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The rapid development of animal husbandry has resulted in serious pollution issues in the livestock and poultry breeding industry, increasing the cost of environmental management. This issue is particularly prominent in China due to its rapid economic development, significant domestic consumption, and aggressive carbon neutrality targets. This study analyses pollution emissions and spatial-temporal variation in China's cattle breeding industry. Using an emission coefficient method and panel data of 31 Chinese provinces/municipalities between 2002 and 2017, we measure the total volume of pollutant emissions from China's cattle breeding industry and five major pollutants: chemical oxygen demand, total nitrogen, total phosphorus, copper, and zinc. We also analyse the dynamic variation of the spatial distribution. The results show that both the total emissions volume and emissions of the five major pollutants have decreased to different extents, among which chemical oxygen demand has decreased the fastest. Spatial divergence is strengthened as the heavy pollution areas have moved from the southeast to the northwest of the country. This study contributes to current research by its focus on the cattle breading industry and by our improvements to the pollutant emission measurement method. • The pollutant emissions brought by cattle breeding has been significant in China. • The emissions show a decreasing trend in total. • Specific five types of major pollutant emissions are examined further. • There is a spatial transferring of emissions from the east to the northwest of China. [ABSTRACT FROM AUTHOR]

Establishing an accurate edge server power model is helpful for resource providers to predict and optimize power consumption within edge data centers. Considering the fact that the accuracy of the previous energy consumption model is easily affected by the workload types, this paper develops an edge server power model based on BP (back propagation) neural network and feature selection, which is denoted by DSBF. For different task types, DSBF leverages "principal component analysis (PCA)" to analyze the contribution of each energy consumption parameter and selects "representative parameter", and then builds a power model based on BP neural network. In contrast to other power models, DSBF can effectively handle the variable workload. To measure the effectiveness of the DSBF model, a series of experiments were conducted. The results suggest that compared with other energy consumption models, DSBF can better adapt to the changing workload and has advantages in predicting the accuracy of the energy consumption model. • Designing the power modeling process, including parameter selection, parameter analysis and screening, model training and evaluation. • Using the "principal component analysis (PCA)" to analyze the factor contribution of each parameter. • Building a new power model named DSBF based on BP neural network and feature selection. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, secure search has become a hot research spot, which is a promising technique that allows a data user to perform privacy-preserving keyword-based search over encrypted cloud data. In this paper, we further consider the secure search problem based on a practical application scenario that a data owner needs to grant different keyword query permissions for different data users to achieve flexible access control on outsourced encrypted data in the cloud computing environment. To address this problem, we propose a fine-grained authorized keyword secure search scheme by leveraging the ciphertext policy attribute-based encryption (ABE), which not only supports privacy-preserving keyword-based search over encrypted data, but also inherits flexible and fine-grained data privilege control properties of ABE. Moreover, our proposed scheme is able to achieve fine-grained search permission update with very small communication and computation cost. By running the attribute revocation sub-protocol and attribute addition sub-protocol, the data owner can flexibly and efficiently update a data user's keyword search permissions when the data user's system role changes. We provide detailed performance analysis and rigorous security proof for our scheme. Extensive experiments demonstrate the correctness and practicality of the proposed scheme. • Presented a fine-grained authorized keyword secure search scheme in cloud computing. • Presented an efficient and fine-grained search permission update protocol. • Analyzed and proved the security of the proposed scheme. • Analyzed the complexity and tested the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]