A critical overview of how English health and social care publications represent autistic adults’ intimate lives

Autistic people face more social barriers to, and experience greater anxiety around, intimate relationships than the general population in our majority neurotypical society, leading to increased loneliness and social isolation. National health and social care policies and publications should recognise these inequalities and guide service systems in reducing them. In this paper, we employ a document analysis design to analyse a cross-section of English national health and social care publications to investigate how autistic adults’ intimate lives are represented and prioritised in these publications. Most publications do not adequately and proportionally recognise or prioritise autistic people's intimate lives. They focus on the risks associated with sex and relationships and overlook autism-specific intimacy needs. They prioritise participation in the workforce while renouncing government responsibility for supporting intimate relationships which can reduce loneliness and alienation. We offer recommendations to ensure that health and social care publication processes better recognise intimate lives

A critical reflection on the development of the Participatory Autism Research Collective (PARC)

Purpose The Participatory Autism Research Collective (PARC) was initially set up with the purpose of bringing autistic people, including scholars and activists (but not exclusively), together with early career researchers and practitioners who work with autistic people, with the aim being to build a community where those who wished to see more significant involvement of autistic people in autism research could share knowledge and expertise. Approach This article explores the development of the PARC network, reflecting upon its activities and ethos within current Higher Education (HE) practices and structures. Findings In supporting autistic individuals in their attempts to establish themselves within academic systems that may not always be considerate or accommodating, the existence of PARC creates a structure with which autistic people can influence social change. PARC serves as a network of support, strengthening the presence of autistic scholars in academia. It also provides a structure through which autistic people are able to demonstrate helpful practices with which to engage more broadly. Value The PARC network is the first autistic-led venture of its kind in the UK to have a sustained impact. PARC is growing to become an important element in the field of autism studies both by supporting the emerging autistic academics and by promoting ethical and participatory research methods and practices

Alkali activation of vitreous calcium aluminosilicate derived from glass fiber waste

