Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation

An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

© 2019, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Gmbh Germany, part of Springer Nature. In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising

Optimistic distributionally robust optimization for nonparametric likelihood approximation

The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood that identifies a probability measure which lies in the neighborhood of the nominal measure and that maximizes the probability of observing the given sample point. We show that when the neighborhood is constructed by the Kullback-Leibler divergence, by moment conditions or by the Wasserstein distance, then our optimistic likelihood can be determined through the solution of a convex optimization problem, and it admits an analytical expression in particular cases. We also show that the posterior inference problem with our optimistic likelihood approximation enjoys strong theoretical performance guarantees, and it performs competitively in a probabilistic classification task

Highly accurate step counting at variouswalking states using low-cost inertial measurement unit support indoor positioning system

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01)

Current advances in seagrass research: A review from Viet Nam

Seagrass meadows provide valuable ecosystem services but are fragile and threatened ecosystems all over the world. This review highlights the current advances in seagrass research from Viet Nam. One goal is to support decision makers in developing science-based conservation strategies. In recent years, several techniques were applied to estimate the size of seagrass meadows. Independent from the method used, there is an alarming decline in the seagrass area in almost all parts of Viet Nam. Since 1990, a decline of 46.5% or 13,549 ha was found. Only in a few protected and difficult-to-reach areas was an increase observed. Conditions at those sites could be investigated in more detail to make suggestions for conservation and recovery of seagrass meadows. Due to their lifestyle and morphology, seagrasses take up compounds from their environment easily. Phytoremediation processes of Thalassia hemprichii and Enhalus acoroides are described exemplarily. High accumulation of heavy metals dependent on their concentration in the environment in different organs can be observed. On the one hand, seagrasses play a role in phytoremediation processes in polluted areas; on the other hand, they might suffer at high concentrations, and pollution will contribute to their overall decline. Compared with the neighboring countries, the total Corg stock from seagrass beds in Viet Nam was much lower than in the Philippines and Indonesia but higher than that of Malaysia and Myanmar. Due to an exceptionally long latitudinal coastline of 3,260 km covering cool to warm water environments, the seagrass species composition in Viet Nam shows a high diversity and a high plasticity within species boundaries. This leads to challenges in taxonomic issues, especially with the Halophila genus, which can be better deduced from genetic diversity/population structures of members of Hydrocharitaceae. Finally, the current seagrass conservation and management efforts in Viet Nam are presented and discussed. Only decisions based on the interdisciplinary cooperation of scientists from all disciplines mentioned will finally lead to conserve this valuable ecosystem for mankind and biodiversity

Anchoring of proteins to lactic acid bacteria

The anchoring of proteins to the cell surface of lactic acid bacteria (LAB) using genetic techniques is an exciting and emerging research area that holds great promise for a wide variety of biotechnological applications. This paper reviews five different types of anchoring domains that have been explored for their efficiency in attaching hybrid proteins to the cell membrane or cell wall of LAB. The most exploited anchoring regions are those with the LPXTG box that bind the proteins in a covalent way to the cell wall. In recent years, two new modes of cell wall protein anchoring have been studied and these may provide new approaches in surface display. The important progress that is being made with cell surface display of chimaeric proteins in the areas of vaccine development and enzyme- or whole-cell immobilisation is highlighted.

Phytophthora antagonism of endophytic bacteria isolated from roots of black pepper (Piper nigrum L.).

