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Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure’s abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach.

The current study fractionated pollution sources and investigated the impacts of seasonal and tidal variations on Saigon River's surface water quality. Ninety-six water samples were collected across dry and rainy seasons, covering ebb and flood tides, and analysed for 19 parameters. During the dry season, ebb tide showed higher levels of electrical conductivity, sulfate, chloride, sodium, and potassium. During the rainy season, ebb tide exhibited greater concentrations of ammonium, total nitrogen, and phosphorus. The water quality index was significantly lower during the dry season's ebb tide compared to the flood tide but remained similar in both tidal phases during the rainy season. Four primary pollution sources were quantified, with seawater intrusion and agricultural activities contributing 62.68% and 28.54%, respectively, to water quality degradation. Briefly, the river's surface water quality was inferior during the dry season compared to the rainy season, primarily due to seawater intrusion and agricultural activities, requiring remediation. [ABSTRACT FROM AUTHOR]

Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure's abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Vietnam is located in the tropical monsoon climate, so there are many storms affecting the marine environment each year. However, Vietnam’s sea also has distinct characteristics due to the continental shelf factors, salinity, sea currents and viscosity water. In this paper, the sea weather effects to the ship in the sea area from BinhThuan province to Ca Mau province are analyzed. Specifically, wave, wind and current which are the three main factors affecting the safety of ship are thoroughly examined. Importantly, the survey parameters have been built from the actual operating environment. In addition, maintaining the stability of dynamic positioning system in Vietnam weather conditions is the main point of this study. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study is to determine the prevalence of the first lower molars that have two roots or three roots and the number of the root canals of the mandibular first molars in the Vietnamese subpopulation using cone-beam computed tomography (CBCT)., Materials and Methods: The study was conducted on 166 patients who had CBCT as indicated by dentists in Nguyen Trai Dental CT Center, Ho Chi Minh City using the Picasso Trio (Ewoo Vatech, Korea). The number of root canals of the first lower molars was examined by moving cross-sectional slices from the pulpal floor to the apex. The orifices, middle thirds, and apical thirds of the canals of the first lower molar were observed, and the root canals of each root of the mandibular first molars were observed in three planes., Results: The prevalence of two, three, and four root canals of the mandibular first molars was 4.5%, 66.8%, and 28.9%, respectively. For the distal roots of these molars, a classification of Vertucci type I was the most common at a rate of 80.8%-97.6%. Whenever these teeth had three roots, a Vertucci type I was the classification of 100% of distolingual roots., Conclusion: Majority of the mandibular first molars has two roots and three canals. CBCT is appropriate equipment useful in investigating the complex root canal morphology of human teeth., Competing Interests: There are no conflicts of interest.

Two new isoflavone glycosides, dalspinosin 7-O-beta-D-apiofuranosyl-(1-->6)-beta-D-glucopyranoside (1) and caviunin 7-O-(5-O-trans-p-coumaroyl)-beta-D-apiofuranosyl-(1-->6)-beta-D-glucopyranoside (2), and two known compounds, caviunin 7-O-beta-D-apiofuranosyl-(1-->6)-beta-D-glucopyranoside (3) and caviunin (4) were isolated from the stems of Dalbergia vietnamensis. Their structures were determined by the combination of spectroscopic and chemical methods, including 1D- and 2D-NMR spectroscopy, as well as by comparing with the NMR data reported in the literature.

One new flavonol glycoside, 4'-O-methylmyricitrin 3'-O-beta-D-glucopyranoside (1), one new sesquiterpene, reynoudiol (11), as well as the 12 known compounds (2-10, 12-14) quercetin 3-O-methyl ether (2), quercitrin (3), isorhamnetin 3-alpha-L-rhamnopyranoside (4), tamarixetin 3-alpha-L-rhamnopyranoside (5), myricitrin (6), 4'-O-methylmyricitrin (7), isorhamnetin 3-O-beta-D-xylopyranosyl (1-2)-O-beta- D-glucopyranoside (8), isorhamnetin 3-O-beta- D-apiofuranosyl(1-2)-O-beta- D-glucopyranoside (9), (+)-catechin (10), 7-drimene-3,11,12-triol (12), clovane-2 beta,9 alpha-diol (13), and a-cadinol (14), were isolated from the methanol extract of Reynoutria japonica roots. Based on in vitro screening of the anti-influenza activity of the isolated compounds, reynoudiol showed significantly higher activity than that of oseltamivir phosphate at the same concentration, and did not induce any detectable cytopathic effect in MDCK cells. The CC50 of reynoudiol was above 50 micro M and could inhibit influenza virus infection with an IC50 of 0.29 +/- 0.01 microM. The therapeutic index (TI) of reynoudiol against influenza infection was 172.4, and thus, this compound can be potentially used to treat oseltamivir-resistant influenza virus infection.