The properties and microstructure of alkali-activated (AA) vitreous calcium aluminosilicate (VCAS) are presented in this paper. VCAS is manufactured from a by-product of the glass fiber industry and has been activated using NaOH and KOH solutions. The microstructure and mechanical properties of AA VCAS pastes and mortars are reported. The results show that depending on the type and concentration of hydroxide solution used, mortar samples with compressive strengths up to 77 MPa can be formed after curing for three days at 65 °C. The research demonstrates the potential of VCAS to produce AA cements and the importance of alkali type and concentration in optimizing properties and microstructure.Mitsuuchi Tashima, M.; Soriano Martinez, L.; Borrachero Rosado, MV.; Monzó Balbuena, JM.; Cheeseman, CR.; Paya Bernabeu, JJ. (2012). Alkali activation of vitreous calcium aluminosilicate derived from glass fiber waste. Journal of Sustainable Cement-Based Materials. 1(3):83-93. doi:10.1080/21650373.2012.742610S839313Mahasenan N, Smith S, Humphreys K. The cement industry and global climate change: current and potential future cement industry CO2emissions. Greenhouse Gas Control Technologies – 6th International Conference. Oxford: Pergamon; 2003. p. 995–1000.Schneider, M., Romer, M., Tschudin, M., & Bolio, H. (2011). Sustainable cement production—present and future. Cement and Concrete Research, 41(7), 642-650. doi:10.1016/j.cemconres.2011.03.019WBCSD – World Business Council for Sustainable Development. Cement industry energy and CO2performance – Getting numbers right. Edited by WBCSD, Geneva-Switzerland (ISBN 978-3-940388-48-3). 2009.Shi, C., Jiménez, A. F., & Palomo, A. (2011). New cements for the 21st century: The pursuit of an alternative to Portland cement. Cement and Concrete Research, 41(7), 750-763. doi:10.1016/j.cemconres.2011.03.016Duxson, P., Fernández-Jiménez, A., Provis, J. L., Lukey, G. C., Palomo, A., & van Deventer, J. S. J. (2006). Geopolymer technology: the current state of the art. Journal of Materials Science, 42(9), 2917-2933. doi:10.1007/s10853-006-0637-zFernández-Jiménez, A., Palomo, A., & Criado, M. (2005). Microstructure development of alkali-activated fly ash cement: a descriptive model. Cement and Concrete Research, 35(6), 1204-1209. doi:10.1016/j.cemconres.2004.08.021Hossain, A. B., Shirazi, S. A., Persun, J., & Neithalath, N. (2008). Properties of Concrete Containing Vitreous Calcium Aluminosilicate Pozzolan. Transportation Research Record: Journal of the Transportation Research Board, 2070(1), 32-38. doi:10.3141/2070-05Neithalath, N., Persun, J., & Hossain, A. (2009). Hydration in high-performance cementitious systems containing vitreous calcium aluminosilicate or silica fume. Cement and Concrete Research, 39(6), 473-481. doi:10.1016/j.cemconres.2009.03.006Tashima MM, Soriano L, Borrachero MV, Monzó J, Payá J. Effect of curing time on the microstructure and mechanical strength development of alkali activated nbinders based on vitreous calcium aluminosilicate (VCAS). Bull. Mater. Sci. in press.Hemmings RT, Nelson RD, Graves PL, Cornelius BJ. White pozzolan composition and blended cements containing same. Patent US6776838. 2004.Provis, J. L., Lukey, G. C., & van Deventer, J. S. J. (2005). Do Geopolymers Actually Contain Nanocrystalline Zeolites? A Reexamination of Existing Results. Chemistry of Materials, 17(12), 3075-3085. doi:10.1021/cm050230iCriado, M., Fernández-Jiménez, A., de la Torre, A. G., Aranda, M. A. G., & Palomo, A. (2007). An XRD study of the effect of the SiO2/Na2O ratio on the alkali activation of fly ash. Cement and Concrete Research, 37(5), 671-679. doi:10.1016/j.cemconres.2007.01.013Rees, C. A., Provis, J. L., Lukey, G. C., & van Deventer, J. S. J. (2007). In Situ ATR-FTIR Study of the Early Stages of Fly Ash Geopolymer Gel Formation. Langmuir, 23(17), 9076-9082. doi:10.1021/la701185gLee, W. K. W., & van Deventer, J. S. J. (2003). Use of Infrared Spectroscopy to Study Geopolymerization of Heterogeneous Amorphous Aluminosilicates. Langmuir, 19(21), 8726-8734. doi:10.1021/la026127eGarcía-Lodeiro, I., Fernández-Jiménez, A., Blanco, M. T., & Palomo, A. (2007). FTIR study of the sol–gel synthesis of cementitious gels: C–S–H and N–A–S–H. Journal of Sol-Gel Science and Technology, 45(1), 63-72. doi:10.1007/s10971-007-1643-6Barbosa VFF. Sintese e caracterização de polissialatos (Synthesis and characterization of polysialates) [PhD thesis] (in Portuguese). Instituto Militar de Engenharia. Rio de Janeiro - Brazil. 190 p. 1999.Bernal, S. A., Rodríguez, E. D., Mejía de Gutiérrez, R., Gordillo, M., & Provis, J. L. (2011). Mechanical and thermal characterisation of geopolymers based on silicate-activated metakaolin/slag blends. Journal of Materials Science, 46(16), 5477-5486. doi:10.1007/s10853-011-5490-zBoccaccini, A. R., Bücker, M., Bossert, J., & Marszalek, K. (1997). Glass matrix composites from coal flyash and waste glass. Waste Management, 17(1), 39-45. doi:10.1016/s0956-053x(97)00035-4Kourti, I., Rani, D. A., Deegan, D., Boccaccini, A. R., & Cheeseman, C. R. (2010). Production of geopolymers using glass produced from DC plasma treatment of air pollution control (APC) residues. Journal of Hazardous Materials, 176(1-3), 704-709. doi:10.1016/j.jhazmat.2009.11.089Lampris, C., Lupo, R., & Cheeseman, C. R. (2009). Geopolymerisation of silt generated from construction and demolition waste washing plants. Waste Management, 29(1), 368-373. doi:10.1016/j.wasman.2008.04.007Wu, H.-C., & Sun, P. (2007). New building materials from fly ash-based lightweight inorganic polymer. Construction and Building Materials, 21(1), 211-217. doi:10.1016/j.conbuildmat.2005.06.052Kourti, I., Amutha Rani, D., Boccaccini, A. R., & Cheeseman, C. R. (2011). Geopolymers from DC Plasma–Treated Air Pollution Control Residues, Metakaolin, and Granulated Blast Furnace Slag. Journal of Materials in Civil Engineering, 23(6), 735-740. doi:10.1061/(asce)mt.1943-5533.000017

