Loughborough University

This work calibrates an empirical material model for a silicone adhesive by finding the best fit to experimental data over a range of high strain rates. Following the literature precedent of using the empirical nature of the Johnson-Cook model for epoxide modelling, the Modified Johnson-Cook material model is repurposed to enable this for silicone adhesive without the need for a new user-defined material or further development within commercial finite-element method (FEM) software, which significantly reduces the complexity of the implementation. To the authors’ knowledge, such a model is not available anywhere in the open literature. The developed material model is verified against experimental split-Hopkinson pressure bar (SHPB) data of ceramic/steel material couples bonded with a silicone adhesive. This work also tests, by numerical simulation, whether several material models, previously used in the literature for high strain rate modelling of epoxides, can replicate SHPB test data of ceramic/steel material couples bonded with such an adhesive. For silicones, the Modified Johnson-Cook material model, with the calibrated parameters determined in this work, is shown to give good agreement with the results of SHPB testing where the simulation predicted 76.2% transmitted strain, closely matched the experimental value of 70.4%. For epoxides, both the Plastic-Kinematic material model with Cowper-Symonds strain rate scaling, and the Johnson-Cook material model with the Grüneisen equation of state, give good agreement with the results of SHPB testing, with 95.5% and 97.6% transmitted strain respectively, versus the experimental value of 91.2%.

Controlling light absorption in digital light processing (DLP) via pigment incorporation offers a versatile approach to tuning curing dynamics, shrinkage behaviour, and mechanical performance in ceramic additive manufacturing. In this study, black pigment (BP) was systematically introduced into a silica-based photopolymer suspension to modulate optical attenuation and enhance the geometric fidelity, surface quality, and dimensional accuracy of printed parts. An optimal 2 wt% BP formulation reduced peeling forces by ≈40% and eliminated recoating resistance, enabling smoother layer separation and improved print stability. Cure uniformity was enhanced, suppressing overcuring artefacts and sharpening lateral features. The degree of polymer conversion increased from ≈50% (no pigment) to ≈76%, correlating with reduced early-stage mass loss during thermogravimetric analysis, indicating a more complete crosslinked network and reduced low-temperature volatilization. These effects led to significantly improved mechanical performance, with green strength reaching ≈36 MPa and sintered strength ≈12 MPa, alongside minimized shrinkage anisotropy (ΔZ–XY ≈ 0.12%). Microstructural and profilometric analyses confirmed improved microstructural integrity and surface quality, with lateral surface roughness reduced to ≈4 μm and cure-induced overgrowth eliminated. Cure depth tuning effectively confined light penetration, improving interlayer bonding and dimensional precision. A full-scale silica-based ceramic core printed with the optimized formulation preserved intricate features and achieved dimensional accuracy within ±0.5 mm after sintering. These findings demonstrate that, within the studied silica-based system, pigment-modified photopolymer formulations enable high-resolution, low-defect DLP ceramic printing, offering a scalable and reliable route for precision components in aerospace, investment casting, and high-temperature tooling. • Black pigment tuned light absorption in DLP ceramic printing, improving cure uniformity and resolution. • At 2 wt.% BP, peeling force dropped ~40%, recoating became resistance-free, and conversion rose to ~76%. • Green and sintered flexural strengths reached ~36 and ~12 MPa, respectively. • Porosity decreased, surface roughness reached ~4 µm Sa, and shrinkage anisotropy was reduced to ~0.12%. • A full-scale silica-based core achieved ±0.5 mm post-sintering accuracy in complex geometries.

Despite its potential as a sustainable energy source, the direct bioenergy conversion of plant-based lignocellulosic biomass in anaerobic digestion (AD) is hindered by its recalcitrant structure, which is attributed to its high lignin content. This study proposes a novel integrated pretreatment that combines Fenton and microbubble plasma technology to enhance biomethane production from maize biomass. The approach disrupts the complex lignocellulosic structure by harnessing the synergistic oxidative effects of Fenton and plasma-generated radicals, facilitated by enhanced mass transfer of plasma-generated reactive oxygen and nitrogen species through microbubble plasma technology. Pretreatments were carried out across varying molar ratios of Fenton reagents (1:25, 1:50, and 1:75) and plasma treatment durations (30, 60, and 90 min). The optimal conditions, 1:25 M reagent ratio and 30 min microbubble-plasma exposure, achieved a 32 % improvement in delignification compared to untreated maize. ATR-FTIR analyses confirmed structural and chemical alterations that improved substrate accessibility. As a result, biomethane yields increased by 26 % in batch tests (348 N mL/g VS) and by 16 % under continuous tests (119 N mL/g VS) in comparison to the untreated biomass. This combined pretreatment approach enhances AD efficiency, as evidenced by improved biomethane yields and process stability. Energy input-output analysis showed that integrated pretreatment required ∼1.67 MJ/kg biomass, while biomethane production yielded a positive fourfold net energy gain. These findings demonstrate that synergistic integration of Fenton and plasma technologies is a promising approach to valorise lignocellulosic biomass and advance circular bioeconomy goals, including waste-to-energy conversion, renewable energy production, resource recovery, and environmental resilience. • Novel Fenton–plasma pretreatment boosts maize biomass digestibility for AD. • 32 % lignin reduction achieved through combined oxidative mechanisms. • Biomethane yield increased by 26 % in batch and 16 % in continuously fed digester. • ATR-FTIR and FEGSEM confirmed enhanced porosity and lignin bond cleavage. • Pretreatment yields 4 times more energy than it consumes.

