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X-ray diffraction of collagen-structured water molecules for cancer detection
No full textThis article belongs to the Section Medicinal ChemistryStructural biomarkers determined by X-ray scattering of the tissues can complement conventional histopathology and facilitate a fast triage procedure of cancer biopsy samples. It has been shown previously that lipid reflexes can distinguish cancerous from benign samples, except for fibroadenomas. In the present study, we demonstrate that fibroadenoma samples can be recognized using X-ray scattering of collagen. Moreover, we show that modifications in collagen structure are manifested in the water reflexes. Examination of diffraction patterns from water using two-dimensional Fourier transformation and machine learning yields excellent classification metrics in both synchrotron images and laboratory diffractometer data.Molecule
An in-depth review on sensing, heat-transfer dynamics, and predictive modeling for aircraft wheel and brake systems
No full textThis article belongs to the Special Issue Sensors and Sensing Technologies for Structural Health Monitoring in Civil, Mechanical, and Aerospace EngineeringAn accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes.Sensor
Dataset: Environmental impacts of low and high order detonations in water
No full textRaw data for physical and chemical analysis of TNT pipe bombThe clearance of dumped munitions often relies on Low Order (LO) and High Order (HO) detonation techniques, both of which pose significant risks to aquatic ecosystems. LO detonation leaves behind substantial explosive residues, whereas HO detonation generates intense shock waves and extensive fragmentation. This study examines the environmental impact of these detonation methods, including partial detonation, under semi-controlled conditions using six 1000-litre IBC tanks. Partial detonation represents an incomplete LO event, leaving exposed explosive material in the water. Experimental results showed that LO detonations left an average of 8.7 mg/L of explosive residue, significantly higher than the 1.2 mg/L observed for HO. Fragmentation analysis revealed that HO produced more than twice the number of fragments compared to LO, increasing the potential for physical damage. By integrating these findings with modelling, we estimated fragment stopping distances and the spatial extent of explosive contamination. These insights inform the development of mitigation zones to minimise both chemical and physical environmental impacts, particularly in the context of World War-era munitions clearance.DN
Data-driven prediction of wave response for a modular floating solar array
No full textFloating photovoltaic (FPV) systems in coastal and nearshore regions are subjected to complex wave loads that significantly influence their hydrodynamic performance. In the future, industrial scale FPV projects can consist of numerous floating bodies that are too large to be modeled using existing modelling methods such as Computational Fluid Dynamics. Therefore, there is a need to develop data-driven rapid prediction approaches. This study presents a data-driven framework to predict the heave and pitch response amplitude operators (RAOs) of FPV arrays under different wave conditions. A verified simulation model is developed, generating a dataset that incorporates the key influencing factors, including incident wave angle, wavelength-to-floater-dimension ratio, and mooring type. Random Forest (RF) and Multilayer Perceptron (MLP) models are trained and optimized through grid search cross-validation, demonstrating that both models accurately capture the spatial distribution and magnitude of RAOs, with the MLP model showing superior generalization capability. Interpretability analysis further reveals that the wavelength-to-floater-width ratio is the dominant factor driving RAO responses, while appropriate mooring strategies can effectively suppress motions under extreme conditions. Compared with conventional hydrodynamic simulations, the proposed approach significantly reduces computational cost and enables rapid evaluation of FPV dynamic responses, which provides a potentially workable approach to facilitate various purposes of large-scale ocean solar projects, such as design, monitoring, and digital twins.This work was supported by the Natural Science Foundation of Jiangsu Province (BK20231319), State Key Laboratory of Mechanics and Control for Aerospace Structures (Nanjing University of Aeronautics and astronautics) (MCAS-E-0124G03), and the Innovate UK Solar2Wave project (10048187, 10081314).Ocean Engineerin
Turbine-based combined cycle design and performance up to Mach 5.0
