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Pharmabiome analyses in tandem with chemometrics can help trace the provenance of falsified medicines :A proof-of-concept study
No full textA lack of robust analytical approaches limits our ability to investigate the clandestine manufacturing origins of falsified medicines. We conducted a proof-of-concept study to test the feasibility of geolocating the production sites of falsified medicines, based on the identification of site-specific biological and chemo-isotopic features using a combination of environmental DNA metabarcoding, Direct Analysis in Real Time - Mass Spectrometry and Isotope Ratio Mass Spectrometry as profiling techniques. We produced tablets at two distant locations (England vs. Thailand), using controlled manufacturing methods, excipient composition and environmental conditions. Sets of tablets produced at separate locations showed distinct bacterial and eukaryotic diversity, particularly influenced by the incorporation of water used during tableting and the background environmental biosignatures of the production site. Tablets showed corresponding site-specific chemometric profiles, but the factors contributing to the observed chemical differences were unclear. When reference samples of known origin are available, our study suggests that site-specific biological and chemical features can be used in modelling approaches to successfully predict product origin. We developed a new mapping approach to exploit the geographic information within the eukaryotic pharmabiome of the falsifications; based on eDNA-derived species identification and the integration of publicly available species distribution data. In the absence of reference samples of known origin, the application of this workflow to our dataset provided partial clues about the product's origin, with limitations likely due to taxonomic resolution and the presence of species with wide distribution ranges. Collectively, our research provides experimental support for the development of integrated, multifaceted tools for tracing the origin of falsified medicines, advancing efforts to combat this pervasive but neglected global health problem.</p
UNHRD’s humanitarian support in South Asia via multistage stochastic programming
Full text linkOne of the main tasks of the United Nations Humanitarian Response Depot (UNHRD) relies on allocating relief aid to save people who suffer from disasters. This task is particularly challenging in areas like South Asia, where relief aid efforts are confronted with complex transportation conditions, significant socioeconomic disparities, and the frequent occurrence of disasters, not to mention that financial resources are often scarce. In this paper, we develop a novel Multistage Stochastic Programming model to help UNHRD support critical decisions regarding site selection and relief aid allocation. Differently from the main literature, where these decisions are often made within a two-stage paradigm, our three-stage perspective takes into account in-kind donation campaigns that are triggered depending on the disaster impact and its effects, and is paramount to improving the effectiveness and fairness of the disaster relief operation. Our objective function maximizes the effectiveness of the disaster relief operation, defined as the extent to which it fulfills the needs of the population. Considering that different regions often exhibit distinct coping capacities, the effectiveness measure also factors in a vulnerability score to encourage relief aid allocation to the most in-need populations. The overall results show the importance of in-kind donation to achieve a more equitable relief aid allocation plan and the benefit of targeting more vulnerable regions under severely scarce resources
Understanding changing patterns of placement type stability in the first two years of placement for looked after children in Scotland: A sequence analysis
Full text linkPlacement stability is an important concern for children looked after. Sequence analysis has been proposed as a promising method by which to quantitatively assess care placement stability for looked after children. Previous uses of sequence analysis to understand care placement stability have focused on a cohort perspective – following children from birth to age 18 and then analysing their whole care histories. This cohort approach is less well suited to understanding care placement stability for the population of children in care at a given time, which is a limitation given interest in understanding how aggregate levels of care placement stability is changing. In this paper we demonstrate a complimentary use of sequence analysis to understand placement type stability in shorter periods. We combined sequence analysis with regression modelling applied to Scottish administrative data for children looked after between 2008–2017 to describe change over time in the average number of transitions between placement types – a placement stability measure derived from sequence analysis – for children in their first two years in care. Our results show that there was a slight overall decrease in placement stability by this measure between 2008–2017, but that this decrease appears attributable to changes in the composition of placement types over the same period
Cognitive factors in code-switching:In response to Bentahila and Davies (1992)
