Validation of non‑stationary precipitation series for site‑specific impact assessment: comparison of two statistical downscaling techniques

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

Synthesis, thermal characterization and rheological properties of a homologous series of polymethacrylate-based side-chain liquid crystal polymers

A new homologous series of side-chain liquid crystal polymers, the poly[omega-(4-cyanoazobenzene-4'-oxy)alkyl methacrylate]s, have been prepared in which the length of the flexible alkyl spacer is varied from 3 to 12 methylene units. All the polymers exhibit liquid crystalline behaviour; specifically, crystal E, smectic A and nematic phases are observed. The glass transition temperatures decrease on increasing spacer length before reaching a limiting value at ca. 30 degrees C. The clearing temperatures exhibit an odd-even effect on varying the length and parity of the spacer. This is attributed to the change in the average shape of the side chain as the parity of the spacer is varied. This rationalization also accounts for the observed alternation in the entropy change associated with the clearing transition. A weak relaxation is observed theologically for several members of this polymer series at temperatures above their respective glass transition temperatures. This is attributed either to specific motions of the smectic layers or to 180 degrees reorientational jumps of the long axis of the mesogenic unit about the polymer backbone. (C) 1997 Elsevier Science Ltd. All rights reserved.

“If I Were Nick”: Men’s Responses to an Interactive Video Drama Series to Support Smoking Cessation

Background: Men continue to smoke in greater numbers than women; however, few interventions have been developed and tested to support men’s cessation. Men also tend to rely on quitting strategies associated with stereotypical manliness, such as willpower, stoicism and independence, but may lack the self‐efficacy skills required to sustain a quit. In this article we describe the development of and reception to an interactive video drama (IVD) series, composed of 7 brief scenarios, to support and strengthen men’s smoking cessation efforts. The value of IVD in health promotion is predicated on the evidence that viewers engage with the material when they are presented characters with whom they can personally identify. The video dramatizes the challenges unfolding in the life of the main character, Nick, on the first day of his quit and models the skills necessary to embark upon a sustainable quit. 
Objective: The objective was to describe men’s responses to the If I were Nick IVD series as part of a pilot study of QuitNow MenTM, an innovative smoking cessation website designed for men. Specific objectives were to explore the resonance of the main character of the IVD series with end‐users, and men’s perceptions of the effectiveness of the IVD series for supporting their quit self‐management. 
Methods: Seven brief IVD scenarios were developed, filmed with a professional actor and uploaded to a new online smoking cessation website, QuitNow MenTM.  A sample of 117 men who smoked were recruited into the study and provided baseline data prior to access to the QuitNow MenTM website for a 6 month period. During this time, 47 men chose to view the IVDs. Their responses to questions about the IVDs were collected in 3‐month and 6‐month online follow‐up surveys and analyzed using descriptive statistics. 
Findings: The majority of participants indicated they related to the main character, Nick. Participants who “strongly agreed” they could relate to Nick perceived significantly higher levels of support from the IVDs than the “neutral” and “disagree” groups (P <.001, d =2.0, P <.001 d =3.1). The “agree” and “neutral” groups were significantly higher on rated support from the videos than the “disagree” (P <.001 d =2.2, P =.01 d = 1.5). Participants’ perception of the main character was independent of participant age, education attainment or previous quit attempts. 
Conclusions: The findings suggest that IVD interventions may be an important addition to men’s smoking cessation programs. Given that the use of IVD scenarios in health promotion is in its infancy, the positive outcomes from this pilot study signal the potential for IVD and warrant ongoing evaluation in smoking cessation and, more generally, men’s health promotion.  

Open Source Telecommunications Framework for Time-Series Data from Smart Grid Sensors.

This paper presents a framework for a telecommunications interface which allows data from sensors embedded in Smart Grid applications to reliably archive data in an appropriate time-series database. The challenge in doing so is two-fold, firstly the various formats in which sensor data is represented, secondly the problems of telecoms reliability. A prototype of the authors' framework is detailed which showcases the main features of the framework in a case study featuring Phasor Measurement Units (PMU) as the application. Useful analysis of PMU data is achieved whenever data from multiple locations can be compared on a common time axis. The prototype developed highlights its reliability, extensibility and adoptability; features which are largely deferred from industry standards for data representation to proprietary database solutions. The open source framework presented provides link reliability for any type of Smart Grid sensor and is interoperable with existing proprietary database systems, and open database systems. The features of the authors' framework allow for researchers and developers to focus on the core of their real-time or historical analysis applications, rather than having to spend time interfacing with complex protocols.

Series Introduction: Centenary Classics

The Centenary Classics contains six titles in this special edition series. The year 2016 marks the beginning of the centenary period of the Irish Free State's establishment. This beautifully produced limited edition series examines the fascinating time of change and evolution in the Ireland of 100 years ago. Each volume is a first-hand account of individuals or events during the 1913-23 revolutionary period. They are each introduced by leading experts and academics in the field - giving a contemporary analysis of the original text - while a general series introduction by Fearghal McGarry sets the scene of the period. The complete series collectively tells the story of the birth of the Irish nation and consist of the following six titles: A Chronicle of Jails - Darrell Figgis; Civil War in Ulster - Joseph Johnston; Free State or Republic? - Padraig de Burca and John F. Boyle; Rising Out - Ernie O'Malley; Victory and Woe - Mossie Harnett and The Victory of Sinn Fein - P. S. O'Hegarty.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.