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Subsea pipelines are the backbone of the modern oil and gas industry, transporting a total of 28% of global oil production. Due to several factors, such as corrosion or deformations, the pipelines might degrade over time, which might lead to serious economic and environmental damages if not addressed promptly. Therefore, it is crucial to detect any serious damage to subsea pipelines before they cause dangerous catastrophes. Inspections of subsea pipelines are usually made using a Remote Operating Vehicle and the inspection data is usually processed manually, which is subject to human errors, and requires experienced Remote Operating Vehicle operators. It is thus necessary to automate the inspection process to enable more efficiency as well as reduce costs. Besides, it is recognised that specific challenges of noisy and low-quality inspection data arising from the underwater environment prevent the industry from taking full advantage of the recent development in the Artificial Intelligence field to the problem of subsea pipeline inspection. In this paper, we developed an ensemble of deep learning classifiers to further improve the performance of single deep learning models in classifying anomalous events on the subsea pipeline inspection data. The output of the proposed ensemble was combined based on a weighted combining method. The weights of base classifiers were found by minimising the difference between the weighted combining result and the given associated ground truth annotation information. Three inspection datasets, gathered from different oil and gas companies in the United Kingdom, were analysed. These datasets were recorded under varying conditions and include a range of anomalies. The results showed that the proposed ensemble achieves around 78% accuracy on two datasets and more than 99% accuracy on one dataset, which is better compared to base classifiers and two popular ensembles. [ABSTRACT FROM AUTHOR]

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification. [ABSTRACT FROM AUTHOR]

Altered glycosylation plays an important role during development and is also a hallmark of increased tumorigenicity and metastatic potentials of several cancers. We report here that Tankyrase-1 (TNKS1) controls protein glycosylation by Poly-ADP-ribosylation (PARylation) of a Golgi structural protein, Golgin45, at the Golgi. TNKS1 is a Golgi-localized peripheral membrane protein that plays various roles throughout the cell, ranging from telomere maintenance to Glut4 trafficking. Our study indicates that TNKS1 localization to the Golgi apparatus is mediated by Golgin45. TNKS1-dependent control of Golgin45 protein stability influences protein glycosylation, as shown by Glycomic analysis. Further, FRAP experiments indicated that Golgin45 protein level modulates Golgi glycosyltransferease trafficking in Rab2-GTP-dependent manner. Taken together, these results suggest that TNKS1-dependent regulation of Golgin45 may provide a molecular underpinning for altered glycosylation at the Golgi during development or oncogenic transformation. Xihua Yue et al. report that Tankyrase-1 controls poly-ADP-ribosylation of Golgin45, a Golgi structural protein, which has a profound influence on glycosyltransferase trafficking and protein glycosylation at the Golgi. This may shed new light onto mechanisms involved in development and oncogenic transformation. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) numerical models are useful tools for studying river morphology. Frequently, 2D numerical models are often applied to predict the impacts of the artificial changes to rivers and estuaries. These changes may be caused by altered watershed hydrology, variations in the sediment supply and the construction of artificial works such as dams, embankments and tidal gutters. The aim of this study was to apply the CCHE2D model in simulating riverbed morphological variation in the Co Chien Estuary of Vietnam with complex morphology under the combined impacts of hydrodynamic processes such as waves, flow field, tidal currents and sediment transport. First, the proposed model was calibrated using water surface level and current speed data during dry and flood seasons in 2010. Calibrated results showed satisfactory coefficients (root mean square error smaller than 0.10 and Brier skill score (BSS) criteria varying between 0.63 and 0.94). Second, the proposed model is applied to simulate riverbed level variation for Co Chien Estuary after the 6-year flood (2010–2015). The results were evaluated comparing deviations between simulated and measured elevations at multiple monitoring cross sections and longitudinal bed profiles after the 6-year flood. Compared results confirmed that the proposed model is suitable for simulating hydrodynamic processes and riverbed morphological changes in the study area with BSS criteria greater than 0.68. The proposed model is a useful tool to help efficiently manage resources and minimizing the unwanted influences of wave, current, tide and sediment transport process. [ABSTRACT FROM AUTHOR]

This work evaluates the impacts of climate change on water use-demand of three rice planting crops including winter–spring (WS), summer–autumn (SA) and autumn–winter crops for Long Xuyen Quadrangle Delta of Vietnam. Climatic variables scenarios were obtained from the updated report on emission scenarios which are issued by Vietnam's Ministry of Natural Resources and Environment for three future timescales (2016–2035, 2046–2065 and 2080–2099) of representative concentration pathways (RCP) 4.5 and RCP8.5 scenarios. Cropwat model was selected to evaluate the irrigation water demand of three rice planting crops based on simulating actual evapotranspiration (ETc) and effective rainfall for the study area. The results showed that the WS and SA planting crops need more irrigation water demand in the growing and developmental stage for timescales of RCP4.5 and RCP8.5 scenarios. Results also pointed out that compared with current climate condition the tendency to decrease in irrigation water demand in the initial and developmental stages of autumn–winter planting crop with arranging from 2.9–12.9 to 10.0–18.2%, respectively, corresponding to timescales of RCP4.5 and RCP8.5 scenarios is found and a significant downward trend in the late stage approximately 5.8–20.0% and 13.6–20.7%, respectively, for RCP4.5 and RCP8.5 scenarios also recorded. [ABSTRACT FROM AUTHOR]

