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In a Daya Bay 120-year dated sediment core(1892-2010), analyses were conducted of grain-size, water content, TOC, TIC, TC, loss on ignition, TN, BSi and TP, to reconstruct the anthropogenic activity history. The entire core was divided into four periods. Multi-parametric measurements, their ratios and interrelations are seen to clearly reflect the development of agriculture, aquaculture, industry and social economy surrounding Daya Bay. The trends of TOC, TOM and BSi after 1990 may be due to mass input of nutritious matter from aquaculture and industry, whereas the trends of BSi, TOC and TOM between 1960 and 1990 were owing to aquaculture and agriculture. Two peaks of BSi, TOC and TOM in 1994 and 2002 imply that the mass input of cooling water from nuclear power plants may be a significant contributor to ecological environment changes. Finally, some proposals were put forward for the healthy and sustainable development of Daya Bay.

The abundance and composition of marine debris including floating marine debris (FMD), seafloor marine debris (SMD) and beached marine debris (BMD) were investigated in coastal seawaters/beaches around the northern South China Sea during 2009 and 2010. The FMD density was 4.947 (0.282-16.891) items/km², with plastics (44.9%) and Styrofoam (23.2%) dominating. More than 99.0% of FMD was small or medium size floating marine debris. The SMD and BMD densities of were 0.693 (0.147-5.000) and 32.82 (2.83-375.00) items/km², respectively. SMD was composed of plastics (47.0%), wood (15.2%), fabric/fiber (13.6%) and glass (12.1%), while BMD was composed of plastics (42.0%) and wood (33.7%). More than 90% of FMD, 75% of SMD and 95% of BMD were not ocean-based sources but land-based sources, mostly attributed to coastal/recreational activity, because of the effect of human activities in the areas.

An autonomous-steerable-vehicle storage and retrieval system is a new storage concept, in which multiple automated storage a retrieval vehicles can concurrently perform compact three-dimensional automated storage or retrieval with the help of lifters. In this paper, six regulations, two corollaries, and one algorithm are presented for the rack design of an autonomous-steerable-vehicle storage and retrieval system. The six regulations describe a framework of the rack design of the system, which avoid collision between vehicles by setting vehicles to move unidirectionally on trunk rails and to pass through rail nodes after being authorized, and avoid deadlock by restricting the trunk rail networks to "no cut edge" configuration. The two corollaries can help designers to check the liveness of the highroad traffic network of the rack. The algorithm illustrates an orientation of the trunk rail network, with which designers can test the rationality of the designed trunk rail network. At last, an example is shown to exhibit how the regulations, corollaries, and algorithm work in the design.

An automated storage and retrieval system is a new storage concept derived from a pallet-shuttle high-density-storage system. This new system (former system) has higher space utilization than the original one (latter system), and can store or retrieve palletized units automatically using automated storage & retrieval vehicles. In order to organize multiple vehicles moving orderly in the system, offline vehicle routing and online vehicle scheduling are performed, based on a path-net graph modeled from the racks and pallet lifters of the system. In offline vehicle routing, path payload and navigation factor are introduced to calculate the path cost, which can make the vehicles not only have short paths but also employ trunk rails and pallet lifters evenly. In online vehicle scheduling, to avoid vehicle collisions at path nodes, buffers are attached to path nodes in the path-net graph model for vehicle parking, and each vehicle can only pass through the path nodes after having acquired the authorization of the system. With the algorithm illuminated in this paper, the system can route and schedule vehicles efficiently, and the utilization rate of vehicles can achieve more than 98% and congestion rate of system less than 2%, with an appropriate number of vehicle.