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In order to maximize the total spectrum efficiency and improve the Quality of Experience (QoE) on Elastic Optical Network (EON), an adaptive modulation method based on Deep Neural Network (DNN) is proposed. Firstly, the network transmission quality in optical fiber is estimated. Then, the route length, hop number and video quality are taken as the three parameters of the request characteristics, and the deep learning method is adopted to selected the modulation system with the maximum spectrum efficiency according to the desired QoE of each requirement. The simulation results show that the average spectral efficiency of the proposed method is 51%and 32%higher in National Science Foundation (NSF) network than that of distance adaptive method and integer programming method, and 43%and 29%higher in China Network (CN) backbone network. In addition, the blocking probability of the proposed method is 0.01 lower than that of the distance adaptive method and integer programming method.

Abstract: It is difficult for conventional temporary bridging methods to seal large pore throats, which usually make the dominant contribution to permeability. The reason is that the particle size of conventional bridging agents is optimized depending on the median pore size of target formation. High-permeability sandstone reservoirs have such features as wide permeability distribution range, high porosity, argillaceous cement, and wide distribution range of pore throat sizes. The key factors of reservoir damage are invaded particles and filtrate of drilling fluids, as well as the hydrate expansion and dispersion of clay. The KPAM polymer drilling fluids are optimized, and ideal packing approach is applied to the drilling fluids, and the instrument of dynamic fluid loss test is used to evaluate return permeability. The test results indicate that the ideal packing approach can reduce the dynamic fluid loss, improve the return permeability, and reduce breakthrough pressure. Key words: high-permeability reservoirs, formation damage, ideal packing, return permeability, formation damage control

A pot experiment was conducted to study the effects of two controlled-release fertilizers CRFA (4% resin-coated, N: P2O5: K2O = 14: 14: 14) and CRFB (4% resin-coated, N: P2O5: K2O = 20: 8:10) on the nutrient uptake and growth of Chrysanthemum morifolium, with common compound fertilizer CCF (N: P2O5: K2O = 15: 15: 15) as the control. Six treatments were installed, i. e., CCF1 (CCF, 6 g N · pot-1), CCF2 (CCF, 3 g N · pot-1), CRFA1 (CRFA, 6 g · pot-1), CRFA2 (CRFA, 3 g · pot(-1)), CRFB1 (CRFB, 6 g · pot-1), and CRFB (CRFB, 3 g · pot-1). On the 30th day of applying common compound fertilizer CCF1 and CCF2, soil available N, P and K contents were 163.29 and 145.26 mg · kg-1, 180.39 and 163.13 mg · kg-1, and 300.08 and 213.15 mg · kg-1, respectively, and decreased rapidly since then. In treatments CRFA1, CRFB1, CRFA2, and CRFB, soil available N content increased slowly, and reached the peak on the 60th day after fertilizing, being 129.51, 138.65, 118.36, and 126.31 mg · kg-1, respectively. Soil available P content had the same variation trend. Its maximum concentration was 169.54 and 133.46 mg · kg-1 in treatments CRFA1 and CRFA2 on the 30th day after fertilizing, and 137.13 and 84.68 mg · kg-1 in treatments CRFB1 and CRFB2 on the 60th day after fertilizing, and decreased slowly then. The agronomic traits such as leaf area, leaf area index, branch number, flowering rate, flower number, and flower diameter, etc., in treatments CRFA and CRFB were obviously better than those of the control, and CRFB was better than CRFA, suggesting that CRFB more matched the nutrient demand of C. morifolium. Under the conditions of present experiment, applying CRFB2 obtained the highest yield. [ABSTRACT FROM AUTHOR]

As the core of blockchain technology,consensus mechanism determines the performance,scalability,and security of blockchain systems.Aiming at the performance and scalability issues of current blockchains and the high cost of the incentives used to maintain the security of the systems,a trust-based dual-layer scalable consensus protocol (TDSCP) is proposed.First,the trust model and consensus algorithm of dual-layer cooperation are designed through a structured network.The trust model determines whether a node gets the right to generate blocks based on its trustworthiness to avoid the high cost of mining.Secondly,the dual-layer consensus algorithm within the partitions improves consensus efficiency,expands the number of nodes involved in consensus,and avoids the problem of system centralization.Finally,the verifiable random function and the multilevel graph partitioning algorithm are combined for partitioning nodes,which can effectively prevent malicious nodes from gathering and reduce the number of cross-partition transactions.The experimental results show that TDSCP improves the scalability of the blockchain system,the latency of its intra-partition algorithm consensus is lower,and the partition method significantly reduces the number of cross-partition transactions.