In recent years, Mekong Delta of Vietnam is severely affected by salinity intrusion and water scarcity due to climate variability. In this study, a comprehensive analysis of meteorology drought was conducted to detect drought events using the Standardized Precipitation Index at 3-, 6-, 9- and 12-month time scale based on monthly precipitation data from 46 precipitation gauge stations for a period of 1984-2015. The aim of the study is to assess the degree of meteorology drought from 1984 to 2015 in potential crop growing areas to provide early warnings and monitor drought events to minimize their negative effects. The results indicated that meteorological drought occurred at the central provinces of the study area in the period 1985-1994, the northeastern and northwestern provinces in the period 1995-2004 and 10 recent years (2005-2014) meteorological drought shifted toward southern coastal provinces. The analyzed results also showed a tendency to decrease in frequency of drought is recorded while a tendency to increase in the spatial distribution of drought with moderate and severe droughts is recorded. Among the major droughts, 1990-1992 was evaluated the most extreme drought with 85% of the study area covered by the extreme drought with peak value of − 2.63 recorded and lasting for 29 months. [ABSTRACT FROM AUTHOR]

Long Xuyen Quadrangle is one of the important agricultural areas of the Mekong Delta of Vietnam accounting for 25% of rice production. In recent years, the area faces drought and salinization problems, as part of climate change impact and sea level rise. These are the main causes that led to the crop water deficits for agricultural production. Therefore, this work was conducted to predict crop water requirement (CWR) based on consideration of other related meteorological factors and further redefine the crop planting calendar (CPC) for three main cropping seasons including winter-spring (WS), summer-autumn (SA) and autumn-spring (AS) using the Cropwat crop model based on the current climate conditions and future climate change scenarios. Meteorological data for the baseline period (2006-2016) and future corresponding to timescales 2020s, 2055s and 2090s of Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios are used to predict CWR and CPC for the study area. The results showed that WS and SA crops needed more irrigation water than AS crop and the highest irrigation water requirement of the WS and SA crops occurred on developmental stage, while the AW crop appeared on growth, developmental and late stage for the baseline and timescales of RCP4.5 and RCP8.5 scenarios. Calculation results of the shift of CPC indicated that the CWR of the AW crop decreased lowest approximately 6.6-20.6% for timescales of RCP4.5 scenario and 20.6-25.5% for RCP8.5 scenario compared with the baseline. [ABSTRACT FROM AUTHOR]

This study presented simulation results of runoff discharge combining the Mike NAM and Mike SHE models for lower Sai Gon-Dong Nai River basin (SG-DNRB). Sai Gon-Dong Nai River basin is Vietnam’s largest population center and main industrial area in Ho Chi Minh City. In recent years, the area faces flooding problems because of heavy rainfalls and high tides, as part of the impacts of climate change and sea level rise. The lower of SG-DNRB was selected as a case study to highlight the necessity to investigate combination of the rainfall-runoff model and the hydrodynamic model for long-term strategies to resource water in the future. The models were calibrated using water level and runoff discharge data during low and flood seasons in 2014. The calibrated results showed satisfactory coefficients (NASH index, R2 up to 0.70 and RMSE is smaller than 0.20). The results confirmed that the combination of Mike NAM and Mike SHE models is well suitable to simulate runoff discharge in the lower SG-DNRB. Combination of the models can be considered a useful tool to help professional agency operator water resources management projects in other areas in the context of climate change and sea level rise. [ABSTRACT FROM AUTHOR]

Numerical models are useful tools that play an important role in many research projects. Mike21C is one of the most well-established models for simulating a variety of processes, including bank erosion, bed level variations, aggradation, and degradation. Such processes are caused by a variety of activities, such as construction and dredging, as well as seasonal flow fluctuations. Mike21C based on an orthogonal curvilinear grid, which enables a computational speed that is faster than that of other grids. The hydrodynamic part of the model is based on solving the Saint-Venant equations. In this research, the Mike21C model was applied to simulate water depth, flow discharge distribution, and suspended transport rate along reaches of the Bassac River the on Vietnam. The data files of the curvilinear grid and bathymetry were used to generate the model. A time series for the discharge and water level of hydrological stations was established. The model was calibrated using the water level and suspended load data collected during high and low flow discharges. The simulation results show that the model can be applied well to other areas. [ABSTRACT FROM AUTHOR]

Studies of sediment transport problems in mountainous rivers with steep slopes are difficult due to rapid variations in flow regimes, abrupt changes in topography, etc. Sediment transport in mountainous rivers with steep slopes is a complicated subject because bed materials in mountainous rivers are often heterogeneous and contain a wide range of bed material sizes, such as gravel, cobbles, boulders, etc. This paper presents a numerical model that was developed to simulate the river morphology in mountainous rivers where the maximum bed material size is in the range of cobbles. The governing equations were discretized using a finite difference method. In addition, an empirical bed load formula was established to calculate the bed load transport rate. The flow and sediment transport modules were constructed in a decoupled manner. The developed model was tested to simulate the river morphology in an artificial channel and in the Asungjun River section of the mountainous Yangyang Namdae River (South Korea). The simulation results exhibited good agreement with field data. [ABSTRACT FROM AUTHOR]

Human blood monocytes are heterogeneous and conventionally subdivided into two subsets based on CD16 expression. Recently, the official nomenclature subdivides monocytes into three subsets, the additional subset arising from the segregation of the CD16+ monocytes into two based on relative expression of CD14. Recent whole genome analysis reveal that specialized functions and phenotypes can be attributed to these newly defined monocyte subsets. In this review, we discuss these recent results, and also the description and utility of this new segregation in several disease conditions. We also discuss alternative markers for segregating the monocyte subsets, for example using Tie-2 and slan, which do not necessarily follow the official method of segregating monocyte subsets based on relative CD14 and CD16 expressions. [ABSTRACT FROM AUTHOR]

A correction to the article "Experimental analysis and numerical simulation of bed elevation change in mountain rivers," by Troung An Dang and Sang Deog Park that was published in the 2016 issue is presented.