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Which MPs get elevated to the House of Lords?
Get PDFUsing logistic regression and a dataset of 816 MPs who sat in the UK House of Commons between 1997 and 2019, we analyse which MPs get elevated to the upper chamber. Drawing on literatures concerning progressive political ambition, the UK Parliament and the wider nature of the British state, we test hypotheses concerning loyalty, expertise and nepotism. We find evidence to support all three but expertise in the form of frontbench experience and, for those MPs without such experience, loyalty appear to be the most important factors driving elevation. Our research has implications for debates surrounding House of Lords reform
Women trail runners' encounters with vulnerability to male harassment in rural off-road spaces
Get PDFThe #metoo movement and high-profile coverage of murders of women in public spaces have reignited investigation of public harassment and women’s actions as they make decisions where and how to engage in outdoor physical activity. This paper draws from the ideas of Lefebvre (1991) and Massey (1994) to understand women trail runners’ spatial experiences in England. Sixteen women who trail run by themselves participated in go-along interviews in their usual running trails. This method allowed participants to recall moments in specific spaces or address spaces that generate particular feelings, and encouraged the researcher to gain a sensory understanding of the spaces which were important to participants. We analyse the production of the trail through runners’ interactions with people and environment inside and outside the trail, and discuss enjoyment as well as perceptions of vulnerability to male harassment and ‘risky’ moments. Ultimately, despite runners regularly feeling vulnerable when running, they refused to stop. At a time when physical activity and natural environments are being promoted as key contributors to personal wellbeing and public health, this research provides evidence of how the production of spaces and safety negotiations affect women’s running experiences
An unsupervised approach for the detection of zero‐day distributed denial of service attacks in Internet of Things networks
Get PDFThe authors introduce an unsupervised Intrusion Detection System designed to detect zero-day distributed denial of service (DDoS) attacks in Internet of Things (IoT) networks. This system can identify anomalies without needing prior knowledge or training on attack information. Zero-day attacks exploit previously unknown vulnerabilities, making them hard to detect with traditional deep learning and machine learning systems that require pre-labelled data. Labelling data is also a time-consuming task for security experts. Therefore, unsupervised methods are necessary to detect these new threats. The authors focus on DDoS attacks, which have recently caused significant financial and service disruptions for many organisations. As IoT networks grow, these attacks become more sophisticated and harmful. The proposed approach detects zero-day DDoS attacks by using random projection to reduce data dimensionality and an ensemble model combining K-means, Gaussian mixture model, and one-class SVM with a hard voting technique for classification. The method was evaluated using the CIC-DDoS2019 dataset and achieved an accuracy of 94.55%, outperforming other state-of-the-art unsupervised learning methods
DeepDetect: an innovative hybrid deep learning framework for anomaly detection in IoT networks
Get PDFThe presence of threats and anomalies in the Internet of Things infrastructure is a rising concern. Attacks, such as Denial of Service, User to Root, Probing, and Malicious operations can lead to the failure of an Internet of Things system. Traditional machine learning methods rely entirely on feature engineering availability to determine which data features will be considered by the model and contribute to its training and classification and “dimensionality” reduction techniques to find the most optimal correlation between data points that influence the outcome. The performance of the model mostly depends on the features that are used. This reliance on feature engineering and its effects on the model performance has been demonstrated from the perspective of the Internet of Things intrusion detection system. Unfortunately, given the risks associated with the Internet of Things intrusion, feature selection considerations are quite complicated due to the subjective complexity. Each feature has its benefits and drawbacks depending on which features are selected. Deep structured learning is a subcategory of machine learning. It realizes features inevitably out of raw data as it has a deep structure that contains multiple hidden layers. However, deep learning models such as recurrent neural networks can capture arbitrary-length dependencies, which are difficult to handle and train. However, it is suffering from exploiting and vanishing gradient problems. On the other hand, the log-cosh conditional variational Autoencoder ignores the detection of the multiple class classification problem, and it has a high level of false alarms and a not high detection accuracy. Moreover, the Autoencoder ignores to detect multi-class classification. Furthermore, there is evidence that a single convolutional neural network cannot fully exploit the rich information in network traffic. To deal with the challenges, this research proposed a novel approach for network anomaly detection. The proposed model consists of multiple convolutional neural networks, gate-recurrent units, and a bi-directional-long-short-term memory network. The proposed model employs multiple convolution neural networks to grasp spatial features from the spatial dimension through network traffic. Furthermore, gate recurrent units overwhelm the problem of gradient disappearing- and effectively capture the correlation between the features. In addition, the bi-directional-long short-term memory network approach was used. This layer benefits from preserving the historical context for a long time and extracting temporal features from backward and forward network traffic data. The proposed hybrid model improves network traffic's accuracy and detection rate while lowering the false positive rate. The proposed model is evaluated and tested on the intrusion detection benchmark NSL-KDD dataset. Our proposed model outperforms other methods, as evidenced by the experimental results. The overall accuracy of the proposed model for multi-class classification is 99.31% and binary-class classification is 99.12%
Stakeholder perspectives on supplemental milk for infants under six months with growth faltering
