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Microanalysis of ฮฒ-(AlxGa1-x)2O3 films grown by MOCVD
Get PDFA combined microanalysis and optical study of ฮฒ-(AlxGa1โx)2O3 films grown on sapphire via metalorganic chemical vapour deposition, with thickness 350โ1000 nm and Al fraction (x) from 0% to 45%, is presented. Al incorporation in the films showed a linear relation with nominal Al composition calculated from precursor flow rate, and the optical bandgap increased from 4.96 eV to 5.44 eV with a bowing parameter of 1.7 ยฑ 0.5 eV. A high Al fraction led to reduced crystallinity, increased surface roughness, and diminished cathodoluminescence intensity. The topography revealed elongated surface features that evolved with Al content, and luminescence spectra exhibited a blueshift in peak emission attributed to the widening of the bandgap. These findings highlight the trade-off between bandgap tuning and material quality, informing future growth strategies for future electronic and optical devices
Optimizing electric vehicle charging infrastructure on highways : a MILP model for balanced demand allocation
No full textThe strategic placement and sizing of electric vehicle (EV) charging stations on highways are critical for alleviating range anxiety and fostering widespread EV adoption. This paper presents a novel mixed-integer linear programming (MILP) model for optimizing the location and capacity of charging stations along highway corridors. Unlike traditional approaches, our formulation explicitly models the distribution of charging demand from each originโdestination (O/D) pair among the multiple stations along its path. The nonlinearities inherent in this flow-sharing mechanism are efficiently handled via a piecewise linear approximation, ensuring model tractability for real-world instances. Using real geographical and demographic data, we generate realistic case studies for two U.S. highways, Iโ70 and Iโ95. Our analysis evaluates the trade-offs between cost, service quality, and infrastructure layout for different charger power levels (50, 150, and 350โkW) and EV adoption scenarios. Results indicate that 350โkW chargers generally offer the most cost-effective solution while significantly reducing expected user times by up to 70% compared to 50โkW chargers. The model provides a practical decision-support tool for planners, balancing computational efficiency with a high-fidelity representation of network-wide charging dynamics
Linear causal discovery with interventional constraints
No full textIncorporating causal knowledge and mechanisms is essential for refining causal models and improving downstream tasks, such as designing new treatments. In this paper, we introduce a novel concept in causal discovery, termed interven-tional constraints, which differs fundamentally from interventional data. While interventional data require direct perturbations of variables, interventional con-straints encode high-level causal knowledge in the form of inequality constraints on causal effects. For instance, in the Sachs dataset, Akt has been shown to be activated by PIP3, meaning PIP3 exerts a positive causal effect on Akt. Existing causal discovery methods allow enforcing structural constraints (e.g., requiring a causal path from PIP3 to Akt), but they may still produce incorrect causal con-clusions, such as learning that โPIP3 inhibits Akt.โ Interventional constraints bridge this gap by explicitly constraining the total causal effect between vari-able pairs, ensuring learned models respect known causal influences. To formalize interventional constraints, we adopt a metric to quantify total causal effects for linear causal models and formulate the problem as a constrained optimization task, solved using a two-stage constrained optimization method. We evaluate our approach on real-world datasets and demonstrate that integrating interven-tional constraints not only improves model accuracy and ensures consistency with established findings, making models more explainable, but also facilitates the discovery of new causal relationships that would otherwise be costly to identify
Regulatory gap analysis for risk-based design of liquefied hydrogen maritime transport vessels
Get PDFThe increasing use of hydrogen for decarbonization and net-zero objectives has positioned the maritime transport of liquefied hydrogen (LH2) as a critical element of the global energy transition. However, safety concepts for LH2 storage, handling, and transportation remain underdeveloped, while technological advancements are still in their infancy. This study examines the fundamental properties of hydrogen and identifies three principal risks: its wide explosion and flammability limits, permeation, and leakage. A comprehensive review of international regulations and classification society guidelines on low-flashpoint fuels reveals that no current framework provides detailed safety requirements for LH2 carriage. To address thesse limitations, this paper emphasizes the need for advanced leakage detection technologies, permeation-resistant materials, and risk assessment methodologies that support the development of robust safety guidelines. The findings contribute to establishing a foundation for the safe design, operation, and regulation of LH2 transport vessels
Theorising respect and disrespect by and about children and young people : a qualitative systematic literature review
Get PDFRespect is a foundational moral and social value, yet its conceptualisation by and about children and young people remains underexplored. This systematic qualitative literature review examines how respect and disrespect are theorised, defined or conceptualised in relation to children and young people, and the extent to which their perspectives are represented in schools, higher education, care and community settings. Guided by PRISMA protocols, 10 databases were searched, yielding 814 records; 26 peer-reviewed articles met the inclusion criteria. Five overarching themes emerged: (1) Recognition and moral worth, emphasising respect as a universal entitlement and basis for rights; (2) Relational and reciprocal dynamics, highlighting mutuality, dialogue and authentic engagement; (3) Respect as a behavioural, emotional and cultural construct, shaped by norms, authority and gendered expectations; (4) Educational and developmental value, positioning respect as a teachable moral and epistemic virtue; and (5) Social justice, inclusion and power, critiquing top-down, punitive respect agendas that alienate young people. Across contexts, respect was most often conceptualised as relational and care-oriented, expressed through attentiveness, fairness and recognition of individuality. Disrespect, conversely, was linked to misrecognition, exclusion and structural inequalities. Future research should recognise young people as capable of contributing to theoretical and practical understandings of moral principles such as respect
