Improved Three-Component Decomposition Technique for Forest Parameters Estimation from PolInSAR Image

Polarimetric SAR interferometry (PolInSAR) is an efficient remote sensing technique that allows to extract forest heights by means of model-based inversion. Recently, there have been plenty of researches on the retrieval of vegetation parameters by single frequency single baseline PolInSAR, such as the ESPRIT method and three-stage inversion method. However, these methods have several shortcomings which tend to underestimate the forest height due to attenuations of the electromagnetic waves in the ground medium. In order to overcome these shortcomings, an improved three-component decomposition technique using PolInSAR image is proposed in this paper. By means of coherence set and a Newton-Raphson method, the proposed method improves the accuracy of forest height estimation. The proposed algorithm performance is evaluated with simulated data from PolSARProSim software and L-band PolInSAR image pair of Tien-Shan test site which is acquired by the SIR-C/X-SAR system

Secrecy performance of underlay cooperative cognitive network using non-orthogonal multiple access with opportunistic relay selection

In this paper, an underlay cooperative cognitive network using a non-orthogonal multiple access (UCCN-NOMA) system is investigated, in which the intermediate multiple relays help to decode and forward two signals x1 and x2 from a source node to two users D-1 and D-2, respectively, under wiretapping of an eavesdropper (E). We study the best relay selection strategies by three types of relay selection criteria: the first and second best relay selection is based on the maximum channel gain of the links Ri-D1, Ri-D-2, respectively; the third one is to ensure a minimum value of the channel gains from the Ri-E link. We analyze and evaluate the secrecy performances of the transmissions x1 and x2 from the source node to the destination nodes D-1, D-2, respectively, in the proposed UCCN-NOMA system in terms of the secrecy outage probabilities (SOPs) over Rayleigh fading channels. Simulation and analysis results are presented as follows. The results of the (sum) secrecy outage probability show that proposed scheme can realize the maximal diversity gain. The security of the system is very good when eavesdropper node E is far from the source and cooperative relay. Finally, the theoretical analyses are verified by performing Monte Carlo simulations.Web of Science113art. no. 38

Framing Analysis of Belt and Road Initiative

In 2013, Chinese president, Xi Jinping, announced the Belt and Road Initiative (BRI) to his audience in Kazakhstan. He stated that this will involve the construction of roads, railways, and even special trade corridors, among others along the ancient Silk Road in a bid to foster economic, political, and social relationship between China and partner countries. This paper focuses on analyzing how leading newspapers in Nigeria, Malaysia, and Vietnam, namely The Sun, Vanguard, The Punch, The Nation, NewStraits Times, Malay Mail, Business Insider, The Star, Saigon Times and Vietnamnet Bridge, have framed and communicated this multi-national project to their various audiences six years after Xi’s announcement. Working on 200 editorial contents published between May 2017 and March 2019 across the selected newspapers, this explores how they framed BRI. We found that while most of the reports have framed BRI positively, others are framed to reflect cautious optimism. We suggest that BRI managers should take necessary steps to engage the media, policy makers, and other stakeholders to properly educate them on the vision and mission of the initiative

Effects of Titanium Dioxide nanoparticles on salinity tolerance of rice (Oryza sativa L.) at the seedling stage

This study aimed to evaluate the effects of titanium dioxide nanoparticles on the salinity tolerance of rice. The effects of five nano titanium dioxide concentrations (0 mg/L, 25 mg/L, 50 mg/L, 75 mg/L, and 100 mg/L) on the physiological and biochemical parameters of rice were evaluated. The results showed that among three rice varieties (ST24, ST25, OM18), only ST25 grew in a better manner with the application of TiO2 nanoparticles and the optimal concentration of TiO2 nanoparticles was 50 mg/mL. It increased the shoot height by 20.07% and the survival rate of rice compared to the control. These growth-promoting effects were simultaneous with increased levels of chlorophyll, carotenoid and proline. The activities of antioxidant enzymes were improved. While activities of enzymes catalase and peroxidase increased significantly, no change in the activities of ascorbate peroxidase was observed. Finding of this study showed that titanium dioxide nanoparticles increased the salinity tolerance of rice by promoting the photosynthetic and anti-oxidative processes in rice seedlings

Chemical components and biological properties from acetone extracts of Conamomum vietnamense

Conamomum vietnamense is an endemic and rare species from Vietnam. The aim of this study is to determine the chemical compositions, antibacterial and antioxidant properties of the acetone extracts obtained from the different organs of this species for the first time. A total of 82 components were identified from the acetone extracts of leaf, flower, and rhizome of C. vietnamense using Gas chromatography–mass spectrometry (GC/MS) technique. Furthermore, the agar disk-diffusion method was also used to determine the antibacterial activity of the C. vietnamense extracts. Accordingly, the leaf extract was found to be effective against eight out of nine bacterial strains while the flower and rhizome extracts displayed activity against four out of nine tested bacteria. In addition, the three organs of C. vietnamense also possessed the high DPPH scavenging properties. The results of this study indicate that C. vietnamense extracts have the potential to be developed into pharmaceutical products in the future

Gene Family Abundance Visualization based on Feature Selection Combined Deep Learning to Improve Disease Diagnosis

Advancements in machine learning in general and in deep learning in particular have achieved great success in numerous fields. For personalized medicine approaches, frameworks derived from learning algorithms play an important role in supporting scientists to investigate and explore novel data sources such as metagenomic data to develop and examine methodologies to improve human healthcare. Some challenges when processing this data type include its very high dimensionality and the complexity of diseases. Metagenomic data that include gene families often have millions of features. This leads to a further increase of complexity in processing and requires a huge amount of time for computation. In this study, we propose a method combining feature selection using perceptron weight-based filters and synthetic image generation to leverage deep-learning advancements in order to predict various diseases based on gene family abundance data. An experiment was conducted using gene family datasets of five diseases, i.e. liver cirrhosis, obesity, inflammatory bowel diseases, type 2 diabetes, and colorectal cancer. The proposed method provides not only visualization for gene family abundance data but also achieved a promising performance level

Physiochemical properties, antibacterial and antioxidant activities of Terminalia catappa seed oils from two extracting processes

Terminalia catappa is a widespread medium tree species in many tropical countries. While the majority of the studies up to date focuses on the aerial part of the plant such as leaf, stem bark and fruit, information about the phytochemical property as well as the biological property of the edible seed is still scarce. This study was the first to explore the fatty acid composition, antibacterial and antioxidant activities of the seed oil from T. catappa grown in Vietnam. The results showed that both the hot-pressed and cold-pressed oils contained a high level of unsaturated fatty acids such as oleic (~32%) and linoleic acids (28.38%-29.2%), as well as saturated fatty acids such as palmitic acid (~33.3%-33.61%). The presence of eicosadienoic acid in T. catappa seed oils was reported in this study for the first time. These oils displayed antibacterial activity against 5 out of 12 tested strains such as Bacillus cereus, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa and Vibrio parahaemolyticus. The antioxidant activity of the oils was also recorded by DPPH radical scavenging assays with IC50 values of 950 µg/ml and 2529 µg/ml for cold-pressed oil and hot-pressed oil respectively. This study has provided promising extracting methods and resulted in oils that could be good candidates for developing food sources with valuable fatty acids, antioxidant and antibacterial capacities against both Gram-positive and negative bacteria in the human diet

Characterization of multilayered carbon-fiber–reinforced thermoplastic composites for assembly process

The aim of this research work is to characterize the mechanical behavior of multilayered carbon-fiber–reinforced polyphenylene sulfide composites with the application to assembly process of nonrigid parts. Two anisotropic hyperelastic material models were investigated and implemented in Abaqus as a user-defined material. An inverse characterization method was applied to identify the parameters of these material models. Finite element simulations at finite strains of a flexible composite sheet were carried out. Numerical results of sheet deformation were compared with the experimental results in order to evaluate the appropriateness of the material models developed for this application

Inverse procedure for mechanical characterization of multi-layered non-rigid composite parts with applications to the assembly process

In assembly process, non-rigid parts in free-state may have different forms compared to the designed model caused by gravity load and residual stresses. For non-rigid parts made by multi-layered fiber-reinforced thermoplastic composites, this process becomes much more complex due to the nonlinear behavior of the material. This paper presented an inverse procedure for characterizing large anisotropic deformation behavior of four-layered, carbon fiber-reinforced polyphenylene sulphide, non-rigid composite parts. Mechanical responses were measured from the standard three points bending test and the surface displacements of composite plates under flexural loading test. An orthotropic hyperelastic material model was implemented as a UMAT user routine in the Abaqus/Standard to analyze the behavior of flexible fiber-reinforced thermoplastic composites. Error functions were defined by subtracting the experimental data from the numerical mechanical responses. Minimizing the error functions helps to identify the material parameters. These optimal parameters were validated for the case of an eight-layered composite material