No full textBilinguals’ tendency to switch between their languages (also known as code-switching) and the cognitive processes driving it are now known to be predicted by a variety of cognitive factors specific to the individual. In this review paper, we reflect on 30 years of progress in the study of the cognitive factors that determine how and when bilinguals switch between languages since Bentahila and Davies’ (1992) chapter on the relationship between code-switching and language dominance. We discuss how their work reflected a growing emphasis on moving beyond a strictly linguistic framework to focus on the psychological and social context in which code-switching occurs, and on the speaker-specific factors that affect the behaviour. We review and evaluate some of the substantial body of subsequent quantitative research about language switching and its relationship to language dominance, lexical access, cognitive control and interactional contexts. Some of these areas, like cognitive control, have seen considerable progress in understanding in the last thirty years and have substantially contributed to the development of psychological theories of bilingualism. Others we are only more recently beginning to understand, such as the effect of interactional contexts on the cognition of code-switching. The data yield a complex and sometimes contradictory picture but overall demonstrate that a range of social and psychological factors affect code-switching behaviours in ways that offer insights into the cognitive basis of code-switching
Deep Learning Joint Extremes of Metocean Variables Using the SPAR Model
Full text linkThis article presents a novel deep learning framework for estimating multivariate joint extremes of metocean variables, based on the Semi-Parametric Angular-Radial (SPAR) model. When considered in polar coordinates, the problem of modelling multivariate extremes is transformed to one of modelling an angular density, and the tail of a univariate radial variable conditioned on angle. In the SPAR approach, the tail of the radial variable is modelled using a generalised Pareto (GP) distribution, providing a natural extension of univariate extreme value theory to the multivariate setting. In this work, we show how the method can be applied in higher dimensions, using a case study for five metocean variables: wind speed, wind direction, wave height, wave period and wave direction. The angular variable is modelled empirically, while the parameters of the GP model are approximated using fully-connected deep neural networks. Our data-driven approach provides great flexibility in the dependence structures that can be represented, together with computationally efficient routines for training the model. Furthermore, the application of the method requires fewer assumptions about the underlying distribution(s) compared to existing approaches, and an asymptotically justified means for extrapolating outside the range of observations. Using various diagnostic plots, we show that the fitted models provide a good description of the joint extremes of the metocean variables considered
Gender, sexuality and viral safety: A mixed-methods examination of the negotiation of risk and precautions through dating apps during a pandemic
Full text linkThis paper examines the role of dating apps as mediators of intimacy and risk during the COVID-19 pandemic. Drawing quantitative and qualitative data from a UK based study of heterosexual and LGBQ+ people’s (lesbian, gay, bisexual, queer plus ‘other’ identified) dating app use, we investigate how users navigated the tensions between their desires for intimate and social connections and the imperatives of viral safety. Existing studies of dating app use tend to be based on samples of mostly heterosexual people, with unidentified or small numbers of LGBQ+ people. This undermines a fuller understanding of the potentially diverse ways in which gender and sexuality interact to shape the negotiation of risk. The paper examines study participants’ practices in negotiating viral risk in app-based interactions, and positions dating apps as actors within broader sociocultural and public health contexts. We argue that while dating apps have potential to facilitate intimacy and viral safety in future pandemics, their use raises sidelined challenges for health promotion that are linked partially to the interaction of gender and sexuality, but more so to trust
Enhancing bolted joint performance of woven composite laminates using 3D printed interlayers with tailored fibre architectures
