The US firm behind the Roomba smart vacuum cleaner, iRobot, has filed for bankruptcy protection after facing competition from Chinese rivals and being hit by tariffs. Under the so-called pre-packaged Chapter 11 process, the main manufacturer of its devices, Shenzhen-based Picea Robotics, will take ownership of the firm. The tough commercial landscape had forced iRobot to cut its prices and make major investments in new technology, according to documents filed on Sunday. US import duties of 46% on goods from Vietnam, where most of iRobot's devices for the American market are made, increased its costs by $23m (£17.2m) this year, the firm said. The loss-making company was valued at $3.56bn in 2021 after the pandemic helped to drive strong demand for its products.

Large language models have been widely applied in many aspects of real life, bringing significant efficiency to businesses and offering distinctive user experiences. In this paper, we focus on exploring the application of ChatGPT, a chatbot based on a large language model, to support higher educator in generating quiz questions and assessing learners. Specifically, we explore interactive prompting patterns to design an optimal AI-powered question bank creation process. The generated questions are evaluated through a "Blind test" survey sent to various stakeholders including lecturers and learners. Initial results at the Banking Academy of Vietnam are relatively promising, suggesting a potential direction to streamline the time and effort involved in assessing learners at higher education institutes.

The COVID-19 pandemic caused great losses worldwide, efforts are taken place to prevent but many countries have failed. In Vietnam, the traceability, localization, and quarantine of people who contact with patients contribute to effective disease prevention. However, this is done by hand, and take a lot of work. In this research, we describe a named-entity recognition (NER) study that assists in the prevention of COVID-19 pandemic in Vietnam. We also present our manually annotated COVID-19 dataset with nested named entity recognition task for Vietnamese which be defined new entity types using for our system.

Nintendo fans across the US are breathing a sigh of relief as they tear apart the boxes housing their new Nintendo Switch 2 video game consoles. On-again, off-again trade tariffs implemented by Donald Trump, which precipitated pre-order delays from Nintendo, made the 5June release date of the highly coveted hardware feel more like a hope than a certainty. A potential price hike up from 450 loomed over launch day, but would-be buyers' fears did not come to fruition. The Japanese console maker managed to luckily launch its device squarely within a 90-day tariff pause issued by the president. If tariffs on countries like India and Japan return to the levels proposed during Trump's "Liberation Day" speech at the start of April, however, experts say Nintendo will have to limber up for yet another delicate trade policy dance.

Accurate household electrical energy demand prediction is essential for effectively managing sustainable Energy Communities. Integrated with the Energy Management System, these communities aim to optimise operational costs. However, most existing forecasting models are region-specific and depend on large datasets, limiting their applicability across different climates and geographical areas. These models often lack flexibility and may not perform well in regions with limited historical data, leading to inaccurate predictions. This paper proposes a global model for 24-hour-ahead hourly electrical energy demand prediction that is designed to perform effectively across diverse climate conditions and datasets. The model's efficiency is demonstrated using data from two distinct regions: Ireland, with a maritime climate and Vietnam, with a tropical climate. Remarkably, the model achieves high accuracy even with a limited dataset spanning only nine months. Its robustness is further validated across different seasons in Ireland (summer and winter) and Vietnam (dry and wet). The proposed model is evaluated against state-of-the-art machine learning and deep learning methods. Simulation results indicate that the model consistently outperforms benchmark models, showcasing its capability to provide reliable forecasts globally, regardless of varying climatic conditions and data availability. This research underscores the model's potential to enhance the efficiency and sustainability of Energy Communities worldwide. The proposed model achieves a Mean Absolute Percentage Error of 8.0% and 4.0% on the full Irish and Vietnamese datasets.

Understanding and monitoring aquatic biodiversity is critical for ecological health and conservation efforts. This paper proposes SuoiAI, an end-to-end pipeline for building a dataset of aquatic invertebrates in Vietnam and employing machine learning (ML) techniques for species classification. We outline the methods for data collection, annotation, and model training, focusing on reducing annotation effort through semi-supervised learning and leveraging state-of-the-art object detection and classification models. Our approach aims to overcome challenges such as data scarcity, fine-grained classification, and deployment in diverse environmental conditions.

