Processing of porous glass ceramics from highly crystallisable industrial wastes

This study was carried out to gain understanding about the sintering behaviour of highly crystallisable industrial waste derived silicate mixtures under direct heating and rapid cooling conditions. The materials used in this study were plasma vitrified air pollution control waste and rejected pharmaceutical borosilicate glass. Powder compacts sintered under direct heating conditions were highly porous; compacts with particle size <38 μm reached a maximum density of 2.74 g cm-3 at 850°C, whereas compacts with particles of size <100 and <250 mm reached maximum densities of 2.69 and 2.72 g cm-3 at 875 and 900°C respectively. Further increase in sintering temperature resulted in a rapid decrease in density of the glass ceramics. Image analysis results were used to link the sudden drop in density to the increase in volume of microsized pores formed in the samples during sintering. In particular, compacts made from ,38 mm particles sintered at 9508C resulted in 65 vol.-% porosity with a pore size of <20 μm. Such materials can be used for sound and thermal insulation purposes

Distance‐based time series classification approach for task recognition with application in surgical robot autonomy

BackgroundRobotic‐assisted surgery allows surgeons to perform many types of complex operations with greater precision than is possible with conventional surgery. Despite these advantages, in current systems, a surgeon should communicate with the device directly and manually. To allow the robot to adjust parameters such as camera position, the system needs to know automatically what task the surgeon is performing.MethodsA distance‐based time series classification framework has been developed which measures dynamic time warping distance between temporal trajectory data of robot arms and classifies surgical tasks and gestures using a k‐nearest neighbor algorithm.ResultsResults on real robotic surgery data show that the proposed framework outperformed state‐of‐the‐art methods by up to 9% across three tasks and by 8% across gestures.ConclusionThe proposed framework is robust and accurate. Therefore, it can be used to develop adaptive control systems that will be more responsive to surgeons’ needs by identifying next movements of the surgeon. Copyright © 2016 John Wiley & Sons, Ltd.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138333/1/rcs1766.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138333/2/rcs1766_am.pd

Automated robot‐assisted surgical skill evaluation: Predictive analytics approach

BackgroundSurgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot‐assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.MethodsEight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise – novice and expert. Three classification methods – k‐nearest neighbours, logistic regression and support vector machines – are applied.ResultsThe result shows that the proposed framework can classify surgeons’ expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.ConclusionThis study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/1/rcs1850.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/2/rcs1850_am.pd

Target highlights in CASP14 : Analysis of models by structure providers

Abstract The biological and functional significance of selected CASP14 targets are described by the authors of the structures. The authors highlight the most relevant features of the target proteins and discuss how well these features were reproduced in the respective submitted predictions. The overall ability to predict three-dimensional structures of proteins has improved remarkably in CASP14, and many difficult targets were modelled with impressive accuracy. For the first time in the history of CASP, the experimentalists not only highlighted that computational models can accurately reproduce the most critical structural features observed in their targets, but also envisaged that models could serve as a guidance for further studies of biologically-relevant properties of proteins. This article is protected by copyright. All rights reserved.Peer reviewe

Development of novel dense glass-ceramics from combination of silicate waste

In the present study, Plasma Vitrified air pollution control Waste (PVW) and recycled pharmaceutical Borosilicate Glass (BSG) are used to form novel glass-ceramic composites. The sinterability of PVW and 11vol%BSG mixtures at different annealing temperature, holding time and particle size are investigated. A simple powder technology processing approach has been followed to ease the number of processing parameters such as heating and cooling rate. The highest density of 2.74g/cm3 of new glass-ceramics was achieved for <38\ub5m particle size powder compacts sintered at 850\ub0C for 30min. Particle sizes of <100\ub5m and <250\ub5m processed under similar conditions showed maximum densities of 2.69 and 2.72g/cm3 at 875\ub0C and 900\ub0C, respectively. The XRD measurements have shown the crystallisation of anorthite and wollastonite phases for the above mentioned processing temperatures, while the complete crystallisation for all samples was observed at 950\ub0C and 30min sintering time. A drastic change in color is observed for different particle sized glass-ceramic composites indicating microstructural differences. This preliminary investigation confirms that the powder route of optimised mixtures of PVW and BSG is suitable to produce glass-ceramics with low porosity for structural applications

Review. Functional glasses and glass-ceramics derived from iron rich waste and combination of industrial residues

Wastes from industrial processes and energy generation facilities pose significant environmental and health issues. Diversion of waste from landfill to favour reuse or recycling options and towards the fabrication of marketable products is of high economic and ecologic interest. Moreover safe recycling of industrial wastes is necessary and even vital to our society because of the dramatically increasing volume being generated. Glasses and glass-ceramics attract particular interest in waste recycling concepts. Novel and/or improved glass-based products from wastes should meet a variety of demands, among which the functional requirements are paramount. The investigations reviewed in this paper focusing on iron rich waste materials demonstrate the potential of turning these silicate based wastes into functional glass-based products. By properly selecting iron oxide containing residues and processing parameters, functional glass-based products with suitable catalytic activity, magnetic, optical and electrical properties can be obtained. The possibility of fabricating highly porous materials using different types of wastes for sound and thermal insulating as well as catalytic support applications is also discussed based on literature results. Thus, porosity can be considered to achieve particular properties in waste derived products