Arching action strength enhancement in laterally-restrained slab strips

The behaviour and ultimate load capacity of laterally-restrained reinforced concrete slabs can be considerably enhanced by the development of arching or compressive membrane action. This paper presents a simple method for predicting the enhanced ultimate load capacity of laterally-restrained slab strips. The method is based on deformation theory and utilizes an elastic-plastic stress-strain criterion for concrete. The loads carried by bending and arching action are calculated separately and then added to give the total ultimate load capacity. A simple equivalent strip approach, based on a three-hinged arch analogy, allows for the degree of lateral restraint. The method of prediction has been validated by correlation with a wide range of test results from various sources.

Climate Change, national politics and grassroots action: an introduction

Climate change continues to dominate academic work within green/environmental politics. Indeed, there appears to be almost an inverse relationship between the lack of political leadership on tackling climate change and the growth in ever more sophisticated academic analyses of this complex and multifaceted problem. There is an increasing disjunction between the growth in our knowledge and understanding of the ethical, political, economic, sociological, cultural, and psychological aspects of climate change and the lack of political achievement in putting in place clear and binding targets, an agreed decarbonisation roadmap, and associated regulatory and policy instruments with enforcement. This gap might be taken as evidence that we do not need more reports on climate change. To quote that most unlikely of green politicians, Arnold Schwarzenegger, former Governor of California: ‘The debate is over. We know the science. We see the threat. And we know that the time for action is now’ (California Energy Commission 2007, p. 1). This special issue focuses on a variety of ways in which climate change is conceptualised in normative political and ethical theory, and addressed in policy and regulations.

Episodic Reasoning for Vision-Based Human Action Recognition

Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning.

Establishing optimal quantitative-polymerase chain reaction assays for routine diagnosis and tracking of minimal residual disease in JAK2-V617F-associated myeloproliferative neoplasms:a joint European LeukemiaNet/MPN&MPNr-EuroNet (COST action BM0902) study

Reliable detection of JAK2-V617F is critical for accurate diagnosis of myeloproliferative neoplasms (MPNs); in addition, sensitive mutation-specific assays can be applied to monitor disease response. However, there has been no consistent approach to JAK2-V617F detection, with assays varying markedly in performance, affecting clinical utility. Therefore, we established a network of 12 laboratories from seven countries to systematically evaluate nine different DNA-based quantitative PCR (qPCR) assays, including those in widespread clinical use. Seven quality control rounds involving over 21,500 qPCR reactions were undertaken using centrally distributed cell line dilutions and plasmid controls. The two best-performing assays were tested on normal blood samples (n=100) to evaluate assay specificity, followed by analysis of serial samples from 28 patients transplanted for JAK2-V617F-positive disease. The most sensitive assay, which performed consistently across a range of qPCR platforms, predicted outcome following transplant, with the mutant allele detected a median of 22 weeks (range 6-85 weeks) before relapse. Four of seven patients achieved molecular remission following donor lymphocyte infusion, indicative of a graft vs MPN effect. This study has established a robust, reliable assay for sensitive JAK2-V617F detection, suitable for assessing response in clinical trials, predicting outcome and guiding management of patients undergoing allogeneic transplant.

Participatory Action Research with and within community activist groups: capturing the collective experience of Ireland's Community and Voluntary Pillar

The inclusion of community activists in policy planning is increasingly recognized at the highest international level. This article shows how the use of Participatory Action Research (PAR) can present a deeper and more holistic picture of the experiences of Civil Society Organizations (CSOs) in shaping national-level social policy. By utilizing action-based research, the Community and Voluntary Pillar (CVP) of Ireland’s system of social partnership is shown to be an important agent in deliberating national bargaining outcomes (known as the Towards 2016 national agreement). The key contribution of this research is the reflective methodological considerations in terms of PAR design, execution and participant integration in the research process as a way to enrich and develop a deeper and more informed community of practice.

Low-rank representation based action recognition

Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features extracted from videos still remains to be a challenging problem. In this paper, we propose a novel method named low-rank representation based action recognition to recognize human actions. Given a dictionary, low-rank representation aims at finding the lowestrank representation of all data, which can capture the global data structures. According to its characteristics, low-rank representation is robust against noises. Experimental results demonstrate the effectiveness of the proposed approach on several publicly available datasets.