Full text linkThis study investigates the effect of incorporating 3D printed interlayers containing continuous carbon fibres into plain weave CFRP laminates. The impact on stress distribution and the mechanical performance of bolted joints is systematically investigated. Three interlayer design strategies were developed to tailor the fibre distribution within the interlayers using filament-based 3D printing, and the resulting tailored-interlayer/woven laminates were assessed through double-shear testing to characterise the fibre load-transfer mechanisms. A filament-level multiscale finite element model was developed to capture the progressive damage evolution of the laminates. The experimental and numerical results demonstrate that incorporating 3D-printed interlayers can substantially enhance joint performance. Relative to the woven laminate baseline, enhancements were achieved across all interlayer cases. Specifically, improvements of up to 86 % in stiffness, 95 % in initial peak strength, and 59 % in ultimate bearing strength were achieved across the evaluated cases. In addition, substantial enhancements in energy absorption capacity were observed, with the initial fracture energy increasing by as much as 496 %, and the ultimate fracture energy by up to 10 %, depending on the specific architectural conditions. Among the designs, fibre steering guided by failure planes yielded most suppression of damage propagation. Together with micro-CT scans of the final failure morphologies, the simulation results provided insight into the damage progression and showed good agreement with the overall mechanical response observed experimentally. This research highlights the effectiveness of stress-adapted fibre steering in laminates and demonstrates the potential of 3D printing as a tool for locally reinforcing CFRP joints
Electro-mechanical tide-to-wire model of a horizontal-axis tidal turbine undergoing turbulent flow
No full textThis work builds up a numerical electro-mechanical coupled model of a laboratory-scale horizontal-axis tidal turbine and analyses the turbulent flow impact in the mechanical and electrical variables of the coupled system. Computational Fluid Dynamics/Large Eddy Simulation simulates turbulent flow, and the Actuator Line Method models the rotor blade dynamics. An electrical generator, controllers, power electronics, transformer, transmission lines and electrical grid compose the electrical system. Balances of hydrodynamic body forces and rotor - electromagnetic torques manage the hydro-mechanical and electro-mechanical interactions, respectively. Time series of the tidal turbine mechanical and electrical characteristics, hydrodynamic forces distribution over rotor blades, and flow development over the computational domain are analysed. Results show that turbulence influences the tidal turbine’s mechanical and electrical components, so compromising the quality of the power supplied to the grid. The coupled system response undergoing turbulent flow is compared by filtering the control signals for maximum power generation. The simulated mechanical variables are compared with laboratory measurements, and a good agreement is found. The control signal filtering allows the mitigation of the turbulent flow impact in the coupled system model. This mitigation is considerable for electromagnetic torque, generator and grid powers at around 70 %
Nested Crystal Graph Neural Networks for Modeling Chemically Complex Materials
No full textSolid solution crystals, in which lattice sites are partially or fully occupied by multiple atomic species, represent a chemically complex class of materials where atomic-scale disorder strongly governs properties. However, geometric representation learning of such systems remains challenging due to the lack of site uniqueness and the presence of short-range order. Here, we introduce the Nested Crystal Graph Neural Network (NCGNN), a general-purpose and scalable framework that hierarchically integrates local compositional disorder and global structural characteristics via a nested graph architecture. NCGNN enables interpretable predictions without large supercells and outperforms existing models by up to 50% across diverse solid solutions, ranging from fully random alloys to systems with sublattice structure. Additionally, NCGNN captures both short- and long-range ordering effects and reveals key composition-structure-property insights. Extensive benchmarks demonstrate that NCGNN is a universal framework for chemically disordered crystals, offering new opportunities for data-driven materials discovery
Innovative multichannel reactor design for solid-state hydrogen storage: A comprehensive study of Mg2Ni, LaNi5, and ZrCo performance
No full textHydrogen is increasingly recognized as a pivotal clean and sustainable energy carrier; however, its efficient storage, particularly in solid-state systems, remains a major technological challenge. This study presents a comprehensive investigation into the thermal and hydrogen absorption performance of three metals, Mg2Ni, LaNi5, and ZrCo, within a novel multichannel reactor design optimized for solid-state hydrogen storage. Using Computational Fluid Dynamics (CFD) simulations in COMSOL 6.2, the effects of key operational parameters, including cooling configurations, hydrogen supply pressure, heat transfer fluid's Re number, and material (reactor body) properties, on heat transfer and hydrogen absorption were systematically evaluated. The results demonstrate that Mg2Ni outperforms LaNi5 and ZrCo, exhibiting higher hydrogen absorption efficiency, faster cooling, and shorter saturation times. Moreover, increases in Re number and hydrogen supply pressure significantly enhance both heat transfer and absorption rates. The study also highlights the importance of reactor geometry in achieving effective thermal management. These findings offer valuable insights into the design of next-generation hydrogen storage systems, emphasizing the need for innovative reactor configurations to support the transition to a hydrogen-based energy
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