Beyond the hype: Organisational adoption of Generative AI through the lens of the TOE framework–A mixed methods perspective

It is widely accepted that the impact of Generative Artificial Intelligence (GenAI) has been nothing short of transformational, with tangible impacts on industry, education, healthcare and government. But beyond the headlines, how are organisations actually using GenAI, what are the key challenges experienced by decision makers and has the reality on the ground matched the hype? This study adopts a mixed-methods approach, utilising the Technology-Organisation-Environment (TOE) framework to reveal greater insights to how organisations are adopting GenAI, the drivers that affect decision making and the key challenges associated with greater use of the technology. This research adopts a mixed method approach incorporating an explorative qualitative step with industry participants followed by a survey of 304 (three hundred and four) decision makers from a cross section of industry sectors from around the world including: North America, Europe, Africa, Australia and Asia, to gain further insight to the underlying factors that drive GenAI adoption. The research model was validated using Structural Equation Modelling (SEM) and reveals the intricate and inherent complexities related to greater levels of GenAI adoption. The analysis highlights the critical role of change capacity of the organisation in moderating complexity and staff skills. This research provides valuable and timely insights for senior management and policy makers that are attempting to better understand the interdependencies and perspectives on the key challenges facing organisations looking to deliver greater impact on organisational performance through GenAI

Design and testing of a universal platform for search and rescue operation: Exploring indoor and outdoor potentials

Large-scale natural and human-caused disasters have created significant challenges for worldwide Search and Rescue (SAR) operations, highlighting persisting concerns related to the efficiency and technical limitations of existing technologies. To address these challenges, the proposed Universal Platform for Search and Rescue integrates various technologies, including a voice-guided control system, advanced 3D reconstruction techniques, and a people tracker and follower system. A central feature of our work is the platform’s universality: our system acts as an additional, modular controller that can connect to any robotic platform—commercial or custom—that supports text-based command communication via network or cable. The system does not replace original robot logic, but rather extends capabilities with minimal integration. Tests showed that the platform can effectively execute voice commands and track a specified route even in high-wind (23 km/h) and noisy environments (70–100 dB for the Drone, 65–99.6 dB for the Quadruped), providing a user-friendly and intuitive interaction for users across different skill levels. Performance metrics indicated strong quality in 3D scene reconstruction with significant similarity between the reconstructed images and reference images (Drone: indoor: 0.82 SSIM, outdoor: 0.81 SSIM; Quadruped: indoor: 0.79 SSIM, outdoor: 0.58 SSIM). Consequently, the immersive 3D mapping reconstruction facilitated prompt and precise terrain assessments for both internal and external operations. Furthermore, the integration of real-time video streaming and cloud-based connectivity optimized the data flow and strengthened communication during operations, allowing person face identification, 3D tracking, and following

Development of a High Integrity Interlayer Joining Technology for High Temperature Aerospace Applications

The purpose of this PhD project was the continual development of the powder interlayer bonding technique for high temperature alloys, more specifically Ti-6Al-2Sn-4Zr-6Mo. The application of this technology is for the potential use as a joining and repair technology on BLISKs. The requirement of extended life cycles for aerospace components such as this is important for the viability of technologies which aim to further improve efficiency within the gas turbine engine. The principle of interlayer bonding is on the utilisation of powder based interlayer which is used to improve bond integrity between two surfaces.Research during this thesis programme has resulted in the development of an interlayer bonding technique that allows for the evaluation of using an inert gas environment, instead of traditional vacuum systems, with a focus on how this technology would eventually be implemented on more complex geometries. The use of argon shielding gas provided the required environment to limit the oxidation of titanium at elevated temperature required for bonding. The results allowed for the mechanical performance of the interlayer bonds to be evaluated, along with the effects of using a post bond heat anneal, with the properties of interlayer bonded Ti-6246 showing only a slight reduction in room temperature properties in comparison to the base material.Preliminary research was also conducted on evaluating the possibility of joining dissimilar titanium alloys, with a focus on Ti-6246 and Ti-6242. With the right balance of the key bonding parameters it was possible to create low porosity bonds between the alloy systems with the tensile results again showing a small debit in strength.The final stages of the programme focused on the potential use of alternate interlayers, including different morphology of alloyed titanium powder as well as Commercially Pure powder and foil with potentially further avenues of research available to investigate