No full text© 2026 by Cranfield University and Rolls-Royce plc.The design of Turbine Based Combined Cycles (TBCCs), consisting of a gas turbine engine and ram- or scramjet, has previously been explored and a number of prototypes have been constructed. The sizing of the TBCC depends heavily on the trajectory flown by the platform, but the definition of the trajectory has been left mainly to optimisation algorithms. This paper presents a methodology that can be used in preliminary engine-airframe sizing studies and explores the impact of some trajectory choices on the performance of a TBCC for three simplified trajectories: constant-EAS climb, climb-dive, and climb-accelerate. It was found that the climb Equivalent Airspeed (EAS), rate of climb, and Top of Climb Mach number all affect the turbojet performance at Mach 2.0; the impact on the ramjet at Mach 5.0 is more noticeable in terms of required intake capture area rather than Specific Fuel Consumption and Specific Net Thrust. When climb-accelerate and climb-dive trajectories are compared to a baseline constant-EAS climb, with the same intake area and Mach 5.0 conditions, trajectories with lower climb EAS (whether climb-dive or climb-accelerate) have improved turbojet performance at Mach 2.0. The qualitative impact of trajectory choices, presented throughout this paper, can inform rudimentary trajectory design with respect to the effect on the engine performance and sizing and, consequentially, on the platform.AIAA SCITECH 2026 Foru
Modelling and optimisation of rapid tow shearing for composite reflective mirrors in space-based laser communication systems
No full textAbstract and slides.This study focuses on the modelling and optimisation of a composite primary reflective mirror for a Schmidt Cassegrain telescope, intended for laser communication systems in low Earth orbit. A novel manufacturing process employing rapid tow shearing (RTS) is developed to fabricate the mirror from aerospace-grade Hexcel 8552/IM7 carbon fibre prepreg. Process modelling includes forming and curing simulations, post process distortion prediction and dynamic analysis to assess the mirror’s performance under operational and launch conditions. Thermomechanical and hygrothermal models are incorporated to predict the behaviour of the mirror under thermal cycling, outgassing and launch acceleration. Optimisation aims to minimise mass and thermal distortion while maintaining a resonance frequency above the launch limit and ensuring structural integrity under launch loads. Comparative analyses are performed between the RTS-fabricated mirror and a conventional straight-fibre counterpart, demonstrating the capability of RTS for weight and distortion reduction. Additionally, the ability to tailor deformation fields through local variation of fibre orientation is investigated as a means of achieving enhanced optical performance. This research aims to validate RTS as an innovative manufacturing process for lightweight, high-performance space optics, paving the way for future technological advancements in space missions.This work is supported by the UK Space Agency under the NSIP Programme. (KS1-040)International Conference on Manufacturing of Advanced Composites 2025 (ICMAC 2025
The Role of Social Capital in Risk Mitigation and Recovery Strategies in Supply Chains - A Systematic Literature Review
No full textAbstract Global supply chains are increasingly exposed to a wide range of disruptions, from natural disasters and pandemics to geopolitical conflicts and technological breakdowns. Against this backdrop, resilience has emerged as a central concern for both scholars and practitioners. This thesis investigates the role of social capital in risk mitigation and recovery strategies in supply chains, focusing on how its structural, relational, and cognitive dimensions contribute to resilience. A systematic literature review (SLR) was conducted, analysing sixty peer-reviewed articles published between 2010 and 2025, drawn from Scopus and EBSCO databases. The thematic synthesis identified five interrelated mechanisms; trust, knowledge sharing, collaboration, flexibility, and shared norms, that explain how social capital operates as a systemic resource for resilience. The findings demonstrate that relational trust accelerates joint responses, structural capital enhances information flows and flexibility, and cognitive alignment through shared norms embeds a risk-aware culture that sustains long-term resilience. Together, these mechanisms highlight the interdependence of social and organisational ties in shaping risk mitigation and recovery capabilities. This research adds to theoretical debates and provides practical insights into an integrative framework of social capital and resilience, and to practice by offering actionable guidance for managers on cultivating trust, collaborative capacity, and cultural alignment in their networks. Limitations are acknowledged in terms of dataset scope and sectoral focus, but this research also identifies avenues for future research, including digital trust, informal networks, and cross-cultural perspectives. In summary, the thesis demonstrates that resilience is not solely a technical or structural capability, but a collective achievement rooted in the networks.MSc in Logistics and Supply Chain Managemen
Investigation into the modelling ability of a hybrid anisotropic K-omega SST/STM for the Onera M6 wing and DNS lab channel flow validation cases
