Thermosets : structure, properties and applications

Structure-property relationships of thermosets are important in the manufacture and application of materials. Understanding the desired properties of a material in a certain application is related to the material's structure and vice versa. The way in which the material is processed is also a determinant of the structure and resulting properties. Many books have been written about the chemistry of thermosets but with only brief consideration of structure-property relationships. This book focuses on how the structure and properties of a range of thermosets affect the final material and applications. It is composed of two parts: I Structure and properties of thermosets and II Applications of thermosets. Part I starts with a comprehensive overview of thermosets covering structure, properties and processing for advanced applications, followed by four chapters addressing mechanical properties, thermal properties, rheology, and nanostructures and toughening. The applications presented in Part II range from the use of thermosets in the building and construction industry to aerospace applications, electrical applications, thermoset adhesives and insulation materials in appliances and other applications. We hope that this book will not only be a useful textbook for advanced undergraduate and postgraduate students, but also a concise reference for researchers in academia and engineers in related industries. I would like to express my sincere gratitude to the staff of Woodhead Publishing Limited, especially Kathryn Picking who invited me to edit this book and helped develop the initial content, also Adam Hooper, Helen Bradley, Emily Cole, Francis Dodds and Rachel Cox for their assistance in many ways during the preparation of the manuscript. Finally, I wish to express my appreciation and respects to all the contributors for their commitment, patience and pleasant cooperation.

Recommendations for improved data processing from expired gas analysis indirect calorimetry

There is currently no universally recommended and accepted method of data processing within the science of indirect calorimetry for either mixing chamber or breath-by-breath systems of expired gas analysis. Exercise physiologists were first surveyed to determine methods used to process oxygen consumption ([OV0312]O 2) data, and current attitudes to data processing within the science of indirect calorimetry. Breath-by-breath datasets obtained from indirect calorimetry during incremental exercise were then used to demonstrate the consequences of commonly used time, breath and digital filter post-acquisition data processing strategies. Assessment of the variability in breath-by-breath data was determined using multiple regression based on the independent variables ventilation (VE), and the expired gas fractions for oxygen and carbon dioxide, FEO 2 and FECO2, respectively. Based on the results of explanation of variance of the breath-by-breath [OV0312]O2 data, methods of processing to remove variability were proposed for time-averaged, breath-averaged and digital filter applications. Among exercise physiologists, the strategy used to remove the variability in sequential [OV0312]O2 measurements varied widely, and consisted of time averages (30 sec [38%], 60 sec [18%], 20 sec [11%], 15 sec [8%]), a moving average of five to 11 breaths (10%), and the middle five of seven breaths (7%). Most respondents indicated that they used multiple criteria to establish maximum [OV0312]O 2 ([OV0312]O2max) including: the attainment of age-predicted maximum heart rate (HRmax) [53%], respiratory exchange ratio (RER) >1.10 (49%) or RER >1.15 (27%) and a rating of perceived exertion (RPE) of >17, 18 or 19 (20%). The reasons stated for these strategies included their own beliefs (32%), what they were taught (26%), what they read in research articles (22%), tradition (13%) and the influence of their colleagues (7%). The combination of VE, FEO 2 and FECO2 removed 96-98% of [OV0312]O2 breath-by-breath variability in incremental and steady-state exercise [OV0312]O2 data sets, respectively. Correction of residual error in [OV0312]O2 datasets to 10% of the raw variability results from application of a 30-second time average, 15-breath running average, or a 0.04 Hz low cut-off digital filter. Thus, we recommend that once these data processing strategies are used, the peak or maximal value becomes the highest processed datapoint. Exercise physiologists need to agree on, and continually refine through empirical research, a consistent process for analysing data from indirect calorimetry.

Parallel monotone spline interpolation and approximation on GPUs

Monotonicity preserving interpolation and approximation have received substantial attention in the last thirty years because of their numerous applications in computer aided-design, statistics, and machine learning [9, 10, 19]. Constrained splines are particularly popular because of their flexibility in modeling different geometrical shapes, sound theoretical properties, and availability of numerically stable algorithms [9,10,26]. In this work we examine parallelization and adaptation for GPUs of a few algorithms of monotone spline interpolation and data smoothing, which arose in the context of estimating probability distributions.

Scaffolding, analysis and materials: contributing factors in an unexpected finding of advanced infant/toddler pretend play?

