Probing Bias-Dependent Electrochemical Gas-Solid Reactions in (LaxSr1-x)CoO3-delta Cathode Materials

Spatial variability of bias-dependent electrochemical processes on a (La0.5Sr0.5)(2)CoO4 +/- modified (LaxSr1-x)CoO3- surface is studied using first-order reversal curve method in electrochemical strain microscopy (ESM). The oxygen reduction/evolution reaction (ORR/OER) is activated at voltages as low as 3-4 V with respect to bottom electrode. The degree of bias-induced transformation as quantified by ESM hysteresis loop area increases with applied bias. The variability of electrochemical activity is explored using correlation analysis and the ORR/OER is shown to be activated in grains at relatively low biases, but the final reaction rate is relatively small. At the same time, at grain boundaries, the onset of reaction process corresponds to larger voltages, but limiting reactivity is much higher. The reaction mechanism in ESM of mixed electronic-ionic conductor is further analyzed. These studies both establish the framework for probing bias-dependent electrochemical processes in solids and demonstrate rich spectrum of electrochemical transformations underpinning catalytic activity in cobaltites.

'Changing attitudes with a little imagination': Imagined contact effects on young children's intergroup bias

The current research tested a recent development in social psychology, namely 'imagined contact', among young children (n = 123, 5 to 10 years). Children imagined interacting with a physically disabled child, or did not take part in this activity (the control group). Compared with the control group, children who engaged in 'imagined contact' subsequently showed reduced intergroup bias in their general attitude and ratings of warmth and competence. Imagined contact also led to more positive intended friendship behavior towards the disabled, but only among 5 – 6 year olds. This provides partial support for our hypothesis that younger children, perhaps as a result of their lack of outgroup experience, are more likely to benefit from imagined contact. Implications for the development of attitudes towards the disabled, imagined contact theory and the development of classroom-based prejudice-reduction techniques based on imagined contact are discussed.

Effect of magnetic bias on Gaussian pulse scattering by stacked nonlinear semiconductor layers

The nonlinear scattering of pulses by periodic stacks of semiconductor layers with magnetic bias has been studied in the self-consistent problem formulation, taking into account mobility of carriers. The three-wave mixing technique has been applied to the analysis of the waveform evolution in the stacks illuminated by two Gaussian pulses with different central frequencies and lengths. The effects of external magnetic bias, and stack physical and geometrical parameters on the properties of the scattered waveforms are discussed. © 2013 IEEE.

Sequential injection of domain walls into ferroelectrics at different bias voltages: Paving the way for “domain wall memristors”

Simple meso-scale capacitor structures have been made by incorporating thin (300 nm) single crystal lamellae of KTiOPO4 (KTP) between two coplanar Pt electrodes. The influence that either patterned protrusions in the electrodes or focused ion beam milled holes in the KTP have on the nucleation of reverse domains during switching was mapped using piezoresponse force microscopy imaging. The objective was to assess whether or not variations in the magnitude of field enhancement at localised “hot-spots,” caused by such patterning, could be used to both control the exact locations and bias voltages at which nucleation events occurred. It was found that both the patterning of electrodes and the milling of various hole geometries into the KTP could allow controlled sequential injection of domain wall pairs at different bias voltages; this capability could have implications for the design and operation of domain wall electronic devices, such as memristors, in the future.

Bias in research

The aim of this article is to outline types of ‘bias’ across research designs, and consider strategies to minimise bias. Evidence-based nursing, defined as the “process by which evidence, nursing theory, and clinical expertise are critically evaluated and considered, in conjunction with patient involvement, to provide the delivery of optimum nursing care,”1 is central to the continued development of the nursing professional. Implementing evidence into practice requires nurses to critically evaluate research, in particular assessing the rigour in which methods were undertaken and factors that may have biased findings.

Attentional bias to food-related visual cues: is there a role in obesity?

The incentive sensitisation model of obesity suggests that modification of the dopaminergic associated reward systems in the brain may result in increased awareness of food-related visual cues present in the current food environment. Having a heightened awareness of these visual food cues may impact on food choices and eating behaviours with those being most aware of or demonstrating greater attention to food-related stimuli potentially being at greater risk of overeating and subsequent weight gain. To date, research related to attentional responses to visual food cues has been both limited and conflicting. Such inconsistent findings may in part be explained by the use of different methodological approaches to measure attentional bias and the impact of other factors such as hunger levels, energy density of visual food cues and individual eating style traits that may influence visual attention to food-related cues outside of weight status alone. This review examines the various methodologies employed to measure attentional bias with a particular focus on the role that attentional processing of food-related visual cues may have in obesity. Based on the findings of this review, it appears that it may be too early to clarify the role visual attention to food-related cues may have in obesity. Results however highlight the importance of considering the most appropriate methodology to use when measuring attentional bias and the characteristics of the study populations targeted while interpreting results to date and in designing future studies.

