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Women and Magna Carta - A Treaty for Control or Freedom?
Get PDFIn 2015 Magna Carta turned eight-hundred. A treaty between king and barons, today Magna Carta is claimed as a fundamental statement of rights. No woman was at Runnymede. Women appear in Magna Carta as attached to men – widows, daughters. So, asks Scutt, does Magna Carta speak for women? Some women spoke independently in medieval Britain, although church and aristocracy circumscribed all women’s sphere. Little is written of women and Magna Carta historically or in women’s rights campaigns. Mary Wollstonecraft demanded rights, and some campaigns reflect Magna Carta’s terms without invoking them directly. For men, Magna Carta is ‘adaptable’, a ‘speaking statute’, encompassing new rights and supporting contemporary claims. Scutt asks if Magna Carta thus promotes women’s rights, or does it symbolise wrongs done to women
Beyond the “Territorial Minorities” Discourse: Theory and Practice of Political Participation of National Minorities in Bosnia and Herzegovina through the Case Study of Jews and Poles
No full textAlthough different patterns of political participation among self-aware minority groups have spurred much debate in the academic circles, especially in stable democracies, this issue remains understudied in the newer post-communist societies and notably so the post-conflict countries of former Yugoslavia. Much of the existing research conducted in established democracies has demonstrated that increased levels of national minority political involvement are directly related to democratic development, but that these groups are shunning more traditional forms of engagement, notably political party membership in favour of direct engagement through informal participation. Nevertheless, there is very little understanding of what national minority political participation represents in post-conflict states, as much scholarly research has termed it as underground, invisible or inexistent. Despite this, there is evidence that in these states formal political participation of national minority groups is still strong, but it remains unknown to what degree this occurs, what factors influence this behavior and to what degree is this behavior present among autochthon minority groups. As active political participation of national minorities plays an important role in the democratization and stabilization of such societies, this represents an important gap in our knowledge.
This thesis aims to investigate the level of conventional political participation and the trigger factors for such engagement of two significant, yet contrasting national minority groups in Bosnia and Herzegovina (BiH), namely Jews and Poles. To do this, a mixed-method approach embedded in the transformative paradigm is employed, combining qualitative and quantitative findings of fieldwork. The thesis assesses eight indicators of formal political participation and reveals whether we can observe new trends when it comes to conventional engagement of these two, but also whether their influence remains limited due to their inability to formally participate in the government. It finds that both groups are political communicators, which choose to opt out of political party membership or financial support to electoral campaigns, because they feel alienated from formal politics due to constitutional limitations. However, this exit from the highest forms of political participation is not coupled with total disengagement, as both groups are actively engaged in other forms of formal political activism. This thesis concludes that new trends of political behaviour are emerging among the two observed groups, and especially so among their youth
Targeting Muslims through women's dress : Using the niqab in the psychological war against Muslims
Get PDFComputer-aided diagnosis of gynaecological abnormality using B-mode ultrasound images
Get PDFUltrasound scan is one of the most reliable imaging for detecting/diagnosing of gynaecological abnormalities. Ultrasound imaging is widely used during pregnancy and has become central in the management of the problems of early pregnancy, particularly in miscarriage diagnosis. Also ultrasound is considered as the most important imaging modality in the evaluation of different types of ovarian tumours. The early detection of ovarian carcinoma and miscarriage continues to be a challenging task. It mostly relies on manual examination, interpretation by gynaecologists, of the ultrasound scan images that may use morphology features extracted from the region of interest. Diagnosis depends on using certain scoring systems that have been devised over a long time. The manual diagnostic process involves multiple subjective decisions, with increased inter- and intra-observer variations which may lead to serious errors and health implications.
This thesis is devoted to developing computer-based tools that use ultrasound scan images for automatic classification of Ovarian Tumours (Benign or Malignant) and automatic detection of Miscarriage cases at early stages of pregnancy. Our intended computational tools are meant to help gynaecologists to improve accuracy of their diagnostic decisions, while serving as a tool for training radiology students/trainees on diagnosing gynaecological abnormalities. Ultimately, it is hoped that the developed techniques can be integrated into a specialised gynaecology Decision Support System.
