满足用户的需要以重塑图书馆未来的工作重心

Theme: Intelligence, Innovation and Library Services在数字时代, 技术成为驱使图书信息服务方式变化的主要力量。 技术创新使图书馆有能力将信息服务随时随地传递到用户桌面。 然而，技术的迅速发展也带来了意想不到的结果，并不断地挑战着图书馆。如今，全球的图书馆员都关注着许多共同的问题，其中包括 ...Technology is a major driving force behind the methods used to deliver library and information services in this Digital Age. Blessed with technological innovation, libraries are empowered to capitalize on the use of technologies to deliver the information services to the desktop of users anytime, anywhere. However, libraries are also constantly challenged by the unintended consequences of these rapid technological developments. Nowadays, librarians worldwide are concerned about numerous issues, including ...published_or_final_versionThe 4Th Shanghai International Library Forum (SILF 2008), Shanghai Library, Shanghai, China, 20-22 October 2008. In Proceedings of the 4th SILF, 2008, p. 65-7

Solving dynamic optimisation problems by combining evolutionary algorithms with KD-tree

In this paper we propose a novel evolutionary algorithm that is able to adaptively separate the explored and unexplored areas to facilitate detecting changes and tracking the moving optima. The algorithm divides the search space into multiple regions, each covers one basin of attraction in the search space and tracks the corresponding moving optimum. A simple mechanism was used to estimate the basin of attraction for each found optimum, and a special data structure named KD-Tree was used to memorise the searched areas to speed up the search process. Experimental results show that the algorithm is competitive, especially against those that consider change detection an important task in dynamic optimisation. Compared to existing multi-population algorithms, the new algorithm also offers less computational complexity in term of identifying the appropriate sub-population/region for each individual

Multi-population methods in unconstrained continuous dynamic environments: The challenges

Themulti-populationmethod has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different peaks to locate and track multiple changing peaks simultaneously. However, to make this approach efficient, several crucial challenging issues need to be addressed, e.g., how to determine the moment to react to changes, how to adapt the number of populations to changing environments, and how to determine the search area of each population. In addition, several other issues, e.g., communication between populations, overlapping search, the way to create multiple populations, detection of changes, and local search operators, should be also addressed. The lack of attention on these challenging issues within multi-population methods hinders the development of multi-population based algorithms in dynamic environments. In this paper, these challenging issues are comprehensively analyzed by a set of experimental studies from the algorithm design point of view. Experimental studies based on a set of popular algorithms show that the performance of algorithms is significantly affected by these challenging issues on the moving peaks benchmark. Keywords: Multi-population methods, dynamic optimization problems, evolutionary computatio

An Open Framework for Constructing Continuous Optimization Problems

Many artificial benchmark problems have been proposed for different kinds of continuous optimization, e.g., global optimization, multi-modal optimization, multi-objective optimization, dynamic optimization, and constrained optimization. However, there is no unified framework for constructing these types of problems and possible properties of many problems are not fully tunable. This will cause difficulties for researchers to analyze strengths and weaknesses of an algorithm. To address these issues, this paper proposes a simple and intuitive framework, which is able to construct different kinds of problems for continuous optimization. The framework utilizes the k-d tree to partition the search space and sets a certain number of simple functions in each subspace. The framework is implemented into global/multimodal optimization, dynamic single objective optimization, multiobjective optimization, and dynamic multi-objective optimization, respectively. Properties of the proposed framework are discussed and verified with traditional evolutionary algorithms

High expression of biglycan is associated with poor prognosis in patients with esophageal squamous cell carcinoma

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Compact metallic RFID tag antennas with a loop-fed method

Several compact, low profile and metal-attachable RFID tag antennas with a loop-fed method are proposed for UHF RFID systems. The structure of the proposed antennas comprise of two parts: (1) The radiator part consists of two shorted patches, which can be treated as two quarter-wave patch antennas or a cavity. (2) A small loop printed on the paper serves as the feeding structure. The small loop provides the needed inductance for the tag and is connected to the RFID chip. The input impedance of the antenna can be easily adjusted by changing loop dimensions. The antenna has the compact size of 80 mm × 25 mm × 3.5 mm, and the realized gain about -3.6 dB. The measured results show that these antennas have good performance when attached onto metallic surfaces. © 2011 IEEE.published_or_final_versio

