The Causes of Chronic and Transient Poverty and Their Implications for Poverty Reduction Policy in Rural China

The study focuses on two components of total poverty: chronic and transient poverty, and investigates their relative importance in total observed poverty, as well as the determinants of each components. We found that transient poverty accounts for a large proportion of total poverty observed in the poor rural areas of China. By analyzing the determinants of the two types of poverty, we found that household demographic characteristics, such as age of the head of households, family sizes, labour participation ratio, and educational level of the head of the households, are very important to the poverty status of households. These factors matter more to chronic poverty than transient poverty, and have greater impacts on the poverty measured by consumption than that measured by income. Besides the demographic factors of households, other household factors like physical stocks, the composition of income, and the amount of cultivated lands also have significant effects on both chronic and transient poverty. It is also confirmed that change in cash holding and saving and borrowing grain are used by rural households to cope with income variation and smooth their consumption. Attributes of community where the households reside are also important to poverty. With very few exceptions, we did not find that poverty programs have significant impact on poverty reduction at the households' level. We interpreted this as the poverty programs benefiting the wealthy more than the poor in a given poor area. The main reason for this could be that the implementation design of these programs fails to target the poor.Income risk, chronic poverty, transient poverty, poverty program evaluation, China

Climate change impacts on food security in Sub-Saharan Africa: Insights from comprehensive climate change scenarios

Climate change impacts vary significantly, depending on the scenario and the Global Circulation Model (GCM) chosen. This is particularly true for Sub-Saharan Africa. This paper uses a comprehensive climate change scenario (CCC) based on ensembles of 17 GCMs selected based on their relative performance regarding past predictions of temperature and precipitation at the level of 2o x 2o grid cells, generated by a recently developed entropy-based downscaling model. Based on past performance, the effects of temperature and precipitation across the 17 GCMs are incorporated into a global hydrological model that is linked with IFPRI's IMPACT water and food projections model to assess the effects of climate change on food outcomes for the region. For Sub-Saharan Africa, the paper finds that the CCC scenario predicts consistently higher temperatures and mixed precipitation changes for the 2050 period. Compared to historic climate scenarios, climate change will lead to changes in yield and area growth, higher food prices and therefore lower affordability of food, reduced calorie availability, and growing childhood malnutrition in Sub-Saharan Africa.Climate change, hydrology, crop yield, food security,

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

Marginal-based methods achieve promising performance in the synthetic data competition hosted by the National Institute of Standards and Technology (NIST). To deal with high-dimensional data, the distribution of synthetic data is represented by a probabilistic graphical model (e.g., a Bayesian network), while the raw data distribution is approximated by a collection of low-dimensional marginals. Differential privacy (DP) is guaranteed by introducing random noise to each low-dimensional marginal distribution. Despite its promising performance in practice, the statistical properties of marginal-based methods are rarely studied in the literature. In this paper, we study DP data synthesis algorithms based on Bayesian networks (BN) from a statistical perspective. We establish a rigorous accuracy guarantee for BN-based algorithms, where the errors are measured by the total variation (TV) distance or the $L^2$ distance. Related to downstream machine learning tasks, an upper bound for the utility error of the DP synthetic data is also derived. To complete the picture, we establish a lower bound for TV accuracy that holds for every $\epsilon$-DP synthetic data generator

Plastic avalanches in metal-organic framework crystals

The compressive properties of metal-organic framework (MOF) crystals are not only crucial for their densification but also key in determining their performance in many applications. We herein investigated the mechanical responses of a classic crystalline MOF, HKUST-1 by using in situ compression tests. A serrated flow accompanied by the unique strain avalanches was found in individual and contacting crystals before their final flattening or fracture with splitting cracks. The plastic flow with serrations is ascribed to the dynamic phase mixing due to the progressive and irreversible local phase transition in HKUST-1 crystals, as revealed by molecular dynamics and finite element simulations. Such pressure-induced phase coexistence in HKUST-1 crystals also induces a significant loading-history dependence of their Young's modulus. The observation of plastic avalanches in HKUST-1 crystals here not only expands our current understanding of the plasticity of MOF crystals but also unveils a novel mechanism for the avalanches and plastic flow in crystal plasticity

