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Sparse outlier-robust PCA for multi-source data
No full textSparse and outlier-robust principal component analysis (PCA) has been a very active field of research recently. Yet, most existing methods apply PCA to a single data set whereas multi-source data—i.e. multiple related data sets requiring joint analysis—arise across many scientific areas. We introduce a novel PCA methodology that simultaneously (i) selects important features, (ii) allows for the detection of global sparse patterns across multiple data sources as well as local source-specific patterns, and (iii) is resistant to outliers. To this end, we develop a regularization problem with a penalty that accommodates global-local structured sparsity patterns, and where an outlier-robust covariance estimator, namely the ssMRCD, is used as plug-in to permit joint, robust analysis across multiple data sources. We provide an efficient implementation of our proposal via the alternating direction method of multipliers and illustrate its practical advantages in simulations and in applications
Introduction: the 2022 Russian invasion of Ukraine and the re-making of the European security order
No full textRussia’s increasing aggression, the return of great power rivalry, and eventually the 2022 invasion of Ukraine have brought back questions about Europe's security order. In response to Russia’s war against Ukraine, both states and security organisations–the EU and NATO–have implemented new policies to address Europe’s vulnerabilities and to support Ukraine politically and militarily. This introduction to this special issue serves two purposes. First, it explores the war, examining debates on the origins of the war and tracing the evolution of the war to summer 2025. Second, it places the war in the context of longer-term debates on European security order, arguing that a largely liberal European security order was consolidated after the Cold War, but that relations between Russia and the West remained partly characterised by more traditional realist dynamics. The articles in this special issue explore how far and in what ways Russia’s full-scale war against Ukraine since 2022 can be viewed as a critical juncture for Europe’s security order, resulting in major and lasting changes to that order. Taken together, the articles highlight that while February 2022 may have been a turning point, the impacts of the war vary in important ways across states and institutions.</p
Experimenting with Analytical Categories as Reflexive Method:Mobility Trajectories to Study Young People with and without Migration Background
No full textThis chapter develops mobility-based categories for studying young people with and without a migration background. Most migrant youth research uses the categories of ethnicity or generation. These categories hide the mobility that young people engage in such as migration but also study abroad, vacations, gap years and family visits; both for those youth who have migration in their biographies and those who do not. In a globalising world the ability of young people to be geographically mobile is increasingly a marker of difference and therefore needs to be considered when studying young people’s lives. The chapter argues that mobility-based categories shed new light on young people’s lives in three ways. First, they allow investigating elements of commonality and difference between youth, irrespective of where they or their parents come from. Second, they take young people’s past and present mobilities into account, allowing a temporal understanding of how mobility affects their current and future lives. Third, they operationalize mobility as a process rather than a one-time move. The article exemplifies mobility-based categories through a recent, large-N, primary data collection project on secondary-school student’s mobility in three European and one African country
Automatic localization of myocardial infarction using vectorcardiography
No full textFor patients experiencing myocardial infarction (MI), localizing the affected cardiac region using electrocardiography (ECG) can reduce the time to reperfusion therapy, reducing morbidity and mortality. Extracting relevant information from ECG signals is not trivial, and computational methods have been developed aiming to assist physicians in making faster and better decisions in emergency situations. However, their clinical adoption remains limited due to the high false alarm rates consequence of the low generalizability of these methods. This research compares the performance of three machine learning techniques - Lasso, Support Vector Machine, and Gradient Boosting Machine - with varying degrees of complexity in localizing MI. Vectorcardiography-derived features were used as input to the models due to their ability to capture spatial and temporal information regarding the heart's electrical activity. An autoencoder was employed to smooth the feature space, facilitating more efficient model training and improving generalization. To further address generalizability challenges, an inter-patient validation approach was employed. Models were trained on the PTB-XL dataset and externally validated on the PTB Diagnostic dataset. Results demonstrate that Lasso, a simpler model, achieved the highest AUC of 0.74 on the external dataset, outperforming more complex models such as SVM (0.72) and GBM (0.68). Also, the combination of Lasso with the autoencoder provided superior generalization compared to other state-of-the-art methods reported in the MI localization literature. This highlights the proposed method's suitability for clinical settings, where model generalizability and reliability are critical. Furthermore, our method offers the advantage of explainability, allowing the extraction of clinical and physiological insights from the data and bridging the gap between computational methods and clinicians
ToF-SIMS spectra of typical substrates in both polarities: Si wafer, Au-coated glass, and ITO-coated glass
No full textToF-SIMS is a surface analysis technique, and as a result, many samples need to be spotted or mounted onto a suitable substrate. Different substrates can be used for ToF-SIMS analysis, and it is essential to properly assign the characteristic chemical signals from the substrate to distinguish them from the sample analyte ions. Here, the authors provide a reference database that provides ToF-SIMS spectra for three widely used substrates in positive and negative polarities. The six spectra were acquired using a 30 keV Bi3+ primary ion beam, with Ar gas cluster ion beam sputter cleaning of the surfaces before analysis.</p
