Intensive livestock and poultry breeding can supply meat products and increase farmers' income. However, it may cause high resource losses (RL) and environmental deterioration if its wastes are not handled correctly. Therefore, sustainable development of scale-breeding industry is very important. In this article, the sustainable development mechanism of livestock and poultry scale breeding was analyzed from the perspective of joint control of RL and external environmental costs (EEC). Further, the estimated models of EEC and RL were built and the sustainable development conditions were discussed. Correspondingly, the internal driving force and external constraining force for the scale-breeding sustainable development were analyzed empirically. The results show that based on the empirical analysis the production activities of all sample farms result in EEC and RL. All sample farms are in profitable state with traditional profit analysis method. If resource consumption and environment effects are considered, the actual profits of most farms decreased greatly. After some measures are taken to control EEC and RL, the number of farms under deficit decreased and the profit of many farms increase by more than 20%. The number of farms in the sustainable development conditions rises by 30%. The joint control of RL and EEC is an effective way to facilitate the sustainability of scale-breeding industry.

This paper proposes a novel high-quality nonparallel many-to-many voice conversion method based on transitive star generative adversarial networks with adaptive instance normalization (Trans-StarGAN-VC with AdaIN). First, we improve the structure of generator with TransNets to make full use of hierarchical features associated with speech naturalness. In TransNets, many shortcut connections share hierarchical features between encoding and decoding part to capture sufficient linguistic and semantic information, which helps to provide natural sounding converted speech and accelerate the convergence of training process. Second, by incorporating AdaIN for style transfer, we enable the generator to learn sufficient speaker characteristic information directly from speech instead of using attribute labels, which also provides a promising framework for one-shot VC. Objective and subjective experiments with nonparallel training data show that our method significantly outperforms StarGAN-VC in both speech naturalness and speaker similarity. The mean values of mean opinion score (MOS) and ABX are increased by 24.5% and 10.7%, respectively. The comparison of spectrogram also shows that our method can provide more complete harmonic structures and details, and effectively bridge the gap between converted speech and target speech.

Scale-breeding family farms can meet the demand of meat market and increase the farmers’ income. However, they may cause serious environmental problems to some extent if the waste is not properly handled. In this paper, the appropriate scale of scale breeding family farms was studied from the circular economy theory. Results show that the resource loss rate of middle-scale family farms is lowest among three kinds of scale-breeding family farms. Therefore, the middle-scale should be chosen as optimistic size for sustainable development of agricultural economy in China.

Shear-wave splitting (SWS) analysis is used to predict fractures in subsurface media. Specifically, two parameters relevant to SWS analysis (the azimuth of the fast shear wave and the time delay between the fast and slow shear waves) are used to quantify the main azimuth and degree of the fracture development, respectively. However, the algorithms of SWS analysis using a grid search have relatively low computational efficiency, as they need to calculate the objective function values of all grid points. To improve the efficiency of SWS analysis, we proposed new algorithms using the gradient descent, Newton, and advance-retreat methods. The new methods use the direction of the fastest gradient descent, the intersection points of the tangent plane of the first-order objective function with the zero plane, and narrowing the range of extremum points to determine the search path. Therefore, this removes the necessity to compare all grid points in the value region. We compared the three methods and the rotation-correlation method, and both synthetic and field data tests indicated that all three methods had higher computational efficiency than the traditional grid search method. Among the proposed methods, the gradient-descent method obtained the most accurate results for both synthetic and field data. Our study shows that SWS analysis combined with the gradient-descent method can accurately and efficiently obtain SWS parameters for fracture prediction.