EFFECT OF SOME DISPERSING AUXILIARIES ON NANOSILICA DISPERSION INTO PASSIVE CHROME TRIVALENT SOLUTION

In this paper, the dispersion of nanosilica in conversion solution trivalent chromium using variety of dispersion aids namely: SDS, PVP, AE-7, OP-10 and epomin. The results showed that the nanosilica could disperse in conversion solution in low pH (pH = 1.5). The solution containing nanosilica was uniform, having no agglomeration with SDS, PVP and AE-7 after 7-day preparation. The results of zeta potential and size distribution illustrated that AE-7 was the most effective for nanosilica dispersion in passive solution trivalent chromium with medium size of nanosilica in C2-nanosilica solution using AE-7 equal ~ 60 nm. However, there was a much diffirence size between the size of nanosilica in passive solution and initial nanosilica. As a result, nanosilica could disperse in passive solution at low pH with AE-7 but this was not effective enough to held dispersed solution in stable state

The results of deep magnetotelluric sounding for studying the Nha Trang - Tanh Linh fault

The profile of deep magnetotelluric sounding (MT) from Duc Trong - Tuy Phong has been carried out in Lam Dong and Binh Thuan  provinces. The length of the Duc Trong - Tuy Phong profile is about 80 km with 15 stations and the distance between the stations measures about 5 km. Two-dimensional MT inversion was used to find a resistivity model that fits the data. The 2D resistivity model allows determining position and development formation of the Nha Trang - Tanh Linh  fault. This is the deep fault, which is showed by the boundaries of remarkable change of resistivity. In the near surface of the Earth (from ground to the depth of 6 km), the angle of inclination of this fault is about 60o; in the next part, the direction of the Nha Trang - Tanh Linh  faut is vertical. Geoelectrical section of the Nha Trang - Tanh Linh  profile shows that the resistivity of mid-crust is higher than that of lower-crust and of upper-crust

A comparison between Hydrochloric acid and Trifluoroacetic acid in hydrolysis method of exopolysaccharide from Ophiocordyceps sinensis in Monosaccharide composition analysis by GC-FID

The monosaccharide composition is one of the crucial factors affecting the bioactivity of exopolysaccharide (EPS) in Cordyceps species. Therefore, many scientists have studied, analyzed monosaccharide composition and structure of EPS from Cordyceps species, especially Ophiocordyceps sinensis (O. sinensis). This study aimed to compare hydrochloric acid (HCl) with trifluoroacetic acid (TFA) in the EPS hydrolysis process in monosaccharide composition analysis by Gas Chromatography with Flame-Ionization Detection (GC-FID). The hydrolysis is a crucial step in forming the acetyl derivative, which helps the GC-FID technique to have good results in monosaccharide composition analysis. The results showed that hydrolysis with HCl gave a higher hydrolysis efficiency and was more suitable than hydrolysis by TFA in pretreatment to EPS for GC-FID. Hydrolysis results were analyzed through thin-layer chromatography and high-performance liquid chromatography (HPLC), then Acetyl derivatives were produced and finally analyzed by GC-FID to determine the monosaccharide composition of EPS. For EPS hydrolyzed by HCl, the analytical results presented that this sample had 6 kinds of monosaccharides, including rhamnose, arabinose, xylose, mannose, glucose, and galactose; the most monosaccharide was glucose. The EPS hydrolyzed by TFA only detected three kinds of monosaccharides, including mannose, arabinose, and galactose, mainly mannose. The study has set a foundation for further analysis of monosaccharide composition and structure of EPS from O. sinensis

Neither Peace Nor War: China's Grey Zone Coercion in the South China Sea

Chinese 'assertiveness' has become a catchphrase in policy and academic discussions regarding the Asia-Pacific security and China's foreign policy more specifically. In the South China Sea, an increase in China's assertive actions to realise its expansionist territorial and maritime claims has led to speculation about the ultimate outbreak of war in the region. This would have tremendous implications for global peace and stability in the long run. A broad reference to 'assertive actions', however, fails to capture the subtlety of actual developments on the ground where China has gradually and ambiguously altered the status quo in the South China Sea at regional actors' policy paralysis. It is, therefore, important to delve beyond the descriptive label and investigate the mechanisms and strategic calculations of China's activities. More research rigour is needed to discern whether these activities belong to a sustained and systemised plan in China's dispute strategy. This dissertation examines China's maritime assertiveness through the lens of grey-zone coercion. It seeks to understand how and why China employs grey-zone coercion to defend and advance its claims in the South China Sea disputes. The questions at stake are manifold: what Chinese grey-zone coercion entails, why it uses grey-zone..

Ani mír, ani válka: Čínský nátlak v šedé zóně v Jihočínském moři

Chinese 'assertiveness' has become a catchphrase in policy and academic discussions regarding the Asia-Pacific security and China's foreign policy more specifically. In the South China Sea, an increase in China's assertive actions to realise its expansionist territorial and maritime claims has led to speculation about the ultimate outbreak of war in the region. This would have tremendous implications for global peace and stability in the long run. A broad reference to 'assertive actions', however, fails to capture the subtlety of actual developments on the ground where China has gradually and ambiguously altered the status quo in the South China Sea at regional actors' policy paralysis. It is, therefore, important to delve beyond the descriptive label and investigate the mechanisms and strategic calculations of China's activities. More research rigour is needed to discern whether these activities belong to a sustained and systemised plan in China's dispute strategy. This dissertation examines China's maritime assertiveness through the lens of grey-zone coercion. It seeks to understand how and why China employs grey-zone coercion to defend and advance its claims in the South China Sea disputes. The questions at stake are manifold: what Chinese grey-zone coercion entails, why it uses grey-zone...Department of Security StudiesKatedra bezpečnostních studiíFakulta sociálních vědFaculty of Social Science

