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Carbon foams have been applied widely in energy, environment and catalyst, but it is still challengeable to exploit simple and facile strategies to construct carbon foams with well-developed three-dimensional connected pore structure. In the present paper, we report a novel method to synthesize carbon foam by direct cross-linking of styrene–butadiene rubber-based powder puff (SBR). The high-density cross-linking bonds can address the thermal instability of SBR, and then endow it with rigid macromolecular framework and carbonizability. Therefore, the obtained carbon foam well inherits the micromorphology from SBR and incorporates the structural characteristics of light weight, honeycomb-like pore structure and tunable surface morphology. Owing to these structural advantages, the obtained carbon foam presents superior oil-water separation performance and Na+ storage property. For example, the carbon foam has high adsorption capacities of pump oil and other solvents (300–500%), and it can be recovered to the original state by combustion. In addition, the reversible Na+ storage capacity of carbon foam can reach 220 mAh g−1.

Stretchable conductors are receiving increasing attention due to their potential applications in wearable electronics. Highly stretchable conductors are fabricated using composites of silver nanowires (Ag NWs) and flakes on elastic polydimethylsiloxane (PDMS) substrates. The resistance of the Ag NW/flake composite conductor decreases to 1.9 × 10−4 Ohm·cm and increases by only 1.8 times after 100 stretch/release cycles at 100% peak strain. The improved conductivity and stretchability of the composite conductors are due to the synergy between the Ag NWs and Ag flakes because the Ag NWs connect the Ag flakes as conductive cables and the Ag flakes fill the gaps among the Ag NWs. Robust Ag NW/flake conductors are promising candidate for wearable electronics.

Generation maintenance scheduling has system level and equipment level aspects. At the system level, large-scale integration of wind power brings about significant challenges for classical generator maintenance scheduling. At the equipment level, forced outages of power equipment caused by wear affect maintenance decisions. Here, the authors report a robust optimisation model for maintenance scheduling considering wind power integration and forced outages of equipment. An uncertainty set is used to describe fluctuations in the wind power, and a method to control the conservativeness of the robust model is developed. An outage model is used to describe the impact of the operating conditions on maintenance decisions. The effects and advantages of the model are demonstrated via numerical simulations.

Traditional outage model for the power equipment usually focus on the behavior of the equipment under random factors, and the availability of the power equipment in system analysis is usually confined to the steady value. However, this model may be inaccurate in the short term analysis, where the transient process of availability has not ended yet. Furthermore, the power equipment in the short term analysis might be influenced by both random factors and deterministic factors, yet the impact of deterministic factors cannot be completely reflected in the traditional outage model. Based on the above issues, a Markov-based transient outage model is proposed in this paper, which describes the deterioration and repair process of an equipment. Both the corrective maintenance and preventive maintenance are concerned in the model. The preventive maintenance in the model is considered as deterministic event, in which the start time and duration are both scheduled. Meanwhile the corrective maintenance and the unexpected failure are modeled as random events. The transient state probability and availability of equipment under preventive maintenance is derived. The effect of deterministic events on the availability of equipment is analyzed on numerical tests. The proposed model can be used in the short-term reliability assessment and maintenance scheduling in actual systems.

According to cellphone customer complaints data and telecommunications equipment failures data provided by company, the correlation analysis and prediction are carried out under environment of Hadoop2.0 for fault prediction timely. Firstly, the main cause of customer's complaints was found out by using hierarchical clustering method. Then, the association rules between customer complaints and equipment failures were mined by using APRIOR algorithm, Finally, the prediction model was established based on decision tree, using it to predict the equipment failures with the main cause of customer complaints. The single-node fault problem of Hadoop1.0 was solved and the cluster implemented independent resource management and scheduling under the architecture of Hadoop2.0.