Vietnam has developed a national network of marine protected areas (MPAs) to conserve its marine biodiversity, but the network remains small and its management effectiveness is uneven. This review synthesises peer-reviewed literature and policy documents on Vietnam’s MPAs, drawing on Web of Science, Scopus, and Google Scholar together with Vietnamese government and institutional sources. Of the 16 MPAs designated under the Prime Minister’s Decision No. 742/QD-TTg (2010), 12 are currently operational, covering about 1,803.96 km2, less than 0.2

Oil pollution is one of the most persistent and harmful anthropogenic pressures on global marine and coastal ecosystems. Accidental discharges, chronic leaks, operational spills from shipping, offshore drilling, and industrial activities release millions of tons of hydrocarbons annually, threatening marine biodiversity, fisheries, and coastal livelihoods. Remote sensing has become the primary technology for oil spill detection, mapping, and monitoring, offering synoptic, repeatable, and objective coverage of extensive marine areas. This paper presents a systematic review of remote sensing for oil spill detection, mapping, and monitoring, grounded in a bibliometric analysis of 2856 verified documents authored by 6473 researchers, retrieved from five major academic databases (OpenAlex, CrossRef, EuropePMC, SemanticScholar, and CORE), spanning the period 2000 to 2026. Annual publication output grew from 16 documents in 2000 to a peak of 244 in 2025, reflecting a 15-fold growth driven by the Deepwater Horizon disaster (2010), the launch of Sentinel-1 (2014-2016), and the proliferation of deep learning frameworks. The review examines the physical principles of oil detection across the electromagnetic spectrum; compares radar, optical, hyperspectral, and thermal sensor platforms; and evaluates developments in artificial intelligence (AI) and data fusion methods for automated detection. Validation protocols, regional case studies from the Gulf of Mexico, North Sea, Mediterranean, Arctic, and Caspian Sea, and the integration of Earth observation with decision-support frameworks are also assessed. Key findings confirm that no single sensor is universally superior: synthetic aperture radar (SAR) provides all-weather, day-night capability, while optical and hyperspectral sensors deliver spectral and compositional insight. Deep learning models, particularly U-Net and transformer-based architectures, have achieved exceptional detection accuracy but face persistent challenges of data scarcity, look-alike discrimination, and limited cross-regional transferability. Emerging innovations in multi-sensor constellations, physics-informed deep learning, and cloud-native processing are identified as pathways toward real-time environmental intelligence and improved ocean governance.

Coastal and marine vegetation, known as blue carbon ecosystems, plays a vital role in the lives of coastal communities throughout Southeast Asia, particularly in Vietnam. Mangrove forests, seagrass meadows, and salt marshes are distributed throughout the coastline of Vietnam with varying species diversity and spatial distribution patterns. This study performed a scientometrics analysis of blue carbon ecosystems in Vietnam, while also examining restoration initiatives and prospects for the country's blue carbon economy. Moreover, we also investigated the trends in blue carbon research in Vietnam, the challenges and issues related to blue carbon, and possible solutions to overcome these challenges. This study also considered the applications of remote sensing in blue carbon research in Vietnam.

This in-depth review investigated the status and prospects of coral reef research in Vietnam. Coral reefs along the coastline of Vietnam characterize a critical element of marine biodiversity and coastal ecosystem services in the country. The 3260 km coastline of Vietnam supports 1100 recorded coral species, and geographically, in addition to the archipelagos, the coral reef zones in Vietnam have been divided into three: the northern, central, and southern zones. Several hard and soft corals, reef fish, invertebrates, and endemic/rare species are found associated with reefs in Vietnam. However, the coral reefs in Vietnam are increasingly threatened by climate change, destructive fishing practices, coastal development, and pollution. Recent assessments show that more than 50 % of Vietnam's coral reefs are in poor condition, with declining live coral cover and biodiversity. Despite their importance and ongoing conservation efforts, research and monitoring remain limited in scope, duration, and geographic coverage in Vietnam. Research priorities include long-term ecological monitoring, studies on coral resilience to thermal stress and acidification, improved reef restoration techniques, and effective integration of socioeconomic and policy research. Technological advancements-such as remote sensing, environmental DNA (eDNA), and artificial intelligence-offer new opportunities for data collection and analysis. To ensure effective reef conservation, Vietnam must invest in scientific capacity, enhance inter-institutional collaboration, and embed research findings into marine governance frameworks. A multidisciplinary and progressive research agenda is essential for protecting and restoring Vietnam's coral reefs amid growing environmental and anthropogenic pressures. By aligning scientific innovation with policy and community-based management, Vietnam can build a more resilient framework for sustaining its coral reef ecosystems. Such an integrated approach will not only safeguard marine biodiversity but also secure the ecological and socioeconomic benefits that coral reefs provide for future generations.

