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Law Enforcement
No full textLaw enforcement can be considered in both the ‘narrow’ sense of the policing and enforcement of law, and in a wider sense of the maintenance of order and reinforcement of societal rules and ideologies. The maintenance of social order, protection of citizens and prevention of and redress for harms against citizens, property and non-human nature is heavily reliant on law enforcement. Effective criminal justice is arguably dependent on law enforcement as a dominant feature of criminal justice systems and the notion of punishment as a tool of social control. Societal construction of harm and definition of unacceptable behaviour often manifests itself in laws, rules and regulations that serve as both control mechanisms and expressions of societal norms. Where societal rules, in the form of laws and regulations, are broken, effective law enforcement is essential both to demonstrate societal disapproval of the ‘deviant’ behaviour and to provide for social sanction through appropriate redress and retributive justice mechanisms. Accordingly, law enforcement and policing are inextricably linked in the context of providing a means through which serious social harms can be dealt with. But law enforcement goes far beyond policing, both conceptually and in respect of the mechanisms that are deployed to express society’s disapproval and ultimately secure redress. In a narrow sense policing can be defined as that which the police (or recognised policing agencies) carry out. This often centres around enforcement of the criminal law and a detection, investigation and apprehension model inextricably linked to ideas of retributive justice. By contrast, law enforcement involves civil and criminal justice agencies, can incorporate administrative and regulatory law mechanisms and even alternative dispute resolution (ADR) as a means of resolving disputes and ensuring appropriate redress. Thus, law enforcement can also extend beyond the confines of retributive criminal justice to incorporate restorative and rehabilitative justice mechanisms. Adopting a critical criminology perspective, this analysis examines law enforcement within the context of the major sources of crime as being the unequal class, race/ethnic, and gender relations that control our society. Thus, law enforcement risks being less about the protection of society and maintenance of order as much as it is about selective enforcement of rules, that somewhat perpetuates unequal power relationships within society. Using examples from environmental justice, human rights, and crimes against non-human nature, this analysis identifies law enforcement as both a wider justice approach concerned with maintaining and reinforcing societal norms and a criminal justice approach extending beyond the retributive justice approach employed in policing. This analysis considers how environmental crimes are often subject to an approach based on regulation and settlement with use of the criminal law as a last resort. This contrasts with the law enforcement approach to mainstream crimes which remains situated within a reactive criminal law-based approach.</p
Self-paced regularized adaptive multi-view unsupervised feature selection
No full text Multi-view unsupervised feature selection (MUFS) is an efficient approach for dimensional reduction of heterogeneous data. However, existing MUFS approaches mostly assign the samples the same weight, thus the diversity of samples is not utilized efficiently. Additionally, due to the presence of various regularizations, the resulting MUFS problems are often non-convex, making it difficult to find the optimal solutions. To address this issue, a novel MUFS method named Self-paced Regularized Adaptive Multi-view Unsupervised Feature Selection (SPAMUFS) is proposed. Specifically, the proposed approach firstly trains the MUFS model with simple samples, and gradually learns complex samples by using self-paced regularizer. -norm () is employed to measure the learning error and as the sparse regularization to accommodate various sparsity requirements across different datasets. Moreover, hypergraph Laplacian matrices are constructed for each view to better preserve the local manifold structure and encode high-order relationships within the data space. They are adaptively assigned weights to learn the underlying correlated and diverse information among different views. An iterative optimization algorithm is proposed to solve SPAMUFS and the convergence and computational complexity are also analyzed. The effectiveness of SPAMUFS is substantiated by comparing with eight state-of-the-art algorithms on nine public multi-view datasets. </p
How to be an effective Boundary Spanner between energy policy and energy Social Sciences & Humanities communities
No full textReaching across policy and research boundaries is essential if low-carbon energy transformations are to be achieved. A Boundary Spanner is an individual/organisation that sits between traditional users and producers of knowledge; thus, Boundary Spanners can and do enable knowledge exchange between research and policy communities. However, despite much discussion of Boundary Spanners in certain literatures, the concept has been sparsely applied to issues of energy policy and governance. In fact, it has been altogether excluded from discussions of how Social Sciences and Humanities (SSH) communities and energy policy actors can better work together towards common goals. This Perspective piece provides 12 recommendations for Boundary Spanners operating in these spaces, which we cluster into four themes: (i) Pay active attention to policy contexts of action; (ii) Use power to build bridges to different (and underrepresented) constituencies; (iii) Ensure institutions promote boundary spanning skill development; and (iv) Sensitively support the agendas of external colleagues. We hope this Perspective contributes to more attention being given to these relatively few Boundary Spanners, who work in the margins between policy and research, and also provides useful guidance on how they may do so.</p
National trends in allergic rhinitis and chronic rhinosinusitis and COVID-19 pandemic related factors in South Korea, from 1998 to 2021
No full textIntroduction: Existing studies provide insights into the prevalence and environmental factors associated with allergic rhinitis (AR) and chronic rhinosinusitis (CRS) globally. However, limitations still persist in these studies, particularly regarding cohort sizes and the duration of follow-up periods, indicating a need for more comprehensive and long-term research in these fields. Our study aimed to investigate the prevalence, long-term trends, and underlying factors of these conditions in the general population of adult participants (≥19 years) in Korea. Method: We analyzed data from adult participants (≥19 years) from the Korea National Health and Nutrition Examination Survey (KNHANES) study to determine the prevalence of AR and CRS from 1998 to 2021. To analyze prevalence trends before and during the COVID-19 pandemic, we employed a weighted linear regression model and obtained β-coefficients with 95% confidence intervals (CI). Results: Between 1998 and 2021, over a span of 24 years, the comprehensive KNHANES study included 146,264 adult participants (mean age: 47.80 years, standard deviation: 16.49 years; 66,177, 49.3% men). The prevalence of AR and CRS increased from 1998 to 2021, with AR prevalence rising from 5.84% (95% CI, 5.57–6.10) in 1998–2005 to 8.99% (8.09–9.91) in 2021 and CRS from 1.84% (1.70–1.97) in 1998–2005 to 3.70% (3.18–4.23) in 2021. However, the increasing trend has slowed down during the COVID-19 pandemic era. Conclusions: The significance of continuous monitoring and focused interventions for AR and CRS is underscored by this study. The observed deceleration in the rising prevalence of AR and CRS during the pandemic indicates the possibility of beneficial impacts from lifestyle modifications triggered by the pandemic. These findings call for additional research to explore potential protective effects in greater depth.</p
The effects of lower limb ischaemic preconditioning: a systematic review
No full textIschaemic preconditioning (IPC) involves the use of repeated occlusions and reperfusions of the peripheral muscle blood supply at a limb. This systematic literature review examines the typical responses in response to the method of application during an IPC applied at the lower limb. This review focuses on the physiological responses for VO2max, haemoglobin, metabolic and genetic responses to various IPC interventions. The literature search was performed using four databases and assessed using the PRISMA search strategy and COSMIN to assess the quality of the articles. Seventeen articles were included in the review, with a total of 237 participants. While there is variation in the method of application, the average occlusion pressure was 222 ± 34 mmHg, ranging from 170 to 300 mmHg typically for 3 or 4 occlusion cycles. The distribution of this pressure is influenced by cuff width, although 8 studies failed to report cuff width. The majority of studies applies IPC at the proximal thigh with 16/17 studies applying an occlusion below this location. The results highlighted the disparities and conflicting findings in response to various IPC methods. While there is some agreement in certain aspects of the IPC manoeuvre such as the location of the occlusion during lower limb IPC, there is a lack of consensus in the optimal protocol to elicit the desired responses. This offers the opportunity for future research to refine the protocols, associated responses, and mechanisms responsible for these changes during the application of IPC.</p
Effect of early versus late onset of partial visual loss on judgments of auditory distance
No full text It is important to know whether early-onset vision loss and late-onset vision loss are associated with differences in the estimation of distances of sound sources within the environment. People with vision loss rely heavily on auditory cues for path planning, safe navigation, avoiding collisions, and activities of daily living. Loss of vision can lead to substantial changes in auditory abilities. It is unclear whether differences in sound distance estimation exist in people with early-onset partial vision loss, late-onset partial vision loss, and normal vision. We investigated distance estimates for a range of sound sources and auditory environments in groups of participants with early- or late-onset partial visual loss and sighted controls. Fifty-two participants heard static sounds with virtual distances ranging from 1.2 to 13.8 m within a simulated room. The room simulated either anechoic (no echoes) or reverberant environments. Stimuli were speech, music, or noise. Single sounds were presented, and participants reported the estimated distance of the sound source. Each participant took part in 480 trials. Analysis of variance showed significant main effects of visual status (p0.05). The findings suggest that early-onset partial vision loss results in significant changes in judged auditory distance in different environments, especially for close and middle distances. Late-onset partial visual loss has less of an impact on the ability to estimate the distance of sound sources. The findings are consistent with a theoretical framework, the perceptual restructuring hypothesis, which was recently proposed to account for the effects of vision loss on audition.</p
Urgent need for better quality control, standards and regulation for the Large Language Models used in healthcare domain
No full textCurrent methodologies for ensuring AI technology safety and efficacy may be adequate for earlier AI iterations predating generative artificial intelligence (GAI). However, governing clinical GAI may necessitate the development of novel regulatory frameworks. As AI technology advances, researchers, academic institutions, funding bodies, and publishers should continue to examine its impact on scientific inquiry and revise their understanding, ethical guidelines, and regulations accordingly.</p
Measuring X inactivation skew for retinal diseases with adaptive nanopore sequencing
No full textX-linked genetic disorders typically affect females less severely than males due to the presence of a second X chromosome not carrying the deleterious variant. However, the phenotypic expression in females is highly variable, which may be explained by an allelic skew in X chromosome inactivation. Accurate measurement of X inactivation skew is crucial to understand and predict disease phenotype in carrier females, with prediction especially relevant for degenerative conditions. We propose a novel approach using nanopore sequencing to quantify skewed X inactivation accurately. By phasing sequence variants and methylation patterns, this single assay reveals the disease variant, X inactivation skew, its directionality, and is applicable to all patients and X-linked variants. Enrichment of X-chromosome reads through adaptive sampling enhances cost-efficiency. Our study includes a cohort of 16 X-linked variant carrier females affected by two X-linked inherited retinal diseases: choroideremia and RPGR-associated retinitis pigmen-tosa. As retinal DNA cannot be readily obtained, we instead determine the skew from peripheral samples (blood, saliva and buccal mucosa), and correlate it to phenotypic outcomes. This revealed a strong correlation between X inactivation skew and disease presentation, confirming the value in performing this assay and its potential as a way to prioritise patients for early intervention, such as gene therapy currently in clinical trials for these conditions. Our method of assessing skewed X inactivation is applicable to all long-read genomic datasets, providing insights into disease risk and severity and aiding in the development of individualised strategies for X-linked variant carrier females.</p
Enhancing Malware Detection Through Machine Learning Using XAI with SHAP Framework
No full textMalware represents a significant cyber threat that can potentially disrupt any activities within an organization. There is a need to devise effective proactive methods for malware detection, thereby minimizing the associated risks. However, this task is challenging due to the ever-growing volume of malware data and the continuously evolving techniques employed by malicious actors. In this context, machine learning models offer a promising approach to identify key malware features and facilitate accurate detection. Machine learning has proven to be effective in detecting malware and has recently gained widespread attention from both the academic and research sectors. Despite their effectiveness, current research on machine learning (ML) models for malware detection often lacks necessary explanations for the selection of key features. This opacity of ML models can complicate the understanding of the outputs, errors, and decision-making processes. To address this challenge, this research uses Explainable AI (XAI), particularly the SHAP framework, to enhance transparency and interpretability. By providing extensive insights into how each feature contributes to the model’s conclusions, the approach further improves the model’s accountability. An experiment was conducted to demonstrate the applicability of the proposed method, beginning with the training of the chosen machine learning models, including Random Forest, Adaboost, Support Vector Machine and Artificial Neural Network, for detecting malware, and concluding with the explanation of the decision-making process using XAI techniques. The results showed high accuracy in malware detection, along with comprehensive explanations of the feature contributions, which justifies the outputs produced by the models.</p
Synthetic Data Generation and Impact Analysis of Machine Learning Models for Enhanced Credit Card Fraud Detection
No full textThe financial industry is currently experiencing a substantial shift in its operating landscape because of the swift integration of technology. This transformation brings with it potential risks and challenges. Heightened occurrence of online fraud is one the key concerns for this sector, which has been exacerbated by the growing prevalence of online payment methods on e-commerce platforms and other websites. The identification of credit card fraud is a challenging task due to nature of imbalanced transactional data to detect and predict any fraudulent activities. In this context, this paper provides a unique approach to create synthetic dataset to tackle imbalanced issue for credit card fraud detection. The approach adopts Synthetic Minority Over-sampling Technique (SMOTE) technique for balancing dataset. An experiment is performed using several ML models including SVM (Support Vector Machines), KNN (K-Nearest Neighbours), and Random Forest to demonstrate the feasibility of using synthetic data. In this study, we have combined resampling techniques like SMOTE for oversampling the minority class with ensemble methods and appropriate evaluation metrics like the F1-score to improve the imbalanced data. The result from the experiment compared with widely used public datasets to evaluate the model performance. The analysis reveals an imbalance in the real ULB (Université Libre de Bruxelles) dataset, with the positive class (frauds) comprising a mere 0.172% of all transactions. The findings clearly show that the Random Forest model performs better than other modes with outstanding precision, recall, accuracy, and F1 score values to detect fraudulent transactions and reduce false positives.</p
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