Characteristics of Metal Droplets in Slag Tapped from the Blast Furnace

Slag samples, hot‐metal samples and hot‐metal temperatures were obtained during tapping of two blast furnaces. Sampling was carried out at different time points during tapping of three separate heats. The size distribution and composition of metal droplets found in the slag were determined using scanning electron microscopy. Only metal droplets above 0.75 μm could be counted and analysed. All droplets were below 8 μm in diameter and the great majority of these droplets were found to be between 0.75 and 2 μm. The size distribution did not differ significantly for different slag samples. Iron was the main droplet component. Electron probe microanalysis showed that the droplets contained small amounts of carbon. The percentage of the area in a studied cross‐section that was covered with metal droplets varied between 0.01 and 0.07%. Calculations based on Stoke's law showed that the distance droplets travel in the slag is in the micron range. Slag samples taken in the beginning of slag tapping contained more droplets than those taken in the middle of slag tapping, an indication that most droplets can be found in the area near the furnace wall. Some droplets were determined to have magnesium enrichment at the external surface.     

Pollution Buildup on Road Surfaces

Samples of sediment found on an urban road in Aberdeen (Scotland) were collected by washing designated surfaces. This method, called the “wet” method, was capable of collecting sediment of the smallest particle size range that is normally left behind by traditional sampling techniques using dry vacuuming. Over 17 months (mainly on a weekly basis), 66 samples were collected and analyzed for sediment loading, particle size distribution, concentration of heavy metals (Zn, Cu, Pb, Cd) in four sediment size fractions, and several dissolved pollutants in the effluent collected from washing (NO3?,?SO42?,?PO43?,?Cl?,?F?,?NH4+, total organic carbon, total carbon). Standard statistical methods, including multiple regressions, were used to determine relationships amongst different sediment characteristics. It was found that sediment loading, as well as concentrations of Cl? and SO42?, were highest in the winter months, especially when snow was present on the road surface. It was observed that 66% of total road sediment loading was found within a 0.5 m strip next to the curb. The average sediment particle sizes found were smaller than those previously recorded in the literature. As expected, the concentrations of heavy metals were highest in the smallest particle size fraction analyzed (<63?μm), and this occurred during the summer months when less sediment was available on the street surface. The antecedent dry weather period had a very weak and negative influence on the loading rate of the smallest particle size fraction next to the curb.     

Optimal decoder for block-transform based video coders

In this paper, we introduce a new decoding algorithm for DCT-based video encoders, such as Motion JPEG (M-JPEG), H26x, or MPEG. This algorithm considers not only the compression artifacts but also the ones due to transmission, acquisition or storage of the video. The novelty of our approach is to jointly tackle these two problems, using a variational approach. The resulting decoder is object-based, allowing independent and adaptive processing of objects and backgrounds, and considers available information provided by the bitstream, such as quantization steps, and motion vectors. Several experiments demonstrate the efficiency of the proposed method. Objective and subjective quality assessment methods are used to evaluate the improvement upon standard algorithms, such as the deblocking and deringing filters included in MPEG-4 postprocessing.     

Distributed Job Scheduling in Rings

We give a distributed approximation algorithm for job scheduling in a ring architecture. In contrast to many other parallel scheduling models, the model we consider captures the influence of the underlying communications network by specifying that task migration from one processor to another takes time proportional to the distance between those two processors in the network. As a result, our algorithm must balance computational load and communication time. The algorithm is simple, requires no global control, and yields schedules of length at most 4.22 times optimal. We also give a lower bound on the performance of any distributed algorithm and the results of simulation experiments which suggest better performance than does our worst-case analysis.     

Remotely sensed indicators of habitat heterogeneity: Use of synthetic aperture radar in mapping vegetation structure and bird habitat

An integrated remote sensing/field ecology project linked the use of synthetic aperture radar (SAR) and aerial photography to studies of landscape spatial heterogeneity and bird community ecology. P-, L-, and C-band SAR data, collected over a section of Kakadu National Park in Australia's Northern Territory during the Joint NASA/Australia DC-8 data acquisition campaign, were analyzed in light of field data integrating vegetation structure and floristics with bird abundances across a heterogeneous study site. Results indicate that SAR data are able to discern structural differences relevant to bird habitat quality within floristically homogeneous stands, while multispectral sensors successfully identified floristic differences among habitat types. Simplifying indices of bird diversity showed ambiguous changes across the site; however, the abundances of individual species were observed to change significantly across both floristic and structural gradients. These results suggest that efforts to map bird diversity should focus on species-specific habitat relationships and that some measure of vegetation structure is needed to understand bird habitat. The approach employed here advances the use of SAR data in the three-dimensional mapping of animal habitats from remotely sensed data, and extends current capabilities for mapping and modeling large-scale patterns in the distribution of biological diversity.     

Evaluating the Endophenotype Model of ADHD Neuropsychological Deficit: Results for Parents and Siblings of Children With ADHD Combined and Inattentive Subtypes.

