艾哈迈德·阿德南·萨伊贡艺术中心,伊兹密尔,土耳其

<正>一座建筑的性能应当如何来衡量?除了通过统治当代建筑舆论的美学、技术以及经济价值来进行描述之外,我们还能够如何来阐释一座建筑,尤其是一座致力于庇护那些渴望展现自己的艺术家的建筑?有人会问成功的表演是如何实现的,表演者是如何进行伪装的、又是如何展现自己的?根据海德格尔的理论,当我们看不到钉子的时候,铁锤的表现才是成功的;钉子本身的出现通常表明出现了问题。     

On evaluating Weibull fits to mechanical testing data

The use of coefficient of determination, R², and the Anderson–Darling (A²) hypothesis test to evaluate the goodness-of-fit to the two-parameter Weibull distribution was investigated. Results of Monte Carlo simulations for sample sizes between 5 and 100 indicated that guidelines provided previously in the literature are too conservative for sample sizes up to 80. New guidelines for the use of R² and A² for sample sizes between 5 and 100 have been developed. The two measures of goodness-of-fit were found to agree more than 95% of the time, regardless of sample size. The use of the new guidelines has been demonstrated on two datasets from the casting literature.     

Using directionality in mobile routing

The increased usage of directional methods of communications has prompted research into leveraging directionality in every layer of the network stack. In this paper, we explore the use of directionality in layer 3 to facilitate routing in highly mobile environments. We introduce Mobile Orthogonal Rendezvous Routing Protocol (MORRP) for mobile ad-hoc networks (MANETs). MORRP is a lightweight, but scalable routing protocol utilizing directional communications (such as directional antennas or free-space-optical transceivers) to relax information requirements such as coordinate space embedding, node localization, and mobility. This relaxation is done by introducing a novel concept called the directional routing table (DRT) which maps a set-of-IDs to each interface direction to provide probabilistic routing information based on interface direction. We show that MORRP achieves connectivity with high probability even in highly mobile environments while maintaining only probabilistic information about destinations. Additionally, we compare MORRP with various proactive, reactive, and position-based routing protocols using single omni-directional interfaces and multiple directional interfaces and show that MORRP gains over 10–14 × additional goodput vs. traditional protocols and 15–20% additional goodput vs. traditional protocols using multiple interfaces. MORRP scales well without imposing DHT-like graph structures (eg: trees, rings, torus etc). We also show that high connectivity can be achieved without the need to frequently disseminate node position resulting increased scalability even in highly mobile environments.     

Effect of nonlinearity on site response and ground motion due to earthquake excitation

Ground motions under earthquake loading are influenced by the local soil conditions, but the non-linearity of the soil behavior makes it difficult to estimate the site response. The paper reports a non-linear analysis developed to elucidate the effect of non-linearity on site response, illustrating its applicability with reference to a petroleum storage tank site subjected to earthquake excitation.     

Early prostate cancer diagnosis by using artificial neural networks and support vector machines

The aim of this study is to design a classifier based expert system for early diagnosis of the organ in constraint phase to reach informed decision making without biopsy by using some selected features. The other purpose is to investigate a relationship between BMI (body mass index), smoking factor, and prostate cancer. The data used in this study were collected from 300 men (100: prostate adenocarcinoma, 200: chronic prostatism or benign prostatic hyperplasia). Weight, height, BMI, PSA (prostate specific antigen), Free PSA, age, prostate volume, density, smoking, systolic, diastolic, pulse, and Gleason score features were used and independent sample t-test was applied for feature selection. In order to classify related data, we have used following classifiers; scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and Levenberg–Marquardt (LM) training algorithms of artificial neural networks (ANN) and linear, polynomial, and radial based kernel functions of support vector machine (SVM). It was determined that smoking is a factor increases the prostate cancer risk whereas BMI is not affected the prostate cancer. Since PSA, volume, density, and smoking features were to be statistically significant, they were chosen for classification. The proposed system was designed with polynomial based kernel function, which had the best performance (accuracy: 79%). In Turkish Family Health System, family physician to whom patients are applied firstly, would contribute to extract the risk map of illness and direct patients to correct treatments by using expert system such proposed.     

