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Learning the limits : how data, diversity, and representation control machine-learning predictions of reorganisation energy
Get PDFAccurate and scalable prediction of hole and electron reorganisation energies (λh and λe) is a persistent bottleneck in the data-driven design of organic semiconductors, as routine ab initio calculations remain impractical for large molecular libraries. This work presents a systematic and interpretable evaluation of how molecular representation, chemical diversity, and dataset size constrain the accuracy and transferability of machine-learning models for predicting λh and λe. Three complementary datasets are analysed: a chemically diverse benchmark of approximately 5000 molecules with paired λh and λe values, a thiophene-focused dataset comprising 1486 molecules, and a sequence of progressively augmented datasets extending to nearly 13 000 structures. Fifteen molecular descriptor schemes and twelve learning algorithms, spanning linear, kernel-based, ensemble, and graph-based models, are benchmarked under consistent training and validation protocols. Across broad chemical space, predictive performance is primarily governed by molecular representation, with hybrid descriptors that combine RDKit features and multiple molecular fingerprints consistently outperforming single-source encodings, while graph neural networks underperform in highly diverse regimes. Constraining chemical diversity leads to substantial accuracy gains, particularly for electron reorganisation energies, whereas increasing dataset size improves robustness and generalisation with rapidly diminishing returns beyond modest augmentation. Model interpretation using SHAP analysis reveals stable and physically meaningful design trends across all datasets, showing that rigid, extended π-conjugation, low conformational flexibility, and balanced charge distribution systematically reduce reorganisation energies. These results define realistic performance limits for machine-learning prediction of reorganisation energy and provide concrete guidance on representation choice, dataset design, and molecular optimisation strategies for high-mobility organic electronic materials
Towards an optimum yield : science, technology, and fisheries development in Lake Malawi, 1930-1964
Get PDFIn the late colonial period, fisheries science and colonial development programs converged in blueprints to better exploit marine resources throughout the British empire. Yet, the historiography of science and colonial development has focused predominantly on the management and exploitation of terrestrial resources with only limited investigation of parallel schemes focused on marine and freshwater resources. Centering on the final decades of the British-ruled Nyasaland Protectorate, this article interrogates the role of science in shaping the regulations and development of Lake Malawi’s fisheries. It argues that the late-colonial fusion of scientific optimization and legislative frameworks tethered government-led fisheries development programs to a vision of optimal resource extraction, governmental custodianship, and technical development that was entrenched in a faith in scientific management but without the necessary data to monitor changes in fishing efforts or the capacity to enforce fishing regulations. Consequently, scientists’ recommendations helped to embed assumptions of custodianship and control over watery environments within legislative frameworks that were increasingly disconnected from the evolving commercial, environmental, and technological contexts shaping Indigenous-led and settler-owned fishing enterprises
Fiscal flows and asset prices
Get PDFThis paper investigates whether incorporating daily U.S. fiscal flows improves the explanatory power of traditional asset pricing models. Using data from October 2005 to April 2024, we assess model performance through both classical alpha testing and Bayesian model comparison following the framework of Bryzgalova, Huang, and Julliard (2023). The results show that the inclusion of the fiscal flow measure reduces average alpha across a variety of test asset portfolios and consistently appears in the top-performing models identified by the Bayesian framework. While the statistical evidence for a single dominant model is modest, these findings suggest that daily fiscal operations contain valuable information for asset pricing
A Human Rights-based Approach to Dignity Education in Health & Social Care Practice – Development and Evaluation of a Massive Open Online Course (MOOC)
No full textPreserving human dignity is a core principle in both health and social care practice. However, the concept of dignity in care using a human rights-based approach has received limited emphasis in healthcare education. To address this gap, a Massive Open Online Course (MOOC), Dignispace, was co-designed with nursing students from two Scottish universities and interdisciplinary expertise, including human rights law. The MOOC is grounded in a human rights-based approach structured around a set of principles known as PANEL—Participation, Accountability, Non-discrimination and equality, Empowerment, and Legality—which provide the overarching framework for promoting dignity in care. This study evaluated learners’ experiences and learning outcomes through analysis of survey responses and comments from MOOC discussion forums. Both quantitative and qualitative data indicated high levels of satisfaction with the course. Dignispace was also found to enhance participants’ knowledge and foster intentions for behavioural change. These findings support the course’s value and effectiveness in advancing dignity-focused education in healthcare. Future research and implementation efforts should prioritise adapting the course to diverse cultural and legal contexts and strengthening policy and institutional support to translate learning into sustained practice change