Remote sensing of dynamics and aboveground biomass of seagrass in Tauranga Harbor (Bay of Plenty), New Zealand

Seagrasses are angiosperm plants that are completely adapted to life in seawater. They are distributed widely across the climatic regions, ranging from the tropics to temperate regions, in both inter-tidal and sub-tidal zones. Multiple ecosystem services are recognized as being supported by seagrass meadows, which recently has included appreciation role in carbon sequestration. Seagrass meadows, however, have been degraded in both terms of area and habitat quality across the globe, leading to a significant loss of ecosystem services and human livelihood support. This ongoing degradation has resulted in an urgent need to develop tools for assessing the temporal changes of extant meadows and accurate estimation of seagrass biological parameters, which will contribute to a sustainable conservation strategy into the future. This thesis describes the use of a range of freely available Earth observation products, including multi-spectral imagery from Landsat and Sentinel-2, and synthetic aperture radar (SAR) products from Sentinel-1, coupled with a range of machine learning (ML) and meta-heuristic optimization algorithms, to develop novel and advanced techniques for remote sensing of seagrass. The work used field validation data from Tauranga Harbor, New Zealand, and specifically targeted mapping, change detection, and estimation of seagrass distribution and biomass. The relatively small and patchy meadows of Zostera muelleri in this harbor can be mapped using a three-category classification (dense, sparse and none) with up to 91% accuracy for dense and 75% for sparse meadows using the machine learning algorithm Rotation Forest with Sentinel-2 imagery. Despite a slightly lower accuracy (90%), the algorithm Canonical Correlation Forest also shows merit for categorical seagrass mapping. Historic Landsat multispectral satellite data used with ML models was able to map accurately the change in distribution of seagrass meadows over 29 years (1989 - 2019). For this binary mapping application (presence/absence) the CatBoost model obtained over 90% accuracy. Historic imagery indicates an approximately 50% of seagrass loss, from 2,424 ha in the year 1989 down to 1,184 ha in the year 2019 in Tauranga Harbor. Most of the early loss was from the northern and southern parts of the harbor and results were consistent with published estimates of change based on aerial photography. In addition, a mapping scheme of seagrass distribution was developed from SAR data and a fusion of the multi-spectral and SAR data was developed for seagrass aboveground biomass (AGB) estimation. Optimal results were obtained using a combination of ML methods and metaheuristic optimization. The seagrass distribution was mapped at an accuracy over 90% using the Extreme Gradient Boost (XGB) whilst the AGB map at 10 m spatial resolution was produced at 75% accuracy using the XGB model together with Sentinel-2 images and Particle Swarm Optimization (PSO). The last part of the thesis describes the development of a web-based application to allow the advances in this research to be shared with a broader community and strengthen international and domestic collaboration in seagrass protection and conservation. This study provides in-depth and advanced methods for seagrass resource inventory, maximizing the utilization of remotely sensed data, state-of-the-art ML and metaheuristic optimization algorithms to accurately map distribution and estimate desired biophysical parameters. The proposed methods are open-source and applicable across the globe, providing a complete toolset for both scientist and managers in aquatic resource management

A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga Harbor, New Zealand

Seagrass has been acknowledged as a productive blue carbon ecosystem that is in significant decline across much of the world. A first step toward conservation is the mapping and monitoring of extant seagrass meadows. Several methods are currently in use, but mapping the resource from satellite images using machine learning is not widely applied, despite its successful use in various comparable applications. This research aimed to develop a novel approach for seagrass monitoring using state-of-the-art machine learning with data from Sentinel–2 imagery. We used Tauranga Harbor, New Zealand as a validation site for which extensive ground truth data are available to compare ensemble machine learning methods involving random forests (RF), rotation forests (RoF), and canonical correlation forests (CCF) with the more traditional maximum likelihood classifier (MLC) technique. Using a group of validation metrics including F1, precision, recall, accuracy, and the McNemar test, our results indicated that machine learning techniques outperformed the MLC with RoF as the best performer (F1 scores ranging from 0.75–0.91 for sparse and dense seagrass meadows, respectively). Our study is the first comparison of various ensemble-based methods for seagrass mapping of which we are aware, and promises to be an effective approach to enhance the accuracy of seagrass monitoring

Total organic carbon estimation in seagrass beds in Tauranga Harbour, New Zealand using multi-sensors imagery and grey wolf optimization

Estimation of carbon stock in seagrass meadows is in challenges of paucity of assessment and low accuracy of the estimates. In this study, we used a fusion of the synthetic aperture radar (SAR) Sentinel-1 (S-1), the multi-spectral Sentinel-2 (S-2), and coupled this with advanced machine learning (ML) models and meta-heuristic optimization to improve the estimation of total organic carbon (TOC) stock in the Zostera muelleri meadows in Tauranga Harbour, New Zealand. Five scenarios containing combinations of data, ML models (Random Forest, Extreme Gradient Boost, Rotation Forest, CatBoost) and optimization were developed and evaluated for TOC retrieval. Results indicate a fusion of S1, S2 images, a novel ML model CatBoost and the grey wolf optimization algorithm (the CB-GWO model) yielded the best prediction of seagrass TOC (R2, RMSE were 0.738 and 10.64 Mg C ha−1). Our results provide novel ideas of deriving a low-cost, scalable and reliable estimates of seagrass TOC globally

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes