Habitual Habitation

This abstract presentation will set out a series of observations and precedents related to the relationships formed between humans and the evolving spaces of personal habitation. The work discusses the prospect of future interlocking and divided spaces that humans use for the process of habitation in both the work and the domestic context. The four key design considerations outlined within this relationship are the arrangement of habitation (design), the occupation of that space (purpose), inhabitation of space (user interaction) and the duration of use (space longevity). Some of the main themes that are discussed are the processes of human habitation, why it occurs, the evolving nature of that habitat and the development of habitat for a defined purpose. The presentation will examine a range of particular, purposeful and peculiar human habitat spaces and use them to explore the interactive relationships between humans and the spaces that are created for specific functions e.g., work, leisure and utility. The typography of human habitation is also explored and critiqued to establish a clear picture of the current and future frameworks in which designers, clients and design users are / will be considering interior space. There is also exploration of how human behaviours differ in space that has been specifically created for a purpose and the realities of actual human use and occupation. There is also exploration of the way certain types of interior spaces influence and often dictate human behaviour. This is then reviewed and a future construct is suggested about human behaviour changing and fragmenting according to the surrounding space. A review of the interior designers role in this construct is identified with examination of spatial tactics and external forces within this process. Does interior design hold the key to future human living and working behaviours? Finally, a future habitation hypothesis is presented along with a review of Peter Sloterdijk and Henri Lefebvre’s suggestions and definitions of “Home” and “Habitus” which helps to synthesise some future ideas on interior design and habitation

Linear Support Vector Machines for Error Correction in Optical Data Transmission

Reduction of bit error rates in optical transmission systems is an important task that is difficult to achieve. As speeds increase, the difficulty in reducing bit error rates also increases. Channels have differing characteristics, which may change over time, and any error correction employed must be capable of operating at extremely high speeds. In this paper, a linear support vector machine is used to classify large-scale data sets of simulated optical transmission data in order to demonstrate their effectiveness at reducing bit error rates and their adaptability to the specifics of each channel. For the classification, LIBLINEAR is used, which is related to the popular LIBSVM classifier. It is found that is possible to reduce the error rate on a very noisy channel to about 3 bits in a thousand. This is done by a linear separator that can be built in hardware and can operate at the high speed required of an operationally useful decode

A pilot study of homeworking: WorkHouse (30)

Workhouse aims to improve technology-based homeworking, through an understanding of working patterns, interactions with architecture and furniture. A log record of participants working environments, their hours of work, and their posture. The 10 participants revealed a range of working patterns (6:30 to midnight); choice of rooms, even with a dedicated study available. There are some issues to be resolved with the logs: recording of working hours, posture, and the need to make further decisions about the data required

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression

This is the pre-peer reviewed version of the following article: Parivash Ashrafi, Yi Sun, Neil Davey, Roderick G. Adams, Simon C. Wilkinson, and Gary Patrick Moss, ‘Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression’, Journal of Pharmacy and Pharmacology, Vol. 70 (3): 361-373, March 2018, which has been published in final form at https://doi.org/10.1111/jphp.12863. Under embargo until 17 January 2019. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Objectives The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters. Methods Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or ‘chemical space’ of the key descriptors to assess the effect of the data range on model quality. Key findings The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure–permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly. Conclusions The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets.Peer reviewedFinal Accepted Versio

Experiential Boundaries

Using an analysis of the door’s semantic, utilitarian and its protective potential as provocations and triggers, [Von Meiss, 1998], this proposal will explore doors long history as a strategic boundary device used to control points of entry, egress, or indeed exclusion to a city or inner sanctuary. However, the scenarios which unfold on the door are seldom used to examine the experiential relationships between people, objects and environments in a way which challenges the marketing strategies, and commercial contexts associated with markets and customer satisfaction. The door remains the architectural micro-site of serendipitous social interactions, transactions and occasional transgressions and psychological threshold seen, increasingly in exclusive, security conscious, gated-communities whose technological dependency reveals anxieties of containment and encroachment. Smith and Topham (2012) described this as communities whose experiential encounters are closed-off from others and unwittingly pave the way for domestic designs that imprison free inhabitants in alarmed paradises. This contradiction opens up the experiential aspects of the door to the concept of doubleness recalling Van Eyck’s of the doors two faces -inside or out, raising interesting questions of what one would design first -its inner domestic face or outer defensive skin and as plane(s) in which the world reverses suggesting a new, emergent anatomy and a language for the doorway. Florida State University Department of Interior Architecture & Design; PARADE (Publication & Research in Art, Architectures, Design and Environments); the interdisciplinary research organisation AMPS (Architecture, Media, Politics, Society) and its academic journal Architecture_MPS

Structural plasticity and associative memory in balanced neural networks with spike-time dependent inhibitory plasticity

Peer reviewe

Prediction of skin penetration using machine learning methods

Improving predictions of the skin permeability coefficient is a difficult problem. It is also an important issue with the increasing use of skin patches as a means of drug delivery. In this work, we applyK-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the permeability coefficient. We obtain a considerable improvement over the quantitative structureactivity relationship (QSARs) predictors. We show that using five features, which are molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups, can produce better predictions than the one using only lipophilicity and the molecular weight. The Gaussian process regression with five compound features gives the best performance in this work

Recommendations for Practitioners Engaged in Antitrafficking Task Forces: An Evalaution of the Enhanced Collaborative Model Task Forces to Combat Human Trafficking

The Enhanced Collaborative Model (ECM) Task Force to Combat Human Trafficking Program funded task forces comprised of law enforcement officials, prosecutors, victim service providers, and other stakeholders at the local, state, and federal levels. This brief details recommendations from the Urban Institute's 10-site evaluation of ECM task forces across the United States. Recommendations were derived from the findings of our analysis and directly from task force stakeholders' responses to interview questions about task force recommendations and best practices. Respondents summarized recommendations across four categories including structure, operation, and funding of ECM task forces; collaboration among stakeholders; survivor engagement and service provision; and task force training, focus, and activities

Support vector regression to estimate the permeability enhancement of potential transdermal enhancers

This is the peer reviewed version of the following article: Shah, A., Sun, Y., Adams, R. G., Davey, N., Wilkinson, S. C. and Moss, G. P. (2016), Support vector regression to estimate the permeability enhancement of potential transdermal enhancers', Journal of Pharmacy and Pharmacology, Vol. 68 (2): 170–184, which has been published in final form at doi:10.1111/jphp.12508. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. © 2016 Royal Pharmaceutical Society.Objectives Searching for chemicals that will safely enhance transdermal drug delivery is a significant challenge. This study applies support vector regression (SVR) for the first time to estimating the optimal formulation design of transdermal hydrocortisone formulations. Methods The aim of this study was to apply SVR methods with two different kernels in order to estimate the enhancement ratio of chemical enhancers of permeability. Key findings A statistically significant regression SVR model was developed. It was found that SVR with a nonlinear kernel provided the best estimate of the enhancement ratio for a chemical enhancer. Conclusions Support vector regression is a viable method to develop predictive models of biological processes, demonstrating improvements over other methods. In addition, the results of this study suggest that a global approach to modelling a biological process may not necessarily be the best method and that a ‘mixed-methods’ approach may be best in optimising predictive models.Peer reviewedFinal Accepted Versio