Engineering habits of mind in preschool children at Scottish forest nurseries and Australian bush kinders

Nature‐based learning environments for early childhood are expanding, as is research into their affordances and pedagogies. Engineering in these environments is not well studied. Previous work considered engineering experiences through the lens of ‘designerly play’, finding that natural materials, the space for larger creations and constructions, the altered group dynamics and less gendered environments, promote engineering play. Other engineering frameworks have not been applied. In this study, we have identified ways in which preschool‐aged children engage with Engineering Habits of Mind while at forest nursery and bush kinder. Ethnographic and video data from two Scottish forest nurseries and two Australian bush kinders have been collected and with our vignettes, we have shown that young children readily engage with all six Engineering Habits of Mind in a variety of different play scenarios when in natural learning environments. As well as demonstrating the benefits of forest nursery and bush kinder to young children's engineering learning, our examples can be used to guide educators looking for ways to increase engineering play in other learning contexts

Uncertainty and loan pricing for public and private firms:evidence from the Brexit referendum

We examine the impact of uncertainty on loan pricing for public and private firms in the UK, using the 2016 Brexit referendum as an exogenous shock of uncertainty. We find that uncertainty leads to a significantly higher cost of borrowing for private firms relative to public firms. However, firm-level foreign exposure, i.e. foreign sales and subsidiaries in the foreign markets, mitigates the adverse impact of uncertainty on loan prices more for private firms than public firms. Moreover, uncertainty increases the number of financial covenants in loans for public firms with high information transparency (i.e. constituents of FTSE 100/250). However, we observe a decline in the number of financial covenants in loans for private firms with low information transparency (i.e. private firms without institutional investors) under uncertainty. Overall, we provide novel evidence highlighting the differences in the design of syndicated loan contracts between public and private firms under uncertainty.</p

Self-learning smart contracts

Creating a reliable and secure smart contract in a crypto project depends on an effective tokenomics model. Such models are built using algebraic specifications and formal methods. The model incorporates crypto platform operations in accordance with the actions of project stakeholders—users, team members, and investors. A smart contract is an implementation of tokenomics and often contains mitigation or preventive measures to maintain the equilibrium and liquidity of the project’s cryptocurrency. In this context, we propose a technology for determining and configuring the parameters of such smart contract actions using machine learning.We consider an initial neural network trained on historical data reflecting changes in the rate of selected tokens in relation to the rates of major cryptocurrencies—Bitcoin or Ethereum. Additional factors, such as celebrity statements, news, world events, and natural disasters that indirectly affect cryptocurrency and token rates, are also taken into account. This combination of a tokenomics model and a neural network forms the basis of the smart contract. During the operation of the smart contract, these data are continually considered and used to retrain the neural network, taking into account the behaviour of the project’s token.Several experiments have demonstrated that predicting changes in token rates and dynamically adjusting parameters is significantly more accurate and effective for the project’s success than relying on a static algorithm. The proposed approach is exemplified by a tokenomics model and a self-learning smart contract, with comparative metrics illustrating its effectiveness.<br/

Multi-criteria decision making to explore the relationship between supply chain mapping and performance

In today’s highly dynamic and volatile business environment, the performance of a supply chain significantly depends on its structure, technological capabilities, and the adaptability of its constituent stages. Supply chain mapping, an approach to represent complex supply chain networks, is crucial for enhancing supply chain performance by identifying critical linkages, flows, and relationships. Despite its strategic importance, the specific impacts of supply chain mapping attributes on various performance indicators remain underexplored. Addressing this research gap, this study investigates the relationships between key supply chain mapping attributes (e.g., information flow, lead-time, mode of transportation) and supply chain performance indicators (e.g., reliability, responsiveness, agility). To achieve this, the study employs a multi-step analytical approach: first, relevant attributes are identified through a systematic literature review; second, these attributes are validated using the Delphi method involving international supply chain experts; finally, the Grey Decision-Making Trial and Evaluation Laboratory (Grey-DEMATEL) technique is applied to establish interrelationships among the attributes. Findings reveal that information flow is the most influential supply chain mapping attribute, significantly impacting multiple performance indicators, especially supply chain responsiveness. The novelty of this research lies in its integrative use of Delphi and Grey-DEMATEL methods, providing practitioners with actionable insights into effectively leveraging supply chain mapping to achieve strategic performance improvements