Physics-informed geometric operators in generative airfoil design with hybrid VAEs
No full textThis work investigates the use of physics-informed geometric operators in tandem with latent variable models to support surrogate learning, dimensionality reduction, and generative design of airfoils. A baseline dataset was constructed via a NURBS-based airfoil parametric model with physically interpretable design variables, and discretized using two schemes: a uniform parametric and a uniform arc-length sampling. A hybrid Variational AutoEncoder (VAE) with the addition of convolutional layers was developed and iteratively refined to evade shape invalidities. A systematic comparison of reconstruction accuracy, robustness, and diversity showed that a loss function based on the mean sum of squared distances had the best performance with sufficient stability during model optimization. However, this can be only established when the reconstruction and Kullback-Leibler terms in the β-VAE objective function are weighted via an appropriately selected β value. Additionally, augmenting geometry with physics-correlated high-level descriptors, such as geometric moments, further improves latent-space quality. Among the tested operators, third-order geometric moments yielded the most consistent robustness gains. Discretization and achieved diversity proved to be linked, with uniform arc-length spacing achieving the best reconstruction accuracy, but with many near-identical designs that degraded the resulting diversity. In contrast, uniform parametric spacing exhibited higher diversity without the need for any special treatment of design distributions and diversity quantification measures. This study consolidates practical guidelines on architecture, loss-function scaling, physics-informed features, and quantification protocols for reliable, data-efficient airfoil generative design with VAEs
A prospective life cycle assessment framework for sustainable renewable fuels in international shipping : hydrogen based e fuels
Get PDFThis paper presents a prospective life cycle assessment (pLCA) framework to evaluate the greenhouse gas (GHG) reduction potential of hydrogen-based e-fuels in international shipping. Maritime transport currently contributes approximately 2.89 % of global anthropogenic GHG emissions, with projections indicating a possible increase to 130 % of 2008 levels by 2050. In response, the International Maritime Organization (IMO) aims for net-zero emissions around 2050, necessitating a shift from fossil-based fuels to renewable alternatives. However, concerns persist regarding the life-cycle GHG emissions of such fuels, especially during the well-to-tank stage. This study develops a pLCA framework that goes beyond conventional attributional or consequential LCA approaches by incorporating future-oriented parameters such as projected transport demand, vessel scrapping rates, and the impact of evolving international regulations (e.g., CII and EEXI). Using a dynamic fleet modeling approach applied to 2062 bulk carriers over 50,000 GT, the study simulates multiple decarbonization pathways and fuel uptake scenarios through 2030, 2040, and 2050. It integrates upstream fuel production emissions—linked to electricity source carbon intensity and technology readiness—with downstream fleet emissions, reflecting regulatory and operational constraints. The results reveal that while renewable e-fuels such as e-ammonia and e-hydrogen show promising GHG reductions, their effectiveness is highly dependent on the carbon intensity of electricity used in fuel synthesis and on policy-driven adoption rates. The proposed framework offers a reproducible tool for policymakers and shipowners to assess the effectiveness of decarbonization strategies in meeting IMO targets, particularly for import-reliant countries like South Korea
Data Resource Profile : The Scottish Combined Medicines Dataset (SCoMeD)
No full textIntroduction: Prescribing data has been collected electronically in Scotland for many years; however, data are collated in individual, non-overlapping datasets based on the origin of the prescription (e.g., primary or secondary care). The vision was to create a unified view of all prescribing data to provide a longitudinal dataset of medicines use for patients treated by the National Health Services (NHS) Scotland, irrespective of where or how that care was provided. Methods: The Scottish Combined Medicines Dataset (SCoMeD) is, in essence, a data virtualisation tool collating information from three previously available prescribing datasets: the Prescribing Information System (PIS); the Hospital Electronic Prescribing and Medicines Administration (HEPMA) national dataset; and the Homecare Medicines (HCM) dataset. This allows the creation of study cohorts (patient groups of interest) that meet specified criteria across all prescribing settings and facilitates the retrieval of the prescribing history for individuals pre-identified from other datasets. Records contain a unique patient identifier (Community Health Index number) which is used to identify patients for inclusion in the dataset and also enables linkage to other routinely collected data, including hospital admission episodes and death records. Results: SCoMeD contains details on the patient (age, sex, geographical information) and on the medication prescribed. Medication-related information includes what was received and when; strength and dose information are also available. The earliest date of data availability depends on the source (PIS, 01.2010; HEPMA, 07.2022; HCM, 01.2019). Data is held by Public Health Scotland. Conclusion: SCoMeD facilitates a range of different studies, including cross-sectional/point-prevalence studies and drug utilisation studies as well as longitudinal studies, e.g., cohort and case-control studies. With the possibility to link to other relevant datasets, additional areas of interest may include health policy evaluations and health economics studies. Access to data is subject to approval; researchers need to contact the electronic Data Research and Innovation Service in the first instance