Finite strain thermoelasticity and the Third Law of thermodynamics

This paper shows that commonly used large strain thermoelastic models in which the specific heat coefficient is constant or, at most, changes with temperature, are incompatible with the Third Law of thermodynamics, namely, that “entropy should be zero at the Kelvin state, that is, absolute zero temperature”. In particular, it will be shown that the Third Law implies that the specific heat coefficient must vary with deformation for the coupling between mechanical and thermal effects to take place. In line with this result, a simple analytical constitutive model consistent with the Third Law will be proposed. The model will be based on a multiplicative decomposition of the specific heat into a deformation dependent part and a temperature dependent component. The resulting thermoelastic model complies with the Third Law and, in addition, the necessary convexity conditions that ensure the existence of real wave speeds. It can replicate existing entropic elasticity models for rubber, describe melting and softening behaviour, and converge to the classical relationships for linear thermoelasticity in the small strain regime

Stretch-based hyperelastic electromechanical constitutive metamodels via gradient enhanced Gaussian predictors using hierarchical structure discovery

This paper introduces a new approach to developing electromechanical constitutive metamodels via the use of Gradient Enhanced Gaussian Predictors (Kriging). The formulation uses principal stretches for the isotropic mechanics, invariants for the electrostatics and coupling terms, and accounts for anisotropy through the relevant inclusion of anisotropic invariants associated with a respective symmetry integrity basis. Three novelties are presented in this paper. The first is the use of orthogonal projections to identify the most appropriate set of inputs - related to material anisotropy - for use in the metamodel. By projecting the stress and electric field data into several derivative bases - defined for each anisotropic class - and then reconstructing the quantities, the errors in reconstruction can be assessed thus inferring the most appropriate class of anisotropy. Furthermore, the procedure forms a pre-processing stage and is particularly useful when an underlying model is completely unknown as seen when modelling Relative Volume Elements. The second novelty arises from the use of a hybrid formulation, namely the principal stretches for isotropic mechanics and the electromechanical anisotropic invariants. This is beneficial during the projection procedure in reducing the cases where the projection matrix becomes singular but requires careful development of the correlation function to maintain physical symmetry conditions. Thirdly, the electromechanical metamodels are calibrated upon the concentric styled data before being integrated within a Finite Element framework and tested upon a range of challenging simulations including bending actuators with induced torsion, frilling due to bending with selected electrode placement, as well as buckling plates tested with three rank-one laminate materials with increasing levels of anisotropy due to physical contrasts. The successful calibration and implementation of the metamodels can be witnessed amongst the wide range of presented numerical examples

Neural networks meet hyperelasticity: A monotonic approach

We propose and apply a novel parametrized physics-augmented neural network (PANN) constitutive model to experimental data of rubber-like materials whose behavior depends on manufacturing parameters. For this, we conduct experimental investigations on a 3D printed digital material at different mix ratios and consider several datasets from literature, including Ecoflex at different Shore hardness, a photocured 3D printing material at different grayscale values, and a EPDM rubber synthesised with different amounts of curatives. We introduce a parametrized hyperelastic PANN model which can represent material behavior at different manufacturing parameters. The proposed model fulfills common mechanical conditions of hyperelasticity. In addition, the hyperelastic potential of the proposed model is monotonic in isotropic isochoric strain invariants of the rightCauchy-Green tensor. In incompressible hyperelasticity, this is a relaxed version of the ellipticity (or rankone convexity) condition. Using this relaxed ellipticity condition, the monotonic PANN model provides more flexibility than comparable approaches from literature that are elliptic by construction by formulating the PANN model to be both monotonic and convex. The monotonic PANN yields excellent results for a variety of different materials with largely varying qualitative and quantitative stress behavior. Although calibrated on uniaxial tensile data only, it leads to a stable numerical behavior of 3D finite element simulations. The findings of our work suggest that monotonicity could be a promising alternative to more constrained PANN models that includeboth convexity and monotonicity, in particular, when considering highly nonlinear and parametrized materials. This paper has three key novelties: (1) We propose a novel parametrized hyperelastic PANN model that is monotonic in both strain invariants and additional parameters. (2) We apply parametrized hyperelastic PANN models to experimental data of rubber-like materials whose behavior depends on manufacturing parameters. (3) With these highly nonlinear datasets, we benchmark the monotonic PANN model against existing PANN model formulations from literature. Furthermore, we compare the performance of different PANN models in terms of material stability and performance in finite element simulations

A comparison between the delivery of genomic and pharmacogenomic education and training for pharmacy undergraduates between the UK and other international countries: A narrative review

Genomics is perceived to impact healthcare in the United Kingdom and pharmacy professionals are believed to have a key role in the delivery of pharmacogenomic services. Purpose: To compare the delivery of genomic education within pharmacy undergraduate training between the UK and other countries. Method: Six electronic databases were searched including MEDLINE, EMBASE and Cochrane Library using variations of the terms pharmacogenomic, genomics and education, looking at all levels of education. No date restrictions were applied. Studies were then screened for duplicates and eligibility for inclusion. Results: Fifty studies were included and categorised into three main themes: identifying training requirements, training methods, and curriculum design/review. Most studies (n = 30) were from the United States. Many international studies highlighted the need to improve pharmacy undergraduate pharmacogenomic training. The pharmacist pharmacogenomic focussed competencies available in the United States have underpinned the development of pharmacist pharmacogenomic education and many studies described a mixed-methods approach to education delivery to ensure pharmacy student pharmacogenomic competence. The curricula evaluation in the Unites States and Australia demonstrated improved pharmacogenomic content within school of pharmacy curriculums but lacks nationwide standardisation. Conclusions: This review demonstrates global growth in pharmacy pharmacogenomic education, particularly in the US, where competencies and delivery methods have been defined and explored across institutions. The United Kingdom should develop its own competency framework to guide pharmacogenomic education for pharmacy undergraduates. This would support efforts to standardise genomic content in UK pharmacy curricula and promote the creation of standardised tools for effective training across all pharmacy schools

Association of Covid-19 vaccination uptake with recorded self-harm, neurodevelopmental disorders and mental health conditions during the Covid-19 pandemic: A nationwide e-cohort study in Wales, UK

Background: Understanding COVID-19 vaccine uptake among individuals who self-harm or with mental health conditions is critical to addressing health inequalities and guiding public health strategies/pandemic preparedness. Evidence on temporal trends and sociodemographic factors shaping vaccine uptake within these populations remains limited.Methods: We linked Wales Immunisation System data to demographic and healthcare records for 2.2 million individuals. Using modified Poisson regressions and growth models, we explored the association between self-harm, neurodevelopmental disorders, mental health conditions, and vaccine uptake from 8 December 2020 to 8 December 2023. Models were adjusted for age, sex, deprivation, ethnicity, and physical comorbidities.Findings: Attention Deficit Hyperactivity Disorder (ADHD), conduct disorder, drug use, and, to a lesser extent, self-harm were associated with lower incidence of vaccination. Conversely, those with autism spectrum disorder, or learning difficulty had slightly higher incidence of vaccination. Individuals with severe mental illness (SMI: schizophrenia, bipolar disorder and other psychotic disorders) exhibited a steeper initial increase and earlier peak in uptake, but their final coverage was lower. Belonging to an ethnic minority group and, to a lesser extent, being male, younger, or leaving in highly deprived areas were also associated with reduced uptake.Interpretation: Disparities in vaccine uptake exist among individuals with self-harm and mental health conditions, driven by intersecting health and social factors. Tailored interventions, effective communication, and trust-building strategies are critical to reducing these inequities. Underserved groups including those with SMI, ADHD, and self-harm, should be prioritised in future vaccination campaigns to improve equity