A methodological review of the Short Form Health Survey 36 (SF-36) and its derivatives among breast cancer survivors

Purpose: A systematic review of the validity, reliability and sensitivity of the Short Form (SF) health survey measures among breast cancer survivors.
Methods: We searched a number of databases for peer-reviewed papers. The methodological quality of the papers was assessed using the COnsenus-based Standards for the selection of health Measurement INstruments (COSMIN).
Results: The review identified seven papers that assessed the psychometric properties of the SF-36 (n = 5), partial SF-36 (n = 1) and SF-12 (n = 1) among breast cancer survivors. Internal consistency scores for the SF measures ranged from acceptable to good across a range of language and ethnic sub-groups. The SF-36 demonstrated good convergent validity with respective subscales of the Functional Assessment of Cancer Treatment—General scale and two lymphedema-specific measures. Divergent validity between the SF-36 and Lymph-ICF was modest. The SF-36 demonstrated good factor structure in the total breast cancer survivor study samples. However, the factor structure appeared to differ between specific language and ethnic sub-groups. The SF-36 discriminated between survivors who reported or did not report symptoms on the Breast Cancer Prevention Trial Symptom Checklist and SF-36 physical sub-scales, but not mental sub-scales, discriminated between survivors with or without lymphedema. Methodological quality scores varied between and within papers.
Conclusion: Short Form measures appear to provide a reliable and valid indication of general health status among breast cancer survivors though the limited data suggests that particular caution is required when interpreting scores provided by non-English language groups. Further research is required to test the sensitivity or responsiveness of the measure.

Bandwidth selection by cross-validation for forecasting long memory financial time series

The paper addresses the issue of choice of bandwidth in the application of semiparametric estimation of the long memory parameter in a univariate time series process. The focus is on the properties of forecasts from the long memory model. A variety of cross-validation methods based on out of sample forecasting properties are proposed. These procedures are used for the choice of bandwidth and subsequent model selection. Simulation evidence is presented that demonstrates the advantage of the proposed new methodology.

The role of personality and self-efficacy in the selection and retention of successful nursing students:a longitudinal study

Aim: This paper is a report of a study to examine the role of personality and self-efficacy in predicting academic performance and attrition in nursing students.

Background: Despite a considerable amount of research investigating attrition in nursing students and new nurses, concerns remain. This particular issue highlights the need for a more effective selection process whereby those selected are more likely to complete their preregistration programme successfully, and remain employed as Registered Nurses.

Method: A longitudinal design was adopted. A questionnaire, which included measures of personality and occupational and academic self-efficacy, was administered to 384 students early in the first year of the study. At the end of the programme, final marks and attrition rates were obtained from university records for a total of 350 students. The data were collected from 1999 to 2002.

Findings: Individuals who scored higher on a psychoticism scale were more likely to withdraw from the programme. Occupational self-efficacy was revealed to be a statistically significant predictor of final mark obtained, in that those with higher self-efficacy beliefs were more likely to achieve better final marks. Extraversion was also shown to negatively predict academic performance in that those with higher extraversion scores were more likely to achieve lower marks.

Conclusion: More research is needed to explore the attributes of successful nursing students and the potential contribution of psychological profiling to a more effective selection process.

Automotive waste heat recovery: Working fluid selection and related boundary conditions

This paper presents the rational for the selection of fluids for use in a model based study of sub and supercritical Waste Heat Recovery (WHR) Organic Rankine Cycle (ORC). The study focuses on multiple vehicle heat sources and the potential of WHR ORC’s for its conversion into useful work. The work presented on fluid selection is generally applicable to any waste heat recovery system, either stationary or mobile and, with careful consideration, is also applicable to single heat sources. The fluid selection process presented reduces the number of potential fluids from over one hundred to a group of under twenty fluids for further refinement in a model based WHR ORC performance study. The selection process uses engineering judgement, legislation and, where applicable, health and safety as fluid selection or de-selection criteria. This paper also investigates and discusses the properties of specific ORC fluids with regard to their impact on the theoretical potential for delivering efficient WHR ORC work output. The paper concludes by looking at potential temperature and pressure WHR ORC limits with regard to fluid properties thereby assisting with the generation of WHR ORC simulation boundary conditions.

Optimal wafer site selection using forward selection component analysis

Reducing wafer metrology continues to be a major target in semiconductor manufacturing efficiency initiatives due to it being a high cost, non-value added operation that impacts on cycle-time and throughput. However, metrology cannot be eliminated completely given the important role it plays in process monitoring and advanced process control. To achieve the required manufacturing precision, measurements are typically taken at multiple sites across a wafer. The selection of these sites is usually based on a priori knowledge of wafer failure patterns and spatial variability with additional sites added over time in response to process issues. As a result, it is often the case that in mature processes significant redundancy can exist in wafer measurement plans. This paper proposes a novel methodology based on Forward Selection Component Analysis (FSCA) for analyzing historical metrology data in order to determine the minimum set of wafer sites needed for process monitoring. The paper also introduces a virtual metrology (VM) based approach for reconstructing the complete wafer profile from the optimal sites identified by FSCA. The proposed methodology is tested and validated on a wafer manufacturing metrology dataset. © 2012 IEEE.

Adaptive uncertain information fusion to enhance plan selection in BDI agent systems

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for possibility theory), called largely partially maximal consistent subsets (LPMCS), can be adapted to Dempster-Shafer (DS) theory. We identify those measures for the degree of uncertainty and internal conflict that are available in DS theory and show how they can be used for guiding LPMCS merging. A simplified real-world power distribution scenario illustrates our framework. We also briefly discuss how our approach can be incorporated into a multi-agent programming language, thus leading to better plan selection and decision making.

Proactive Relay Selection with Joint Impact of Hardware Impairment and Co-channel Interference

In this paper, we investigate the end-to-end performance of dual-hop proactive decode-and-forward relaying networks with Nth best relay selection in the presence of two practical deleterious effects: i) hardware impairment and ii) cochannel interference. In particular, we derive new exact and asymptotic closed-form expressions for the outage probability and average channel capacity of Nth best partial and opportunistic relay selection schemes over Rayleigh fading channels. Insightful discussions are provided. It is shown that, when the system cannot select the best relay for cooperation, the partial relay selection scheme outperforms the opportunistic method under the impact of the same co-channel interference (CCI). In addition, without CCI but under the effect of hardware impairment, it is shown that both selection strategies have the same asymptotic channel capacity. Monte Carlo simulations are presented to corroborate our analysis.

Transmit Antenna Selection in Cognitive MIMO Relaying with Multiple Primary Transceivers

We consider transmit antenna selection (TAS) in cognitive multiple-input multiple-output (MIMO) relay networks, as an interference-aware design for secondary users (SUs) to ensure power and interference constraints of multiple primary users (PUs). In doing so, we derive new exact and asymptotic expressions for the outage probability of TAS with maximal ratio combining (TAS/MRC) and with selection combining (TAS/SC) over Rayleigh fading. The proposed analysis and simulations highlight that TAS/MRC and TAS/SC with decode-and-forward relaying achieve the same diversity order in cognitive MIMO networks, which scales with the minimum number of antennas at the SUs. Furthermore, we accurately characterize the outage gap between TAS/MRC and TAS/SC relaying as a concise ratio of their array gains.

Relay Selection for Security Enhancement in Cognitive Relay Networks

This letter proposes several relay selection policies for secure communication in cognitive decode-and-forward (DF) relay networks, where a pair of cognitive relays are opportunistically selected for security protection against eavesdropping. The first relay transmits the secrecy information to the destination,
and the second relay, as a friendly jammer, transmits the jamming signal to confound the eavesdropper. We present new exact closed-form expressions for the secrecy outage probability. Our analysis and simulation results strongly support our conclusion that the proposed relay selection policies can enhance the performance of secure cognitive radio. We also confirm that the error floor phenomenon is created in the absence of jamming.

Generalized Selection Combining for Cognitive Relay Networks over Nakagami-m Fading

We consider transmit antenna selection with receive generalized selection combining (TAS/GSC) for cognitive decodeand-forward (DF) relaying in Nakagami-m fading channels. In an effort to assess the performance, the probability density function and the cumulative distribution function of the endto-end SNR are derived using the moment generating function, from which new exact closed-form expressions for the outage probability and the symbol error rate are derived. We then derive a new closed-form expression for the ergodic capacity. More importantly, by deriving the asymptotic expressions for the outage probability and the symbol error rate, as well as the high SNR approximations of the ergodic capacity, we establish new design insights under the two distinct constraint scenarios: 1) proportional interference power constraint, and 2) fixed interference power constraint. Several pivotal conclusions are reached. For the first scenario, the full diversity order of the
outage probability and the symbol error rate is achieved, and the high SNR slope of the ergodic capacity is 1/2. For the second scenario, the diversity order of the outage probability and the symbol error rate is zero with error floors, and the high SNR slope of the ergodic capacity is zero with capacity ceiling.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.