Development potential for rooftop solar photovoltaic: case studies in commercial and industrial sectors of Hue City

A growing interest in solar energy development to ensure national power security and slow the onset of the current climate crisis has spurred the deployment of rooftop solar photovoltaic (PV) in cities in Vietnam. This paper aims to examine the technical and financial potential for rooftop solar PV development of two enterprises in Hue City, namely Truong Tien Plaza Commercial Center and Huetronics Joint Stock Company. The Google Earth Pro, the Roof Pitch Factor, and the on-site survey were employed to determine the rooftop characteristics of the two facilities. The technical potentials of Huetronics Company and Truong Tien Plaza were estimated at 1,256 kWh/day and 1,437 kWh/day, respectively. The financial analysis with Cost-Benefit Analysis reveals that both reference cases proved to be financially viable: the Discounted Payback Period is 5.8 years in the case of Huetronics and five years for Truong Tien Plaza; the Net Present Values are greater than zero, and the Internal Rate of Return is higher than the Cost of Capital. Such results are expected to assist in making informed policy decisions on the commercial and industrial rooftop solar PV development in Hue City

Lung Volume Reduction Surgery in Patients with Heterogenous Emphysema: Selecting Perspective

BACKGROUND: Lung volume reduction surgery (LVRS) was introduced to alleviate clinical conditions in selected patients with heterogenous emphysema. Clarifying the most suitable patients for LVRS remained unclear. AIM: This study was undertaken to specifically analyze the preoperative factor affecting to LVRS. METHODS: The prospective study was conducted at 103 Military Hospital between July 2014 and April 2016. Severe heterogenous emphysema patients were selected to participate in the study. The information, spirometry, and body plethysmographic pulmonary function tests in 31 patients who underwent LVRS were compared with postoperative outcomes (changing in FEV1 and CAT scale). RESULTS: Of the 31 patients, there was statistically significant difference in the outcome of functional capacity, lung function between two groups (FEV1 ≤ 50% and > 50%) (∆FEV1: 22.46 vs 18.32%; p = 0.042. ∆CAT: 6.85 vs 5.07; p = 0.048). Changes of the FEV1 and CAT scale were no statistically significant differences in three groups residual volume. Patients with total lung capacity < 140% had more improved than others (∆FEV1: 23.81 vs 15.1%; p = 0.031). CONCLUSION: Preoperative spirometry and body plethysmographic pulmonary function tests were useful measures to selected severe heterogenous emphysema patients for LVRS. Patients with FEV1 ≤ 50%, TLC in the range of 100-140% should be selected

Effect of Biosurfactants on Gram-negative Bacterial Biofilm Formation

2020【要旨

Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time

U-shaped deep-learning models for island ecosystem type classification, a case study in Con Dao Island of Vietnam

The monitoring of ecosystem dynamics utilises time and resources from scientists and land-use managers, especially in wetland ecosystems in islands that have been affected significantly by both the current state of oceans and human-made activities. Deep-learning models for natural and anthropogenic ecosystem type classification, based on remote sensing data, have become a tool to potentially replace manual image interpretation. This study proposes a U-Net model to develop a deep learning model for classifying 10 island ecosystems with cloud- and shadow-based data using Sentinel-2, ALOS and NOAA remote sensing data. We tested and compared different optimiser methods with two benchmark methods, including support vector machines and random forests. In total, 48 U-Net models were trained and compared. The U-Net model with the Adadelta optimiser and 64 filters showed the best result, because it could classify all island ecosystems with 93 percent accuracy and a loss function value of 0.17. The model was used to classify and successfully manage ecosystems on a particular island in Vietnam. Compared to island ecosystems, it is not easy to detect coral reefs due to seasonal ocean currents. However, the trained deep-learning models proved to have high performances compared to the two traditional methods. The best U-Net model, which needs about two minutes to create a new classification, could become a suitable tool for island research and management in the future

Deep learning models integrating multi-sensor and -temporal remote sensing to monitor landslide traces in Vietnam

