Benchmarking best manufacturing practices: a study into four sectors of Turkish industry

Reports on a benchmarking study conducted to quantify how well companies operating in various sectors of Turkish industry match up to best practice, both in the practices they adopt and in the operational outcomes that result, and to test the hypothesis that the closer a company is to best practice, the more likely it is for that company to achieve higher business performance. The survey conducted in 1997 and 1998 included 82 companies from the Turkish electronics, cement, automotive sectors and part and component suppliers to the appliance industry. For data gathering. employs the Competitive Strategies and Best Practices Benchmarking Questionnaire, supported ly, some follow-up interviews and one-day site visits. Classifies two small groups of companies as leaders and laggers, depending on how close they were to best practice. Shows that the leaders have performed better than the laggers in adopting best manufacturing practices and in the achievement of high performance La,els. The leaders also have achieved substantially higher business performance than the laggers. Furthermore, observes that large-sized companies outperform the rest both in terms of their success in implementing best manufacturing practices and in achieving high operational outcomes and that there is no appreciable difference between industrial sectors in implementing best manufacturing practices and in achieving high operational outcomes

Asteroseismology of the {\it Kepler} target KIC\,9204718

The high precision data obtained by the {\it Kepler} satellite allows us to detect hybrid type pulsator candidates more accurately than the data obtained by ground-based observations. In this study, we present preliminary results on the new analysis of the {\it Kepler} light curve and high resolution spectroscopic observations of pulsating Am star KIC\,9204718. Our tentative analysis therefore show that the star has hybrid pulsational characteristics.Comment: 'Proceedings of Wide Field variability surveys : a 21 st Century 22nd Los Alamos Stellar Pulsation Conference San Pedro De Atacama ,Chile Nov 28-Dec 2, 2016' to be published by the EPJ Web of Conference

Reducing query overhead through route learning in unstructured peer-to-peer network

Cataloged from PDF version of article.In unstructured peer-to-peer networks, such as Gnutella, peers propagate query messages towards the resource holders by flooding them through the network. This is, however, a costly operation since it consumes node and link resources excessively and often unnecessarily. There is no reason, for example, for a peer to receive a query message if the peer has no matching resource or is not on the path to a peer holding a matching resource. In this paper, we present a solution to this problem, which we call Route Learning, aiming to reduce query traffic in unstructured peer-to-peer networks. In Route Learning, peers try to identify the most likely neighbors through which replies can be obtained to submitted queries. in this way, a query is forwarded only to a subset of the neighbors of a peer, or it is dropped if no neighbor, likely to reply, is found. The scheme also has mechanisms to cope with variations in user submitted queries, like changes in the keywords. The scheme can also evaluate the route for a query for which it is not trained. We show through simulation results that when compared to a pure flooding based querying approach, our scheme reduces bandwidth overhead significantly without sacrificing user satisfaction. (C) 2008 Elsevier Ltd. All rights reserved

A connection management protocol for promoting cooperation in Peer-to-Peer networks

Cataloged from PDF version of article.The existence of a high degree of free riding in Peer-to-Peer (P2P) networks is an important threat that should be addressed while designing P2P protocols. In this paper we propose a connection-based solution that will help to reduce the free riding effects on a P2P network and discourage free riding. Our solution includes a novel P2P connection type and an adaptive connection management protocol that dynamically establishes and adapts a P2P network topology considering the contributions of peers. The aim of the protocol is to bring contributing peers closer to each other on the adapted topology and to push the free riders away from the contributors. In this way contribution is promoted and free riding is discouraged. Unlike some other proposals against free riding, our solution does not require any permanent identification of peers or a security infrastructure for maintaining a global reputation system. It is shown through simulation experiments that there is a significant improvement in performance for contributing peers in a network that applies our protocol. © 2007 Elsevier B.V. All rights reserved

Static index pruning in web search engines: Combining term and document popularities with query views

Cataloged from PDF version of article.Static index pruning techniques permanently remove a presumably redundant part of an inverted file, to reduce the file size and query processing time. These techniques differ in deciding which parts of an index can be removed safely; that is, without changing the top-ranked query results. As defined in the literature, the query view of a document is the set of query terms that access to this particular document, that is, retrieves this document among its top results. In this paper, we first propose using query views to improve the quality of the top results compared against the original results. We incorporate query views in a number of static pruning strategies, namely term-centric, document-centric, term popularity based and document access popularity based approaches, and show that the new strategies considerably outperform their counterparts especially for the higher levels of pruning and for both disjunctive and conjunctive query processing. Additionally, we combine the notions of term and document access popularity to form new pruning strategies, and further extend these strategies with the query views. The new strategies improve the result quality especially for the conjunctive query processing, which is the default and most common search mode of a search engine

A framework for use of wireless sensor networks in forest fire detection and monitoring

Cataloged from PDF version of article.Forest fires are one of the main causes of environmental degradation nowadays. Current surveillance systems for forest fires lack in supporting real-time monitoring of every point of a region at all times and early detection of fire threats. Solutions using wireless sensor networks, on the other hand, can gather sensory data values, such as temperature and humidity, from all points of a field continuously, day and night, and, provide fresh and accurate data to the fire-fighting center quickly. However, sensor networks face serious obstacles like limited energy resources and high vulnerability to harsh environmental conditions, that have to be considered carefully. In this paper, we propose a comprehensive framework for the use of wireless sensor networks for forest fire detection and monitoring. Our framework includes proposals for the wireless sensor network architecture, sensor deployment scheme, and clustering and communication protocols. The aim of the framework is to detect a fire threat as early as possible and yet consider the energy consumption of the sensor nodes and the environmental conditions that may affect the required activity level of the network. We implemented a simulator to validate and evaluate our proposed framework. Through extensive simulation experiments, we show that our framework can provide fast reaction to forest fires while also consuming energy efficiently. (C) 2012 Elsevier Ltd. All rights reserved

Cluster searching strategies for collaborative recommendation systems

Cataloged from PDF version of article.In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster- skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures. © 2012 Elsevier Ltd. All rights reserved

Second chance: A hybrid approach for dynamic result caching and prefetching in search engines

Cataloged from PDF version of article.Web search engines are known to cache the results of previously issued queries. The stored results typically contain the document summaries and some data that is used to construct the final search result page returned to the user. An alternative strategy is to store in the cache only the result document IDs, which take much less space, allowing results of more queries to be cached. These two strategies lead to an interesting trade-off between the hit rate and the average query response latency. In this work, in order to exploit this trade-off, we propose a hybrid result caching strategy where a dynamic result cache is split into two sections: an HTML cache and a docID cache. Moreover, using a realistic cost model, we evaluate the performance of different result prefetching strategies for the proposed hybrid cache and the baseline HTML-only cache. Finally, we propose a machine learning approach to predict singleton queries, which occur only once in the query stream. We show that when the proposed hybrid result caching strategy is coupled with the singleton query predictor, the hit rate is further improved. © 2013 ACM