New insights into bioactive Ga(III) hydroxyquinolinate complexes from UV-vis, fluorescence and multinuclear high-field NMR studies

There is current interest in the anticancer and antimicrobial activities of Ga(III) tris-hydroxyquinolinate complexes, and hence their solution and solid-state chemistry. Here, we have studied the formation, stability and structure of a novel tris-5,7-dibromo-8-hydroxyquinolinate Ga(III) complex [Ga(Br2-HQ)3]. Reactions of 5,7-dibromo-8-hydroxyquinoline with Ga(NO)3 in DMSO were followed using electronic absorption and emission spectroscopy, and revealed the slow but concerted coordination of three chelated ligands, with ligand deprotonation being the apparent rate-limiting step, facilitated by basic Ga(III) hydroxido species. The emissive excited state of [Ga(Br2-HQ)3] in DMSO had a short half-life of 1.2 ns, and the fluorescence (550 nm, λex = 400 nm) was characterized by TDDFT calculations as arising from a ligand-centred singlet S1 state. We compared the structures of [Ga(Br2-HQ)3] and the clinical tris-hydroxyquinolinate complex [Ga(HQ)3] using high-field magic-angle-spinning solid-state 1D and 2D 850 MHz and 1 GHz 1H, 13C and 71Ga NMR spectroscopy. The similarity of their coordination spheres was confirmed by their 71Ga chemical shifts of 101 and 98 ppm, respectively, and quadrupolar coupling constants of 9.265 MHz and 9.282 MHz. 1H-1H 2D NOESY experiments revealed second coordination sphere interactions between an acetic acid solvent molecule and the bound hydroxyquinolinate ligands of [Ga(HQ)3]·0.5CH3CO2H. This finding suggests that carboxylic acids could play a role in modifying the formulation properties of this drug for clinical use.</p

Digital Rock Analysis: A Morphological Approach from Micro- to Meso-Scale in Petrophysics

Constructing 3D digital rock models is essential for accurately estimating petrophysical properties using porescale modelling. These models represent rock microstructures and capture complex features such as porosity, pore geometry, connectivity, and grain size distribution that are vital for computing petrophysical rock properties such as permeability. Reconstructing 3D models from two-dimensional images or statistical methods joins the micro and core scales, enabling applications in contexts lacking physical samples.In this paper, we adopt the morphological approach (MA) to construct 3D digital rock models using 2D SEM and micro-CT images in tarmat-bearing formation. This methodology provides accurate, physics-based insights into porosity, permeability, and their relationship, enabling more reliable predictions.Machine learning (ML) techniques, such as SliceGAN, have been conducted to reconstruct realistic 3D models from 3D images, hence tackling difficulties in regions like “tarmat”, where direct 3D imaging poses difficulty. The integration of machine learning into the 3D reconstruction process with MA offers a practical approach for checking the reliability of real rock structures in the 3D reconstruction process by using MA, hence enabling advancements in numerical modelling at the mesa and pore scale

Duoethnographic inquiry into translingualism and language teacher identity

Recent investigations into multilingualism and translanguaging by language teachers have highlighted the importance of individual identity and social context in determining the scope or ability to carry out translingual practices and enact preferred identities (Nagashima &amp; Lawrence, 2020). In this chapter we take up the call issued by Lee and Canagarajah (2019) to examine the ways in which “contradicting ideologies about language and language teaching and their experience of power, privilege, marginalization or other lived experiences and identities interplay in enacting translingual dispositions” (p. 361). We do this by adopting a two-stage duoethnographic approach to explore the experiences of two multilingual migrant English teachers; one a “non-native speaker” teaching in the “native” English environment of the UK, and the other a “native speaker” teaching in the “non-native” environment of Japan. The study reveals that monolingualism and native-speakerism in the local communities have heavily influenced our willingness to claim a bilingual identity, preventing us from adopting a translingual disposition. It also puts constraints on our autonomy in professional identity negotiation and results in us de-emphasising or concealing our national origins. However, our stories also show that teachers’ small acts of resistance can afford them the possibility to challenge existing ideologies