Landslides pose significant threats to lives and public infrastructure in mountainous regions. Real-time landslide monitoring presents challenges for scientists, often involving substantial costs and risks due to challenging terrain and instability. Recent technological advancements offer the potential to identify landslide-prone areas and provide timely warnings to local populations when adverse weather conditions arise. This study aims to achieve three key objectives: (1) propose indicators for detecting landslides in both field and remote sensing images; (2) develop deep learning (DL) models capable of automatically identifying landslides from fusion data of Sentinel-1 (SAR) and Sentinel-2 (optical) images; and (3) employ DL-trained models to detect this natural hazard in specific regions of Vietnam. Twenty DL models were trained, utilizing three U-shaped architectures, which include U-Net and U-Net3+, combined with different data-fusion choices. The training data consisted of multi-temporal Sentinel images and increased the accuracy of DL models using Adam optimizer to 99% in landslide detection with low loss function values. Using two bands of the Sentinel-1 could not define the characteristics of landslide traces. However, the integration between Sentinel-2 data and these bands makes the landslide detection process more effective. Therefore, the authors proposed a consolidated strategy based on three models: (1) UNet using four S2-bands, (2) UNet3+ using four S2-bands, (3) UNet using four S2-bands and VV S1-band, and (4) UNet using four S2-bands and VH S1-band for fully detect landslides. This integrated strategy uses the capabilities of each model and overcomes model result constraints to better describe landslide traces in varied geographical locations

A Bayesian Belief Network for assessing ecosystem services and socio-economic development in threatened estuarine regions

Estuaries feature diverse ecosystems with great biological production and favourable resources and landscapes for ecotourism. Increasing natural disasters have threatened the lives and safety of over 70% of the region's population in recent years. Rapid urbanisation and tourism have changed land use. This changes ecosystem structure and function, impacting service provision. This study developed a Bayesian Belief Network (BBN) model to assess the imbalance between socio-economic development and resource conservation using an ecosystem services (ES) approach. The BBN model helps synthesise and exchange information, provide decision-making data, evaluate trade-off possibilities and anticipate future situations when assessing ES. The BBN network model probabilistically evaluates ecosystem services using expertise, statistical modelling, geographic information systems and interviews. We assessed the comprehensive value of 17 forms of ES for four ecosystem groups over a period of 30 years. As a result, the cultural ecosystem services of some estuarial regions in Vietnam have the highest value and are showing an increasing trend, while the regulating ecosystem services are continuously fluctuating and decreasing. Provisioning ecosystem services are stable with small changes. This study also examined ES values in six landscape categories and created two ES change scenarios. The findings can help managers choose land-use and resource exploitation policies, understand the value of ecosystem services at the regional level and develop estuary sustainability strategies for long-term ecosystem service balance

Current Plastic Waste Status and Its Leakage at Tam Giang–Cau Hai Lagoon System in Central Vietnam

Plastic waste poses a significant threat to the environment, impacting both aquatic ecosystems and human health. This study aimed to quantify the leakage of plastic waste from urban and rural areas into the Tam Giang–Cau Hai lagoon system area in Vietnam. The research involved conducting surveys and sampling plastic waste in wards and communes surrounding the Tam Giang–Cau Hai lagoon system, as well as utilizing a waste flow diagram to calculate the amount of plastic waste leakage into the environment. The findings of the study reveal that the annual plastic leakage in this study area is approximately 479 tons. The majority of this waste enters the water body system, accounting for 74.1% of the total leakage, followed by land areas at 23.4% and land burning at 2.5%. Among the sources contributing to the wastewater flow in the area, households and markets were found to be the two primary contributors. Household waste accounted for 70.4% (2806 tons year−1) of the total, while the market sources accounted for 16.9% (675 tons year−1). This study marks the inaugural effort to assess the extent of plastic waste released from Hue City into the Tam Giang–Cau Hai lagoon system. It plays a pivotal role in examining the makeup, source of plastic waste and path of plastic waste leakage

New Approach to Assess Multi-Scale Coastal Landscape Vulnerability to Erosion in Tropical Storms in Vietnam

The increase of coastal erosion due to intense tropical storms and unsustainable urban development in Vietnam demands vulnerability assessments at different research scales. This study proposes (1) a new approach to classify coastlines and (2) suitable criteria to evaluate coastal vulnerability index (CVI) at national and regional/local scales. At the national scale, the Vietnamese coastline was separated into 72 cells from 8 coast types based on natural features, whereas the Center region of Vietnam was separated into 495 cells from 41 coast types based on both natural and socio-economic features. The assessments were carried out by using 17 criteria related to local land use/cover, socio-economic, and natural datasets. Some simplified variables for CVI calculation at the national scale were replaced by quantitative variables at regional/local scales, particularly geomorphology and socio-economic variables. As a result, more than 20% of Vietnam’s coastline has high CVI values, significantly more than 350 km of the coasts in the center part. The coastal landscapes with residential and tourism lands close to the beaches without protection forests have been strongly affected by storms’ erosion. The new approach is cost-effective in data use and processing and is ideal for identifying and evaluating the CVI index at different scales