In and out of home care decisions: The influence of confirmation bias in developing decision supportive reasoning

The aims of this study were to identify the themes Social Workers regard as important in supporting decisions to remove children from, or return them to, the care of their parents. To further elicit underlying hypotheses that are discernible in interpretation of evidence. A case study, comprising a two-part vignette with a questionnaire, recorded demographic information, child welfare attitudes and risk assessments, using scales derived from standardised instruments, was completed by 202 Social Workers in Northern Ireland. There were two manipulated variables, mother’s attitude to removal and child’s attitude to reunification2 years later. In this paper we use data derived from respondents’ qualitative comments explaining their reasoning for in and out of home care decisions. Some 60.9% of respondent’s chose the parental care option at part one, with 94% choosing to have the child remain in foster care at part two. The manipulated variables were found to have no significant statistical effect. However, three underlying hypotheses were found to underpin decisions; (a)child rescue, (b) kinship defence and (c) a hedged position on calculation of risk subject to further assessment. Reasoning strategies utilised by social workers to support their decision making suggest that they tend to selectively interpret information either positively or negatively to support pre-existing underlying hypotheses. This finding is in keeping with the literature on ‘confirmation bias.’ The research further draws attention to the need to incorporate open questions in quantitative studies, to help guard against surface reading of data, which often does not ‘speak for itself.’

The Equiprobability Bias from a Mathematical and Psychological Perspective

The equiprobability bias (EB) is a tendency to believe that every process in which randomness is involved corresponds to a fair distribution, with equal probabilities for any possible outcome. The EB is known to affect both children and adults, and to increase with probability education. Because it results in probability errors resistant to pedagogical interventions, it has been described as a deep misconception about randomness: the erroneous belief that randomness implies uniformity. In the present paper, we show that the EB is actually not the result of a conceptual error about the definition of randomness. On the contrary, the mathematical theory of randomness does imply uniformity. However, the EB is still a bias, because people tend to assume uniformity even in the case of events that are not random. The pervasiveness of the EB reveals a paradox: The combination of random processes is not necessarily random. The link between the EB and this paradox is discussed, and suggestions are made regarding educational design to overcome difficulties encountered by students as a consequence of the EB.

Outperformance in exchange-traded fund pricing deviations: Generalized control of data snooping bias

An investigation into exchange-traded fund (ETF) outperforrnance during the period 2008-2012 is undertaken utilizing a data set of 288 U.S. traded securities. ETFs are tested for net asset value (NAV) premium, underlying index and market benchmark outperformance, with Sharpe, Treynor, and Sortino ratios employed as risk-adjusted performance measures. A key contribution is the application of an innovative generalized stepdown procedure in controlling for data snooping bias. We find that a large proportion of optimized replication and debt asset class ETFs display risk-adjusted premiums with energy and precious metals focused funds outperforming the S&P 500 market benchmark. 

Failure of a numerical quality assessment scale to identify potential risk of bias in a systematic review: a comparison study

BACKGROUND: Assessing methodological quality of primary studies is an essential component of systematic reviews. Following a systematic review which used a domain based system [United States Preventative Services Task Force (USPSTF)] to assess methodological quality, a commonly used numerical rating scale (Downs and Black) was also used to evaluate the included studies and comparisons were made between quality ratings assigned using the two different methods. Both tools were used to assess the 20 randomized and quasi-randomized controlled trials examining an exercise intervention for chronic musculoskeletal pain which were included in the review. Inter-rater reliability and levels of agreement were determined using intraclass correlation coefficients (ICC). Influence of quality on pooled effect size was examined by calculating the between group standardized mean difference (SMD).

RESULTS: Inter-rater reliability indicated at least substantial levels of agreement for the USPSTF system (ICC 0.85; 95% CI 0.66, 0.94) and Downs and Black scale (ICC 0.94; 95% CI 0.84, 0.97). Overall level of agreement between tools (ICC 0.80; 95% CI 0.57, 0.92) was also good. However, the USPSTF system identified a number of studies (n = 3/20) as "poor" due to potential risks of bias. Analysis revealed substantially greater pooled effect sizes in these studies (SMD -2.51; 95% CI -4.21, -0.82) compared to those rated as "fair" (SMD -0.45; 95% CI -0.65, -0.25) or "good" (SMD -0.38; 95% CI -0.69, -0.08).

CONCLUSIONS: In this example, use of a numerical rating scale failed to identify studies at increased risk of bias, and could have potentially led to imprecise estimates of treatment effect. Although based on a small number of included studies within an existing systematic review, we found the domain based system provided a more structured framework by which qualitative decisions concerning overall quality could be made, and was useful for detecting potential sources of bias in the available evidence.

Data-Augmentation for Reducing Dataset Bias in Person Re-identification

In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based re-identification systems, and that changing the image background yields further improvements.