Wind turbine blade steel T-joint test data

This data supports the paper titled “Integrated testing and modelling of substructures using full-field imaging and data fusion” published in the journal “Engineering Structures”. The paper describes the testing of a wind turbine blade spar cap/web joint T-joint substructure mock-up specimen made from steel. The data folder contains CAD models of the testing facility used, the captured white light images for Digital Image Correlation (DIC), and infrared images for Thermoelastic Stress Analysis (TSA). The data can be used for exploring full-field imaging techniques and/or for the development of Finite Element model validation strategies on the substructure scale

Versatile Method for Preparing Two-Dimensional Metal Dihalides

Ever since the ground-breaking isolation of graphene, numerous two-dimensional (2D) materials have emerged with 2D metal dihalides gaining significant attention due to their intriguing electrical and magnetic properties. In this study, we introduce an innovative approach via anhydrous solvent-induced recrystallization of bulk powders to obtain crystals of metal dihalides (MX2, with M = Cu, Ni, Co and X = Br, Cl, I), which can be exfoliated to 2D flakes. We demonstrate the effectiveness of our method using CuBr2 as an example, which forms large layered crystals. We investigate the structural properties of both the bulk and 2D CuBr2 using X-ray diffraction, along with Raman scattering and optical spectroscopy, revealing its quasi-1D chain structure, which translates to distinct emission and scattering characteristics. Furthermore, microultraviolet photoemission spectroscopy and electronic transport reveal the electronic properties of CuBr2 flakes, including their valence band structure. We extend our methodology to other metal halides and assess the stability of the metal halide flakes in controlled environments. We show that optical contrast can be used to characterize the flake thicknesses for these materials. Our findings demonstrate the versatility and potential applications of the proposed methodology for preparing and studying 2D metal halide flakes.<br/

Exploring social workers' views on assessing child neglect in England and Wales

Child neglect poses many issues for social work, notably in terms of effective assessment leading to informed intervention targeting the needs of children and families. In response to this challenge, our multiphase research project is developing a new multiagency child neglect measurement tool. The phase of the project reported in this article administered an online survey via Qualtrics to explore the views of children and families social workers on assessing child neglect. One hundred and twenty-nine completed responses were received from registered children and families social workers in England and Wales. The main findings are that social workers are regularly undertaking child neglect assessments and feel relatively confident in completing them. They also feel relatively confident that their assessments are inclusive of social harms such as poverty and social isolation, but less confident they are accurate and informed by research evidence. Almost two-thirds are using a child neglect assessment tool, but they lack confidence in these accurately assessing neglect or being quick and simple to use. The findings illustrate that social workers require both the work conditions and tools to use in which they feel confident to undertake balanced and accurate assessments of child neglect

Ideas, Coalition Magnets and Policy Change:Comparing Variation in Early Childhood Education and Care Policy Expansion across Four Latecomer Countries

This article examines variation in early childhood education and care (ECEC) expansion in four ‘latecomer’ reformers: Germany, England, South Korea and Japan. Taking a comparative approach through an analysis of policy documents, it focuses on the role of ideas as coalition magnets in explaining the more extensive and sustained policy shifts in Germany and Korea, in contrast to the more limited and fragmented reforms in England and Japan. As the comparative literature struggles to explain variation in ECEC expansion, this focus on ideas provides a significant contribution, highlighting why ECEC reform became supported by a broad cross-class coalition in Germany and Korea but not in England or Japan. The theoretical contribution argues that coalition magnets are formed when the polysemic potential of a policy is drawn out by key actors strategically linking it to several problem definitions, which can appeal to diverse political actors and forge lasting consensus for reform

AR atmospherics and virtual social presence impacts on customer experience and customer engagement behaviours

Purpose – Few studies have examined technology-enhanced atmospheres for strengthening customer experience and brand engagement in physical store settings. This study builds on the social presence theory to test for the first time the moderating effects of virtual social presence on customer responses, through AR adoption in-store. Our study aims to understand the impact of technology-enhanced in-store atmospherics (TEISAs) with emphasis on AR elements and virtual social presence on customer experience and engagement behaviours (CEBs) in luxury settings.Design/methodology/approach – Hypotheses are developed and a survey using 566 responses were collected using Qualtrics. T-tests, two-way ANOVA and structural equation modelling were used for analysis of CEBs. Moreover, using PLS-SEM, we test whether virtual social presence moderates this relationship in a cross-country context; Britain and China, two of the largest economies for luxury growth.Findings – The findings demonstrate that TEISAs have a positive impact on emotion and perceived value, with virtual social presence moderating this relationship. The cross-cultural comparison results show that the impact of TEISAs on emotion and perceived value is stronger for British than for Chinese millennials. Originality/value – Our model is the first to incorporate technology into various store atmospherics, to employ virtual social presence as a new moderator, and to provide empirical evidence on the effects of AR on customer experience and CEBs in the real-time luxury retail environment. This study is also the first to consider virtual social presence on social media as a moderating variable