A team-oriented design methodology for mixed model assembly systems

Team-oriented approaches are widely being used in modern real life assembly systems, as are other modern systems. In this paper, first the literature of single and mixed model assembly line balancing, which plays an important role for the design of assembly systems, is reviewed. The associated literature matrixes reveal that team-oriented approaches do not have an intensive research area. Second, a team-oriented mathematical programming model for creating assembly teams (physical stations) in mixed model assembly lines is devised. Owing to the fact that this model is NP hard, a scheduling based heuristic algorithm is developed. The mixed model assembly line design methodology, which includes a team-oriented algorithm as a step, is proposed. Both model sequencing and worker transfer systems are included in the methodology. The algorithms for each step of the methodology are also coded by using MATLAB and MS Excel is used as the user interface. Furthermore, the presented methodology was applied in a chosen segment of a real life mixed model tractor assembly system.     

Preparation of PbS thin films: A new electrochemical route for underpotential deposition

Nanostructured thin films of lead sulfide have been synthesized by a new electrochemical approach based on the underpotential deposition (UPD) of Pb and S from the saturated solution of PbS containing excess of PbS particles as a source of Pb²⁺ and S²⁻ at various temperatures.We have demonstrated that this new electrochemical route is a simple method with several advantages, including better control of the growth conditions and a one-step process to obtain the nanostructures of PbS. Scanning probe microscopy studies indicate that the growth of PbS nanofilms follows a two-dimensional layer-by-layer growth kinetics at the beginning of electrodeposition but a three-dimensional growth dominates after the formation of the first few layers. The results of morphological and structural investigations reveal that PbS nanostructures grown by this method are single-crystalline in cubic structure and have a preferential orientation along the [2 0 0] direction. The optical absorption spectra of PbS nanostructures show the blue-shift with respect to those of the bulk counterpart, which are attributed as quantum-size effect.     

A quasi-static analysis for a class of induced-current EIT systems using discrete coils

A discrete coil EIT system is investigated for the general case of an eccentric circular inhomogeneity. The solution methodology of the forward problem of this system is explained. An optimization procedure using this forward problem solution is developed to find optimum currents that maximize the distinguishability. For an eccentric inhomogeneity problem, it is shown that the coil currents can be optimized to focus the current density in a region of interest. Optimum coil currents under limited peak coil currents constraint and limited total power constraint are obtained. Representative examples that demonstrate the performance of the system are presented.     

Efficient string matching with wildcards and length constraints

This paper defines a challenging problem of pattern matching between a pattern P and a text T, with wildcards and length constraints, and designs an efficient algorithm to return each pattern occurrence in an online manner. In this pattern matching problem, the user can specify the constraints on the number of wildcards between each two consecutive letters of P and the constraints on the length of each matching substring in T. We design a complete algorithm, SAIL that returns each matching substring of P in T as soon as it appears in T in an O(n+klmg) time with an O(lm) space overhead, where n is the length of T, k is the frequency of P's last letter occurring in T, l is the user-specified maximum length for each matching substring, m is the length of P, and g is the maximum difference between the user-specified maximum and minimum numbers of wildcards allowed between two consecutive letters in P.SAIL stands for string matching with wildcards and length constraints. Gong Chen received the B.Eng. degree from the Beijing University of Technology, China, and the M.Sc. degree from the University of Vermont, USA, both in computer science. He is currently a graduate student in the Department of Statistics at the University of California, Los Angeles, USA. His research interests include data mining, statistical learning, machine learning, algorithm analysis and design, and database management. Xindong Wu is a professor and the chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM),an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He is the 2004 ACM SIGKDD Service Award winner. Xingquan Zhu received his Ph.D degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent 4 months with Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From 2001 to 2002, he was a postdoctoral associate in the Department of Computer Science at Purdue University, West Lafayette, IN. He is currently a research assistant professor in the Department of Computer Science, the University of Vermont, Burlington, VT. His research interests include data mining, machine learning, data quality, multimedia computing, and information retrieval. Since 2000, Dr. Zhu has published extensively, including over 50 refereed papers in various journals and conference proceedings. Abdullah N. Arslan got his Ph.D. degree in Computer Science in 2002 from the University of California at Santa Barbara. Upon his graduation he joined the Department of Computer Science at the University of Vermont as an assistant professor. He has been with the computer science faculty there since then. Dr. Arslan's main research interests are on algorithms on strings, computational biology and bioinformatics. Dr. Arslan earned his Master's degree in Computer Science in 1996 from the University of North Texas, Denton, Texas and his Bachelor's degree in Computer Engineering in 1990 from the Middle East Technical University, Ankara, Turkey. He worked as a programmer for the Central Bank of Turkey between 1991 and 1994. Yu He received her B.E. degree in Information Engineering from Zhejiang University, China, in 2001. She is currently a graduate student in the Department of Computer Science at the University of Vermont. Her research interests include data mining, bioinformatics and pattern recognition.