683 research outputs found
How do we learn new meanings for words already known? Evidence from EEG and MEG studies
In addition to learning new words, people often learn new meanings for words they already know. For example, one might learn that the word ‘skate’ refers to a type of fish long after knowing its more common roller- or ice-skating meaning. Different from learning a new word, this type of learning requires updating the lexical knowledge by associating new information with an existing word and involves the co-activation of new and prior word knowledge. This dissertation investigates the mechanisms underlying the learning of new meanings for known words. In particular, it focuses on the influence of overnight consolidation on the learning of new meanings for known words and the role of the left posterior middle temporal gyrus (pMTG) in binding new meanings to known words. Study 1 showed that the processing of both new and original meanings became faster after overnight sleep. This indicated reduced interference between new and original meanings over time, especially after overnight consolidation occurred. However, the event-related potential (ERP) data showed that accessing the new meanings was still mainly supported by episodic retrieval even 24 hours after learning. To investigate how new meanings are associated with known words, Study 2a first demonstrated that presenting word meanings as verbal definitions were sufficient to drive a semantic category effect. Based on this result, Study 2b further showed that the left pMTG, in addition to sensorimotor cortices relevant to the representation of new meanings, was involved in binding new meanings to known words. Combined with the previous findings on learning novel words, the results suggest that the co-activation of new and prior knowledge is essential to the integration of new word knowledge into the mental lexicon. The left pMTG not only supports the formation of novel form-meaning associations, but also the associations between new meanings and previously known words
Adaptive Neural Network Robust Control for Space Robot with Uncertainty
The trajectory tracking problems of a class of space robot manipulators with parameters and non-parameters uncertainty are considered. An adaptive robust control algorithm based on neural network is proposed by the paper. Neutral network is used to adaptive learn and compensate the unknown system for parameters uncertainties, the weight adaptive laws are designed by the paper, System stability base on Lyapunov theory is analysised to ensure the convergence of the algorithm. Non-parameters uncertainties are estimated and compensated by robust controller. It is proven that the designed controller can guarantee the asymptotic convergence of tracking error. The controller could guarantee good robust and the stability of closed-loop system. The simulation results show that the presented method is effective
On plasmon modes in multi-layer structures
In this paper, we consider the plasmon resonance in multi-layer structures.
The conductivity problem associated with uniformly distributed background field
is considered. We show that the plasmon mode is equivalent to the eigenvalue
problem of a matrix, whose order is the same to the number of layers. For any
number of layers, the exact characteristic polynomial is derived by a
conjecture and is verified by using induction. It is shown that all the roots
to the characteristic polynomial are real and exist in the span [-1, 2].
Numerical examples are presented for finding all the plasmon modes, and it is
surprisingly to find out that such multi-layer structures may induce so called
surface-plasmon-resonance-like band
Understanding Attitudes towards Proenvironmental Travel: An Empirical Study from Tangshan City in China
Understanding people’s attitudes towards proenvironmental travel will help to encourage people to adopt proenvironmental travel behavior. Revealed preference theory assumes that the consumption preference of consumers can be revealed by their consumption behavior. In order to investigate the influences on citizens’ travel decision and analyze the difficulties of promoting proenvironmental travel behavior in medium-sized cities in China, based on revealed preference theory, this paper uses the RP survey method and disaggregate model to analyze how individual characteristics, situational factors, and trip features influence the travel mode choice. The field investigation was conducted in Tangshan City to obtain the RP data. An MNL model was built to deal with the travel mode choice. SPSS software was used to calibrate the model parameters. The goodness-of-fit tests and the predicted outcome demonstrate the validation of the parameter setting. The results show that gender, occupation, trip purpose, and distance have an obvious influence on the travel mode choice. In particular, the male gender, high income, and business travel show a high correlation with carbon-intensive travel, while the female gender and a medium income scored higher in terms of proenvironmental travel modes, such as walking, cycling, and public transport
Optimization of Hybrid Hub-and-Spoke Network Operation for Less-Than-Truckload Freight Transportation considering Incremental Quantity Discount
This paper presents a mixed integer linear programming model (MILP) for optimizing the hybrid hub-and-spoke network operation for a less-than-truckload transportation service. The model aims to minimize the total operation costs (transportation cost and transfer cost), given the determined demand matrix, truck load capacity, and uncapacitated road transportation. The model also incorporates an incremental quantity discount function to solve the reversal of the total cost and the total demand. The model is applied to a real case of a Chinese transportation company engaged in nationwide freight transportation. The numerical example shows that, with uncapacitated road transportation, the total costs and the total vehicle trips of the hybrid hub-and-spoke network operation are, respectively, 8.0% and 15.3% less than those of the pure hub-and-spoke network operation, and the assumed capacity constraints in an extension model result in more target costs on the hybrid hub-and-spoke network. The two models can be used to support the decision making in network operations by transportation and logistics companies
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