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Featured Application: Yielding- and relief-pressure support technology of a lining filled with granular material is proposed. This technology will guide the design and construction of tunnel projects' supporting structures in the future. For high ground-stress soft-rock tunnels surrounding rock with large deformation, rapid deformation rate, a long creep time, and a high likelihood of to causing initial and secondary lining damage, the yielding and relief-pressure support technology of a lining filled with a granular material is proposed. A layer of granular material is placed at the reserved deformation layer of the tunnel to provide the surrounding rock with a certain amount of deformation space. Confined compression tests were undertaken to study the laws of compressive strain, load reduction law, and horizontal force variation of different granular materials under different rock stresses. The research showed that the compressibility and load reduction performance of 8 mm soil was optimal. Its maximum compressive strain reached 47.6%, and the total load reduction rate reached 71%. The yielding- and relief-pressure effects of the granular sand-filled lining support were analyzed from the angles of deflection, pressure, and energy. The results show that the highest reduction rate of deflection was 36.7%, and the greatest load reduction rate of pressure was 78%. The grainy filling material can remove part of the load imposed by the surrounding rock on the support structure of the secondary lining through yielding pressure and relief pressure, which dramatically reduces the damage to the secondary lining from the surrounding rock. The research results have specific reference significance for designing and constructing tunnel support structures. [ABSTRACT FROM AUTHOR]

In this study, acid treatment coal-series kaolin (AK) materials (named 2AK, 3AK, 5AK, 10AK) were synthesized by a facile method. Batch adsorption experiments were carried out using Congo red (CR) as a model dye pollutant in various experimental conditions. The samples were studied by XRF, XRD, FTIR, SEM, Zeta potential and N2 adsorption techniques. The results indicate that the acid treatment materials exhibit increased specific surface area and pore structures, and showed obvious effects on the adsorption performance of kaolin. Owing to the structural edges of kaolin receiving protons from the hydrochloric acid aqueous solution, the prepared 5AK exhibited excellent adsorption performance for Congo red (CR) anionic dye. The adsorption process followed a pseudo-second-order rate model and the Langmuir isotherm was found a better fit with a maximum adsorption capacity of 237.53 mg/g, which is very close to the experimental data. Thermodynamic studies showed Congo red adsorption on samples was exothermic and spontaneous in nature. The acid treatment coal-series kaolin shows significant potential applications in the fields of adsorption, pH-responsive delivery and other environmental remediation. [ABSTRACT FROM AUTHOR]

Recently, with the development of IP and elastic optical networks (EONs), the network control and management (NC&M) scheme for IP-over-EONs, which can facilitate effective cross-layer orchestration (XLyr-O), has become an interesting but challenging research topic. In this paper, we consider a software-defined IP-over-EON (SD-IPoEON), leverage deep learning (DL) to analyze and predict the traffic fluctuation on established lightpaths in it, and design a proactive DL-assisted XLyr-O scheme. Specifically, we study the DL-assisted XLyr-O scheme from algorithm design to system prototype. A DL module based on the long/short-term memory based neural network (LSTM-NN) is first designed and optimized for precise IP traffic prediction. Then, we develop algorithms to explore the traffic prediction for realizing proactive XLyr-O to deal with hard/soft failures constantly, i.e., making intelligent online decisions to re-groom and reroute IP flows and to reconfigure lightpaths such that the performance tradeoff among lightpath utilization, congestion probability, and reconfiguration frequency is balanced well. Finally, we implement our proposed algorithm in a small-scale but real SD-IPoEON testbed to prototype the DL-assisted XLyr-O, and conduct experiments with it. Experimental results demonstrate that compared with the reactive benchmark without DL-assistance, our proposal not only invokes less network reconfigurations but also reduces packet losses significantly. [ABSTRACT FROM AUTHOR]

Inter-datacenter (inter-DC) networks are essential for large enterprises to deliver high-quality services to end-users. Since DCs are vulnerable to natural disasters, an inter-DC network operator needs an effective emergency backup plan to evacuate the endangered data out in case of a progressive disaster whose status can be predicted by an early warning system. In this paper, we try to solve the problem of emergency backup in inter-DC networks with progressive disasters. We first utilize the time-expanded network (TEN) approach to model the time-variant inter-DC network during a progressive disaster as a variant TEN (VTEN) and convert the dynamic flow scheduling for emergency backup to a static one. Then, with the VTEN, we formulate an optimization model to maximize the profit from the emergency backup in consideration of data values and resource costs. Although this large-scale optimization can be solved in a distributed way by leveraging the alternation direction method of multipliers (ADMM), we find that one of its subproblems is nontrivial in the distributed setting. We propose a novel inexact ADMM approach to resolve the issue induced by the subproblem, and prove that the proposed algorithm can converge to the optimal solution. The results from extensive simulations confirm that our algorithm is robust and time-efficient, and outperforms several benchmarks in terms of backup profit and running time. [ABSTRACT FROM AUTHOR]