Emotional and behavioural problems in children with language impairments and children with autism spectrum disorders

Background: Although it is well-established that children with language impairment (LI) and children with autism spectrum disorders (ASD) both show elevated levels of emotional and behavioural problems, the level and types of difficulties across the two groups have not previously been directly compared. Aims: To compare levels of emotional and behavioural problems in children with LI and children with ASD recruited from the same mainstream schools. Methods & Procedures: We measured teacher-reported emotional and behavioural problems using the Strengths and Difficulties Questionnaire (SDQ) in a sample of 5-to-13-year old children with LI (N=62) and children with ASD (N=42) attending mainstream school but with identified special educational needs. Outcomes & Results: Both groups showed similarly elevated levels of emotional, conduct and hyperactivity problems. The only differences between the LI and ASD groups were on subscales assessing peer problems (which were higher in the ASD group) and prosocial behaviours (which were higher in the LI group). Overall, there were few associations between emotional and behavioural problems and child characteristics, reflecting the pervasive nature of these difficulties in children with LI and children with ASD, although levels of problems were higher in children with ASD with lower language ability. However, in the ASD group only, a measure of family social economic status was associated with language ability and attenuated the association between language ability and emotional and behavioural problems. Conclusions & Implications: Children with LI and children with ASD in mainstream school show similarly elevated levels of emotional and behavioural problems, which require monitoring and may benefit from intervention. Further work is required to identify the child, family and situational factors that place children with LI and children with ASD at risk of emotional and behavioural problems, and whether these differ between the two groups. This work can then guide the application of evidence-based interventions to these children.

Preoperative predictors for postoperative problems in heart transplantation: psychiatric and psychosocial considerations

The psychiatric and psychosocial evaluation of the heart transplant candidate can identify particular predictors for postoperative problems. These factors, as identified during the comprehensive evaluation phase, provide an assessment of the candidate in context of the proposed transplantation protocol. Previous issues with compliance, substance abuse, and psychosis are clear indictors of postoperative problems. The prolonged waiting list time provides an additional period to evaluate and provide support to patients having a terminal disease who need a heart transplant, and are undergoing prolonged hospitalization. Following transplantation, the patient is faced with additional challenges of a new self-image, multiple concerns, anxiety, and depression. Ultimately, the success of the heart transplantation remains dependent upon the recipient's ability to cope psychologically and comply with the medication regimen. The limited resource of donor hearts and the high emotional and financial cost of heart transplantation lead to an exhaustive effort to select those patients who will benefit from the improved physical health the heart transplant confers.

Variational data assimilation for very large environmental problems

Variational data assimilation is commonly used in environmental forecasting to estimate the current state of the system from a model forecast and observational data. The assimilation problem can be written simply in the form of a nonlinear least squares optimization problem. However the practical solution of the problem in large systems requires many careful choices to be made in the implementation. In this article we present the theory of variational data assimilation and then discuss in detail how it is implemented in practice. Current solutions and open questions are discussed.

Applying robust control theory to solve problems in bio-medical sciences: study of an apoptotic model

Biological models of an apoptotic process are studied using models describing a system of differential equations derived from reaction kinetics information. The mathematical model is re-formulated in a state-space robust control theory framework where parametric and dynamic uncertainty can be modelled to account for variations naturally occurring in biological processes. We propose to handle the nonlinearities using neural networks.

Challenges in conducting and publishing research on the Middle East and Africa in leading journals

Purpose – The purpose of this paper is to examine the reasons for the lack of research attention paid to the Middle East (ME) and Africa regions. In particular, this study seeks to identify the reasons for and implications of the paucity of ME- and Africa-based studies in high-quality international journals in the marketing field with a specific focus on the challenges in conducting and publishing research on these regions. Design/methodology/approach – The authors conducted a systematic review of the literature on the ME and Africa regions to identify papers published in 23 high-quality marketing, international business, and advertising journals. This search resulted in 301 articles, among which 125 articles were based on primary or secondary data collected from a local source in those regions. The authors of these 125 articles constitute the Delphi study sample. These academics provided input in an effort to reach a consensus regarding the two proposed models of academic research in both regions. Findings – This paper differs from previous studies, where academic freedom emerged as the most important inhibitor to conducting and publishing research. The most frequently mentioned challenges in conducting research in Africa were access to data, data collection issues, diversity of the region, and lack of research support infrastructure. For the ME, the most often described challenges included validity and reliability of data, language barriers, data collection issues, and availability of a network of researchers. Editors’ and reviewers’ low interest and limited knowledge were ranked high in both regions. South Africa, Israel, and Turkey emerged as outliers, in which research barriers were less challenging than in the rest of the two regions. The authors attribute this difference to the high incidence of US-trained or US-based scholars originating from these countries. Originality/value – To the best of the knowledge, no marketing studies have discussed the problems of publishing in high-quality international journals of marketing, international business, and advertising for either region. Thus, most of the issues the authors discuss in this paper offer new insightful results while supplementing previous research on the challenges of conducting and publishing research on specific world regions.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

