Calorimeter tests with liquid ionization chambers

Configurations of sampling calorimeters with iron, lead and uranium as absorbers have been investigated using liquid ionization chambers as active elements. As liquid tetramethylsilane has been used. Results of beam tests with electrons, pions and muons in the energy range of 2 to 6 GeV are presented and compared with Monte Carlo simulations. In particular the questions regarding which configuration can compensate and the separation of sampling from intrinsic fluctuations have been studied.     

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.     

Rare-earth catalysts in C-C-linkage of olefins I ytterbium catalyzed linear oligomerization of ethylene

Ytterbium catalysts such as YbCl₃/n-BuLi and (Cp₂YbCl)₂/RLi (R = Me, t-Bu, n-Bu,-CH₂SiMe₃) have been applied for the C-C-linkage of ethylene. Highly linear oligomers (n-alkanes) and polymers (high molecular weight polyethylene) were obtained. A reaction mechanism based on ytterbium-hydrides is proposed.     

Visual Analysis of Time‐Series Similarities for Anomaly Detection in Sensor Networks

We present a system to analyze time‐series data in sensor networks. Our approach supports exploratory tasks for the comparison of univariate, geo‐referenced sensor data, in particular for anomaly detection. We split the recordings into fixed‐length patterns and show them in order to compare them over time and space using two linked views. Apart from geo‐based comparison across sensors we also support different temporal patterns to discover seasonal effects, anomalies and periodicities. The methods we use are best practices in the information visualization domain. They cover the daily, the weekly and seasonal and patterns of the data. Daily patterns can be analyzed in a clustering‐based view, weekly patterns in a calendar‐based view and seasonal patters in a projection‐based view. The connectivity of the sensors can be analyzed through a dedicated topological network view. We assist the domain expert with interaction techniques to make the results understandable. As a result, the user can identify and analyze erroneous and suspicious measurements in the network. A case study with a domain expert verified the usefulness of our approach.     

Light extinction by fine atmospheric particles in the White Mountains region of New Hampshire and its relationship to air mass transport

Chemical, optical, and physical measurements of fine aerosols (aerodynamic diameter < or = 2.5 microm) have been performed at a mountaintop location adjacent to the White Mountain National Forest in northern NH, USA. A 1-month long sampling campaign was conducted at Cranmore Mountain during spring 2000. We report on the apportionment of light extinction by fine aerosols into its major chemical components, and relationships between variations in aerosol parameters and changes in air mass origin. Filter-based, 24-h integrated samples were collected and analyzed for major inorganic ions, as well as organic (OC), elemental (EC), and total carbon. Light scattering and light absorption coefficients were measured at 5-min intervals using an integrating nephelometer and a light absorption photometer. Fine particle number density was measured with a condensation particle counter. Air mass origins and transport patterns were investigated through the use of 3-day backward trajectories and a synoptic climate classification system. Two distinct transport regimes were observed: (1) flow from the north/northeast (N/NE) occurred during 9 out of 18 sample-days; and (2) flow from the west/southwest (W/SW) occurred 8 out of 18 sample-days. All measured and derived aerosol and meteorological parameters were separated into two categories based on these different flow scenarios. During W/SW flow, higher values of aerosol chemical concentration, absorption and scattering coefficients, number density, and haziness were observed compared to N/NE flow. The highest level of haziness was associated with the climate classification Frontal Atlantic Return, which brought polluted air into the region from the mid-Atlantic corridor. Fine particle mass scattering efficiencies of (NH4)2SO4 and OC were 5.35 +/- 0.42 m2 g(-1) and 1.56 +/- 0.40 m2 g(-1), respectively, when transport was out of the N/NE. When transport was from the W/SW the values were 4.94 +/- 0.68 m2 g(-1) for (NH4)2SO4 and 2.18 +/- 0.91 m2 g(-1) for OC. EC mass absorption efficiency when transport was from the N/NE was 9.66 +/- 1.06 m2 g(-1) and 10.80 +/- 1.76 m2 g(-1) when transport was from the W/SW. Results from this work can be used to predict visual air quality in the White Mountain National Forest based on a forecasted synoptic climate classification and its associated visibility.     

Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?

Machine Learning (ML), or the ability of self-learning computer algorithms to autonomously structure and interpret data, is a methodological approach to solve complicated optimization problems based on abundant data. ML is recently gaining momentum as algorithmic applications, computing potency, and available data sets increased manifold over the past two decades, providing an information-rich environment in which human reasoning can partially be replaced by computer reasoning. In this paper, we want to assess the implications of ML for Design of Experiments (DoE), a statistical methodology widely used in Quality Management for quantifying effects and interactions of factors with influence on the production quality or the process yield. We specifically want to assess the future role and importance of DoE: Will it remain unaltered by ML, will it be made obsolete, or will it be reinforced? With this, we want to contribute to the discussion of the future use of traditional Quality Management methodologies in production, as our ML assessment can in principle be applied to other statistical methodologies as well. While we are convinced that ML will heavily impact the field of Quality Management and its predominant set of statistical methodologies, we find reason to expect that this impact will be a mutual one. As this is the first paper addressing the joint force potential of the two methodologies ML and DoE, we expect a range of follow-up papers being written on the subject and a spark in specialized applications addressing DoE's ML-enhanced vital functionality for process improvements.     

Indexing the solution space: a new technique for nearest neighborsearch in high-dimensional space

Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest-neighbor search which corresponds to a computation of the Voronoi cell of each data point. In a second step, we store conservative approximations of the Voronoi cells in an index structure efficient for high-dimensional data spaces. As a result, nearest neighbor search corresponds to a simple point query on the index structure. Although our technique is based on a precomputation of the solution space, it is dynamic, i.e., it supports insertions of new data points. An extensive experimental evaluation of our technique demonstrates the high efficiency for uniformly distributed as well as real data. We obtained a significant reduction of the search time compared to nearest neighbor search in other index structures such as the X-tree