Shaping Luminescent Properties of Yb3+ and Ho3+ Co‐Doped Upconverting Core–Shell β‐NaYF4 Nanoparticles by Dopant Distribution and Spacing

At the core of luminescence color and lifetime tuning of rare earth doped upconverting nanoparticles (UCNPs), is the understanding of the impact of the particle architecture for commonly used sensitizer (S) and activator (A) ions. In this respect, a series of core@shell NaYF₄ UCNPs doped with Yb³⁺ and Ho³⁺ ions are presented here, where the same dopant concentrations are distributed in different particle architectures following the scheme: YbHo core and YbHo@…, …@YbHo, Yb@Ho, Ho@Yb, YbHo@Yb, and Yb@YbHo core–shell NPs. As revealed by quantitative steady‐state and time‐resolved luminescence studies, the relative spatial distribution of the A and S ions in the UCNPs and their protection from surface quenching has a critical impact on their luminescence characteristics. Although the increased amount of Yb³⁺ ions boosts UCNP performance by amplifying the absorption, the Yb³⁺ ions can also efficiently dissipate the energy stored in the material through energy migration to the surface, thereby reducing the overall energy transfer efficiency to the activator ions. The results provide yet another proof that UC phosphor chemistry combined with materials engineering through intentional core@shell structures may help to fine‐tune the luminescence features of UCNPs for their specific future applications in biosensing, bioimaging, photovoltaics, and display technologies.     

Double Auction-based Scheduling of Scientific Applications in Distributed Grid and Cloud Environments

Economy models have long been considered as a promising complement to the classical distributed resource management not only due of their dynamic and decentralized nature, but also because the concept of financial valuation of resources and services is an inherent part of any such model. In its broadest sense, scheduling of scientific applications in distributed Grid and Cloud environments can be regarded as a market-based negotiation between a scheduling service optimizing user-centric objectives (execution time, budget), and a resource manager optimizing provider-centric metrics (resource utilization, income, job throughput). In this paper, we propose a new instantiation of the negotiation protocol between the scheduler and resource manager using a market-based Continuous Double Auction (CDA) model. We analyze different scheduling strategies that can be applied and identify general strategic patterns that can lead to a fast and cheap work ow execution. In the experimental study, we demonstrate that under certain circumstances one can benefit by applying an aggressive scheduling strategy.     

Modelling and solving grid resource allocation problem with network resources for workflow applications

A problem of allocating resources of a grid to workflow applications is considered. The problem consists, generally, in allocating distributed grid resources to tasks of a workflow in such a way that the resource demands of each task are satisfied. Grid resources are divided into computational resources and network resources. Computational tasks and transmission tasks of a workflow are distinguished. We present a model of the problem, and an algorithm for finding feasible resource allocations. A numerical example is included, showing the importance of the resource allocation phase on a grid. Some conclusions and directions for future research are given.     

Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach

Mobile devices could facilitate human interaction and access to knowledge resources anytime and anywhere. With respect to wide application possibilities of mobile learning, investigating learners’ acceptance towards it is an essential issue. Based on activity theory approach, this research explores positive factors for the acceptance of m-learning systems. In the research, we developed an m-learning system for learners’ knowledge management and invited 152 participants who knew how to use the m-learning system then report on their experience. The results show that enhancing learners’ satisfaction, encouraging learners’ autonomy, empowering system functions, and enriching interaction and communication activities have a significant positive influence on the acceptance of m-learning systems.     

Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions

This paper presents a method for multi-scale segmentation of surface data using scale-adaptive region growing. The proposed segmentation algorithm is initiated by an unsupervised learning of optimal seed positions through the surface attribute clustering with a two-criterion score function. The seeds are selected as consecutive local maxima of the clustering map, which is computed by an aggregation of the local isotropic contrast and local variance maps. The proposed method avoids typical segmentation errors caused by an inappropriate choice of seed points and thresholds used in the region-growing algorithm. The scale-adaptive threshold estimate is based on the image local statistics in the neighborhoods of seed points. The performance of this method was evaluated on LiDAR surface images.     

TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

This paper presents an experimental study for turning process in machining by using Takagi-Sugeno-Kang (TSK) fuzzy modeling to accomplish the integration of multi-sensor information and tool wear information. It generates fuzzy rules directly from the input-output data acquired from sensors, and provides high accuracy and high reliability of the tool wear prediction over a wide range of cutting conditions. The experimental results show its effectiveness and satisfactory comparisons relative to other artificial intelligence methods.     

A Fast Algorithm for Adaptive Prefix Coding

In this paper we present a new algorithm for adaptive prefix coding. Our algorithm encodes a text S of m symbols in O(m) time, i.e., in O(1) amortized time per symbol. The length of the encoded string is bounded above by (H+1)m+O(nlog ² m) bits where n is the alphabet size and H is the entropy. This is the first algorithm that adaptively encodes a text in O(m) time and achieves an almost optimal bound on the encoding length in the worst case. Besides that, our algorithm does not depend on an explicit code tree traversal. A preliminary version of this paper appeared in the Proceedings of the 2006 IEEE International Symposium on Information Theory (ISIT 2006). M. Karpinski’s work partially supported by a DFG grant, Max-Planck Research Prize, and IST grant 14036 (RAND-APX). Y. Nekrich’s work partially supported by IST grant 14036 (RAND-APX).