survey of workers from four major ride-hailing and delivery platforms, conducted in Ho Chi Minh City (HCMC) between February and March 2021. From May 2019 to August 2023, the researchers also interviewed and re-interviewed 50 workers and 15 other actors, including company managers, union cadres, state officials, and labour law experts. Moreover, data for this research was drawn from observations made by the researchers and surveyors during their conversations with workers and participation in workers’ Facebook groups. Finally, the research analysed numerous documents including-among other thingsplatforms’ contracts, policies, and documents; statutory laws; administrative regulations; court judgments; government papers and reports; academic works; and newspaper articles. These documents were collected until October 2023. The authors would like to express their deep appreciation to the Friedrich-Ebert-Stiftung (FES) for funding this major project. We are particularly indebted to Axel Blaschke, Timo Rinke, Nguyen Thi Ha Giang, Nguyen Thanh Thuy, and Pham Hung Son for their patience, support, and valuable advice throughout the project. Our gratitude also goes to the people involved in data collection, especially the interviewees and surveyors. We appreciate Dr. Do Quynh Chi, Dr. Pham Sy Thanh, and participants in the webinar on Labour Research in Digital Platforms in Viet Nam organised by the Southern Institute of Social Sciences, FES, and the Viet Nam Labour Research Network in October 2021 for their insightful feedback on our early work. Last, but not least, special thanks go to Laurence Newman and Simon Drought for their proofreading. All errors are ours.
Tạp chí Khoa học & Công nghệ Đại học Thái Nguyên (Số 229.03/Q1)