Sumitomo and SBI Holdings will each acquire a 20% stake in a unit of Vietnam's software and telecommunications conglomerate FPT to foster artificial intelligence adoption in Japan, according to a statement. Sumitomo and SBI will invest in FPT Smart Cloud Japan, which oversees FPT's Japan AI data center, according to a statement from the Vietnamese technology firm. FPT will remain the unit's major stakeholder, it said. SBI Holdings late last year signed a memorandum of understanding to acquire as much as a 35% stake in FPT's Japan cloud unit. FPT is setting up a Japan AI data center, with an initial investment of 200 million.

Kuala Lumpur, Malaysia – China's President Xi Jinping has arrived in Malaysia as part of a Southeast Asian tour which is seen as delivering a personal message that Beijing is a more reliable trading partner than the United States amid a bruising trade war with Washington. Xi arrived in the capital, Kuala Lumpur, on Tuesday evening in what is his first visit to Malaysia since 2013. He flew in from Vietnam where he had signed dozens of trade cooperation agreements in Hanoi on everything from artificial intelligence to rail development. On touching down, Xi said that deepening "high-level strategic cooperation" was good for the common interests of both China and Malaysia, and good for peace, stability and prosperity in the region and the world", according to the official Malaysian news agency Bernama. Xi's three-country tour and his "message" that Beijing is Southeast Asia's better friend than the truculent administration of US President Donald Trump comes as many countries in the 10-member Association of Southeast Asian Nations (ASEAN) bloc are unhappy with their treatment after the US imposed huge tariffs on countries around the world. "This is a very significant visit.

Lung cancer is one of the major causes of death worldwide, and Vietnam is not an exception. This disease is the second most common type of cancer globally and the second most common cause of death in Vietnam, just after liver cancer, with 23,797 fatal cases and 26,262 new cases, or 14.4% of the disease in 2020. Recently, with rising disease rates in Vietnam causing a huge public health burden, lung cancer continues to hold the top position in attention and care. Especially together with climate change, under a variety of types of pollution, deforestation, and modern lifestyles, lung cancer risks are on red alert, particularly in Vietnam. To understand more about the severe disease sources in Vietnam from a diversity of key factors, including environmental features and the current health state, with a particular emphasis on Vietnam's distinct socioeconomic and ecological context, we utilize large datasets such as patient health records and environmental indicators containing necessary information, such as deforestation rate, green cover rate, air pollution, and lung cancer risks, that is collected from well-known governmental sharing websites. Then, we process and connect them and apply analytical methods (heatmap, information gain, p-value, spearman correlation) to determine causal correlations influencing lung cancer risks. Moreover, we deploy machine learning (ML) models (Decision Tree, Random Forest, Support Vector Machine, K-mean clustering) to discover cancer risk patterns. Our experimental results, leveraged by the aforementioned ML models to identify the disease patterns, are promising, particularly, the models as Random Forest, SVM, and PCA are working well on the datasets and give high accuracy (99%), however, the K means clustering has very low accuracy (10%) and does not fit the datasets.

In this paper, we identify a critical problem, "lost-in-retrieval", in retrieval-augmented multi-hop question answering (QA): the key entities are missed in LLMs' sub-question decomposition. "Lost-in-retrieval" significantly degrades the retrieval performance, which disrupts the reasoning chain and leads to the incorrect answers. To resolve this problem, we propose a progressive retrieval and rewriting method, namely ChainRAG, which sequentially handles each sub-question by completing missing key entities and retrieving relevant sentences from a sentence graph for answer generation. Each step in our retrieval and rewriting process builds upon the previous one, creating a seamless chain that leads to accurate retrieval and answers. Finally, all retrieved sentences and sub-question answers are integrated to generate a comprehensive answer to the original question. We evaluate ChainRAG on three multi-hop QA datasets$\unicode{x2013}$MuSiQue, 2Wiki, and HotpotQA$\unicode{x2013}$using three large language models: GPT4o-mini, Qwen2.5-72B, and GLM-4-Plus. Empirical results demonstrate that ChainRAG consistently outperforms baselines in both effectiveness and efficiency.