Our approach is to deal with this problem by adopting a standard image-based pattern recognition research framework that involve the extraction of appropriate feature vector modelling of the investigated tumours, select appropriate classifiers, and test the performance of such schemes using sufficiently large and relevant datasets of ultrasound scan images. We aim to complement the automation of certain parameters that gynaecologist experts and radiologists manually determine, by image-content information attributes that may not be directly accessible without advanced image transformations. This is motivated by, and benefit from, advances in computer vision that led the emergence of a variety of image processing/analysis techniques together with recent advances in data mining and machine learning technologies.
An expert observer makes a diagnostic decision with a level of certainty, and if not entirely certain about their diagnostic decisions then often other experts’ opinions are sought and may be essential for diagnosing difficult “Inconclusive cases”. Here we define a quantitative measure of confidence in decisions made by automatic diagnostic schemes, independent of accuracy of decision.
In the rest of the thesis, we report on the development of a variety of innovative diagnostic schemes and demonstrate their performances using extensive experimental work. The following is a summary of the main contributions made in this thesis.
1. Using a combination of spatial domain filters and operations as pre-processing procedures to enhance ultrasound images for both applications, namely miscarriage identification and ovarian tumour diagnosis. We show that the Non-local means filter is effective in reducing speckle noise from ultrasound images, and together with other filters we succeed in enhancing the inner border of malignant tumours and reliably segmenting the gestational sac.
2. Developing reliable automated procedures to extract several types of features to model gestational sac dimensional measurements, few of which are manually determined by radiologist and used by gynaecologists to identify miscarriage cases. We demonstrate that the corresponding automatic diagnostic schemes yield excellent accuracy when classified by the k-Nearest Neighbours.
3. Developing several local as well as global image-texture based features in the spatial as well as the frequency domains. The spatial domain features include the local versions of image histograms, first order statistical features and versions of local binary patterns. From the frequency domain, we propose a novel set of Fast Fourier Geometrical Features that encapsulates the image texture information that depends on all image pixel values. We demonstrate that each of these features define Ovarian Tumour diagnostic scheme that have relatively high power of discriminating Benign from Malignant tumours when classified by Support Vector Machine. We show that the Fast Fourier Geometrical Features are the best performing scheme achieving more than 85% accuracy.
4. Introducing a simple measure of confidence to quantify the goodness of the automatic diagnostic decision, regardless of decision accuracy, to emulate real life medical diagnostics. Experimental work in this theis demonstrate a strong link between this measure and accuracy rate, so that low level of confidence could raise an alarm.
5. Conducting sufficiently intensive investigations of fusion models of multi-feature schemes at different level. We show that feature level fusion yields degraded performance compared to all its single components, while score level fusion results in improved results and decision level fusion of three sets of features using majority rule is slightly less successful. Using the measure of confidence is useful in resolving conflicts when two sets of features are fused at the decision level. This leads to the emergence of a Not Sure decision which is common in medical practice. Considering the Not Sure label is a good practice and an incentive to conduct more tests, rather than misclassification, which leads to significantly improved accuracy.
The thesis concludes with an intensive discussion on future work that would go beyond improving performance of the developed scheme to deal with the corresponding multi-class diagnostics essential for a comprehensive gynaecology Decision Support System tool as the ultimate goal
Smart identification of MANET nodes using AODV routeing protocol
Get PDFMANET routeing protocols can be either straightforward focusing on establishing and maintaining the path only, or too sophisticated with heavy key-based authentication/encryption algorithms. The consequence for both cases creates issues in the QoS implementation of MANET. This thesis focuses on providing three
enhancements to the well-known AODV routeing protocol, without altering the functionality or impeding its performance. It proposes a scheme that improves AODV
routeing discovery process without the overhead associated with integrity/authenticity that we called SIMAN (Smart Identification for Mobile Ad-hoc Networks). First, SIMAN introduces a prime number based mathematical algorithm in a thin layer between the communication links of the IP layer of the AODV routeing protocol. The algorithm replaces existing AODV “retrieval of node addresses” from the routeing table, with a “prime factorization of two values”. These two values are calculated during the RREP process, and thus enhances the AODV routeing protocol to provide knowledge of nodes in the RREP path beyond neighbouring nodes that are out of the transmission range.