Including Photoexcitation Explicitly in Trajectory-Based Nonadiabatic Dynamics at No Cost

Over the last decades, theoretical photochemistry has produced multiple techniques to simulate the nonadiabatic dynamics of molecules. Surprisingly, much less effort has been devoted to adequately describing the first step of a photochemical or photophysical process: photoexcitation. Here, we propose a formalism to include the effect of a laser pulse in trajectory-based nonadiabatic dynamics at the level of the initial conditions, with no additional cost. The promoted density approach (PDA) decouples the excitation from the nonadiabatic dynamics by defining a new set of initial conditions, which include an excitation time. PDA with surface hopping leads to nonadiabatic dynamics simulations in excellent agreement with quantum dynamics using an explicit laser pulse and highlights the strong impact of a laser pulse on the resulting photodynamics and the limits of the (sudden) vertical excitation. Combining PDA with trajectory-based nonadiabatic methods is possible for any arbitrary-sized molecules using a code provided in this work

Exploring the catastrophic regime::thermodynamics and disintegration in head-on planetary collisions

Head-on giant impacts (collisions between planet-sized bodies) are frequently used to study the planet formation process as they present an extreme configuration where the two colliding bodies are greatly disturbed. With limited computing resources,focusing on these extreme impacts eases the burden of exploring a large parameter space. Results from head-on impacts are often then extended to study oblique impacts with angle corrections or used as initial conditions for other calculations, for example, the evolution of ejected debris. In this study, we conduct a detailed investigation of the thermodynamic and energy budget evolution of high-energy head-on giant impacts, entering the catastrophic impacts regime, for target masses between 0.001 and 12 M⊕. We demonstrate the complex interplay of gravitational forces, shock dynamics, and thermodynamic processing in head-on impacts at high energy. Our study illustrates that frequent interactions of core material with the liquid side of the vapour curve could have cumulative effects on the post-collision remnants, leading to fragmentary disintegration occurring at lower impact energy. This results in the mass of the largest remnant diverging significantly from previously developed scaling laws. These findings suggest two key considerations: (1) head-on planetary collisions for different target masses do not behave similarly, so caution is needed when applying scaling laws across a broad parameter space; and (2) an accurate model of the liquid-vapour phase boundary is essential for modelling giant impacts. Our findings highlight the need for careful consideration of impact configurations in planetary formation studies, as head-on impacts involve a complex interplay between thermodynamic processing, shocks,gravitational forces, and other factors

Extending Contextual Self-Modulation:Meta-Learning Across Modalities, Task Dimensionalities, and Data Regimes

Contextual Self-Modulation (CSM) is a potent regularization mechanism for the Neural Context Flow (NCF) framework which demonstrates powerful meta-learning of physical systems. However, CSM has limitations in its applicability across different modalities and in high-data regimes. In this work, we introduce two extensions: $i$CSM, which expands CSM to infinite-dimensional tasks, and StochasticNCF, which improves scalability. These extensions are demonstrated through comprehensive experimentation on a range of tasks, including dynamical systems with parameter variations, computer vision challenges, and curve fitting problems. $i$CSM embeds the contexts into an infinite-dimensional function space, as opposed to CSM which uses finite-dimensional context vectors. StochasticNCF enables the application of both CSM and $i$CSM to high-data scenarios by providing an unbiased approximation of meta-gradient updates through a sampled set of nearest environments. Additionally, we incorporate higher-order Taylor expansions via Taylor-Mode automatic differentiation, revealing that higher-order approximations do not necessarily enhance generalization. Finally, we demonstrate how CSM can be integrated into other meta-learning frameworks with FlashCAVIA, a computationally efficient extension of the CAVIA meta-learning framework (Zintgraf et al. 2019). FlashCAVIA outperforms its predecessor across various benchmarks and reinforces the utility of bi-level optimization techniques. Together, these contributions establish a robust framework for tackling an expanded spectrum of meta-learning tasks, offering practical insights for out-of-distribution generalization. Our open-sourced library, designed for flexible integration of self-modulation into contextual meta-learning workflows, is available at \url{github.com/ddrous/self-mod}