No full textThe chosen turbulence model for a RANS based computational fluid dynamics simulation is crucial for accurate CFD results. Many strong models exist such as the K-omega SST, K-epsilon and Spalart Allmaras turbulence models however, each of these models makes a major assumption which in nature is profoundly incorrect. Most commercially available turbulence models assume that the Reynolds stress tensor is isotropic, when in reality any shear, rotational or wall bounded flows are anisotropic in nature. This paper aims to investigate a newly proposed anisotropic hybrid K-omega SST / stochastic turbulence model developed by Könözsy The model has been tested for two validation cases: an Onera M6 transonic test case and a traditional channel flow case. Data has been plotted and compared to experimental data and DNS data and good alignment was found for both cases between the two data sets. The model outperforms the K-omega SST in its physical modelling however does still need some refinement in its model constants approach as shown by its anisotropic Reynolds stress modelling for the channel flow case.MSc in Aerospace Computational Engineerin
Business intelligence competence as a strategic enabler of organizational agility within supply networks
No full textPurpose:
This study investigates how Business Intelligence (BI) competence functions as a strategic enabler of organizational agility within supply networks. It examines the moderating role of BI competence in two critical relationships: between agile drivers and agile capabilities, and between agile capabilities and agile practices. In doing so, the study advances organizational agility theory by conceptualizing BI competence as a strategic and relational enabler that shapes agility development, rather than merely a technological support tool.
Design/methodology/approach:
Using targeted sampling, 157 responses were collected through a structured questionnaire from manufacturing firms. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).
Findings:
The results suggest that BI competence serves not only as a predictor of agile capabilities but also as a contextual moderator, particularly influencing the relationship between supply network capabilities and practices. This highlights a previously underexplored pathway in agility theory; whereby BI competence enables more effective translation of capabilities into actionable agile practices.
Originality/value:
This is the first empirical work to examine the theoretical interplay between BI competence and agility constructs within a chain-network framework. It contributes to theory by advancing the understanding of BI as a strategic enabler and moderating force, rather than merely a technological tool. The study also offers practical insights for manufacturing firms in emerging economies seeking to build resilience and responsiveness within supply networks.Strategy & Leadershi
Developing the through-transmission technique in pulsed thermography for material characterisation
No full textZhao, Yifan - Associate SupervisorPulsed Thermography (PT) is a reliable, non-contact, and non-intrusive non-
destructive testing (NDT) technique for assessing the structural health of
materials. Based on the relative positioning of the thermal excitation source and
the infrared radiometer, measurements can be conducted in either reflection or
transmission mode. While reflection mode is widely adopted due to its single-
sided accessibility, transmission mode offers superior lateral resolution but
remains limited in use due to the lack of reliable depth quantification methods. In
the context of thermal diffusivity evaluation, the transmission mode has
demonstrated greater reliability; however, the existing literature lacks a
deterministic approach to systematically assess this in laboratory settings. This
research investigates the current state-of-the-art in through-transmission
thermography and identifies key knowledge gaps. A transparent and repeatable
methodology is developed to evaluate thermal diffusivity using both finite element
models (FEM) and controlled laboratory experiments. The FEM is also used to
assess the temporal behaviour of a sample containing subsurface defects, and a
physical sample is fabricated to validate the simulation results. A novel method
for defect depth quantification is then proposed by establishing a relationship with
the Fourier number. This approach demonstrated a 63% improvement in depth
estimation accuracy (from a 29.3% measurement error to 10.75%) compared to
the Log Second Derivative (LSD) method derived from thermographic signal
reconstruction (TSR) in the simulation environment across all defect sizes and
depths. Additionally, the technique shows potential for estimating impact damage
in carbon fibre-reinforced polymer (CFRP) samples subjected to varying impact
energy levels. By addressing the challenges of thermal property measurement
and depth quantification within the transmission mode, this thesis provides a
foundation for improved material characterisation and supports renewed
research interest in through-transmission pulsed thermography.PhD in Manufacturin