Hoang Anh Le

Cement production and thermal power generation are two primary industries in Ninh Binh province, but they are also among the sources of emissions that can negatively affect to local and regional air quality. This study employs the AERMOD air dispersion modeling system to assess the air pollution levels caused by the operations of the cement and thermal power plants in Ninh Binh province. The results show that the highest average hourly concentrations of TSP, SO2, and NOx are 30.1 µg/m3, 73.2 µg/m3, and 361 µg/m3, respectively; the highest average daily concentrations are 3.77 µg/m3, 2.17 µg/m3, and 49.0 µg/m3, respectively. The predominant direction of pollutant dispersion is towards the West and Southwest, affecting areas within Ninh Binh City and Tam Diep City. The AERMOD model results indicate that pollutant concentrations are lower than the relevant national technical standards. However, it is concerning that a small region within Cuc Phuong National Park shows signs of being affected by these air pollutants. Therefore, stringent control measures on emissions from thermal power and cement plants must be in place to ensure the local air quality and public health.

Vector geographic data play an important role in the natural resources and environment sector as well as in other location information services. This is also one of the types of data where the cost to create it is relatively large because of the difficulty in surveying, collecting, and authorizing. The rapid development of the Internet has created many advantages in the distribution, exploitation, and use of vector geographic data, but it also gives rise to many problems such as duplication, redistribution, forgery, and illegal data use. The theft on the Internet is becoming more and more sophisticated and the number of violations is increasing, showing the urgent need to research and develop an effective solution to protect the copyright of vector geographic data and prevent them from being illegally collected and used. Among the major studies and solutions, digital watermarking emerges as an effective method and is an active research area for copyright protection. Towards a good solution for copyright protection of vector geographic data, our study proposes a new algorithm with three main contributions, including: (1) generating short, pseudo-random meaningful watermarks to in-crease robustness and to enable automated as well as visual manual verifying; (2) building a uniformly distributed mapping between the vertex coordinates and the watermark bit indexes to increase the robustness of the watermarks; and (3) integrating two types of watermarks, namely, spatial domain-based watermarking and zero-watermarking to be resistant to most common attacks on geographic vector data. The algorithm works on all types of vector geographic data, including points, polylines, and polygons.

Air pollution is one of the most concerning environmental issues, wherein PM2.5 concentration plays an important role. This study monitored and evaluated t

Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.