17628 research outputs found
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LiRUL: A physics-constrained and lightweight learning framework for edge-based RUL prediction
Full text linkAccurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for ensuring operational safety, reliability, and cost-effective lifecycle management in electric vehicles and energy storage systems. However, deploying RUL prediction models on real-world edge devices remains challenging due to limited computational resources, nonlinear degradation behaviours, and the need for physically interpretable yet efficient models. Existing physics-based approaches offer high fidelity but are computationally intensive, whereas deep learning models provide accuracy at the cost of transparency and scalability. To bridge this gap, this paper proposes LiRUL, a hybrid and lightweight learning framework specifically designed for RUL prediction under edge computing constraints. LiRUL integrates a compact feature engineering pipeline that fuses statistical compression with physics-informed domain variables, such as temperature and C-rate, to preserve degradation interpretability while minimising model complexity. Furthermore, a physics-constrained loss regularisation is introduced within temporal learning models to enforce monotonic degradation consistency, aligning predictions with electrochemical ageing behaviour. Experiments conducted on the LG M50 dataset demonstrate that LiRUL achieves up to 40% lower error and 60% faster inference latency compared to conventional LSTM and GRU models, while maintaining smooth and physically consistent degradation trajectories. These results highlight LiRUL’s capability to deliver an effective trade-off between accuracy, interpretability, and computational efficiency, establishing it as a deployable and generalisable framework for next-generation edge-based battery health management systems
FinTech, AI and green outcomes
No full textThis paper examines dynamic, frequency-based volatility connectedness using daily data from October 24, 2018, to July 6, 2023. It covers indices in artificial intelligence and Fintech, along with key green market indicators like clean energy, green bonds, and carbon emissions. The dynamic analysis shows that volatility connectedness peaks during major global shocks, such as the COVID-19 pandemic, and increases again during the Russia-Ukraine conflict. Frequency analysis reveals that short-term connectedness is dominant, although significant long-term connectedness also exists. Additionally, AI and Fintech are identified as the primary sources of volatility across different time horizons. Robustness tests confirm the reliability and consistency of these findings. Overall, our study highlights the growing integration between technology-driven and environmentally focused markets, especially in times of crisis
Mapping the Preparation, Administration, and Handling Process for Hazardous Drugs against the Occupational Exposure Risk Points
No full textThe aim of this review is to systematically map the existing literature on the occupational exposure risks associated with the preparation, administration and handling of hazardous drugs. The review will examine the type of risks encountered and the measures used to reduce or eliminate these risks. It seeks to identify the available evidence supporting best practice for healthcare professionals who handle hazardous drugs, as well as and to highlight gaps in the current evidence base that require further investigation
Why Are Bone Torsional Rigidity and Stiffness Measurements Often Inaccurate?
No full textThis study aims to raise awareness about the widespread use of flawed torsion test setups for measuring the stiffness-related torsional mechanical properties of bone, both in previous studies and in current practice. The widely adopted machine-mounted angle transducer (MMAT) introduces significant measurement errors by capturing combined rotations from the specimen, machine components, and fixtures, including slippage and mechanical tolerances, rather than the true rotation of the bone specimen itself. This results in distorted estimations of torsional rigidity and stiffness, undermining the accuracy of biomechanical analyses and the design of orthopaedic devices. To correct this, a specimen-mounted angle transducer (SMAT) was developed using standard laboratory equipment, including linear variable differential transformers (LVDTs) and metal pins. The SMAT focuses solely on the central bone specimen’s rotation, offering more precise measurements. Torsion tests were conducted on eight porcine femur samples, and finite-element twin (FE-twin) models were developed through 3D scanning of bone geometry. These models were calibrated using MMAT and SMAT data to evaluate differences in torsional rigidity and shear modulus. The results revealed significant discrepancies between MMAT-based data and literature values, while SMAT-based tests aligned more accurately with theoretical predictions and strain gauge measurements. Additionally, scanning electron microscopy (FE-SEM) and X-ray diffraction (XRD) analyses highlighted microstructural differences between fractured and unfractured bones. The findings underscore the importance of accurate torsion testing for clinical and research applications, offering improved methods for designing orthopaedic devices and assessing bone health.This paper delivers an important message: bone torsional rigidity and stiffness measurements are unreliable unless the torsion test setup includes direct measurement sensors, either contact or non-contact, mounted on or focused directly on the specimen
Structural assessment of historic masonry lighthouses under critical environmental actions: the case study of the Bell Rock Lighthouse
Full text linkHistoric masonry lighthouses are important navigational aids but also historical and cultural landmarks, that face increasing threats from environmental factors like severe weather and rising sea levels. Situated in the North Sea, the Bell Rock, the world’s oldest surviving sea-washed lighthouse has guided mariners for over 200 years whilst being exposed to the climatic elements. With support from its current managing authority in Scotland, the Northern Lighthouse Board (NLB), this research study aims at investigating the structural reliability of this 35m tapered cylindrical tower, which largely relies to its self-weight and dovetail joints to withstand the force of the North Sea. Structural analysis is undertaken by Finite Element Modelling with the software ABAQUS, where a variety of load scenarios have been considered. With the focus being on wind and wave loading, extensive research and analytical work provided realistic load cases and determined critical combinations. Assessment of results takes place in the form of stress and displacement criteria, as well as the failure mechanisms of overturning and sliding. Based on this numerical output and comparison with data from case studies and site observations, areas of further research are identified