Land surface temperature (LST) is one of the crucial variables in urban microclimate studies. Satellite-based thermal data and vegetation indices, like the normalized difference vegetation index (NDVI), help to understand changes in LST and the development of urban heat islands (UHI). We analyzed the variations in LST and vegetation coverage in two rapidly urbanizing provinces, located in southern Vietnam and Cambodia, respectively, over the 10 years from 2013 to 2025. Additionally, complementary ERA5 Interim air temperature data were also utilized. The satellite and in situ data analysis have been used to understand the impacts of urbanization on LSTs. Spatiotemporal changes in NDVI showed rapid urbanization in the eastern region of Battambang city (39.2 km2 to 47.8 km2) and throughout the southern areas of Binh Duong Province (387 km2 to 464.3 km2). Time-series analysis indicated a consistent increase in LST in both study sites. There has been a notable increase in minimum LST since 2017 in the entire city of Battambang, whereas the central area of Battambang has become consistently warmer after 2020. The minimum estimated LST in Battambang varied between 16.1 °C and 28.58 °C (and increased 0.35 °C per year), whereas the maximum LST varied between 29.2 °C to 40.23 °C (and increased 0.36 °C per year). The LST in southern Binh Duong increased gradually during the study period, primarily due to rapid urbanization and vegetation loss. The minimum estimated LST in Binh Duong varied between 13.2 °C to 24.73 °C (and increased 0.26 °C per year), whereas the maximum LST varied between 34.6 °C to 41.3 °C (and increased 0.024 °C per year). The outcome of this study holds considerable importance, as the phenomenon of UHI formation has been documented in rapidly expanding cities and impervious surfaces globally, especially in Southeast Asia.

Plastic pollution is an ongoing environmental problem because of anthropogenic activities. Low-income South and Southeast Asian countries have witnessed an unprecedented increase in terrestrial and aquatic plastic litter and aerial micro- and nanoplastic pollution. The present review discusses the sources and pathways of plastic pollution in the soil, water, and air, focusing on South and Southeast Asia. In addition, the consequences of plastic pollution on terrestrial, aquatic, and airborne organisms were also analyzed. Fragmentation and degradation pathways of plastic pollutants are highly complex and unpredictable. The circulation of micro- and nanoplastics in the food web and accumulation in living faunal tissues raise health concerns. Challenges in curbing plastic pollution due to technical, legal, behavioral, and socioeconomic conditions were discussed. An extensive list of sustainable solutions to plastic-related hazards focusing on South and Southeast Asia is also provided.

High transaction fees, security vulnerabilities, and inefficiencies in traditional blockchain systems have long hindered the scalability and cost-effectiveness of real-time sensor networks in the Smart buildings. This study addresses these critical issues by introducing a novel integration of sensor nodes with IOTA-based Distributed Ledger Technology (DLT) networks. The proposed system not only reduces operational costs and network overhead but also enhances security through secure device authentication and communication. Employing state-of-the-art encryption techniques ensures secure and confidential data transmission between sensor nodes and the DLT network with cryptographic operations (0.00017s encryption, 0.00016s decryption), creating a tamper-resistant exchange system. Framed within the context of smart buildings, this research explores scalable and interconnected solutions designed to meet the dynamic security and performance needs of urban environments. The experimental validation demonstrates the system's effectiveness through comprehensive analysis of encryption and decryption performance, network transaction throughput (0.331 TPS), low latency (0.0168s), and energy-efficient power consumption across two distinct scenarios: (1) Normal Transaction conditions and (2) Traffic Congestion environment, where the system shows resilience despite 68% throughput reduction and significant latency increases under network stress.

The Internet of Things (IoT) is experiencing rapid growth, with projections indicating that the number of IoT devices will surpass 40.6 billion by 2034. This expansion, however, is accompanied by the potential threat posed by advancements in quantum computing. The security protocols of IoT devices must undergo significant modifications to effectively address emerging threats. Currently, IoT systems predominantly employ RSA and ECC. However, these cryptographic methods are vulnerable to quantum attacks facilitated by Shor’s algorithm. Centralized IoT systems are vulnerable to single points of failure and data integrity issues. This study proposes a security framework that integrates SPHINCS+ digital signatures, which are known for their resistance to quantum attacks, with blockchain technology for device registration and verification. The system can process approximately 19 transactions per second with an average latency of 94.7 ms. The execution speeds of smart contract operations varied, with the addTransaction operation demonstrating the greatest consistency, averaging 49.7 ms. The deployContract operation requires 69.8 ms because of its complexity. The SPHINCS+ signature operations have an average duration of 42.2 ms. However, this delay can extend to 52-53 ms under conditions of high activity. The framework demonstrates that post-quantum cryptography can be integrated into blockchain-based IoT systems with minimal impact on their performance. This integration provides a practical solution for protecting future IoT systems from both traditional and quantum security threats.