The second SIMAN enhancement is to attach the node’s geographical coordinates to the RREP message to enable the trilateration calculation of newly joined nodes. This
process enhances AODV further by providing the nodes with the knowledge of the physical location of every node inside the path. Consequently, by combining both enhancements, AODV can have abstract authentication to prevent from hidden nodes like wormholes.
The final enhancement is to enable SIMAN to construct most efficient paths with nodes that have high battery energy. This is achieved by adding each node’s battery level to the RREP message, where the source will examine the available knowledge of the possible routes that can work efficiently without disconnections or link breakage. The OPNET simulation platform is used for the implementation, verification and testing of this scheme. The results show that the AODV route discovery procedure was not affected in function or performance by our scheme and that the overhead caused by our three enhancements has improved the performance of AODV in certain conditions
Vehicle type recognition using multiple-feature combinations
No full textThis paper proposes a real-time vehicle tracking and type
recognition system. An object tracker is recruited to detect vehicles within CCTV video footage. Subsequently, the vehicle regionof-interest within each frame are analysed using a set of features that consists of Region Features, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) histogram features.
Finally, a Support Vector Machine (SVM) is recruited as the classification tool to categorize vehicles into two classes: cars and vans. The proposed technique was tested on a dataset of 60 vehicles comprising of a mix of frontal/rear and angular views. Experimental results prove that the proposed technique offers a very high level of accuracy thereby promising applicability in real-life situation
Uh-oh! What Have We Missed? A Qualitative Investigation into Everyday Insight Experience
Get PDFInsight has been defined quite specifically in relation to problem solving events as a sudden solution moment usually described as being accompanied by an emotional feeling of Aha (see review by Weisberg, 2014). Others provide more general descriptions of insight such as the experience of a new thought or understanding, without constraining it to instances of problem solving (e.g., Klein & Jarosz, 2011). While affective aspects are often alluded to in reference to Aha or Eureka moments, the focus of insight research has neglected to investigate this emotional component and in the main has concentrated on cognitive processes. Perhaps due to the unpredictable nature of insight the prevailing approach to its study has been to artificially elicit insight moments under controlled conditions. Consequently, there is little research into naturalistic insight experiences (Chu & MacGregor, 2011; Jarman, 2014). This study aims to address these issues and investigate evidence for the existing assumptions about insight using qualitative research methods
Are the physicochemical properties of antibacterial compounds really different from other drugs?
No full textBackground:
It is now widely recognized that there is an urgent need for new antibacterial drugs, with novel mechanisms of action, to combat the rise of multi-drug resistant bacteria. However, few new compounds are reaching the market. Antibacterial drug discovery projects often succeed in identifying potent molecules in biochemical assays but have been beset by difficulties in obtaining antibacterial activity. A commonly held view, based on analysis of marketed antibacterial compounds, is that antibacterial drugs possess very different physicochemical properties to other drugs, and that this profile is required for antibacterial activity.
Results:
We have re-examined this issue by performing a cheminformatics analysis of the literature data available in the ChEMBL database. The physicochemical properties of compounds with a recorded activity in an antibacterial assay were calculated and compared to two other datasets extracted from ChEMBL, marketed antibacterials and drugs marketed for other therapeutic indications. The chemical class of the compounds and Gram-negative/Grampositive profile were also investigated. This analysis shows that compounds with antibacterial activity have physicochemical property profiles very similar to other drug classes.
Conclusions:
The observation that many current antibacterial drugs lie in regions of physicochemical property space far from conventional small molecule therapeutics is correct. However, the inference that a compound must lie in one of these “outlier” regions in order to possess antibacterial activity is not supported by our analysis
Should people in the minimally conscious state have a (recognised) right to reassessment?
Get PDFThis article examines whether there should be a recognised right to reassessment of patients in a minimally conscious state. It examines the result of focus groups done with senior managers, including health care workers and lawyers