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

The increasing importance of logistics outsourcing and availability of logistics services providers (LSPs) highlights the significance and complexity of the LSP evaluation and selection process. Most existing LSP evaluation and selection studies use historical performance data and assume independence among decision criteria. This paper proposes an integrated logistics outsourcing approach to evaluate and select LSPs based on their logistics resources and capabilities. This novel approach combines a fuzzy decision making trial, valuation laboratory (FDEMATEL) and fuzzy techniques to order preferences by similarity to ideal solution (FTOPS IS) methods. The new multi-criteria decision making (MCDM) model addresses the impact relationships between decision criteria and ranks LSP alternatives against weighted resources and capabilities. The effectiveness of this approach is demonstrated through a real case study and a two-phase sensitivity analysis confirms its robustness

Evolutionary fleet sizing in static and uncertain environments with shuttle transportation tasks - the case studies of container terminals

This paper aims to identify the optimal number of vehicles in environments with shuttle transportation tasks. These environments are very common industrial settings where goods are transferred repeatedly between multiple machines by a fleet of vehicles. Typical examples of such environments are manufacturing factories, warehouses and container ports. One very important optimisation problem in these environments is the fleet sizing problem. In real-world settings, this problem is highly complex and the optimal fleet size depends on many factors such as uncertainty in travel time of vehicles, the processing time of machines and size of the buffer of goods next to machines. These factors, however, have not been fully considered previously, leaving an important gap in the current research. This paper attempts to close this gap by taking into account the aforementioned factors. An evolutionary algorithm was proposed to solve this problem under static and uncertain situations. Two container ports were selected as case studies for this research. For the static cases, the state-of-the-art CPLEX solver was considered as the benchmark. Comparison results on real-world scenarios show that in the majority of cases the proposed algorithm outperforms CPLEX in terms of solvability and processing time. For the uncertain cases, a high-fidelity simulation model was considered as the benchmark. Comparison results on real-world scenarios with uncertainty show that in most cases the proposed algorithm could provide an accurate robust fleet size. These results also show that uncertainty can have a significant impact on the optimal fleet size

An experimental study of combining evolutionary algorithms with KD-tree to solving dynamic optimisation problems

This paper studies the idea of separating the explored and unexplored regions in the search space to improve change detection and optima tracking. When an optimum is found, a simple sampling technique is used to estimate the basin of attraction of that optimum. This estimated basin is marked as an area already explored. Using a special tree-based data structure named KD-Tree to divide the search space, all explored areas can be separated from unexplored areas. Given such a division, the algorithm can focus more on searching for unexplored areas, spending only minimal resource on monitoring explored areas to detect changes in explored regions. The experiments show that the proposed algorithm has competitive performance, especially when change detection is taken into account in the optimisation process. The new algorithm was proved to have less computational complexity in term of identifying the appropriate sub-population/region for each individual. We also carry out investigations to find out why the algorithm performs well. These investigations reveal a positive impact of using the KD-Tree

An improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals

This paper improves our previous attempts in which we studied a combination of an evolutionary algorithm (EA) and Monte Carlo simulation (MCS). Results of those studies showed the process of sampling in MCS is very time consuming. This prevents the EA from producing an accurate estimation of the robust solutions within reasonable time. Thus the present work improves the performance of the EA to make it possible to reach high quality solutions in reasonable time, therefore yielding a number of more practical solutions in real cases. Firstly, it proposes a new sampling technique to generate samples that better reflect the worst-case scenarios. This helps the EA to find more robust solutions using smaller sample sizes. Secondly, it proposes a new adaptive sampling technique to adjust the sample size during evolution. Subsequently, to evaluate the proposed algorithm we tested it in a typical environment with shuttle transport tasks: container terminal. Experimental results show that such improvements led to a significantly improved performance of the EA, thus making the proposed algorithm perfectly usable for empirical cases