[[abstract]]Abstract: In this study, 90 root samples were collected from 30 black pepper farms in three provinces in the Central Highlands of Vietnam. A total of 352 endophytic bacteria were isolated and their morphology described. An in vitro assay on the ntifungal activity of these isolates was then conducted and 47 isolates were found to have antagonistic activity on Phytophthora fungi. The antifungal activity of the 47 isolates was evaluated in vivo by shoot assay. Among these 47 isolates, 6 were selected for further investigation. The six isolates were classified and identified by sequencing the 16S RNA gene and phylogeny. The results showed that all six endophytic bacteria belong to the following species of Bacillus genus: B. siamensis, B. amyloliquefaciens, B. velezenis, and B. methylotrophiycus. Enzymatic activity related to the antifungal activity of the six potent isolates was determined; it showed that they possessed high chitinase and protease activities. These isolates were applied for black pepper seedlings in greenhouse. The results showed three promising isolates: B. siamensis EB.CP6, B. velezensis EB.KN12, and B. methylotrophycus EB.KN13. Black pepper seedlings treated with the promising bacteria had the lowest rate of root disease (8.45–11.21%) and lower fatal rate (11.11–15.55%) compared to the control group (24.81% and 24.44%). In addition, the three promising isolates strongly affected the growth of the black pepper seedlings in greenhouse. The plant height, length of roots, and fresh biomass of the seedlings in the treated plots were higher than those in the control plots. Thus, the endophytic bacterial isolates have the potential to act as biocontrol agent for the sustainable production of black pepper.[[sponsorship]]科技部[[notice]]補正完

Detection and monitoring of cancers with biosensors in Vietnam

Biosensors are able to provide fast, accurate and reliable detec-tions and monitoring of cancer cells, as well as to determine the effectiveness of anticancer chemotherapy agents in cancer treatments. These have attracted a great attention of research communities, especially in the capabilities of detecting the path-ogens, viruses and cancer cells in narrow scale that the conven-tional apparatus and techniques do not have. This paper pre-sents technologies and applications of biosensors for detections of cancer cells and related diseases, with the focus on the cur-rent research and technology development about biosensors in Vietnam, a typical developing country with a very high number of patients diagnosed with cancers in recent years, but having a very low cancer survival rate. The role of biosensors in early detections of diseases, cancer screening, diagnosis and treat-ment, is more and more important; especially it is estimated that by 2020, 60-70% new cases of cancers and nearly 70% of cancer deaths will be in economically disadvantaged countries. The paper is also aimed to open channels for the potential R&D collaborations with partners in Vietnam in the areas of innovative design and development of biosensors in particular and medical technology devices in general

PRIMARY EDUCATION IN VIETNAM AND PUPIL ONLINE ENGAGEMENT

This paper focuses on exploring the disparities in social awareness and use of the Internet between urban and rural school children in the North of Vietnam. Design/methodology/approach: A total of 525 pupils, aged 9 to 11 years old, randomly selected from 7 urban and rural schools, who are Internet users, participated in the study and consented to responding to a questionnaire adapted from an equivalent European Union (EU) study. A comparative statistical analysis of the responses was then carried out, using IBM SPSS v21, which consisted of a descriptive analysis, an identification of personal self-development opportunities, as well as issues related to pupils’ digital prowess and knowledge of Internet use, and Internet safety, including parental engagement in their offspring’s online activities. Findings: The study highlights the fact that children from both the urban and rural regions of the North of Vietnam mostly access to the Internet from home, but with more children in the urbanized areas accessing it at school than their rural counterparts. Although children from the rural areas scored lower on all the Internet indicators, such as digital access and online personal experience and awareness, there was no disparity in awareness of Internet risks between the two sub-samples. It is noteworthy that there was no statistically significant gender difference towards online activities that support self-development. In relation to safe Internet usage, children are likely to seek advice from their parents, rather than through teachers or friends. However, they are not yet provided with an effective safety net while exposing themselves to the digital world. Originality/value: Although the Vietnamese national curriculum on the Computer Science subject does not explicitly cover the use of the Internet and its related aspects, the majority of children who took part in this study claimed to have used the Internet in their learning activities. This emphasises the urgent need for the MoE and educators in the country to not only improve ICT facilities in schools, but also to revise the Computer Science curriculum in order to (a) provide a supportive environment for learning development and (b) collectively advocate the dynamics of Internet use in order to ensure safe access and use by the children