Multivariate Statistical Approach for Anomaly Detection and Lost Data Recovery in Wireless Sensor Networks

Data loss due to integrity attacks or malfunction constitutes a principal concern in wireless sensor networks (WSNs). The present paper introduces a novel data loss/modification detection and recovery scheme in this context. Both elements, detection and data recovery, rely on a multivariate statistical analysis approach that exploits spatial density, a common feature in network environments such as WSNs. To evaluate the proposal, we consider WSN scenarios based on temperature sensors, both simulated and real. Furthermore, we consider three different routing algorithms, showing the strong interplay among (a) the routing strategy, (b) the negative effect of data loss on the network performance, and (c) the data recovering capability of the approach. We also introduce a novel data arrangement method to exploit the spatial correlation among the sensors in a more efficient manner. In this data arrangement, we only consider the nearest nodes to a given affected sensor, improving the data recovery performance up to 99%. According to the results, the proposed mechanisms based on multivariate techniques improve the robustness of WSNs against data loss.This work has been partially supported by Spanish MICINN (Ministerio de Ciencia e Innovación) through Project TEC2011-22579, by Spanish MINECO (Ministerio de Economía y Competitividad) through Project TIN2014-60346-R, and the FPU P6A grants program of the University of Granada

Multivariate statistical process control based on principal component analysis: implementation of framework in R

The interest in multivariate statistical process control (MSPC) has increased as the industrial processes have become more complex. This paper presents an industrial process involving a plastic part in which, due to the number of correlated variables, the inversion of the covariance matrix becomes impossible, and the classical MSPC cannot be used to identify physical aspects that explain the causes of variation or to increase the knowledge about the process behaviour. In order to solve this problem, a Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) approach was used and an R code was developed to implement it according some commercial software used for this purpose, namely the ProMV (c) 2016 from ProSensus, Inc. (www.prosensus.ca). Based on used dataset, it was possible to illustrate the principles of MSPC-PCA. This work intends to illustrate the implementation of MSPC-PCA in R step by step, to help the user community of R to be able to perform it.FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2013

An empirical approach towards the efficient and optimal production of influenza-neutralizing ovine polyclonal antibodies demonstrates that the novel adjuvant CoVaccine HT(TM) is functionally superior to Freund's adjuvant