As part of a longitudinal study, infant/toddler pretend play development and maternal play modelling were investigated in dyadic context. A total of 21 children were videotaped in monthly play sessions with their mothers, from age 8 to 17 months. Child and mother pretend play frequencies and levels were measured using Brown’s Pretend Play Observation Scale. Child IQ assessments at 5 years (Stanford–Binet IV) indicated average to high ability levels (M = 122.62). Descriptive analyses showed that children’s levels of pretend development were markedly in advance of age-typical expectations. With a previous analysis showing no specific associations between play levels and IQ, intensive maternal scaffolding, data analysis approaches and use of abstract play materials are proposed as possible contributory factors to the children’s advanced pretend play development.

Digital marketing of unhealthy foods to Australian children and adolescents

The emergence of new media—including branded websites, social media and mobile applications—has created additional touch points for unhealthy food and beverage companies to target children and adolescents. The aim of this study was to perform an audit of new media for three top selling food and beverage brands in Australia. The top selling brand in three of the most advertised food and beverage categories was identified. Facebook, websites and mobile phone applications from these three brands were assessed using a combination of descriptive analyses and structured data collection during June and July 2013. Information on target audience, main focus of the activity, marketing strategies employed and connectivity were collected. Promotional activities were assessed against industry self-regulatory codes. McDonald's, Coca-Cola and Cadbury Dairy Milk were audited, with 21 promotional activities identified. These promotional activities appeared to use a number of marketing strategies, with frequent use of indirect product association, engagement techniques and branding. We identified strategic targeting of both children and adolescents. We found that while all promotional activities technically met self-regulatory codes (usually due to media-specific age restrictions) a number appeared to employ unhealthy food or beverage marketing directed to children. Brands are using engaging content via new media aimed at children and adolescents to promote unhealthy food and beverages. Given the limitations of self-regulatory codes in the context of new media, strategies need to be developed to reduce exposure of children and adolescents to marketing of unhealthy food and beverage products via these avenues.

Smartphone apps to improve fitness and increase physical activity among young people: protocol of the Apps for IMproving FITness (AIMFIT) randomized controlled trial

BACKGROUND: Physical activity is a modifiable behavior related to many preventable non-communicable diseases. There is an age-related decline in physical activity levels in young people, which tracks into adulthood. Common interactive technologies such as smartphones, particularly employing immersive features, may enhance the appeal and delivery of interventions to increase levels of physical activity in young people. The primary aim of the Apps for IMproving FITness (AIMFIT) trial is to evaluate the effectiveness of two popular "off-the-shelf" smartphone apps for improving cardiorespiratory fitness in young people.

METHODS/DESIGN: A three-arm, parallel, randomized controlled trial will be conducted in Auckland, New Zealand. Fifty-one eligible young people aged 14-17 years will be randomized to one of three conditions: 1) use of an immersive smartphone app, 2) use of a non-immersive app, or 3) usual behavior (control). Both smartphone apps consist of an eight-week training program designed to improve fitness and ability to run 5 km, however, the immersive app features a game-themed design and adds a narrative. Data are collected at baseline and 8 weeks. The primary outcome is cardiorespiratory fitness, assessed as time to complete the one mile run/walk test at 8 weeks. Secondary outcomes are physical activity levels, self-efficacy, enjoyment, psychological need satisfaction, and acceptability and usability of the apps. Analysis using intention to treat principles will be performed using regression models.

DISCUSSION: Despite the proliferation of commercially available smartphone applications, there is a dearth of empirical evidence to support their effectiveness on the targeted health behavior. This pragmatic study will determine the effectiveness of two popular "off-the-shelf" apps as a stand-alone instrument for improving fitness and physical activity among young people. Adherence to app use will not be closely controlled; however, random allocation of participants, a heterogeneous group, and data analysis using intention to treat principles provide internal and external validity to the study. The primary outcome will be objectively assessed with a valid and reliable field-based test, as well as the secondary outcome of physical activity, via accelerometry. If effective, such applications could be used alongside existing interventions to promote fitness and physical activity in this population. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12613001030763. Registered 16 September 2013.

The adaptable buffer algorithm for high quantile estimation in non-stationary data streams

The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semi-automatic surveillance analytics systems which detect abnormalities in close-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the memory for storing observations is limited. We make several major contributions: (i) we derive an important theoretical result which shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data, (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings, (iii) we introduce a novel algorithm which implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity, and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic data sets and three large 'real-world' streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high valued and the available buffer capacity severely limited.