Neurogenetic models predict neuropsychological weaknesses in the relatives of children with attention-deficit/ hyperactivity disorder (ADHD). The authors examined executive and regulatory measures in 386 relatives (307 parents, 79 siblings) of children with ADHD combined type, ADHD inattentive type, and controls. Predicted deficits were seen on trailmaking (relatives of ADHD combined type only), stop-signal reaction times (relatives of girls only), and response variability (mothers only) but not on naming or output speed. Effects generally held, even with relatives' ADHD status controlled. A neuropsychologically impaired subgroup of children with ADHD had relatives with clear neuropsychological weaknesses. The authors conclude that a neurogenetic model of ADHD etiology is supportable only for a subset of executive functions and that neuropsychological heterogeneity warrants more examination in ADHD. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Role of Melodic and Temporal Cues in Perceiving Musical Meter.

A number of different cues allow listeners to perceive musical meter. Three experiments examined effects of melodic and temporal accents on perceived meter in excerpts from folk songs scored in 6/8 or 3/4 meter. Participants matched excerpts with 1 of 2 metrical drum accompaniments. Melodic accents included contour change, melodic leaps, registral extreme, melodic repetition, and harmonic rhythm. Two experiments with isochronous melodies showed that contour change and melodic repetition predicted judgments. For longer melodies in the 2nd experiment, variables predicted judgments best at the beginning of excerpts. The final experiment, with rhythmically varied melodies, showed that temporal accents, tempo, and contour change were the strongest predictors of meter. The authors' findings suggest that listeners combine multiple melodic and temporal features to perceive musical meter. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Speech Recognition and In-Vehicle Telematics Devices: Potential Reductions in Driver Distraction

Speech Recognition is frequently cited as a potential remedy to distraction resulting from drivers' operation of in-vehicle devices. This position typically assumes that the introduction of speech recognition will result in reduced cognitive workload and improved driving performance. Past research neither fully supports nor fully discounts this assumption. However, it is difficult to compare many of these studies, due to differences in device operation tasks, the pacing of those tasks, speech recognition system performance, and system interface designs. In an effort to directly address the effect of voice recognition on driver distraction, the present authors developed a capability to manipulate the performance characteristics of a speech recognition system through a Wizard of Oz speech recognition system and installed this system in a simulated driving environment. The sensitivity of the simulated driving environment and speech recognition accuracy manipulation were evaluated in an initial study comparing driver cognitive workload and driving performance during self-paced simulated operation of a personal digital assistant (PDA) during no PDA use, manual control of the PDA, and speech control of the PDA. In the Speech PDA condition, speech recognition accuracy was varied between drivers. Analysis of drivers' emergency braking response times and rated cognitive workload revealed significantly lower cognitive demand and better performance in the No PDA condition when compared to the Manual PDA condition. The Speech PDA condition resulted in response times and rated cognitive workload levels that were between the No PDA and Manual PDA conditions, but not significantly different from either of these conditions. Further analysis of emergency braking performance revealed a non-significant trend towards better performance in conjunction with higher speech recognition accuracy levels. The potential for reducing driver distraction through the careful development and evaluation of speech recognition systems is discussed.     

A comparative evaluation of dental luting cements by fracture toughness tests and fractography

In recent years there has been a shift from traditional methods of investigating dental materials to a fracture mechanics approach. Fracture toughness (KIC) is an intrinsic material property which can be considered to be a measure of a material's resistance to crack propagation. Glass-ionomer cements are biocompatible and bioactive dental restorative materials, but they suffer from poor fracture toughness and are extremely susceptible to dehydration. The main objective of this study was to evaluate the fracture toughness of three types of commercially available dental cements (polyacid-modified composite resin, resin-modified and conventional glass ionomer) using a short-rod chevron-notch test and to investigate and interpret the results by means of fractography using scanning electron microscopy. Ten specimens of each cement were fabricated according to manufacturers' instructions, coated in varnish, and stored at ambient laboratory humidity, 100 per cent relative humidity, or in water at 37 degrees C for 7 days prior to preparation for testing. Results indicated that significant differences existed between each group of materials and that the fracture toughness ranged from 0.27 to 0.72 MN/m3/2. It was concluded that the resin-modified glass-ionomer cement demonstrated the highest resistance to crack propagation. Fractographs clearly showed areas of stable and unstable crack growth along the fractured surfaces for the three materials examined.     

Data Work: How Energy Advisors and Clients Make IoT Data Accountable

We present fieldwork findings from the deployment of an interactive sensing system that supports the work of energy advisors who give face-to-face advice to low-income households in the UK. We focus on how the system and the data it produced are articulated in the interactions between professional energy advisors and their clients, and how they collaboratively anticipate, rehearse, and perform data work. In addition to documenting how the system was appropriated in advisory work, we elaborate the ‘overhead cost’ of building collaborative action into connected devices and sensing systems, and the commensurate need to support discrete workflows and accountability systems to enable the methodical incorporation of the IoT into collaborative action. We contribute an elaboration of the social, collaborative methods of data work relevant to those who seek to design and study collaborative IoT systems.