“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.

Determinants of pediatric cataract program outcomes and follow-up in a large series in Mexico.

PURPOSE:

To report determinants of outcomes and follow-up in a large Mexican pediatric cataract project.

SETTING:

Hospital Luis Sanchez Bulnes, Mexico City, Mexico.

METHODS:

Data were collected prospectively from a pediatric cataract surgery program at the Hospital Luis Sanchez Bulnes, implemented by Helen Keller International. Preoperative data included age, sex, baseline visual acuity, type of cataract, laterality, and presence of conditions such as amblyopia. Surgical data included vitrectomy, capsulotomy, complications, and use of intraocular lenses (IOLs). Postoperative data included final visual acuity, refraction, number of follow-up visits, and program support for follow-up.

RESULTS:

Of 574 eyes of 415 children (mean age 7.1 years +/- 4.7 [SD]), IOLs were placed in 416 (87%). At least 1 follow-up was attended by 408 patients (98.3%) (mean total follow-up 3.5 +/- 1.8 months); 40% of eyes achieved a final visual acuity of 6/18 or better. Children living farther from the hospital had fewer postoperative visits (P = .04), while children receiving program support had more visits (P = .001). Factors predictive of better acuity included receiving an IOL during surgery (P = .04) and provision of postoperative spectacles (P = .001). Predictive of worse acuity were amblyopia (P = .003), postoperative complications (P = .0001), unilateral surgery (P = .0075), and female sex (P = .045).

CONCLUSIONS:

The results underscore the importance of surgical training in reducing complications, early intervention before amblyopia (observed in 40% of patients) can develop, and vigorous treatment if amblyopia is present. The positive impact of program support on follow-up is encouraging, although direct financial support may pose a problem for sustainability. More work is needed to understand reasons for worse outcomes in girls.

Parallel Time Series Analysis and Income Inequality: A New Approach to Comparative Case Studies

This case study deals with the role of time series analysis in sociology, and its relationship with the wider literature and methodology of comparative case study research. Time series analysis is now well-represented in top-ranked sociology journals, often in the form of ‘pooled time series’ research designs. These studies typically pool multiple countries together into a pooled time series cross-section panel, in order to provide a larger sample for more robust and comprehensive analysis. This approach is well suited to exploring trans-national phenomena, and for elaborating useful macro-level theories specific to social structures, national policies, and long-term historical processes. It is less suited however, to understanding how these global social processes work in different countries. As such, the complexities of individual countries - which often display very different or contradictory dynamics than those suggested in pooled studies – are subsumed. Meanwhile, a robust literature on comparative case-based methods exists in the social sciences, where researchers focus on differences between cases, and the complex ways in which they co-evolve or diverge over time. A good example of this is the inequality literature, where although panel studies suggest a general trend of rising inequality driven by the weakening power of labour, marketisation of welfare, and the rising power of capital, some countries have still managed to remain resilient. This case study takes a closer look at what can be learned by applying the insights of case-based comparative research to the method of time series analysis. Taking international income inequality as its point of departure, it argues that we have much to learn about the viability of different combinations of policy options by examining how they work in different countries over time. By taking representative cases from different welfare systems (liberal, social democratic, corporatist, or antipodean), we can better sharpen our theories of how policies can be more specifically engineered to offset rising inequality. This involves a fundamental realignment of the strategy of time series analysis, grounding it instead in a qualitative appreciation of the historical context of cases, as a basis for comparing effects between different countries.

Beyond managerial talent: ‘key group’ identification and differential compensation practices in multinational companies

With the maturation of strategic human resource management scholarship, there appears to be a greater call to move from monolithic workforce management to a more strategic and differentiated emphasis on employees with the greatest capacity to enhance competitive advantage. There has been little consideration in the literature as to whether organizations formally identify key groups of employees based on their impact on organizational learning and core competences. Using survey evidence from 260 multinational companies (MNCs), this paper explores the extent to which key groups of employees are formally recognized and whether they are subject to differential compensation practices. The results demonstrate that just in excess of half of these MNCs identify a key group. There was considerable differentiation in the compensation practices between these key groups, managers and the largest occupational group in the workforce. The results give rise to questions worthy of future investigation, namely whether the differentiated approaches used lead to improved performance outcomes.

IQ Imbalance in Multiuser Systems: Channel Estimation and Compensation

In this paper, we consider the uplink of a single-cell multi-user single-input multiple-output (MU-SIMO) system with in-phase and quadrature-phase imbalance (IQI). Particularly, we investigate the effect of receive (RX) IQI on the performance of MU-SIMO systems with large antenna arrays employing maximum-ratio combining (MRC) receivers. In order to study how IQI affects channel estimation, we derive a new channel estimator for the IQI-impaired model and show that the higher the value of signal-to-noise ratio (SNR) the higher the impact of IQI on the spectral efficiency (SE). Moreover, a novel pilot-based joint estimator of the augmented MIMO channel matrix and IQI coefficients is described and then, a low-complexity IQI compensation scheme is proposed which is based on the
IQI coefficients’ estimation and it is independent of the channel gain. The performance of the proposed compensation scheme is analytically evaluated by deriving a tractable approximation of the ergodic SE assuming transmission over Rayleigh fading channels with large-scale fading. Furthermore, we investigate how many MSs should be scheduled in massive multiple-input multiple-output (MIMO) systems with IQI and show that the highest SE loss occurs at the optimal operating point. Finally,
by deriving asymptotic power scaling laws, and proving that the SE loss due to IQI is asymptotically independent of the number of BS antennas, we show that massive MIMO is resilient to the effect of RX IQI.