Passive immunotherapies utilising polyclonal antibodies could have a valuable role in preventing and treating infectious diseases such as influenza, particularly in pandemic situations but also in immunocompromised populations such as the elderly, the chronically immunosuppressed, pregnant women, infants and those with chronic diseases. The aim of this study was to optimise current methods used to generate ovine polyclonal antibodies. Polyclonal antibodies to baculovirus-expressed recombinant influenza haemagglutinin from A/Puerto Rico/8/1934 H1N1 (PR8) were elicited in sheep using various immunisation regimens designed to investigate the priming immunisation route, adjuvant formulation, sheep age, and antigen dose, and to empirically ascertain which combination maximised antibody output. The novel adjuvant CoVaccine HT™ was compared to Freund’s adjuvant which is currently the adjuvant of choice for commercial production of ovine polyclonal Fab therapies. CoVaccine HT™ induced significantly higher titres of functional ovine anti-haemagglutinin IgG than Freund’s adjuvant but with fewer side effects, including reduced site reactions. Polyclonal hyperimmune sheep sera effectively neutralised influenza virus in vitro and, when given before or after influenza virus challenge, prevented the death of infected mice. Neither the age of the sheep nor the route of antigen administration appeared to influence antibody titre. Moreover, reducing the administrated dose of haemagglutinin antigen minimally affected antibody titre. Together, these results suggest a cost effective way of producing high and sustained yields of functional ovine polyclonal antibodies specifically for the prevention and treatment of globally significant diseases.Natalie E. Stevens, Cara K. Fraser, Mohammed Alsharifi, Michael P. Brown, Kerrilyn R. Diener, John D. Haybal

Visual Analysis of a Cold Rolling Process Using Data-Based Modeling

International Conference on Engineering Applications of Neural Networks (13th. 2012. Coventry Univ, Otaniemi, Finland

Latent Structures based-Multivariate Statistical Process Control: a paradigm shift

