Women and girls represent only a minority in the penitentiary system and in forensic mental health care. About 6%–10% of both prison and forensic psychiatric populations in Western countries comprise women (see for the most recent offi cial statistics in the UK w ww.gov. uk/government, in Canada w ww.statcan.gc.ca, and in the US w ww.bjs.gov) . However, there seems to be widespread agreement that in the past 20 years female offending has been on the rise, especially violent offending and particularly among young women ( Miller, Malone, and Dodge, 2010; M oretti, Catchpole, and Odgers, 2005) . Overall, a disproportionate growth of females entering the criminal justice system and forensic mental health care has been observed in many countries (for reviews, see Nicholls, Cruise, Greig, and Hinz, 2015; Odgers, Moretti, and Reppucci, 2005 ; Walmsley, 2015) . In addition, it should be noted that the ‘dark number’ for women is suggested to be bigger than for men. Offi cial prevalence rates of female offending might constitute an underestimation as women usually commit less reported offences, for example, domestic violence (N icholls, Greaves, Greig, and Moretti, 2015) . Furthermore, it has been found that – if apprehended – girls and women are treated more leniently by professionals and the criminal justice system. Generally, they receive lower prison sentences and are more often admitted to civil psychiatric institutions instead of receiving a prison sentence or mandatory forensic treatment after committing violence ( Javdani, Sadeh, and Verona, 2011 ; Jeffries, Fletcher, and Newbold, 2003 ). Hence, although female offenders compared to male offenders are a minority, female violence is a substantial problem that deserves more attention. Our understanding of female offenders is hindered by the general paucity of theoretical and empirical investigations of this population. In order to improve current treatment and assessment practices, our knowledge and understanding of female offenders should be enlarged and optimised (d e Vogel and Nicholls, 2016 ).
This three day module focuses on the role of strategic environmental assessment in relation to integrating health issues. During the module participants will be introduced to SEA concepts and process dynamics, as well as to health issues when considered from a strategic perspective. Participants will obtain a full understanding of SEA as a process and instrument by means of a policy case study which illustrates the need to look at health concerns in a strategic context.Mini-lectures will be supported by hands-on practical exercises and feed-back on exercises conducted. The cases used in these exercises focus on strategic energy policies. Participants are expected to actively participate in the module work and a final presentation of the groups’ work can be held during the last hour of the module.
MULTIFILE
Resistance to damage, fracture and failure is critical for high performance polymers, especially so in safety applications where they protect equipment or human life. In this project we investigate the use of molecular mechanochemistry tools for the measurement and analysis of mechanical impact in high performance polymers and their composites. While typically performed in a laboratory setting, these measurements hold promise for studying damage in large scale realistic samples. For this we will to develop fluorescent imaging techniques and chemistry, necessary to produce mechanoresponsive samples. This proposal will also draw correlations between imaging and mechanical testing, which can ultimately allow us to study realistic samples and recover the history of the impact they have sustained during operation.
Even though considerable amounts of valuable wood are collected at waste collection sites, most of it remains unused and is burned: it is too labor-intensive to sort, process and upcycle useable parts. Valuable wood thus becomes worthless waste, against circular economy principles. In MoBot-Wood, waste collection organizations HVC and the municipality of Amsterdam, together with Rolan Robotics, Metabolic and AUAS investigate how waste wood can be sorted and processed at waste collection sites, using an easy-to-deploy robotic solution. In various preceding and on-going projects, AUAS and partners are exploring circular wood intake, sorting and processing using industrial robots, including processes like machine vision, 3D scanning, sawing, and milling. These projects show that harvesting waste wood is a challenging matter. Generally, the wood is only partially useable due to the presence of metal, excessive paint, deterioration by fungi and water, or other contamination and damages. To harvest useable wood thus requires intensive sorting and processing. The solution of transporting all the waste wood from collection sites to a central processing station might be too expensive and have a negative environmental impact. Considering that much of collected wood will need to be discarded, often no wood is harvested at all, due to the costs for collection and shipping. Speaking with several partners in related projects, the idea emerged to develop a mobile robotic station, which can be (temporarily) deployed at waste collection sites, to intake, sort and process wood for upcycling. In MoBot-Wood, research entails the design of such station, its deployment conditions, and a general assessment of its potential impact. The project investigates robotic sorting and processing on location as a new approach to increase the amount of valuable, useable wood harvested at waste collection sites, by avoiding material transport and reducing the volume of remaining waste.
Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection.
