カドキ ニ オケル ベトナム トチ セイサク

政治問題の性格を有する土地政策は世界中の国民、とりわけ発展途上国の国民の社会・経済的成長に影響を及ぼしてきている。2004年の世銀報告書は「土地は着実な成長、効果的な国民経済の運営、都市・農村の全ての人々、とりわけ貧しい人々、に対して開かれた福祉・経済機会の提供に関して欠かすことのできない役割を果たしている」と指摘した。本論文は、土地政策が農民、経済社会にもたらした成果を評価するために、1981年から今日までの過渡期ベトナムにおける土地政策を簡潔に述べると同時に、近い将来に向けた土地政策の改善に寄与できるような政策課題を明確にすることを目的としている。Land, a kind of political issue, has influenced on socio-economic growth of all the nations in the world, especially in the developing countries. Policy Research of World Bank (2004) reported as follows: Land plays an indispensable role to the steady growth, the effective national management, welfare and economic opportunities opening for all rural and urban people, particularly for the poor. The aim of the article is to summarize land policy in the transition period in Vietnam from 1981 till now in order to evaluate achievements that the policy creates towards farmers, economy and society and at the same time to identify shortcomings of the policy so it can be changed or adjusted better in the near future

Marine algal species and their distribution in Phu Quoc marine protected area

This article presents the raw data in relation to the status of, and the distribution of, the 41 marine algal species occurring around and within the An Thoi coral reef strictly protected zone, Phu Quoc Marine Protected Area. The data, which were collected in May 2017, include the detailed description of the locations, the oceanographical conditions, and the photographs of the 41 marine algal species. For more insight, please see “Marine algal species and marine protected area management: A case study in Phu Quoc, Kien Giang, Vietnam” Huynh and Nguyen, 2019

Vietnamese Version of Cornell Scale for Depression in Dementia at an Outpatient Memory Clinic: A Reliability and Validity Study

Background: In Vietnam, there has been, currently, no standardized tool for depression assessment for people with dementia (PWD). Cornell Scale for Depression in Dementia (CSDD) is a widely studied and used scale for PWD worldwide. Objectives: The aim of this study was to standardize the Vietnamese version of the CSDD (V-CSDD) in depression assessment in PWD through reliability and validity examination. Methods: V-CSDD was rated in terms of reliability and validity with gold standard regarding "major depressive episode"and "major depressive-like episode"of DSM-5. Cronbach's α, ICC, exploratory factor analysis (EFA), and receiver operating characteristic analysis were performed. Results: V-CSDD was found to have a high internal consistency reliability (Cronbach's α = 0.80), inter-rater reliability at sound ranking (ICC = 0.89; 95% CI = 0.81-0.94), maximum cut-off mark of 13 (sensitivity = 70%, specificity = 92%), and EFA, which suggested that V-CSDD may comprise 5 factors. Conclusions: Results indicate the V-CSDD to be a reliable and valid assessment and to be beneficial in classifying and diagnosing depression in dementia outpatients in clinical contexts

Ecological Engineering and Restoration of Eroded Muddy Coasts in South East Asia: Knowledge Gaps and Recommendations

Ecological engineering (EE) was employed for developing strategies for stabilizing eroded muddy coasts (EMCs). However, there was a limited analysis of these EE strategies with respect to design, performance, and lessons learned. This study employed a critical review for addressing the limitations. There were four EE models designed with different restoration interventions for stabilizing EMCs. The models using active interventions have not been cost-effective in controlling erosion because the interventions failed to achieve their goals or were costly and unnecessary. Of the two passive intervention models, the one with structures constructed from onshore proved to be more cost-effective in terms of construction costs, the survival rate of transplanted seedlings, and levels of sea mud accumulation. Interventions with adequate consideration of the muddy coastal ecological processes and the ecological reasoning for the positioning of these interventions play a crucial role in stabilizing EMCs. A passive restoration model using gradually expanded interventions should be promoted in order to ensure sustainable management of EMCs in the future

Modeling of parallel power MOSFETs in steady-state

In high-power applications, multiple power MOSFETs are connected in parallel and treated as a single switch in order to handle much larger total currents. In this paper, a parallel power MOSFETs model from the turnoff state until they reach their steady state is introduced. The model represents the relationship between each power MOSFET's gate voltage and the current distribution among them. The study's key purpose is to use the model for dealing with the asymmetry in sharing current and power loss between these semiconductor devices during the steady state region.Comment: 10 pages, 7 figures, The 2023 INTERNATIONAL SYMPOSIUM ON ADVANCED ENGINEERING (ISAE2023

Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks

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

Impact of Frequency Shift on Nonlinear Compensation Using Optical Phase Conjugation for M-QAM Signals

Nonlinear compensation using optical phase conjugation (OPC) have been considered a promising technique to increase the reach of high-speed fiber-optic transmission systems. OPC-based nonlinear compensation employs an optical phase conjugation located at a middle of the fiber link to generate a complexed conjugated signal with respect the signal in the first half of the link for propagation in the second half. OPC technique assumes a symmetry for signal propagating in the first and second half to obtain a perfect nonlinear and chromatic dispersion. However, as most of practical OPC schemes are realized by nonlinear effects such as four-wave mixing or a combination of second-harmonic generation and difference frequency generation, the frequency shift induced by OPC affects the signal symmetrical requirement for nonlinear compensation because the chromatic dispersion is different for the first and second half transmissions. In this paper, we investigate the impact of frequency shift on the nonlinear compensation using OPC for high symbol rate, high level modulation format signals. This will be important to understand the tolerance of the OPC techniques against such a practical condition for actual system implementations

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

Approximation solution for steel concrete beam accounting high-order shear deformation using trigonometric-series

Steel concrete beams have a reasonable structure in terms of using material and high load carrying capacity. This paper deals with an approximate solution based on a trigonometric series for the static of steel concrete beams. The displacement field is based on the higher-order theory using Reddy’s hypothesis. The governing equations are derived from variation principles. An approximate solution based on the representation of displacement fields by trigonometric series is developed to solve the static problem of steel concrete beams. In order to verify the accuracy of the present approximate solution, numerical results are compared with those of exact solutions using classical beam theory. The displacements and nominal stress distributions in the depth direction are obtained with various high of beams. The present approximate approach can accurately predict the displacements and stresses of steel concrete beams