Contamination of Selected Persistent Organic Pollutants (POPs) in Sediment of Some Areas in Vietnam

This chapter evaluates the contamination of selected persistent organic pollutants (S-POPs) in the sediment of some typical areas in Vietnam. S-POPs are composed of dichlorodiphenyltrichloroethanes (DDTs), hexachlorocyclohexanes (HCHs), polychlorinated biphenyl (PCBs), and polybrominated diphenyl ethers (PBDEs). The collected data and analyzed results indicated the wide occurrence of significant S-POPs residues in studied areas. The main sources of S-POPs are discussed by using composition analyses and diagnostic ratios of S-POPs indicator. Ecotoxicological risk of S-POPs is assessed. The obtained results have contributed to the assessment of S-POPs fate in the environmental sediment in Vietnam

Residue of Selected Persistent Organic Pollutants (POPs) in Soil of Some Areas in Vietnam

This chapter evaluates the contamination of selected persistent organic pollutants (S-POPs) in soil of some typical areas in Vietnam (mangrove forest, industrial, and urban areas in northern part). S-POPs are composed of polychlorinated biphenyls (PCBs) and polyaromatic hydrocarbons (PAHs). The collected data and analyzed results indicated the wide occurrence of significant S-POPs residues in study areas. The main sources of S-POPs are discussed by using composition analyses and diagnostic ratios of S-POPs indicator. Risk assessment of S-POPs in soil is assessed by using the guidance of the US Environmental Protection Agency. The obtained results have contributed to assess the S-POPs fate in the soil environment in Vietnam

Lignin and Cellulose Extraction from Vietnam’s Rice Straw Using Ultrasound-Assisted Alkaline Treatment Method

The process of cellulose and lignin extraction from Vietnam’s rice straw without paraffin pretreatment was proposed to improve economic efficiency and reduce environmental pollution. Treatment of the rice straw with ultrasonic irradiation for 30 min increased yields of lignin separation from 72.8% to 84.7%. In addition, the extraction time was reduced from 2.5 h to 1.5 h when combined with ultrasonic irradiation for the same extraction yields. Results from modern analytical methods of FT-IR, SEM, EDX, TG-DTA, and GC-MS indicated that lignin obtained by ultrasound-assisted alkaline treatment method had a high purity and showed a higher molecular weight than that of lignin extracted from rice straw without ultrasonic irradiation. The lignin and cellulose which were extracted from rice straw showed higher thermal stability with 5% degradation at a temperature of over 230°C. The ultrasonic-assisted alkaline extraction method was recommended for lignin and cellulose extraction from Vietnam’s rice straw

Deep Learning-Aided Multicarrier Systems

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity

Deep Neural Network-Based Detector for Single-Carrier Index Modulation NOMA

In this paper, a deep neural network (DNN)-based detector for an uplink single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA) system is proposed, where SC-IM-NOMA allows users to use the same set of subcarriers for transmitting their data modulated by the sub-carrier index modulation technique. More particularly, users of SC-IMNOMA simultaneously transmit their SC-IM data at different power levels which are then exploited by their receivers to perform successive interference cancellation (SIC) multi-user detection. The existing detectors designed for SC-IM-NOMA, such as the joint maximum-likelihood (JML) detector and the maximum likelihood SIC-based (ML-SIC) detector, suffer from high computational complexity. To address this issue, we propose a DNN-based detector whose structure relies on the model-based SIC for jointly detecting both M-ary symbols and index bits of all users after trained with sufficient simulated data. The simulation results demonstrate that the proposed DNN-based detector attains near-optimal error performance and significantly reduced runtime complexity in comparison with the existing hand-crafted detectors

Sub-optimal Deep Pipelined Implementation of MIMO Sphere Detector on FPGA

Sphere detector (SD) is an effective signal detection approach for the wireless multiple-input multiple-output (MIMO) system since it can achieve near-optimal performance while reducing significant computational complexity. In this work, we proposed a novel SD architecture that is suitable for implementation on the hardware accelerator. We first perform a statistical analysis to examine the distribution of valid paths in the SD search tree. Using the analysis result, we then proposed an enhanced hybrid SD (EHSD) architecture that achieves quasi-ML performance and high throughput with a reasonable cost in hardware. The fine-grained pipeline designs of 4 × 4 and 8 × 8 MIMO system with 16-QAM modulation delivers throughput of 7.04 Gbps and 14.08 Gbps on the Xilinx Virtex Ultrascale+ FPGA, respectively

Challenges in Employing BASEL II at Military Commercial Joint Stock Bank

This paper is conducted for examining the framework for risk management in the Basel II accord, the Basel II risk management model at the Military Commercial Joint Stock Bank. Data were collected from annual reports for the period from 2015 to 2017 of the Military Commercial Joint Stock Bank. The results show that the implementation of risk management under Basel II at Military Bank still faces many difficulties in the pressure of capital increase, database system, human resource quality, and cost of implementation. The study suggests some solutions for Military Bank to implement successfully Basel II, emphasizing the role of human resource quality, modernizing the data system and the specific mechanism for raising capital. The results of this research is a reference for Vietnamese commercial banks in identifying, controlling and responding various risks in banking activities in the context of Vietnam in particular and in emerging countries in general. Keywords: Basel II, Risk management, Military Ban

Current Development in Lead-Free Bi

The lead-free piezoelectric ceramics display good piezoelectric properties which are comparable with Pb(Zr,Ti)O3 (PZT) and these materials overcome the hazard to the environment and human health. The Bi0.5(Na,K)0.5TiO3 (BNKT) is rapidly developed because of good piezoelectric, ferroelectric, and dielectric properties compared to PZT. The origin of giant strain of BNKT piezoelectric materials was found at morphotropic phase boundary due to crystal change from tetragonal to orthorhombic and/or precipitation of cubic phases, in addition to domain switching mechanism. The dopants or secondary phases with ABO3 structure as solid solution are expected to change the crystal structure and create the vacancies which results in enhancement of the piezoelectric properties. In this work, we reviewed the current development of BNKT by dopants and secondary phase as solid solution. Our discussion will focus on role of dopants and secondary phase to piezoelectric properties of BNKT. This result will open the direction to control the properties of lead-free piezoelectric materials

TiO 3 -Based Piezoelectric Materials

The lead-free piezoelectric ceramics display good piezoelectric properties which are comparable with Pb(Zr,Ti)O 3 (PZT) and these materials overcome the hazard to the environment and human health. The Bi 0.5 (Na,K) 0.5 TiO 3 (BNKT) is rapidly developed because of good piezoelectric, ferroelectric, and dielectric properties compared to PZT. The origin of giant strain of BNKT piezoelectric materials was found at morphotropic phase boundary due to crystal change from tetragonal to orthorhombic and/or precipitation of cubic phases, in addition to domain switching mechanism. The dopants or secondary phases with ABO 3 structure as solid solution are expected to change the crystal structure and create the vacancies which results in enhancement of the piezoelectric properties. In this work, we reviewed the current development of BNKT by dopants and secondary phase as solid solution. Our discussion will focus on role of dopants and secondary phase to piezoelectric properties of BNKT. This result will open the direction to control the properties of lead-free piezoelectric materials

Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector