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Research has demonstrated that gambling is becoming increasingly normalised for women. As limited research has sought to understand women's perspectives on this issue, we sought women's opinions about the factors that may contribute to the normalisation of gambling for women, and the strategies that may counter this normalisation. Semi‐structured interviews were conducted with 41 women in young and middle adulthood, aged 20‐40 years. Participants suggested that gambling was normal for women because gambling environments had been designed to appeal to women, newer technologies had removed the stigma of attending physical venues, and the growing equality and independence of women. To de‐normalise gambling, women suggested addressing the influential role of marketing, designing new public education strategies, addressing the availability and accessibility of gambling, and restricting engagement with gambling products. This study highlighted women's perceptions of strategies to address the normalisation of gambling and the importance of providing risk information paired with broader policy reform and prevention initiatives to address the range of determinants that normalise gambling for women. Involving women in advocacy and understanding their perspectives is important in developing relevant public health responses to the normalisation of gambling for women.

The popularity of digital histopathology is growing rapidly in the development of computer aided disease diagnosis systems. However, the color variations due to manual cell sectioning and stain concentration make the process challenging in various digital pathological image analysis such as histopathological image segmentation and classification. Hence, the normalization of these variations are needed to obtain the promising results. The proposed research intends to introduce a reliable and robust new complete color normalization method, addressing the problems of color and stain variability. The new complete color normalization involves three phases, namely enhanced fuzzy illuminant normalization, fuzzy-based stain normalization, and modified spectral normalization. The extensive simulations are performed and validated on histopathological images. The presented algorithm outperforms the existing conventional normalization methods by overcoming the certain limitations and challenges. As per the experimental quality metrics and comparative analysis, the proposed algorithm performs efficiently and provides promising results.

Standards are the means by which we construct realities. There are established standards for professional accreditation, the environment, consumer products, animal welfare, the acceptable stress for highway bridges, healthcare, education -- for almost everything. We are surrounded by a vast array of standards, many of which we take for granted but each of which has been and continues to be the subject of intense negotiation. In this book, Lawrence Busch investigates standards as \"recipes for reality.\" Standards, he argues, shape not only the physical world around us but also our social lives and even our selves. Busch shows how standards are intimately connected to power -- that they often serve to empower some and disempower others. He outlines the history of formal standards and describes how modern science came to be associated with the moral-technical project of standardization of both people and things. Busch suggests guidelines for developing fair, equitable, and effective standards. Taking a uniquely integrated and comprehensive view of the subject, Busch shows how standards for people and things are inextricably linked, how standards are always layered (even if often addressed serially), and how standards are simultaneously technical, social, moral, legal, and ontological devices.

The CORASO (COmpromise Ranking from Alternative SOlutions) method is one of the more recent MCDM (Multi-Criteria Decision-Making) methods, developed in 2024. It is a straightforward method that has been proven to be highly accurate in ranking alternatives. However, the method becomes unusable if a maximization-type criterion in the decision matrix has a maximum value of zero for a certain alternative, or if a minimization-type criterion has a value of zero for any alternative. This issue is directly related to the existing data normalization technique used within the CORASO method. This study was conducted to identify alternative data normalization methods that can be used with the CORASO method. Three specific data normalization techniques are investigated in this study: linear normalization method (the default method in CORASO), Weitendorf normalization method, and the vector normalization method. These three normalization methods were combined with the CORASO method to solve three different decision-making problems, each with varying numbers of alternatives to be ranked, as well as different numbers and types of criteria. The ranking results obtained using the CORASO method (when combined with the three aforementioned normalization methods) were compared with the results from other established MCDM methods. The findings confirmed that both the Weitendorf normalization and Vector normalization methods are suitable for use with the CORASO method. These two normalization methods were then applied in a specific case where the native CORASO normalization method could not be used. The results consistently demonstrated that these two normalization methods fully meet the requirements when integrated with the CORASO method

London introduced the world’s most stringent emissions zone, the Ultra Low Emission Zone (ULEZ), in April 2019 to reduce air pollutant emissions from road transport and accelerate compliance with the EU air quality standards. Combining meteorological normalisation, change point detection, and a regression discontinuity design with time as the forcing variable, we provide an ex-post causal analysis of air quality improvements attributable to the London ULEZ. We observe that the ULEZ caused only small improvements in air quality in the context of a longer-term downward trend in London’s air pollution levels. Structural changes in nitrogen dioxide (NO 2 ) and ozone (O 3 ) concentrations were detected at 70% and 24% of the (roadside and background) monitoring sites and amongst the sites that showed a response, the relative changes in air pollution ranged from −9% to 6% for NO 2 , −5% to 4% for O 3 , and −6% to 4% for particulate matter with an aerodynamic diameter less than 2.5 μm (PM 2.5 ). Aggregating the responses across London, we find an average reduction of less than 3% for NO 2 concentrations, and insignificant effects on O 3 and PM 2.5 concentrations. As other cities consider implementing similar schemes, this study implies that the ULEZ on its own is not an effective strategy in the sense that the marginal causal effects were small. On the other hand, the ULEZ is one of many policies implemented to tackle air pollution in London, and in combination these have led to improvements in air quality that are clearly observable. Thus, reducing air pollution requires a multi-faceted set of policies that aim to reduce emissions across sectors with coordination among local, regional and national government.

Introduction The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner. Objectives Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow. Methods We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples. Results Our studies have shown that once data are filtered, missing values are not correlated with m/z , retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance. Conclusion The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis – LDA, Support Vector Machine – SVM, Weighted Robust Distance – WeiRD, Gaussian Naïve Bayes – GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA. •We provide Python and MATLAB tutorials for key analysis steps.•We compared dissimilarity measures and preprocessing choices for MEG MVPA.•Multivariate noise normalisation is a key preprocessing step.•LDA, SVM and WeiRD are recommended classifiers for decoding.•The cross-validated Euclidean distance is a reliable and unbiased choice for RSA.