Characteristics of the O(1S) to O(1D) 557.7 nm green emission observed in an argon plasma jet

An extensive study on the green auroral emission characterization is presented based on a single dielectric barrier discharge geometry argon plasma jet driven by a kHz sine voltage. The plasma was generated by using 99.999% pure argon and the observed 557.7 nm green line resulted from the excited O(1S) state. An optical emission spectroscopy method using line ratios of argon was used to obtain the electron density and electron temperature under different conditions in the downstream region. The characteristics of discharge and green emission with variations in interelectrode distance, applied voltage (power) and flow rate are discussed. The spatially diffuse distribution of O(1S), owing to its long lifetime, is shown by the short exposure imaging. Two discharge regimes are presented, accompanied by two distinct branches of the green emission intensity, with a clear conclusion that the 557.7 nm emission is favored in the low electron temperature environment. In this work, the intense and diffuse green plume only forms when the downstream electron density is approximately lower than 1 × 1014 cm−3 and the electron temperature is lower than 1.1 eV. By charging the two electrodes in two opposite ways, it is shown that the green emission from oxygen is favored in the case where the electric field and the electron drift are not continuous

Characteristics of the O(1S) to O(1D) 557.7 nm green emission observed in an argon plasma jet

An extensive study on the green auroral emission characterization is presented based on a single dielectric barrier discharge geometry argon plasma jet driven by a kHz sine voltage. The plasma was generated by using 99.999% pure argon and the observed 557.7 nm green line resulted from the excited O(1S) state. An optical emission spectroscopy method using line ratios of argon was used to obtain the electron density and electron temperature under different conditions in the downstream region. The characteristics of discharge and green emission with variations in interelectrode distance, applied voltage (power) and flow rate are discussed. The spatially diffuse distribution of O(1S), owing to its long lifetime, is shown by the short exposure imaging. Two discharge regimes are presented, accompanied by two distinct branches of the green emission intensity, with a clear conclusion that the 557.7 nm emission is favored in the low electron temperature environment. In this work, the intense and diffuse green plume only forms when the downstream electron density is approximately lower than 1 × 1014 cm−3 and the electron temperature is lower than 1.1 eV. By charging the two electrodes in two opposite ways, it is shown that the green emission from oxygen is favored in the case where the electric field and the electron drift are not continuous

A BAC-NOMA Design for 6 G umMTC With Hybrid SIC: Convex Optimization or Learning-Based?

This paper presents a new backscattering communication (BackCom)-assisted non-orthogonal multiple access (BAC-NOMA) transmission scheme for device-to-device (D2D) communications. This scheme facilitates energy and spectrum cooperation between BackCom devices and cellular downlink users in 6 G ultra-massive machine -type communications (umMTC) scenarios. Given its quasi-uplink nature, the hybrid successive interference cancellation (SIC) is applied to further improve performance. The data rate of BackCom devices with high quality of service (QoS) requirements is maximized by jointly optimizing backscatter coefficients and the beamforming vector. The use of hybrid SIC and BackCom yields two non-concave sub-problems involving transcendental functions. To address this problem, this paper designs and compares convex optimization-based and unsupervised deep learning-based algorithms. In the convex optimization, the closed-form backscatter coefficients of the first sub-problem are obtained, and then semi-definite relaxation (SDR) is utilized to design the beamforming vector. On the other hand, the second sub-problem is approximated by using a combination of sequential convex approximation (SCA) and SDR. For unsupervised deep learning-based optimization, a loss function is properly designed to satisfy constraints. Computer simulations show the following instructive results: i) the superiority of the hybrid SIC strategy; ii) the distinct sensitivities and efficacies of these two algorithms in response to varying parameters; iii) the superior robustness of the unsupervised deep learning-based optimization