Two maximum entropy-based algorithms for running quantile estimation in nonstationary data streams

The need to estimate a particular quantile of a distribution is an important problem that frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semiautomatic surveillance analytics systems that detect abnormalities in close-circuit television footage using statistical models of low-level motion features. In this paper, we specifically address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We make the following several major contributions: 1) we highlight the limitations of approaches previously described in the literature that make them unsuitable for nonstationary streams; 2) we describe a novel principle for the utilization of the available storage space; 3) we introduce two novel algorithms that exploit the proposed principle in different ways; and 4) we present a comprehensive evaluation and analysis of the proposed algorithms and the existing methods in the literature on both synthetic data sets and three large real-world streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that both of the proposed methods are highly successful and vastly outperform the existing alternatives. We show that the better of the two algorithms (data-aligned histogram) exhibits far superior performance in comparison with the previously described methods, achieving more than 10 times lower estimate errors on real-world data, even when its available working memory is an order of magnitude smaller.

Correlated differential privacy: hiding information in non-IID data set

Privacy preserving on data mining and data release has attracted an increasing research interest over a number of decades. Differential privacy is one influential privacy notion that offers a rigorous and provable privacy guarantee for data mining and data release. Existing studies on differential privacy assume that in a data set, records are sampled independently. However, in real-world applications, records in a data set are rarely independent. The relationships among records are referred to as correlated information and the data set is defined as correlated data set. A differential privacy technique performed on a correlated data set will disclose more information than expected, and this is a serious privacy violation. Although recent research was concerned with this new privacy violation, it still calls for a solid solution for the correlated data set. Moreover, how to decrease the large amount of noise incurred via differential privacy in correlated data set is yet to be explored. To fill the gap, this paper proposes an effective correlated differential privacy solution by defining the correlated sensitivity and designing a correlated data releasing mechanism. With consideration of the correlated levels between records, the proposed correlated sensitivity can significantly decrease the noise compared with traditional global sensitivity. The correlated data releasing mechanism correlated iteration mechanism is designed based on an iterative method to answer a large number of queries. Compared with the traditional method, the proposed correlated differential privacy solution enhances the privacy guarantee for a correlated data set with less accuracy cost. Experimental results show that the proposed solution outperforms traditional differential privacy in terms of mean square error on large group of queries. This also suggests the correlated differential privacy can successfully retain the utility while preserving the privacy.

Synthesis and characterisation of graphene hybrid nanoarchitechures for potential sensing applications

 The main focus of our project is to find a novel method to construct graphene hybrid systems and functionalised AuNPs with graphene which opens a new pathway for the potential and highly sensing applications in the area of graphene hybrid nanoarchitecture such as actuators and touch sensors. Adsorption of different CH3 and COOH alkanethiols on the surface of modified Au electrode with different CRGO's sheets to increase the efficient electron pathways for the development of new class graphene electrodes.

Networking for big data

Big data is an emerging hot research topic due to its pervasive application in human society, such as government, climate, finance, and science. Currently, most research work on big data falls in data mining, machine learning, and data analysis. However, these amazing top-level killer applications would not be possible without the underneath support of networking due to their extremely large volume and computing complexity, especially when real-time or near-real-time applications are demanded.

Optimized fuzzy association rule mining for quantitative data

With the advance of computing and electronic technology, quantitative data, for example, continuous data (i.e., sequences of floating point numbers), become vital and have wide applications, such as for analysis of sensor data streams and financial data streams. However, existing association rule mining generally discover association rules from discrete variables, such as boolean data (`O' and `l') and categorical data (`sunny', `cloudy', `rainy', etc.) but very few deal with quantitative data. In this paper, a novel optimized fuzzy association rule mining (OFARM) method is proposed to mine association rules from quantitative data. The advantages of the proposed algorithm are in three folds: 1) propose a novel method to add the smoothness and flexibility of membership function for fuzzy sets; 2) optimize the fuzzy sets and their partition points with multiple objective functions after categorizing the quantitative data; and 3) design a two-level iteration to filter frequent-item-sets and fuzzy association-rules. The new method is verified by three different data sets, and the results have demonstrated the effectiveness and potentials of the developed scheme.