This article presents a structured dataset of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra acquired from natural and man-made textile fibres, compiled to support research in forensic, analytical, and environmental science. The collection comprises 160 spectra obtained from 137 verified textile samples originating from multiple sources, including industry reference collections and academic textile archives. The dataset includes several processing levels: (1) raw instrument files in PerkinElmer .sp format; and (2-5) tabular feature matrices containing unprocessed transmission spectra, baseline-corrected spectra, averaged baseline-corrected spectra grouped by fibre subtype, and fully pre-processed spectra prepared for chemometric and machine-learning workflows. All spectra were recorded using a Frontier FT-IR spectrometer by PerkinElmer equipped with a single-reflection diamond ATR accessory. Measurements were performed over the 4000-550 cm⁻¹ range at 4 cm⁻¹ resolution with four co-added scans per acquisition. Metadata describing sample identity and fibre characteristics are supplied. The resulting collection provides an openly accessible resource for material identification, spectral comparison, and the development or benchmarking of classification algorithms.

This paper proposes a novel powertrain configuration for electric vehicles incorporating symmetrical six-phase machines, based on an eight-switch voltage-source converter. The proposed configuration leverages the spatial phase opposition of symmetrical six-phase machine windings to perform propulsion and onboard battery charging functions with minimized system component count. In propulsion mode, the eight-switch converter efficiently drives the machine with the same torque capability as the conventional twelve-switch converter, while mitigating voltage fluctuations across dc-link capacitors by eliminating the need for dc-link midpoint connection. In charging mode, the configuration seamlessly accommodates both ac and dc charging while ensuring zero electromagnetic torque. During the ac charging mode, the eight-switch converter is controlled as an interleaved single-phase active rectifier, interfacing the single-phase ac mains through the machine windings. Whereas in dc charging mode, the converter operates as a dual two-phase interleaved dc-dc boost converter enabling the efficient power transfer from dc sources to the vehicle’s battery. The implementation of interleaving approach in both ac and dc charging modes significantly suppresses the charging current ripple. The transition between propulsion and charging modes is realized using simple hardware reconfiguration. Experimental prototyping based on a 4 kW six-phase induction machine validates the effectiveness of the proposed configuration in both propulsion and charging modes. The eight-switch converter is evaluated versus its twelve-switch and nine-switch counterparts, to assess key performance metrics such as efficiency, voltage utilization factor, and power losses.

We study a class of dynamical systems generated by random substitutions, which contains both intrinsically ergodic systems and instances with several measures of maximal entropy. In this class, we show that the measures of maximal entropy are classified by invariance under an appropriate symmetry relation. All measures of maximal entropy are fully supported and they are generally not Gibbs measures. We prove that there is a unique measure of maximal entropy if and only if an associated Markov chain is ergodic in inverse time. This Markov chain has finitely many states and all transition matrices are explicitly computable. Thereby, we obtain several sufficient conditions for intrinsic ergodicity that are easy to verify. A practical way to compute the topological entropy in terms of inflation words is extended from previous work to a more general geometric setting.

The Men's 2026 Fédération Internationale de Football Association (FIFA) World Cup (Canada, Mexico and the USA) will expose teams to an unprecedented combination of environmental challenges (heat, altitude, air pollution, allergens and travel). Conditions will vary significantly between host cities, creating a significant threat to player health and performance. These unique stressors may require teams to adopt and adapt strategies to best protect player health and performance. A related open access Sports Medicine review outlines these environmental challenges and the evidence-based guidelines to prepare for and mitigate their effects. Motivation for the present review stems from the unique practical and logistical complexities of 'tournament football'-especially at the expanded 2026 FIFA World Cup-alongside the limitations of previous football-facing reviews, where 'evidence-informed practice' is often not underpinned by football-specific research and thus limits external/ecological validity. Building on the recommendations of the main review, this partner review focuses on integrating those guidelines into practice. It aims to provide teams with a practice-compatible framework to implement evidence-based strategies to protect player health and optimise performance at the 2026 FIFA World Cup.

This paper evaluates an Adaptive LASSO-MGARCH model for multivariate volatility forecasting, with applications in green and conventional bonds, equities, energy commodities, and EU carbon allowances. By introducing coefficient-specific adaptive penalisation directly into the multivariate GARCH variance equations, the model delivers a sparse and data-driven volatility spillover structure while preserving the positive definiteness of the conditional covariance matrix. Using daily data on green and conventional bonds, equities, energy commodities, and carbon allowances, we show that adaptive regularisation substantially reduces model complexity and improves economic interpretability relative to an unpenalised MGARCH benchmark. Out-of-sample forecasting experiments at multiple horizons demonstrate that the Adaptive LASSO-MGARCH model consistently achieves lower covariance forecast losses, and statistical tests based on the White reality check confirm that these improvements are significant across alternative loss functions.

Essential hypertension (EH) refers to elevated arterial blood pressure with unknown etiology, which becomes more prevalent with age. Although the D allele of the ACE (I/D) polymorphism has been linked to EH, this association is not consistent across global populations. This systematic review and meta-analysis examined the relationship between the ACE (I/D) polymorphism and EH in diverse populations to determine the comparability of effect sizes and explore potential implications for public health planning. Case-control and cohort studies published in the last 20 years were reviewed from the main databases (PubMed, Scopus and Embase) using specific inclusion and exclusion criteria. Genotype data were used in meta-analyses using different genetic models. Twenty-two studies with 7690 participants (3886 cases and 3804 controls) were included. Significant associations were observed between the ACE D allele and EH across allelic (OR = 1.37, 95% CI: 1.14-1.63), recessive (OR = 1.61, 95% CI: 1.21-2.13), dominant (OR = 1.37, 95% CI: 1.13-1.67), and homozygote (OR = 1.79, 95% CI: 1.31-2.45) models. Subgroup analyses showed significant associations in Indian and European populations, while African, Middle Eastern and Hispanic groups showed no statistically significant associations. The findings support a significant association between the ACE D allele and EH in several populations, though associations vary by ethnicity.