Adapting floating offshore wind-hydrogen systems for emerging markets : the case of the Ica region, Peru
No full textThe transition to sustainable energy systems is essential for reducing the carbon footprint of maritime and port infrastructure, particularly along South Americaโs Pacific coast. This paper investigates the design of a floating platform for stand-alone offshore wind-powered hydrogen production, focusing on the central coast of Peru, which holds exceptional offshore wind potential. The system integrates hydrogen production facilities with the IEA 15-MW reference floating offshore wind turbine mounted on a semi-submersible platform, emphasizing key design aspects from the perspective of motion dynamics. Stability and site-specific hydrodynamic analyses are conducted under representative sea states and wind conditions of the Ica offshore region to evaluate the accelerations imposed on hydrogen production equipment. Results highlight the benign yet energetic environment of Ica, which offers favourable wind consistency, moderate waves, and reduced extremes compared to benchmark North Atlantic sites. These findings confirm the technical feasibility of floating windโhydrogen integration in Peru, demonstrating that platform responses remain well within recommended design criteria. By coupling renewable power generation with hydrogen production at sea, the study demonstrates a pathway to decarbonize port operations and strengthen the sustainability and resilience of maritime infrastructure in Peru and the wider Pacific region
Best least squares paraunitary approximation : analytic Procrustes problem
No full textThis paper addresses the analytic Procrustes problem, which aims to find the best least-squares paraunitary approximation of a square matrix of analytic transfer functions, or the best paraunitary transformation between two rectangular analytic matrices. This is accomplished by generalising the Procrustes solution from ordinary matrices to the case of matrices of analytic functions via their analytic singular value decomposition (SVD). Different from the ordinary matrix case, the analytic SVD is not restricted to singular values being nonnegative. In the case that singular values do not possess any zero crossings, we can find an analytic paraunitary matrix analogously to the standard Procrustes approach. In the case that singular values exhibit any zero crossings, the solution does not only depend on the left- and right-singular vectors, but also on a discontinuous and hence non-analytic switching function that forces those analytic singular values to become nonnegative real. We show that a close approximation of this switching function can be achieved via a complex-valued allpass filter, for which we suggest a new suitable design to minimise the overall least squares error of the fit. In addition, we propose a DFT domain algorithm to approximate this polynomial Procrustes solution, which avoids ambiguities in the analytic SVD, and possesses proven convergence. Generally, this solution requires a delay for causality, and this delay grows with the approximation order. Examples and simulations demonstrate our proposed method
'It's an impossible job' : pre-service primary teachers' beliefs and attitudes towards differentiation within Scottish ITE
Get PDFDifferentiation is a major challenge within education. To explore the beliefs and attitudes of Pre-Service Teachers (PSTs) towards differentiation, an interpretivist methodology was deployed with PSTs working through a Professional Graduate Diploma in Education (PGDE) within Scotland. Unstructured focus group interviews were conducted and elements of grounded theory and thematic analysis were used for data analysis. From the data, PSTs shared a willingness to differentiate to work towards making teaching and learning accessible for all. However, the three most significant negative themes generated demonstrate tensions PSTs encounter with differentiation: the role of โabilityโ; pressure and stress; and conflicting messages
M. Gabriela M. Gomes, Ibrahim Mohammed and Chris Robertson's contribution to the Discussion of โSome statistical aspects of the Covid-19 responseโ by Wood et al
Get PDFThis contribution to the discussion of \citep{Wood2025} addresses `Epidemic dynamic models' (Section 4)
Autonomous agentic AI with policy adaptation for physics-informed spectral learning in structural health monitoring
Get PDFThis study develops a physics-grounded agentic artificial intelligence (AI) framework for Structural Health Monitoring (SHM) that integrates perception, cognition, action, and reflection with structural dynamics and stochastic system identification. The framework differs from conventional machine learning or deep learning pipelines by optimizing long-horizon monitoring objectives, autonomously adapting sensing and inference to meet safety-critical decision requirements. The methodology establishes a direct bridge from multi-degree-of-freedom structural dynamics to output-only spectral identification through Frequency Domain Decomposition (FDD), and subsequently to Bayesian inference of damage hypotheses and adaptive policy optimization. The proposed framework introduces three main contributions. First, it represents the explicit integration of agentic AI with SHM, embedding reasoning, planning, and learning within a closed-loop structure that is physically interpretable. Second, it provides a rigorous connection between computer science formulations such as partially observable Markov decision processes and reinforcement learning with the established domain of linear structural dynamics and modal analysis. Third, it demonstrates reproducibility and effectiveness through a numerical study on a five-degree-of-freedom shear building subjected to Gaussian white noise excitation. The results indicate high-fidelity modal identification and accurate localization of stiffness loss, with reliability assessed against theoretical baseline modes using the Modal Assurance Criterion (MAC). Further, the robustness of the agentic AI is validated using the ASCE SHM benchmark structure. The findings point toward a new generation of monitoring systems that are physics-informed, uncertainty-aware, and capable of resilient and adaptive operation across interconnected infrastructure networks
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