Preliminary efficacy of an online intervention based on Acceptance and Commitment Therapy for family caregivers of people with dementia:a feasibility study
No full textWith the rising number of dementia cases, supporting family caregivers to maintain their well-being is crucial. Acceptance and Commitment Therapy (ACT) shows promise in promoting psychological flexibility and positive behaviour change. However, it is still developing in caregiving contexts. This study evaluated the preliminary efficacy of a fully online ACT intervention for caregivers of people with dementia. This study employed a pre-post design with two follow-up assessments at 3 and 6 months. A 9-week web-based self-help ACT program, including individual goal setting prior to the intervention, and minimal contact motivational coaching, was provided to 30 family caregivers in the Netherlands. Linear mixed-effect models based on a complete-case analysis showed significant changes in depressive symptoms (mean difference: −3.34, d = −0.78). Significant and sustained improvements were observed in stress (mean difference: −6, d = −1.13) and anxiety (mean difference: −5.55, d = −1.38), both of which were clinically significant. Sense of competence increased (mean difference: 1.1, d = 0.45). ACT-specific measures, including psychological flexibility, engaged living, and inflexibility, also showed significant improvements with medium-to-large effect sizes. This online intervention demonstrated promising preliminary evidence of ACT’s potential efficacy on caregivers’ well-being, warranting further research in larger-scale controlled trials.</p
Optimizing Bone Health in Diabetes:Strategies for Fracture Risk Reduction in Public Healthcare
No full textPurpose of reviewIn this review, we explore the under-recognized burden of fractures in diabetes, focusing on resource-constrained healthcare systems. We examine the epidemiology, assessment methodologies, and management approaches to osteoporosis in diabetes and discuss strategies to improve skeletal health outcomes.Recent findingsPublic healthcare strategies for fracture risk reduction in diabetes include educating healthcare providers, empowering patients, and integrating fracture liaison services for secondary prevention. Community-based awareness programs, digital health solutions, and screening tools such as FRAX (R) (with diabetes-specific adjustments) facilitate early identification and management. Policies supporting insurance coverage and cost-effective management strategies are likewise crucial.SummaryDiabetes-related bone fragility, characterized by altered bone quality and increased fracture risk despite relatively preserved bone density, creates a significant yet underrecognized health burden. Fracture prevention in diabetes is both a clinical necessity and an economic imperative. In this expanding cohort, multidisciplinary, policy-supported strategies can reduce morbidity, mortality, and costs associated with fragility fractures
Empathy and Offending, a Study About the Role of Cognitive and Affective Empathy in Sexual Violence
No full textEmpathy deficits are common among offenders, including those with sexual offenses. While empathy is not a major risk factor for sexual offending, it is linked to other risk factors like offense-supportive attitudes and hostility. Research often fails to differentiate between cognitive (understanding emotions) and affective (emotional resonance) empathy, hindering identification of specific empathy impairments in offenders. This study examines cognitive and affective empathy in Dutch males with sexual offenses (N = 33), violent offenses (N = 30), and the general population (N = 91). Using the Interpersonal Reactivity Index (IRI) and considering impulsivity as a covariate, the study found that sexual offenders had higher levels of both cognitive and affective empathy compared to violent offenders. However, only affective empathy differed significantly from the general population, with sexual offenders showing higher levels. The study suggests nuanced empathy deficits in sexual offenders, though self-reporting may have influenced results.</p
Mapping the Processes of Pharmacist Therapeutic Reasoning:A Scoping Review and Development of the Pharmacist Therapeutic Reasoning Model
No full textBackground: Pharmacists make complex therapeutic decisions. Yet the reasoning processes that result in these choices, therapeutic reasoning (TR), are poorly defined. Existing models of clinical reasoning often overlook how pharmacists weigh risks and benefits of treatment options. Aim: To develop a conceptual model that characterizes the processes, subprocesses, and cognitive strategies used during pharmacist TR based on current literature. Methods: A scoping review was conducted in February 2024 to identify studies describing pharmacist or pharmacy student reasoning during therapeutic decision-making. Data were extracted by two researchers using a standardized form and inductively analyzed. Codes were thematically organized based on shared properties: discrete knowledge, reasoning connections, or modifying influences. Theory use was assessed using the Continuum of Theory Talk framework. Results: Ten studies met inclusion criteria representing diverse contexts, scope, and reasoning stimuli. A total of 109 unique codes were identified and synthesized into a conceptual pharmacist therapeutic reasoning model (Pharm-TRv1). It consists of three knowledge domains (drug, disease, and patient information), three core reasoning processes connecting these domains (drug–patient, drug–disease, patient–disease), and three to four related subprocesses. The model includes five influencing factors: two external (decision context and entry and exit from reasoning) and three internal cognitive modifiers (metacognition, closing a knowledge gap, and reflection). Conclusion: Pharm-TRv1 provides a foundational model of pharmacist therapeutic reasoning grounded in current literature. It offers a structured way to describe, teach, and study how pharmacists evaluate treatment options. Future research should further explore specific processes and subprocesses, validate the model, and explore broader theoretical perspectives