Efficiency and risk in sustaining China’s food production and security: Evidence from micro-level panel data analysis of Japonica rice production

Sustainable food production and food security are always challenging issues in China. This paper constructs a multi-element two-level constant-elasticity-of-substitution (CES) model to assess technological progress in, and its contribution to, japonica rice production in China. The results show that the speed of technological progress in the production of japonica rice on average was 0.44% per annum in 1985–2013, and technological progress has contributed significantly to the growth of japonica rice production in China. Robustness checks show that the results appear to be sensitive to which sub-sample is used. Labour and some other inputs are found to be significant but negative, especially during the middle sampling period of 1994–2006 and in eastern and western regions. This has important policy implications on the impact of rural-to-urban migration and farmers’ human development. View Full-Tex

Low Carbon Energy Policy Research

AbstractCase study of Korea, Low carbon energy efficiency labeling schemes (Energy Efficiency Label and Standard Program, High efficiency Appliance Certification Program, e-Standby Program) play a key role in carrying out the energy efficiency improvement policy in the appliances and equipment sector in Korea. Korea operates these Programs in an effort to improve energy efficiency in appliances and equipments. Mandatory energy efficiency standard which bans production and sales of low energy efficiency products which fall below the minimum energy performance standard. Ministry of Knowledge of Economy (MKE) and Korea Energy Management Corporation (KEMCO) is the key organizations in implementing energy efficiency standards and labeling. National energy efficiency efforts can be realized through energy efficiency improvements with the successful implementation of an energy efficient appliances dissemination policy and the phase out of low efficiency appliances. Through the implementation of the Energy Efficiency Label and Standard Program (1992), High-efficiency Appliance Certification Program (1996) and e-Standby Program (1999), significant energy efficiency improvements have been achieved, and 1.37 billion USD worth of energy savings

A Cotransformation Method To Identify a Restriction-Modification Enzyme That Reduces Conjugation Efficiency in Campylobacter jejuni

Conjugation is an important mechanism for horizontal gene transfer in Campylobacter jejuni, the leading cause of human bacterial gastroenteritis in developed countries. However, to date, the factors that significantly influence conjugation efficiency in Campylobacter spp. are still largely unknown. Given that multiple recombinant loci could independently occur within one recipient cell during natural transformation, the genetic materials from a high-frequency conjugation (HFC) C. jejuni strain may be cotransformed with a selection marker into a low-frequency conjugation (LFC) recipient strain, creating new HFC transformants suitable for the identification of conjugation factors using a comparative genomics approach. To test this, an erythromycin resistance selection marker was created in an HFC C. jejuni strain; subsequently, the DNA of this strain was naturally transformed into NCTC 11168, an LFC C. jejuni strain, leading to the isolation of NCTC 11168-derived HFC transformants. Whole-genome sequencing analysis and subsequent site-directed mutagenesis identified Cj1051c, a putative restriction-modification enzyme (aka CjeI) that could drastically reduce the conjugation efficiency of NCTC 11168 (\u3e5,000-fold). Chromosomal complementation of three diverse HFC C. jejuni strains with CjeI also led to a dramatic reduction in conjugation efficiency (∼1,000-fold). The purified recombinant CjeI could effectively digest the Escherichia coli-derived shuttle vector pRY107. The endonuclease activity of CjeI was abolished upon short heat shock treatment at 50°C, which is consistent with our previous observation that heat shock enhanced conjugation efficiency in C. jejuni. Together, in this study, we successfully developed and utilized a unique cotransformation strategy to identify a restriction-modification enzyme that significantly influences conjugation efficiency in C. jejuni

Precommitted Investment Strategy versus Time-Consistent Investment Strategy for a Dual Risk Model

We are concerned with optimal investment strategy for a dual risk model. We assume that the company can invest into a risk-free asset and a risky asset. Short-selling and borrowing money are allowed. Due to lack of iterated-expectation property, the Bellman Optimization Principle does not hold. Thus we investigate the precommitted strategy and time-consistent strategy, respectively. We take three steps to derive the precommitted investment strategy. Furthermore, the time-consistent investment strategy is also obtained by solving the extended Hamilton-Jacobi-Bellman equations. We compare the precommitted strategy with time-consistent strategy and find that these different strategies have different advantages: the former can make value function maximized at the original time t=0 and the latter strategy is time-consistent for the whole time horizon. Finally, numerical analysis is presented for our results

Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification.

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straight forward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification

Precommitted Investment Strategy versus Time-Consistent Investment Strategy for a General Risk Model with Diffusion

We mainly study a general risk model and investigate the precommitted strategy and the time-consistent strategy under mean-variance criterion, respectively. A lagrange method is proposed to derive the precommitted investment strategy. Meanwhile from the game theoretical perspective, we find the time-consistent investment strategy by solving the extended Hamilton-Jacobi-Bellman equations. By comparing the precommitted strategy with the time-consistent strategy, we find that the company under the time-consistent strategy has to give up the better current utility in order to keep a consistent satisfaction over the whole time horizon. Furthermore, we theoretically and numerically provide the effect of the parameters on these two optimal strategies and the corresponding value functions

A NUMERICAL SIMULATION OF POOL BOILING USING CAS MODEL

ABSTRACT This paper presents a new numerical model, called the CAS model, for boiling heat transfer. The CAS model is based on the cellular automata technique that is integrated into the popular-SIMPLER algorithm for CFD problems. In the model, the cellular automata technique deals with the microscopic nonlinear dynamic interactions of bubbles while the traditional CFD algorithm is used to determine macroscopic system parameters such as pressure and temperature. The popular SIMPLER algorithm is employed for the CFD treatment. The model is then employed to simulate a pool boiling process. The computational results show that the CAS model can reproduce most of the basic features of boiling and capture the fundamental characteristics of boiling phenomena. The heat transfer coefficient predicted by the CAS model is in excellent agreement with the experimental data and existing empirical correlations

Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution

Due to the limitations of hyperspectral imaging systems, hyperspectral imagery (HSI) often suffers from poor spatial resolution, thus hampering many applications of the imagery. Hyperspectral super-resolution refers to fusing HSI and MSI to generate an image with both high spatial and high spectral resolutions. Recently, several new methods have been proposed to solve this fusion problem, and most of these methods assume that the prior information of the Point Spread Function (PSF) and Spectral Response Function (SRF) are known. However, in practice, this information is often limited or unavailable. In this work, an unsupervised deep learning-based fusion method - HyCoNet - that can solve the problems in HSI-MSI fusion without the prior PSF and SRF information is proposed. HyCoNet consists of three coupled autoencoder nets in which the HSI and MSI are unmixed into endmembers and abundances based on the linear unmixing model. Two special convolutional layers are designed to act as a bridge that coordinates with the three autoencoder nets, and the PSF and SRF parameters are learned adaptively in the two convolution layers during the training process. Furthermore, driven by the joint loss function, the proposed method is straightforward and easily implemented in an end-to-end training manner. The experiments performed in the study demonstrate that the proposed method performs well and produces robust results for different datasets and arbitrary PSFs and SRFs