Vietnam's coastal regions are highly vulnerable to natural hazards and human-induced changes, posing significant challenges to their ecological and socio-economic systems. The country's mangrove vegetation spans its entire coastline and has been depleted for decades in many regions. Notably, Vietnam's proactive stance on climate change mitigation received significant recognition during the 26th Conference of the Parties (COP26) to the United Nations Framework Convention on Climate Change. This study investigated five critical coastal environmental features (shoreline dynamics, drought conditions, soil salinity trends, mangrove deforestation, and reforestation, as well as spatiotemporal variations in aquaculture and salt farming areas) using satellite data and geospatial analysis. Findings revealed a 58% decline in mangrove areas between 1989 and 2023, with a sharp decline between 1989 and 2001, followed by a gradual recovery. Furthermore, soil salinity along the Ninh Thuan coast indicated a continuous increase, except during the strong La Niña period in 2001. Additionally, aquaculture and salt marshes have expanded significantly, changing land use patterns. These findings highlight the urgent need for integrated coastal zone management to mitigate environmental degradation and enhance ecosystem resilience. Future studies should investigate the socio-economic implications of these changes and evaluate restoration strategies for sustainable coastal development.

Chronic Kidney Disease (CKD) pose a global health challenge due to their increasing incidence and delayed detection, compounded by the burden they place on healthcare systems worldwide. This surge is particularly pronounced in both industrialized and emerging nations, fueled by the rising prevalence of conditions such as diabetes, hypertension, and obesity. While Artificial Intelligence (AI) shows promise for early CKD detection, issues with data security, model transparency, and decision-making trust have created uncertainty among both patients and healthcare providers. To tackle these issues, a decentralized collaborative learning framework utilizing explainable AI to detect CKD is proposed. This approach combines the privacy-preserving nature of blockchain technology with the interpretability of explainable AI models.Areal-time environmentwas set up to conduct these experiments, simulating a network of healthcare providers collaborating on CKD detection while maintaining patient privacy.

Sri Lanka's extensive 1740-km shoreline boasts a wealth of carbon-sequestering marine habitats, encompassing coastal forests, underwater meadows, and tidal wetlands. This review paper discussed the current status, recent changes, and future potential of the trio of carbon-rich coastal habitats in Sri Lanka. As with other countries in South Asia, including India and Bangladesh, mangrove research in Sri Lanka has advanced well, and regional-scale quantitative analyses of mangrove distribution, biomass, and carbon stocks have been conducted in many coastal areas of Sri Lanka. However, studies on seagrass meadows and saltmarshes are limited to a few sites and the objectives of these studies were mostly restricted to species diversity and distribution. This study analysis focuses on Sri Lankan studies related to mangroves, salt marshes, seagrass, and blue carbon ecosystems. Economic analysis of blue carbon ecosystems, and country-level quantification of carbon stocks in mangrove forests, seagrass meadows, and saltmarshes are yet to be conducted in the country. Moreover, most of the blue carbon ecosystems in Sri Lanka are in degraded conditions or under threat. Therefore, it is essential to enhance knowledge about carbon-sequestering coastal habitats within the nation and develop effective preservation and rehabilitation strategies, to guarantee responsible stewardship of Sri Lanka's shorelines. While there is some very good data available insufficient attention has been given to studying seagrasses and saltmarshes in Sri Lanka, despite their ecological value.

Mood is a prevailing emotional state that remains consistent over time and throughout many settings, reflecting an individual’s overall well-being and outlook. Precisely identifying mood is essential for enhancing human-machine interaction, since it allows for tailored recommendations and a deeper understanding of psychology. Artificial intelligence greatly enhances the accurate detection of mood by evaluating facial expressions and other physiological indicators. Although AI can accurately identify mood, it is as important to control and handle these emotions. Music possesses a formidable capability to manipulate and regulate emotions once it is acknowledged. This study presents a streamlined AI-powered approach for identifying and influencing moods by leveraging music. Furthermore, a camera linked to Internet of Things (IoT) device is incorporated into the cloud-based framework to recognize emotions and provide responses in the form of music. Ecosystem evaluation is performed by utilizing numerous factors.