Towards (more) integrity in academia: transcending neo-liberal assumption of research and knowledge creation and the “rules” supressing academic freedom

European researchers across heterogeneous disciplines voice concerns and argue for new paths towards a brighter future regarding scientific and knowledge creation and communication. Recently, in biological and natural sciences concerns have been expressed that major threats are intentionally ignored. These threats are challenging Europe’s future sustainability towards creating knowledge that effectively deals with emerging social, environmental, health, and economic problems of a planetary scope. Within social science circles however, the root cause regarding the above challenges, have been linked with macro level forces of neo-liberal ways of valuing and relevant rules in academia and beyond which we take for granted. These concerns raised by heterogeneous scholars in natural and the applied social sciences concern the ethics of today’s research and academic integrity. Applying Bourdieu’s sociology may not allow an optimistic lens if change is possible. Rather than attributing the replication of neo-liberal habitus in intentional agent and institutional choices, Bourdieu’s work raises the importance of thoughtlessly internalised habits in human and social action. Accordingly, most action within a given paradigm (in this case, neo-liberalism) is understood as habituated, i.e. unconsciously reproducing external social fields, even ill-defined ways of valuing. This essay analyses these and how they may help critically analyse the current habitus surrounding research and knowledge production, evaluation, and communication and related aspects of academic freedom. Although it is acknowledged that transformation is not easy, the essay presents arguments and recent theory paths to suggest that change nevertheless may be a realistic hope once certain action logics are encouraged.

Advances in the study of boundary value problems for nonlinear integrable PDEs

In this review I summarise some of the most significant advances of the last decade in the analysis and solution of boundary value problems for integrable partial differential equations in two independent variables. These equations arise widely in mathematical physics, and in order to model realistic applications, it is essential to consider bounded domain and inhomogeneous boundary conditions. I focus specifically on a general and widely applicable approach, usually referred to as the Unified Transform or Fokas Transform, that provides a substantial generalisation of the classical Inverse Scattering Transform. This approach preserves the conceptual efficiency and aesthetic appeal of the more classical transform approaches, but presents a distinctive and important difference. While the Inverse Scattering Transform follows the "separation of variables" philosophy, albeit in a nonlinear setting, the Unified Transform is a based on the idea of synthesis, rather than separation, of variables. I will outline the main ideas in the case of linear evolution equations, and then illustrate their generalisation to certain nonlinear cases of particular significance.

Challenging climate change and migration discourse: different understandings of time-scale and temporality in the Maldives

This article draws on ongoing research in the Maldives to explore differences between elite and non-elite perceptions of climate change and migration. It argues that, in addition to variations in perceptions based on diverse knowledge, priorities and agendas, there exists a more fundamental divergence based upon different understandings of the time-scale of climate change and related ideas of urgency and crisis. Specifically, elites tend to focus on a distant future which is generally abstracted from people’s everyday lived realities, as well as utilise the language of a climate change-induced migration ‘crisis’ in their discussions about impacts in a manner not envisaged by non-elites. The article concludes that, rather than unproblematically mapping global, external facing narratives wholesale onto ordinary people’s lives and experiences, there needs to be more dialogue between elites and non-elites on climate change and migration issues. These perspectives should be integrated more effectively in the development of policy interventions designed to help people adapt to the impacts of global environmental change.

Application of complex extreme learning machine to multiclass classification problems with high dimensionality: a THz spectra classification problem

We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.

Rethinking disability theory and practice: challenging essentialism

This edited volume argues that even in recent Critical Disability Studies which have sought to critique essentialist assumptions in relation to Disability, nevertheless essentialisms remain which predetermine and predirect definitions and arguments in the field. This volume analyses such essentialisms in a wide range of areas such as childhood, gender, sexuality, reproduction, ADHD, autism, the animal, d/Deafness, hirsutism, the body, and vision. Particularly issues such as 'agency', 'voice' and 'body' are explored in terms of their political implications.