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The differences among group **means** range from -0.2% to 2.8%, with the methods using cresolphthalein
complexone and ion selective electrodes having the closest agreement (**Table** 1). The difference between these two methods meets the suggested bias criterion for 3 out of the 4 samples, while the difference between the other methods’ **means** is two to three times higher than the suggested measurement bias. Across all four samples, the differences among **method** **means** appear similar, which suggests that these differences are mainly due to variances in assay calibration rather than assay specificity. Further studies are needed to verify these assertions.

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A Wireless Sensor Network (WSN) is a network of small sensor nodes which are energy constraint devices and have limited data transmission and computational power. Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Most of the research in this area has focused on energy-efficient solutions, but has not thoroughly analyzed the network performance, e.g. in terms of data collection rate and time. In this paper we are presenting the clustering of wireless sensor network by using k-**means** approach, over a large dynamic network. As it is the oldest and simplest **method** of clustering. This **method** requires only local communication and synchronization. Due to growing in area of peer to peer and mobile sensor networks, data analysis in large, dynamic network in large garner importance in the near future. Our algorithm shows best result for the large dynamic network. We tested our algorithm in a simulated environment up to 100 nodes in a dynamic environment and analyze its behavior with good accuracy.

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6. CONCLUDING REMARKS
In this paper, we settled the smoothed running time of the k-**means** **method** for ar- bitrary k and d. The exponents in our smoothed analysis are constant but large. We did not make a huge effort to optimize the exponents as the arguments are intricate enough even without trying to optimize constants. Furthermore, we believe that our approach, which is essentially based on bounding the smallest possible improvement in a single step, is too pessimistic to yield a bound that matches experimental obser- vations. A similar phenomenon occurred already in the smoothed analysis of the 2-opt heuristic for the TSP [Englert et al. 2007]. There it was possible to improve the bound for the number of iterations by analyzing sequences of consecutive steps rather than single steps. It is an interesting question if this approach also leads to an improved smoothed analysis of k-**means**.

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We present polynomial upper and lower bounds on the number of iterations per- formed by the k-**means** **method** (a.k.a. Lloyd’s **method**) for k-**means** clustering. Our upper bounds are polynomial in the number of points, number of clusters, and the spread of the point set. We also present a lower bound, showing that in the worst case the k-**means** heuristic needs to perform Ω(n) iterations, for n points on the real line and two centers. Surprisingly, the spread of the point set in this construction is poly- nomial. This is the first construction showing that the k-**means** heuristic requires more than a polylogarithmic number of iterations. Furthermore, we present two alternative algorithms, with guaranteed performance, which are simple variants of the k-**means** **method**. Results of our experimental studies on these algorithms are also presented.

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WSN consist of hundreds of thousands of small and cost effective sensor nodes. Sensor nodes are used to sense the environmental or physiological parameters like temperature, pressure, etc. For the connectivity of the sensor nodes, they use wireless transceiver to send and receive the inter-node signals. Sensor nodes, because connect their selves wirelessly, use routing process to route the packet to make them reach from source to destination. These sensor nodes run on batteries and they carry a limited battery life. Clustering is the process of creating virtual sub-groups of the sensor nodes, which helps the sensor nodes to lower routing computations and to lower the size routing data. There is a wide space available for the research on energy efficient clustering algorithms for the WSNs. LEACH, PEGASIS and HEED are the popular energy efficient clustering protocols for WSNs. In this research, we are working on the development of a hybrid model using LEACH based energy efficient and K-**means** based quick clustering algorithms to produce a new cluster scheme for WSNs with dynamic selection of the number of the clusters automatically. In the proposed **method**, finding an optimum „k‟ value is performed by Elbow **method** and clustering is done by k-**means** algorithm, hence routing protocol LEACH which is a traditional energy efficient protocol takes the work ahead of sending data from the cluster heads to the base station. The results of simulation show that at the end of some certain part of running the proposed algorithm, at some point the marginal gain will drop dramatically and gives an angle in the graph. The correct „k‟

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The goal of this paper is to bound the smoothed running-time of the k-**means** **method**. There are ba- sically two reasons why the smoothed running-time of the k-**means** **method** is a more realistic measure than its worst-case running-time: First, data obtained from measurements is inherently noisy. So even if the original data were a bad instance for k-**means**, the data mea- sured is most likely a slight perturbation of it. Second, if the data possesses a meaningful k-clustering, then slightly perturbing the data should preserve this clus- tering. Thus, smoothed analysis might help to obtain a faster k-**means** **method**: We take the data measured, perturb it slightly, and then run k-**means** on the per-

