Promoting Harmonious Relations and Equitable Well-Being: Peace Psychology and “Intractable” Conflicts

The chapter explores Bar-Tal’s legacy in relation to key concepts, perspectives, and findings that comprise the growing field of peace psychology, specifically the promotion of sustainable peace through the indivisible constructs of harmonious relations and equitable wellbeing. Analyzed through a peace psychology lens, Bar-Tal’s work highlights both the barriers to and bridges for achieving sustainable peace. Central concepts from his work, such as fear, insecurity, and an ethos of conflict, demonstrate key obstacles to fostering harmonious intergroup relations based on social justice. Bar-Tal’s work also identifies processes that can overcome these barriers, which is consistent with peace psychology’s emphasis on the development of constructive responses to violence and conflict. For example, the chapter outlines how confidence-building mechanisms, mutually respectful identities, and reconciliation processes, may help foster an ethos of peace that can be embedded in the structure of societies through peace education. The chapter concludes with implications and suggestions for future research, with a focus on the role of young people in settings of prolonged intergroup division and generational approaches to peacebuilding, as conceptualized through a peace psychology lens.

Predictors of numeracy performance in undergraduate psychology, nursing and medical students

Psychology, nursing and medicine are undergraduate degrees that require students to attain a level of numerical competence for graduation. Yet, the numeracy aspect of these courses is often actively disliked and poorly performed. This study's aim was to identify what factors most strongly predict performance in such courses. Three hundred and twenty-five undergraduate students from these three disciplines were given measures of numeracy performance, maths anxiety, maths attitudes and various demographic and educational variables. From these data three separate path analysis models were formed, showing the predictive effects of affective, demographic and educational variables on numeracy performance. Maths anxiety was the strongest affective predictor for psychology and nursing students, with motivation being more important for medical students. Across participant groups, pre-university maths qualifications were the strongest demographic/educational predictor of performance. The results can be used to suggest ways to improve performance in students having difficulty with numeracy-based modules.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

Research Methods in Psychology For Dummies

Your hands-on introduction to research methods in psychology.  
Looking for an easily accessible overview of research methods in psychology? This is the book for you! Whether you need to get ahead in class, you're pressed for time, or you just want a take on a topic that's not covered in your textbook, Research Methods in Psychology For Dummies has you covered. 
Written in plain English and packed with easy-to-follow instruction, this friendly guide takes the intimidation out of the subject and tackles the fundamentals of psychology research in a way that makes it approachable and comprehensible, no matter your background. Inside, you'll find expert coverage of qualitative and quantitative research methods, including surveys, case studies, laboratory observations, tests and experiments—and much more. 
- Serves as an excellent supplement to course textbooks - Provides a clear introduction to the scientific method - Presents the methodologies and techniques used in psychology research- Written by the authors of Psychology Statistics For Dummies 
If you're a first or second year psychology student and want to supplement your doorstop-sized psychology textbook—and boost your chances of scoring higher at exam time—this hands-on guide breaks down the subject into easily digestible bits and propels you towards success.

Maths anxiety in psychology undergraduates: A mixed-methods approach to formulating and implementing interventions

A key challenge to educators in disciplines that, while not maths based, nevertheless
contain some maths component, is mathematics anxiety. Over the years, a number of
intervention strategies have been tested, seeking reduce maths anxiety in undergraduates.
Many of these studies, however, contain methodological issues that challenge their validity. It
is also unclear how many of these studies decide which type of interventions to use. This
research sought to correct both of these issues. In Study 1, focus groups were carried out to
explore which interventions students believed would most likely reduce their maths anxiety.
Study 2 implemented those interventions that Study 1 showed to be practical and potentially
effective, utilising a large sample of Year 1 and Year 2 psychology undergraduates,
controlling for potential methodological confounds. Results showed that only one
intervention (teaching quantitative research methods using real-life examples) had any
significant effect on maths anxiety, and this was slight. These results, while not impressive by
themselves, do suggest ways in which larger-scale interventions could seek to proceed in
terms of reducing maths anxiety.

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.