On the application of optimal control techniques in lossy fieldbuses

The performance of real-time networks is under continuous improvement as a result of several trends in the digital world. However, these tendencies not only cause improvements, but also exacerbates a series of unideal aspects of real-time networks such as communication latency, jitter of the latency and packet drop rate. This Thesis focuses on the communication errors that appear on such realtime networks, from the point-of-view of automatic control. Specifically, it investigates the effects of packet drops in automatic control over fieldbuses, as well as the architectures and optimal techniques for their compensation. Firstly, a new approach to address the problems that rise in virtue of such packet drops, is proposed. This novel approach is based on the simultaneous transmission of several values in a single message. Such messages can be from sensor to controller, in which case they are comprised of several past sensor readings, or from controller to actuator in which case they are comprised of estimates of several future control values. A series of tests reveal the advantages of this approach. The above-explained approach is then expanded as to accommodate the techniques of contemporary optimal control. However, unlike the aforementioned approach, that deliberately does not send certain messages in order to make a more efficient use of network resources; in the second case, the techniques are used to reduce the effects of packet losses. After these two approaches that are based on data aggregation, it is also studied the optimal control in packet dropping fieldbuses, using generalized actuator output functions. This study ends with the development of a new optimal controller, as well as the function, among the generalized functions that dictate the actuator’s behaviour in the absence of a new control message, that leads to the optimal performance. The Thesis also presents a different line of research, related with the output oscillations that take place as a consequence of the use of classic co-design techniques of networked control. The proposed algorithm has the goal of allowing the execution of such classical co-design algorithms without causing an output oscillation that increases the value of the cost function. Such increases may, under certain circumstances, negate the advantages of the application of the classical co-design techniques. A yet another line of research, investigated algorithms, more efficient than contemporary ones, to generate task execution sequences that guarantee that at least a given number of activated jobs will be executed out of every set composed by a predetermined number of contiguous activations. This algorithm may, in the future, be applied to the generation of message transmission patterns in the above-mentioned techniques for the efficient use of network resources. The proposed task generation algorithm is better than its predecessors in the sense that it is capable of scheduling systems that cannot be scheduled by its predecessor algorithms. The Thesis also presents a mechanism that allows to perform multi-path routing in wireless sensor networks, while ensuring that no value will be counted in duplicate. Thereby, this technique improves the performance of wireless sensor networks, rendering them more suitable for control applications. As mentioned before, this Thesis is centered around techniques for the improvement of performance of distributed control systems in which several elements are connected through a fieldbus that may be subject to packet drops. The first three approaches are directly related to this topic, with the first two approaching the problem from an architectural standpoint, whereas the third one does so from more theoretical grounds. The fourth approach ensures that the approaches to this and similar problems that can be found in the literature that try to achieve goals similar to objectives of this Thesis, can do so without causing other problems that may invalidate the solutions in question. Then, the thesis presents an approach to the problem dealt with in it, which is centered in the efficient generation of the transmission patterns that are used in the aforementioned approaches.

Optimal alarms systems and its application to financial time series

This thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.

Integer-valued APARCH processes in the analysis of time series of counts

The Asymmetric Power Arch representation for the volatility was introduced by Ding et al.(1993) in order to account for asymmetric responses in the volatility in the analysis of continuous-valued financial time series like, for instance, the log-return series of foreign exchange rates, stock indices or share prices. As reported by Brannas and Quoreshi (2010), asymmetric responses in volatility are also observed in time series of counts such as the number of intra-day transactions in stocks. In this work, an asymmetric power autoregressive conditional Poisson model is introduced for the analysis of time series of counts exhibiting asymmetric overdispersion. Basic probabilistic and statistical properties are summarized and parameter estimation is discussed. A simulation study is presented to illustrate the proposed model. Finally, an empirical application to a set of data concerning the daily number of stock transactions is also presented to attest for its practical applicability in data analysis.

The Ethical Gold series

A series of five articles, published over six months in 2010, which explained the key ethical issues relating to sourcing gold. The Ethical Gold Series was written for practising jewellers, industrial jewellery manufacturers and the wider design community. The Ethical Gold Series was published in the Benchpeg Newsletter, an online jewellery industry journal, which in 2010 had over 5,500 subscribers. The material for the articles was drawn from a year of ethnographic fieldwork in the UK jewellery sector, funded by the AHRC, and field visits to preserved, operating and proposed gold mining sites in Alaska, California, Sweden, Wales and Scotland. The ethnographic fieldwork included ‘ethical gold’ promotional events, private industry meetings and jewellery trade shows and meetings with jewellery manufacturers and retailers, assayers, gold refiners and traders and environmental and fair trade campaigners.

Joint compensation of IQ-imbalance and carrier phase sychronization errors in communication receivers

This work addresses the joint compensation of IQimbalances and carrier phase synchronization errors of zero- IF receivers. The compensation scheme based on blind-source separation which provides simple yet potent means to jointly compensate for these errors independent of modulation format and constellation size used. The low-complexity of the algorithm makes it a suitable option for real-time deployment as well as practical for integration into monolithic receiver designs.