Development of a data pre-processing tool for marine systems sensor data
No full textWhile an undeniable progression is taking place in the shipping industry in terms of smart maintenance studies, the development of frameworks for the data pre-processing of marine systems sensor data has not yet been fully addressed. Data pre-processing is critical due to the current challenges that need to be addressed within the sector concerning unreliable outcomes and the appearance of distinct operational states. Such challenges need to be adequately addressed to avert under-utilization of data and computational inefficiency, whilst ensuring data quality and integrity. Accordingly, a data pre-processing tool is proposed to contribute towards implementing more sophisticated methods for addressing the challenges currently experienced within the sector. This data pre-processing tool is comprised of three distinct modules: (1) data imputation, (2) outlier detection and (3) operational state identification. A tool that is combined with a user interface to make accessible data pre-processing methodologies within the shipping sector
Mutualism and social welfare
No full textThis chapter provides an overview of the history of mutual-aid societies in different parts of the world since ancient times. It begins by looking at the history of mutual aid in Antiquity before examining the rise of guilds and confraternities in the Middle Ages and the development of friendly societies and other sickness insurance societies between the eighteenth and twentieth centuries. It highlights the importance of conviviality in cementing the social bonds which united the societies’ members and helped to establish the bonds of trust on which their insurance functions depended. It also considers the value of friendly societies as sources for the history of health and sickness, and their relationship to the rise of welfare states. It concludes by surveying the place of mutual aid in the modern world and identifying some issues for future research
Professionalism, professional identity, and community pharmacy culture : the context of substance dependency through the lens of student and early career pharmacists
Get PDFAims: This study aimed to explore the reflections of student and newly qualified pharmacists (NQPs) surrounding community pharmacy culture around substance dependency. This study explored professionalism and professional identity formation, and the possibility that a fragmented professional identity may impact behaviours and the provision of compassionate care. Design: Qualitative study: semi-structured interviews were conducted with student and exploring stigma within community pharmacy environments in relation to people with substance dependency, the community pharmacy culture and their own ideas of professionalism and their professional identity formation. Interviews were undertaken by six pharmacy student researchers, under the supervision of two experienced researchers. Setting: Community pharmacies across Scotland. Participants: Twenty-eight participants were recruited, including undergraduates based at Scottish Schools of Pharmacy (n = 20); Foundation Year Pharmacy students (n = 2) and NQPs (n = 6). Recruitment utilised university networks and social media platforms. Measurements: Interviews were conducted between September and November 2023 on Microsoft Teams®, each lasting 17–60 minutes. Data underwent inductive thematic analysis via NVivo® through data familiarisation, initial coding, theme searching, reviewing and defining and reporting. Findings: Stigmatisation of people with substance dependency attending a pharmacy was a prominent observation. This included negative stereotyping, adverse treatment because of judgements made about substance use and structural stigma relating to barriers to accessing care. Positive care provision in pharmacies was evident. Pharmacy staff who were empathetic, respectful, professional and who formed long-term relationships with people with substance dependency were valuable role models for students and influenced their professional identify formation. Students appreciated the exposure to practice and the opportunity to make judgements that would mould the type of pharmacist they aspired to become. A number of participants reported that their university course poorly prepared them for the reality of supporting people with substance dependency. Conclusions: Pharmacy practice in Scotland appears to be characterised by stigma and lack of professionalism towards people with substance dependency, although there are examples of compassionate care. Observing staff in practice allowed participants of this study to develop their own professional identity and attitudes, yet there is a need to better prepare students in undergraduate curricula
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