Low voltage (LV) distribution transformers face accelerating demand growth while replacement lead times and costs continue to rise, making improved utilisation of existing assets essential. Static and conservative protection devices (PDs) in distribution transformers are inflexible and limit the available headroom of the transformer. This paper presents a probabilistic framework for dynamically forecasting optimal thermal protection settings. The proposed approach directly predicts the day-ahead scale factor which maximises the dynamic thermal rating of the transformer from historical load, temperature, and metadata using clustered quantile regression models trained on 644 UK LV transformers. Probabilistic forecasting quantifies overheating risk directly through the prediction percentile, enabling risk-informed operational decisions. Results show a 10–12% additional capacity gain compared to static settings, with hotspot temperature risk matching the selected percentile, including under realistic temperature forecast errors. These results demonstrate a practical approach for distribution network operators to take advantage of PDs with adaptive settings to maximise capacity and manage risk on operational time scales.
MXenes, a rapidly expanding family of two-dimensional transition metal carbides and nitrides, have emerged as promising materials for hospital-on-chip (HoC) diagnostics because of their high electrical conductivity, chemically tunable surfaces, and versatile biofunctionalization. Despite substantial advances in device performance, the molecular origins of signal generation, selectivity, and long-term stability remain poorly defined. This review establishes coordination chemistry as a mechanistic framework for interpreting MXene-based biosensing, highlighting how metal–ligand interactions at the biointerface can govern the analytical performance alongside intrinsic electronic conductivity. Ligand-field effects, hard–soft acid–base (HSAB) principles, redox-active coordination environments, and coordination-mediated charge transfer were examined in relation to biomolecular recognition, interfacial electron transfer, and signal transduction. Particular emphasis is placed on the influence of surface terminations (–O, –OH, –F, and –Cl), defect-associated metal sites, and dynamic ligand exchange on sensitivity, selectivity, antifouling behavior, signal fidelity, and operational stability. Coordination-engineered architectures, including MXene–metal nanoparticle hybrids, metalloporphyrin- and phthalocyanine-functionalized MXenes, MXene–metal–organic framework heterostructures, and assemblies incorporating metalloenzymes, aptamers, and antibodies, have been evaluated across electrochemical, optical, field-effect transistor, photoelectrochemical, and piezoelectric sensing platforms. Coordination-controlled nanozyme catalysis, oxidative degradation, biofouling, and interfacial electron transfer pathways are further considered in the context of device reliability and clinical translation. Oxidative instability, heterogeneous surface chemistry, limited clinical validation, and the absence of standardized manufacturing processes remain major barriers to implementation. Emerging strategies for next-generation MXene-enabled HoC diagnostics include ligand-programmable interfaces, single-atom coordination sites, metalloprotein-inspired biointerfaces, computational ligand field engineering, and artificial intelligence-assisted materials discovery.
This study presents a new system performance function for process plant reliability analysis, formulated to capture both structural topology and process sequencing constraints. Built on a modified maximum-flow framework and solved via linear programming, the proposed function efficiently quantifies the maximum feasible flow through a series of interconnected stages. It addresses limitations of existing models — such as fault trees and event trees — that overlook flow continuity and topological dependencies. The system function is integrated into a reliability assessment framework, enabling the evaluation of system failure probability and reliability-based component importance measures. Application to two benchmark examples, including a gas supply plant with 57 nodes and 102 edges, demonstrates the effectiveness of the proposed system function and the validity of the resulting reliability assessment for risk-informed layout planning. We further illustrate the formula’s use for reliability-based component importance measures, multi-state component damage, and cascading-failure analysis. A reconfiguration guided by component importance measures yields up to an 86% reduction in system failure probability, underscoring the importance of reliability-based equipment and pipeline layout.
Wearable personalised health monitoring requires advanced materials that combine biocompatibility, mechanical compliance, and stable signal transduction under dynamic physiological conditions. Poly(vinyl alcohol) (PVA) hydrogels have emerged as a versatile materials platform due to their high-water content, tissue-like softness, and tunable chemical structure. Recent advances have enabled the transformation of PVA hydrogels into multifunctional conductive systems by incorporating nanofillers, conducting polymers, and ionic species, while maintaining structural integrity and hydration stability. This review critically examines current strategies for enhancing conductivity in PVA hydrogels, including network design, synergistic charge transport mechanisms, and scalable fabrication techniques. Their integration into wearable platforms is discussed across key applications, including strain and pressure sensing, electrophysiological monitoring (ECG, EMG, EEG), energy harvesting, and controlled drug delivery. Fundamental challenges, such as dehydration, electromechanical trade-offs, and scalability, are analysed alongside emerging solutions, including self-healing networks, antifreeze approaches, and sustainable composites. Importantly, this review highlights the need to couple materials innovation with standardised evaluation protocols and regulatory frameworks to ensure reproducibility, benchmarking, and clinical translation of PVA-based wearable bioelectronic systems.
Composite manufacturing processes are governed by transient, nonlinear and coupled partial differential equations, making repeated high-fidelity simulations computationally expensive during process optimisation. This work develops a Fourier-encoded, physics-augmented neural-network framework for modelling multi-physics problems in composites, with a focus on frontal polymerisation. The approach combines physics-informed loss functions, model-specific Fourier feature encoding and a neural-network architecture to predict coupled temperature, degree of cure and mechanical deformation. The framework is applied to frontal polymerisation, a rapid out-of-autoclave curing process governed by coupled heat transfer, cure kinetics and mechanical response. A thermo-chemical neural network predicts the evolution of temperature and degree of cure, while a second Fourier-encoded mechanical network uses these predicted fields to infer displacement. It is observed that Fourier encoding significantly improves the representation of transient behaviour compared with a conventional feed-forward neural network. The results show close agreement with direct numerical reference solutions and demonstrate accurate prediction of propagating cure fronts, front velocity and thermo–chemo–mechanical deformation across material and process parameters. The proposed Fourier-encoded surrogate provides an efficient route for optimising the energy-efficient manufacturing process.