Get PDFBackground Growth faltering is a significant public health issue among infants aged <6 months (m). Supplemental milk is commonly used for infants with growth faltering, with variations in type and duration. We synthesized qualitative evidence on stakeholder perspectives about equity, feasibility, and acceptability of the type and duration of supplemental milk for infants aged <6m with growth faltering. Methods We conducted a comprehensive search of six electronic databases in addition to manual searches to identify all qualitative studies published during January 2000-June 2022. Identified articles were screened in two stages against an inclusion criteria with titles and abstract screened first followed by full-text screening. Included studies were quality appraised using the Critical Appraisal Skills Programme checklist. The primary outcome was equity, feasibility, and acceptability of various supplemental milk for infants <6mwith growth faltering. Results Eighteen studies, reporting perspectives of mothers, fathers, grandmothers and healthcare providers were included. Studies were conducted in North America (9), Africa (5) and Asia (3) and South Australia (1). Donor human milk (DHM) (13) and infant formula(9) were the main supplementary milk reported followed by cow/goat milk (2). Key sub-themes derived were: education/awareness, socio-economic status (SES), race and religion, practicality, availability of resources, sustainability, cost, affective attitude, perceived effectiveness and ethicality. Maternal/caregiver SES was a key sub-theme across all three supplemental milk feeds, acting either as a barrier or facilitator for uptake. Conclusions DHM and infant formula were the most commonly reported supplemental feed for infants aged <6m with growth faltering. Maternal/care giver factors were perceived as key to ensure equity, feasibility, and acceptability with respect to type and duration of supplemental milk for infants aged <6m with growth faltering. Key messages • Stakeholders perceived donor human milk and infant formula as main supplemental milk for infants aged <6 months with growth faltering. • Maternal/care giver factors are key to ensure equity, feasibility, and acceptability of supplemental milk for infants aged <6 months with growth faltering
Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain
Get PDFThe management of a food supply chain is difficult and complex because of the product's short shelf-life, time-sensitivity, and perishable nature which must be carefully considered to minimize food waste. Temperature-controlled perishable food supply chain provides the highly crucial facilities necessary to maintain the quality and safety of the product. The storage temperature is the most vital factor in maintaining both the quality and shelf-life of a perishable food. Adequate storage temperature control ensures that perishable foods are transported to the end-users in good quality and safe to consume. This paper presents perishable food storage temperature control through mathematical optimal control model where the storage temperature is regarded as the control variable and the deterioration of the perishable food's quality follows the first-order reaction. The optimal storage temperature for a single perishable food is determined by applying the Pontryagin's maximum principle to solve the optimal control model problem. For multi-temperature commodities supply chain, an unsupervised machine learning (ML) method, called k-means clustering technique is used to determine the temperature clusters for a range of perishables. Based on descriptive analysis, it is observed that the k-means clustering technique is effective in identifying the best suitable storage temperature clusters for quality control of multi-commodity supply chain
'Colorblind game' can enhances awareness of color blindness
Get PDFThis study investigates whether awareness of color blindness can be enhanced through a virtual colorblind gaming experience. We conducted two user studies—one with undergraduates and one with working adults—using ‘colorblind’ color schemes in a digital game to explore whether such an experience enhances their assessment of their own knowledge about color blindness and awareness of its associated disadvantages in society, workplaces, and private life. The results with undergraduates showed increases in general, but not much in workplace disadvantages. In contrast, the results with working adults showed a significant improvement in the assessment of knowledge but not in other aspects. Thus, a virtual colorblind gaming experience can enhance awareness of color blindness, yet the interpretation of the experience may vary significantly depending on the players’ backgrounds
Implementation and impact of the Practice Principles for responding to child exploitation and extra-familial harm
Get PDFThe eight Practice Principles set out an approach for responding to child exploitation and extra-familial harm, aiming to support and align multi-agency responses. Developed by the Tackling Child Exploitation (TCE) Support Programme1 the Principles are evidence-informed2, offering a way to navigate a complex landscape, focusing on behaviours and cultures at all levels. Interrelated and interdependent, they are designed to complement and support existing guidance and local working arrangements
Satellite image restoration via an adaptive QWNNM model
Get PDFDue to channel noise and random atmospheric turbulence, retrieved satellite images are always distorted and degraded and so require further restoration before use in various applications. The latest quaternion-based weighted nuclear norm minimization (QWNNM) model, which utilizes the idea of low-rank matrix approximation and the quaternion representation of multi-channel satellite images, can achieve image restoration and enhancement. However, the QWNNM model ignores the impact of noise on similarity measurement, lacks the utilization of residual image information, and fixes the number of iterations. In order to address these drawbacks, we propose three adaptive strategies: adaptive noise-resilient block matching, adaptive feedback of residual image, and adaptive iteration stopping criterion in a new adaptive QWNNM model. Both simulation experiments with known noise/blurring and real environment experiments with unknown noise/blurring demonstrated that the effectiveness of adaptive QWNNM models outperformed the original QWNNM model and other state-of-the-art satellite image restoration models in very different technique approaches
What next for desistance and youth justice?
Get PDFThe roots of this book lie in conversations about desistance and children in early 2021 that originated following an online event with academics, practitioners and policymakers who energetically critiqued and commented on the relevance and application of desistance theories to youth justice-involved children. While the purpose of the online event was to launch a briefing paper on desistance and youth justice, and thus mark the culmination of the National Association for Youth Justice’s (NAYJ) work on the topic, the discussions led to a number of reflections and questions. What helps children to move away from offending? To what extent is the concept and theorisation of desistance useful to explaining this during childhood and adolescence? Does the application of desistance theories risk problematising rather than normalising children’s behaviour? How is desistance thinking understood, interpreted and implemented in youth justice policy and practice
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