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This study proposed a neural-network-based model to estimate the ocean vertical water temperature from the surface temperature in the northwest Pacific Ocean. The performance of the model and the sources of errors were assessed using the Gridded Argo dataset including 576 stations with 26 vertical levels from surface (0 m)–2,000 m over the period of 2007–2009. The parameter selection, model building, stability of the neural network were also investigated. According to the results, the averaged root mean square error (RMSE) of estimated temperature was 0.7378 °C and the correlation coefficient R was 0.9967. More than 67% of the estimates from the four selected months (January, April, July and October) lay within ± 0.5 °C. When counting with errors lower than ± 1°C, the lowest percentage was 83%
Nowadays automation is a trend of container terminals all over the world. Although not applied in current automated container terminals, storage allocation is indispensable in conventional container terminals, and promising for automated container terminals in future. This paper seeks into the storage allocation problem in automated container terminals and proposed a two level structure for the problem. A mixed integer programming model is built for the upper level, and a modified Particle Swarm Optimization (PSO) algorithm is applied to solve the model. The applicable conditions of the model is investigated by numerical experiments, so as the performance of the algorithm in different problem scales. It is left to future research the lower level of the problem and the potential benefit of storage allocation to automated container terminals
Although diversity-ecosystem theory predicts that ecosystem functioning is strongly determined by species number, species traits play an important role in regulating ecosystem-level dynamics. We analyze responses of species attributes to diversity level and resource availability, and explore their consequences for ecosystem functioning and ultimately assess the contributions of five traits (vegetative plant height, clonal growth, root depth, cespitose habit and seed mass) to ecosystem functioning defined by spatial stability of community biomass. We found that functional traits disproportionately affected spatial stability. Relationships between species functional traits and spatial stability of community biomass indicated that diversity of vegetative plant height facilitated stability of a nitrogen fertilized undisturbed natural community (NAT), and that of a phosphorus fertilized forb, legume and bunchgrass community (FLB). The clonal growth form was also identified as a stabilizing trigger for a unfertilized undisturbed natural community (NAT), whereas diversity in root depth, cespitose habit and seed mass were related to destabilization of a nitrogen fertilized rhizomatous grass community (RRR). Studies quantifying interactions among plant traits, community structure and ecological functioning will contribute much more to understanding of the effects of the ecological behavior of specific traits on the ecosystem functioning.
Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online searching algorithm, efficiency of which is significantly affected by case size. In this study, a Deep Q-Learning Network (DQN) is proposed to solve ship stowage planning problem. With DQN, massive calculation and training is done in pre-training stage, while in application stage stowage plan can be made in seconds. To formulate network input, decision factors are analyzed to compose feature vector of stowage plan. States subject to constraints, available action and reward function of Q-value are designed. With these information and design, an 8-layer DQN is formulated with an evaluation function of mean square error is composed to learn stowage planning. At the end of this study, several production cases are solved with proposed DQN to validate the effectiveness and generalization ability. Result shows a good availability of DQN to solve ship stowage planning problem
Stowage planning is the core of ship planning. It directly influences the seaworthiness of container ship and the handling efficiency of container terminal. As the latter step of container ship stowage plan, terminal stowage planning optimizes terminal cost according to pre-plan. Group-Bay stowage planning is the smallest sub problem of terminal stowage planning problem. A group-bay stowage planning model is formulated to minimize relocation, crane movement and target weight gap satisfying both ship owner and container terminal. A GA-A* hybrid algorithm is designed to solve this problem. Numerical experiment shown the validity and the efficiency
Groundwater inrush within faults is an important issue in underground engineering. The process of water permeation through the soil-rock mixture has been numerically investigated. The simulated soil-rock mixture was presented with rock blocks, and filled with selected types of soil particles. The Euler-Euler method was employed with multiphase interaction. Meanwhile, the filling soil was assumed to be Bingham fluid with additional user-defined function. Then the detailed evolutions of water permeation through the soil-rock mixture were presented visually, including water distribution, water velocity field, permeation time, and penetration time. It is shown that water permeation changes with time and space in the soil-rock mixture, and the overall process of water permeation can be divided into three different stages. Moreover, major variables including water velocity, size of soil particles, and yield stress of soil were considered, which clearly influenced water permeation. Soil density showed little effect on water permeation, and the permeation time decreases with increasing water velocity. Water permeation through the soil-rock mixture is easier when the filling soil consists of smaller particles. The permeation rate of water obviously decreases with increasing yield stress. Meanwhile, different types of soils were considered with corrections on the dynamic viscosities. We found that sand and soil behave differently when water permeates through the soil-rock mixture. Furthermore, selected results on water permeation were compared with the relevant studies, and reasonable agreements were reached. The presented stimulation results provide detailed information for further understanding on the mechanical mechanism of water permeation through the soil-rock mixture used in underground engineering.
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