Deep Learning Ensemble for Methane Emissions Detection in Satellite Imagery

Methane emissions are a significant contributor to global warming and their effective detection and monitoring is essential to achieve NetZero goals. The role of methane in global warming is widely acknowledged, prompting initiatives such as the Global Methane Pledge and Oil and Gas Methane Partnership 2.0 to drive meaningful environmental action. Although methane reductions in the energy sector are often considered low-hanging fruit, significant technological challenges remain in detecting and quantifying methane emissions accurately under manageable monitoring costs.Satellite imagery has emerged as the most promising and cost-effective solution to this issue. However, detecting methane emissions below 1 metric ton/hour continues to be a challenge.This work outlines a novel methodology of an AI-based system for detecting methane emission locations using Sentinel-2 multispectral satellite imagery. The proposed technology employs at its core a bespoke ensemble of AI models to identify methane emission signals and the distinctive shapes of methane plumes, which are concealed in the short-wave infrared (SWIR) satellite bands. Our system can accurately distinguish methane signal and methane plume shape from the background noise with detection limit of 300 kg/hour. This capability is built upon a unique dataset of real-world methane leak events and leverages public-domain, multispectral satellite data

The Financialisation of Real Estate Firm Investment Behaviour – Evidence from a Panel of Real Estate Developers in selected ASEAN economies

The effects of financialisation on the investment behaviour of nonfinancial firms have become the subject matter of some recent studies. Another strand of the literature focuses on the implications of sector specific (particularly the housing sector) financialisation. This study combines these two strands of literature by estimating the impact of financialisation on the investment behaviour of a panel of real estate firms in Malaysia, Thailand and the Philippines. The study extends the current knowledge of this subject area by enabling a more micro-level analysis of real estate firm behaviour that uses accounting data, while also drawing important observations about the similarities and differences in how real estate firms in various countries respond to financialisation. Our main findings can be summarised as follows. First, financialisation has a negative effect on the investment behaviour of real estate firms in Malaysia and the Philippines, but not Thailand. Second, past investment decisions, profitability and sales performance tend to reinforce current investment behaviour. Third, increased past leverage discourages investments. The negative impact of financialisation on investment in Malaysia and the Philippines could imply that more financialisation is associated with a tendency to reduce investments in construction activities in these countries. Some recommendations for policy are proposed

Framing the unseen: feminist Photovoice and intersectionality in shaping urban exclusion and agency for ESEA women

This article advances feminist urban and methodological scholarship by reconfiguring Photovoice, a method that invites individuals to use photography to explore and communicate their lived experiences, as both a participatory method and a feminist spatial intervention. Focusing on the urban experiences of East and Southeast Asian (ESEA) women in Scotland, the research explores how racialized hypervisibility and systemic erasure emerge as concurrent, spatialized conditions that shape experiences of exclusion, identity, and agency. Employing Photovoice as a feminist methodology grounded in situated knowledge and epistemic justice, participants’ images and narratives highlight how urban space is negotiated, contested, and reimagined through everyday acts of resistance. The analysis centres the lived knowledge of ESEA women, revealing how intersecting forms of oppression are materially and symbolically embedded in the built environment, and how layered exclusions are inscribed across the lived geographies and everyday spatial experiences of the city. In response, the study demonstrates how grassroots solidarities, community interventions, and everyday navigational practices operate as feminist spatial strategies that challenge dominant urban narratives. In doing so, this work contributes new theoretical and methodological insights into feminist understandings of space, intersectionality, and participatory praxis, offering clear implications for more inclusive urban policy and planning