Abstract Private information retrieval (PIR) means a user retrieves a file while revealing no information on which file is being retrieved. In classic PIR, the user needs to wait for the responses of all the storage nodes. However, in general, there may be some nodes that are unresponsive, in which case it is formulated as v ‐robust PIR in the network coding (NC) structured distributed storage system (DSS), where v denotes the number of unresponsive nodes. We consider PIR with unresponsive nodes under the framework of NC structured DSS. In particular, the storage nodes adopt combination property with zigzag decodable (CP‐ZD) storage code since it has the advantage of extremely low decoding complexity, which is promised by zigzag decoding (ZD) within a binary field. We design a v ‐robust PIR scheme with its download communication cost slightly larger than existing studies, but its decoding complexity is significantly reduced when compared with existing studies.

This paper investigates structural variation of agricultural productivity growth in China using a multiple-frontier approach. Agricultural productivity gain in Sichuan province is decomposed into technological progress, technical, mix and scale efficiency changes in terms of economic and environmental performance. The results show that agricultural Luenberger productivity indicator in Sichuan presents a significant increase trend with annual growth rate of 2.5% over the period of 1997-2017. The agricultural green growth in Sichuan is most driven by technological progress. Of the decline in the OI changes, over 90% results from the decline of environmental inefficiency. The technological progress is the main driven force of the economic growth while the TEC (technical inefficiency) incumbers its growth. The structural effects (mix and scale components) incumber environmental growth. The results imply that policymakers should pay more attention to new agricultural technological development and resource misallocation to improve agricultural green growth in Sichuan.

For one moving Internet of Things (IoT) collector to download files from nearby industrial IoT devices, storing network coded files into multiple IoT devices can achieve good reliability performance if the following (n, k) erasure property (EP) is fulfilled: k source packets are encoded into n packets, and k packets chosen from these n packets in an arbitrary manner can reconstruct all the source packets. Besides, low decoding complexity is desired for time-sensitive applications and energy-limited moving IoT collectors, and binary zigzag decoding (BZD) achieves significantly low decoding complexity. The objective of previous EP-BZD designs is unilateral, which limits its application scenarios. In this article, a novel EP-BZD code is designed, which achieves good tradeoff among largest storage room overhead (SRO), SRO variance, and wide range of (n, k). To implement such a code, the source packets are shifted by several bits, respectively, and then Xored together. The numbers of bits shifted are represented by a matrix, which is obtained from a specially constructed triangle by taking a certain maximal submatrix. The triangle is obtained by a series of steps, including the construction of base vector, parallelogram, trapezoid, etc. The proof that the proposed code possesses EP and BZD simultaneously is also provided. A series of comparisons verify that the proposed code achieves significantly better tradeoff among triple objectives than existing EP-BZD codes.

A new technique from EC (evolutionary computation), PSO (particle swarm optimization), is implemented to model the end milling process and predict the resulting surface roughness. Data is collected from CNC cutting experiments using DOE approach. The data is used for model calibration and validation. The inputs to the model consist of feed, speed and depth of cut while the output from the model is surface roughness. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining.