Acoustic emission (AE) measurements to determine the dynamic flying characteristics of optical sliders

Flying heads carrying a magnetic coil and a high numerical aperture lens for magneto-optic recording or a solid immersion lens for optical phase change recording, respectively, are promising approaches for increasing the data density. A classic approach to dynamically evaluate the flight attitude of flying heads in data storage is acoustic emission (AE) testing of the head/disk interaction using special glide heads. In our paper we introduce the application of AE test heads for measuring the flying characteristics of optical far field and near field sliders. In comparison to conventional external AE sensors, i.e. sensors mounted somewhere in the drive, these AE elements are directly mounted on the sliders thus yielding a higher pulse shape and a better signal to noise ratio. This set-up allows a clear identification of the head disk contact. To calibrate the sensor a bump disk was fabricated and used for AE measurements.     

Integrated Simulation of Machine Tool and Process Interaction for Turning

The interaction between process and machine tool behaviour can lead to process instabilities in terms of self‐excited vibrations where the energy of the machine tool oscillation is generated by process excitation. The regenerative chatter effects lead to wavy surfaces on the work‐piece. This effect has been simulated for turning processes with an integrated simulation approach, which couples a time domain simulation model for the machine tool and the workpiece with an analytical turning model. In this paper a procedure is illustrated for coupling an FEA‐based 3‐dimensional turning model with the time domain model for the machine tool under consideration of the resulting workpiece surface. In comparison to an analytical approach for calculating the mechanical tool load, the 3D‐FEA‐model has the potential to determine the resulting cutting forces for even complex‐shaped tool geometries, e.g. a complicated chip breaker or a varying cutting edge radius on the main or minor cutting edge. As a matter of the huge model size the calculation time in particular for 3‐dimensional problems is comparatively long to analytical cutting force models. Therefore, in this paper an approach to reduce the calculation time by using characteristic diagrams for the calculated process forces in the FEA‐model is presented. The research has also been focused on the current major problem in the FEA‐based modelling that the thrust and feed forces are generally underestimated in the simulation.     

Cognitive ability predicts objectively measured counterproductive work behaviors.

Over the past 2 decades, increasing attention has been directed at the relationship between individual differences and counterproductive work behaviors (CWB). However, most of this research has focused on personality variables as potential predictors of CWB; surprisingly little research has investigated the link between counterproductivity and cognitive ability. This study presents the first focal investigation of the cognitive ability-CWB relationship. The authors measured organizational and interpersonal CWB using organizational records of formally recorded incidents (e.g., destruction of property, physical violence). In a predictive study, for a large sample of law enforcement job applicants, a standardized psychometric test of cognitive ability predicted CWB, whereas educational attainment did not. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Melting and crystallization of UHMWPE skived film

Commercial skived film from ultra-high molecular-weight polyethylene (UHMWPE) with considerable uniaxial orientation of lamellae is studied by ultra-small-angle X-ray scattering (USAXS) and wide-angle X-ray scattering (WAXS) during melting and crystallization in order to identify its mechanisms of crystallization. For the analysis of the nanostructure two-dimensional USAXS patterns are analyzed by means of the multidimensional chord distribution function (CDF) method. WAXS shows that crystallization is always isotropic and fast. WAXS reflections are observed before—under certain processing conditions—the SAXS pattern becomes anisotropic. Thus crystallization is decoupled from a slower process of oriented nanostructure formation (nanoforming). If nanoforming is performed isothermally at 105 °C, the evolving nanodomain layers obtain some preferential orientation, as long as the orientation of the melt has not previously been erased by melt-annealing at temperatures of 140 °C or above. Crystallization at temperatures ≥110 °C followed by quenching leads to isotropic nanostructure. Although crystallization is always observed early in the WAXS patterns, the USAXS patterns exhibit only weak discrete scattering during isothermal treatment at temperatures of 110 °C and higher. At 105 °C anisotropic isothermal nanoforming starts after 1.5 min. The melting of the original material resembles an inverted random car-parking mechanism. Only next-neighbor correlations are observed among the crystalline layers. The average nanodomain layer thickness is only slightly increasing (26–30 nm), whereas the long period increase is strong (from 60 nm to 140 nm).     

Microwave radiometry for cement kiln temperature measurements.

The maximum temperature inside a cement kiln is a critical operating parameter, but is often difficult or impossible to measure. We present here the first data that show a correlation between cement kiln temperature measured using a microwave radiometer and product chemistry over an eight-hour period. The microwave radiometer senses radiation in the 12-13 GHz range and has been described previously [Stephan and Pearce (2002), JMPEE 37: 112-124].     

Fast Image‐Based Modeling of Astronomical Nebulae

Astronomical nebulae are among the most complex and visually appealing phenomena known outside the bounds of the Solar System. However, our fixed vantage point on Earth limits us to a single known view of these objects, and their intricate volumetric structure cannot be recovered directly. Recent approaches to reconstructing a volumetric 3D model use the approximate symmetry inherent to many nebulae, but require several hours of computation time on large multi‐GPU clusters. We present a novel reconstruction algorithm based on group sparsity that reaches or even exceeds the quality of prior results while taking only a fraction of the time on a conventional desktop PC, thereby enabling end users in planetariums or educational facilities to produce high‐quality content without expensive hardware or manual modeling. In principle, our approach can be generalized to other transparent phenomena with arbitrary types of user‐specified symmetries.     

A multi-sensor climatological view of double ITCZs over the Indian Ocean

We characterize the climatological features of the double inter-tropical convergence zones (DITCZs) over the western Indian Ocean during November–December by a synergistic analysis of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS III) data (1988–2005) and the National Aeronautics and Space Administration's (NASA's) A-Train data (2002–2009). We investigate rainfall, freshwater flux and cloud liquid water, cloud fraction and relative humidity over the DITCZs. In addition, the daily rainfall data from the Global Precipitation Climatology Project (GPCP) are used to document the DITCZs during the El Niño southern oscillation (ENSO) events. An analysis of the GPCP data shows that the DITCZs are clearly discernible during strong ENSO events (1997, 2002 and 2006), in sharp contrast to the DITCZs in the eastern Pacific Ocean, where they are absent during ENSOs. Further, these convergence zones on either side of the equator are of short duration, approximately 3–6 pentads during November and December. All satellite sensor data sets consistently capture the major features of DITCZs. As an accurate simulation of DITCZs in coupled global climate models remains a challenge, the results from the present study would provide a platform for evaluating these models.     

Automatic learners with feedback queries

Automatic classes are classes of languages for which a finite automaton can decide whether a given element is in a set given by its index. The present work studies the learnability of automatic families by automatic learners which, in each round, output a hypothesis and update a long-term memory, depending on the input datum, via an automatic function. Many variants of automatic learners are investigated: where the long-term memory is restricted to be the current hypothesis whenever this exists, cannot be of length larger than the length of the longest datum seen, or has to consist of a constant number of examples seen so far. Learnability is also studied with respect to queries which reveal information about past data or past computation history; the number of queries per round is bounded by a constant.