The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. This is a strategic issue for industrial success in the tremendously competitive global market.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Ferrer, A. (2014). Latent Structures based-Multivariate Statistical Process Control: a paradigm shift. Quality Engineering. 26(1):72-91. https://doi.org/10.1080/08982112.2013.846093S7291261Aparisi, F., Jabaioyes, J., & Carrion, A. (1999). Statistical properties of the lsi multivariate control chart. Communications in Statistics - Theory and Methods, 28(11), 2671-2686. doi:10.1080/03610929908832445Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International, 23(5), 517-543. doi:10.1002/qre.829Bharati, M. H., & MacGregor, J. F. (1998). Multivariate Image Analysis for Real-Time Process Monitoring and Control. Industrial & Engineering Chemistry Research, 37(12), 4715-4724. doi:10.1021/ie980334lBharati, M. H., MacGregor, J. F., & Tropper, W. (2003). Softwood Lumber Grading through On-line Multivariate Image Analysis Techniques. Industrial & Engineering Chemistry Research, 42(21), 5345-5353. doi:10.1021/ie0210560Bisgaard, S. (2012). The Future of Quality Technology: From a Manufacturing to a Knowledge Economy & From Defects to Innovations. Quality Engineering, 24(1), 30-36. doi:10.1080/08982112.2011.627010Box, G. E. P. (1954). Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification. The Annals of Mathematical Statistics, 25(2), 290-302. doi:10.1214/aoms/1177728786Camacho, J., & Ferrer, A. (2012). Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: theoretical aspects. Journal of Chemometrics, 26(7), 361-373. doi:10.1002/cem.2440Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003Efron, B., & Gong, G. (1983). A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. The American Statistician, 37(1), 36-48. doi:10.1080/00031305.1983.10483087Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304Fuchs, C. (1998). Multivariate Quality Control. doi:10.1201/9781482273731Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9Helland, I. S. (1988). On the structure of partial least squares regression. Communications in Statistics - Simulation and Computation, 17(2), 581-607. doi:10.1080/03610918808812681Höskuldsson, A. (1988). PLS regression methods. Journal of Chemometrics, 2(3), 211-228. doi:10.1002/cem.1180020306Hunter, J. S. (1986). The Exponentially Weighted Moving Average. Journal of Quality Technology, 18(4), 203-210. doi:10.1080/00224065.1986.11979014Edward Jackson, J. (1985). Multivariate quality control. Communications in Statistics - Theory and Methods, 14(11), 2657-2688. doi:10.1080/03610928508829069Jackson, J. E., & Mudholkar, G. S. (1979). Control Procedures for Residuals Associated With Principal Component Analysis. Technometrics, 21(3), 341-349. doi:10.1080/00401706.1979.10489779Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Kourti, T. (2006). Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis. Critical Reviews in Analytical Chemistry, 36(3-4), 257-278. doi:10.1080/10408340600969957Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Liu, R. Y. (1995). Control Charts for Multivariate Processes. Journal of the American Statistical Association, 90(432), 1380-1387. doi:10.1080/01621459.1995.10476643Liu, R. Y., Singh, K., & Teng*, J. H. (2004). DDMA-charts: Nonparametric multivariate moving average control charts based on data depth. Allgemeines Statistisches Archiv, 88(2), 235-258. doi:10.1007/s101820400170Liu, R. Y., & Tang, J. (1996). Control Charts for Dependent and Independent Measurements Based on Bootstrap Methods. Journal of the American Statistical Association, 91(436), 1694-1700. doi:10.1080/01621459.1996.10476740LOWRY, C. A., & MONTGOMERY, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800-810. doi:10.1080/07408179508936797MacGregor, J. F. (1997). Using On-Line Process Data to Improve Quality: Challenges for Statisticians. International Statistical Review, 65(3), 309-323. doi:10.1111/j.1751-5823.1997.tb00311.xMason, R. L., Champ, C. W., Tracy, N. D., Wierda, S. J., & Young, J. C. (1997). Assessment of Multivariate Process Control Techniques. Journal of Quality Technology, 29(2), 140-143. doi:10.1080/00224065.1997.11979743Montgomery, D. C., & Woodall, W. H. (1997). A Discussion on Statistically-Based Process Monitoring and Control. Journal of Quality Technology, 29(2), 121-121. doi:10.1080/00224065.1997.11979738Nelson, P. R. C., Taylor, P. A., & MacGregor, J. F. (1996). Missing data methods in PCA and PLS: Score calculations with incomplete observations. Chemometrics and Intelligent Laboratory Systems, 35(1), 45-65. doi:10.1016/s0169-7439(96)00007-xNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Prats-Montalbán, J. M., Ferrer, A., Malo, J. L., & Gorbeña, J. (2006). A comparison of different discriminant analysis techniques in a steel industry welding process. Chemometrics and Intelligent Laboratory Systems, 80(1), 109-119. doi:10.1016/j.chemolab.2005.08.005Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026Bisgaard, S., Doganaksoy, N., Fisher, N., Gunter, B., Hahn, G., Keller-McNulty, S., … Wu, C. F. J. (2008). The Future of Industrial Statistics: A Panel Discussion. Technometrics, 50(2), 103-127. doi:10.1198/004017008000000136Stoumbos, Z. G., Reynolds, M. R., Ryan, T. P., & Woodall, W. H. (2000). The State of Statistical Process Control as We Proceed into the 21st Century. Journal of the American Statistical Association, 95(451), 992-998. doi:10.1080/01621459.2000.10474292Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 24(2), 88-95. doi:10.1080/00224065.1992.12015232Wierda, S. J. (1994). Multivariate statistical process control—recent results and directions for future research. Statistica Neerlandica, 48(2), 147-168. doi:10.1111/j.1467-9574.1994.tb01439.xWold, S. (1978). Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models. Technometrics, 20(4), 397-405. doi:10.1080/00401706.1978.10489693Woodall, W. H. (2000). Controversies and Contradictions in Statistical Process Control. Journal of Quality Technology, 32(4), 341-350. doi:10.1080/00224065.2000.11980013Woodall, W. H., & Montgomery, D. C. (1999). Research Issues and Ideas in Statistical Process Control. Journal of Quality Technology, 31(4), 376-386. doi:10.1080/00224065.1999.11979944Yu, H., & MacGregor, J. F. (2003). Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods. Chemometrics and Intelligent Laboratory Systems, 67(2), 125-144. doi:10.1016/s0169-7439(03)00065-0Yu, H., MacGregor, J. F., Haarsma, G., & Bourg, W. (2003). Digital Imaging for Online Monitoring and Control of Industrial Snack Food Processes. Industrial & Engineering Chemistry Research, 42(13), 3036-3044. doi:10.1021/ie020941