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However, the extension of the logarithm, identric and Seiﬀert **means** from two to three or more variables does not appear to be obvious from the above expressions of these **means**.
In this sense, we refer the reader to [, –] for some extensions about the logarithmic and identric **means**. Here, we will derive other extensions of these latter **means** from our above study. In fact, the above transformation for **means** with two variables can be imme- diately stated in a similar manner for **means** involving several variables. For instance, we can deﬁne

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A. A. POPOFF
Automechanical Institute, Moscow, U.S.S.R.
1. Introduction. This paper contains a new **method** of integration which is partly graphical, partly analytical.1 It permits a simple determination of integrals of the form J<t>i{x)<t>k{x)dx, where 4>i(x) is given graphically and tf>k(x) is given either graphi- cally or analytically. The **method** requires the construction of certain diagrams, called scales, showing the abscissae of the centroids of certain areas associated with <t>kix), and is based on some properties of the so-called orthogonality foci. Finally, the **method** is applied to interpolation, Fourier analysis, and the evaluation of Mohr in- tegrals in the theory of structures.

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Filtering the simulated data sets that have been tested has proven to be beneficial. It could be clearly seen that the number of false rejections has been reduced, where this was not necessarily the case for the total number of rejections. This leads to the conclusion that more correct rejections have been made, hence more precise detections have been achieved. What could improve the detection of a wanted signal even more is applying the Holm-Bonferroni **method** [8] on the simulated data. Briefly explained, this **method** is used for multiple-hypothesis testing to control the family-wise error rate - meaning the probability that one or more errors of Type I will occur. Since this paper was dealing with multiple-hypothesis testing for simulated data, and further on the Kepler data, this **method** could have been useful.

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This online database contains the full-text of PhD dissertations and Masters’ theses of University of Windsor students from 1954 forward. These documents are made available for personal[r]

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The surficial waves of the 1999 Turkish earth- quakes obtained from the seismic stations located in western Greece were studied mainly for diffractions of Love waves and the crustal thickness of the northwestern Anatolia was calculated as about 33 km (Novotny et al., 2001). By application of experimental relations to **gravity** anomaly data, it was determined that the crustal thickness values for Anatolia varies between 26.4 km and 49.5 km (Maden et al., 2005). Later on, two dimensional radial mean power spectrum technique was applied to ano- maly map to find the average regional depth as 47 km. At the second stage, the one dimen- sional sliding window power spectrum **method** was applied to the same map to investigate the

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Hummel, 2010; Shen, 2010; Tavakoli and Gerami, 2013). In this **method**, a target word in a foreign language L2 can be learned by a native speaker of another language L1 in two main steps: 1) one or more L1 words, possibly referring to a concrete entity, are chosen based on orthographic or pho- netic similarity with the target word; 2) an L1 sen- tence is constructed in which an association be- tween the translation of the target word and the keyword(s) is established, so that the learner, when seeing or hearing the word, immediately recalls the keyword(s). To illustrate, for teaching the Ital- ian word cuore which **means** heart in English, the learner might be asked to imagine “a lonely heart with a hard core”.

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The problem this paper addresses is the **automation** of transition methods, particularly those modeled with the SPEM. The 4SRS was modeled with the SPEM in order to formalize it as a software process. It had to be automated so that it could be enlivened by **means** of a tool. The SPEM was chosen because it is standard, therefore it would be possible to benefit from the advantages of using a standard that is available to every professional of process modeling. Assuming that disciplines are sets of tasks that can be grouped according to their particular relevance in specific phase(s) from large software development processes into which small dedicated software development processes can be plugged into, transition methods are methods that describe how to transform artifacts from one discipline of a large software development process into artifacts from another discipline of such a process.

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The height data H in the matrix S are filtered to obtain the minimum distance dis to obtain the corresponding angle value β, and the deflection angle θ is obtained by the equation (9). In this experiment, the ideal angle is 180 degrees. The actual shortest distance corresponds to an angle of 182.5 degrees, which **means** the laser scanning ranging radar coordinate system is deflected by 2.5 degrees in the counterclockwise direction relative to the world coordinate system. Then, in the point of the laser scanning ranging radar coordinate system, the coordinates in the world coordinate system can be obtained by the homogeneous rotation transformation and the homogeneous translation transformation.

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