Weighted Sum Rate Maximization for RIS-mounted UAV-aided Cell-Free ISAC Systems network with URISs
Full text linkThis paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfig-urable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)' transmit beamformings, RISs' phase shifts, user-RIS association, and UAVs' locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solutions by decomposing the original problem into three subproblems. In particular, for optimizing APs' transmit beamformings, RISs' phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs' locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocate
Assessing blockchain technology's technical utility in construction supply chains: A multi-KPI decision support approach via use cases
Full text linkBlockchain technology (BCT) holds significant potential to transform construction supply chains (CSCs) by addressing longstanding challenges related to transparency, efficiency, and traceability. This study investigates and develops a rigorous, KPI-centric framework that systematically maps blockchain’s enabling capabilities (ECs) to key performance indicators (KPIs) critical to CSC performance. Through a hybrid methodology combining content analysis and design science research (DSR), the paper introduces a web-based Decision Support Tool (DST) to guide stakeholders in evaluating the technical suitability of blockchain for construction projects. The DST operates in two phases: first, assessing blockchain applicability through a structured diagnostic; second, recommending ‘best-fit’ blockchain stacks by aligning selected KPIs with relevant use cases and ECs. Validation via simulated case scenarios demonstrates the DST’s robustness in supporting early-stage, technically grounded decision-making and recommends blockchain solutions tailored to user-defined KPIs and use cases. The findings reveal that BCT, through automation, immutable data sharing, decentralized governance, and the like, can significantly improve CSCs' performance. By bridging the gap between conceptual promise and practical application, this research provides both theoretical advancements and actionable insights for digital transformation in the construction industry. It contributes a replicable decision-support architecture for technology adoption and performance optimization in complex, multi-stakeholder supply chain environments
Digital economy, energy trade, and the path to sustainability: A G7 perspective
No full textThis study develops an integrated analytical framework to investigate how the digital economy influences the energy trilemma, comprising energy security, energy equity, and environmental sustainability. Using a balanced panel dataset of G7 countries spanning 2000–2023, we employ panel regression techniques and extensive robustness checks to quantify the digital economy's impact on each dimension of the trilemma. Our results reveal a significant positive effect, suggesting that digitalization can enhance energy efficiency and sustainability while improving access and reliability. We further examine the mediating roles of energy trade cooperation, market openness, and energy structure adjustment in amplifying these effects. The findings underscore the strategic importance of aligning digital economy policies with energy sector reforms to address long-term energy challenges. Policy recommendations include fostering cross-border energy cooperation, enhancing digital trade infrastructure, accelerating renewable energy adoption, and implementing targeted incentives to fully leverage digitalization for sustainable energy development
Does Eyewitness Confidence Calibration Vary by Target Race?
Full text linkAfter making a lineup decision, eyewitnesses may be asked to indicate their confidence in their decision. Eyewitness confidence is considered an important reflector of accuracy. Previous studies have considered the confidence-accuracy (CA) relationship—that is, the relationship between participants’ confidence in their lineup decision and the accuracy of that decision. However, the literature is limited and mixed concerning the CA relationship in cross-race scenarios. We considered the CA relationship for White and Asian participants and targets (fully crossed) using sequential lineups. Participants completed four trials (two White targets and two Asian targets). For each trial, they watched a mock-crime video, performed a distractor task, made a sequential lineup decision (target-present or target-absent), and indicated confidence in their lineup decision. White participants had higher identification accuracy with White than Asian targets, while Asian participants were similarly accurate with White and Asian targets. White participants’ confidence was better calibrated for White than Asian targets, except for when they had medium-high confidence (no difference). This finding is not only theoretically relevant—showing support for the optimality hypothesis—but also practically relevant—suggesting that the CA relationship may differ for target races at some levels of confidence
Timber Offsite MMC Living Lab: Case Study of a UK Mid-Century Bungalow Retrofit
Full text linkThe built environment, particularly ageing housing stock, significantly impacts the global energy consumption and energy related carbon dioxide (CO2) emissions. Retrofitting these dwellings, particularly in the UK due to its aging housing stock, is a critical step towards reducing environmental impacts and achieving net-zero emissions. This case study adopts a “living lab” approach to investigate the phased renovation of an early 1950s bungalow in Edinburgh conducted over two phases (2011–2017 and 2021–2023). The extension and fabric upgrade measures have utilised an array of offsite and innovative Modern Methods of Construction (MMC) approaches including the 1st retrofit and extension application of UK-grown mass timber. Phase 1, totalled in 10.8 tCO2e of embodied carbon emissions, while phase 2 resulted in 11.47 tCO2e. However, the combined offset emissions of both phases amounted -23.86 tCO2e when considering biogenic carbon storage. The property’s usable space area more than doubled from 91m2 to 197m2 and resulted in a 27% reduction in total energy consumption from 19,782 kWh/year to 15,679 kWh/year. As a result, the property’s market value has increased by 70%. The findings provide an insight into using off-site timber MMC for the retrofit of existing dwellings and offer a replicable model for net-zero transition
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