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Widespread influence of artificial light at night on ecosystem metabolism
No full textThe FLUXNET2015 data analysed in this study are available at https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/ (ref. 55) and are subject to the FLUXNET data policy (https://fluxnet.org/data/data-policy). As the redistribution of raw half-hourly flux data is not permitted, we provide only derived products, including daily and annual summaries, processed variables and model outputs, which are available under a CC-BY 4.0 license via Figshare at https://doi.org/10.6084/m9.figshare.29958455 (ref. 58). The ALAN metrics used here are available at https://doi.org/10.3390/rs9060637 (ref. 32). The R code used for data processing and analysis in this study is available via Figshare at https://doi.org/10.6084/m9.figshare.29958455 (ref. 58).Artificial light pollution is increasing worldwide with pervasive effects on ecosystem structure and function, yet its influence on ecosystem metabolism remains largely unknown. Here we combine artificial light at night (ALAN) intensity metrics with eddy covariance observations across 86 sites in North America and Europe to show that ALAN indirectly decreases annual net ecosystem exchange by enhancing ecosystem respiration (Re). At half-hourly and daily scales, we detect consistent nonlinear interactions between ALAN and night duration, with Re increasing under higher ALAN and partially decoupling from gross primary production. At the annual scale, gross primary production shows no direct ALAN response and is instead influenced by the growing season length and urban proximity, whereas Re responds more strongly and consistently across timescales. Our findings show that ALAN disrupts the fundamental energetic constraints on ecosystem metabolism, warranting the inclusion of light pollution in global change and carbon–climate feedback assessments.This work was supported by a Natural Environment Research Council grant (grant no. NE/W003031/1)Nature Climate Chang
Robustness of crystal plasticity parameters validation with Digital Image Correlation for fatigue modeling
No full textAdvances in computational power has made microstructure-sensitive modeling more accessible in industry. For example, crystal plasticity models enable the understanding of the microstructure influence on fatigue crack initiation and propagation. However, these models require extended parameterizations with multiple coefficients, making it not only time consuming, but often inaccessible to engineers. Although multiple methods have approached parameters estimation by correlating models with macroscopic experimental measurements from a monotic test, macroscale calibrations carry significant local uncertainties. In this sense, Digital Image Correlation (DIC) can offer mesoscale validation by providing a full-field measurement of strain that can be compared to the simulation. The main difficulty of this approach is the strain accuracy and the signal-to-noise ratio. This work employs DIC to study parameter sensitivity and adequate parameterization strategies. We explore the strain difference after a model parameter variation using virtual experiments with different loading scenarios. A simple criterion comparing strain sensitivity to the model parameters and the measured DIC noise is proposed to assess the calibration robustness. Different case studies highlight the difficulty of validation under cyclic paths owing to a low signal-to-noise ratio. This is the main limitation for calibrations of models that aim to capture the cyclic behavior. We conclude with further recommendations to use DIC for model parameterization.This work was supported by the Federal Ministry for Economic Affairs and Climate Action of Germany (BMWK) within the framework LuFo Klima VI-1.International Journal of Fatigu
Data for RealFake-450K: A Balanced Dataset of Authentic and Manipulated Images for Deepfake Detection
No full textThis research introduces a large-scale deepfake image dataset designed to support the development and evaluation of deep learning models for detecting manipulated facial content. With the growing threat of synthetic media, particularly deepfakes, the ability to distinguish real from fake images has become vital for ensuring digital trust and authenticity.
The dataset comprises 451,440 images, evenly split between real and fake samples, making it one of the most comprehensive deepfake image collections to date. It combines data from six reputable sources, including FaceForensics++, Celeb-DF, CDDB, WildDeepfake, DeeperForensics-1.0, and Kaggle’s 140K Real and Fake Faces dataset. These images capture a diverse range of facial identities, lighting conditions, compression levels, and forgery techniques, providing a robust foundation for training models that generalize well in real-world applications.
This dataset was instrumental in developing and benchmarking an enhanced deepfake detection framework for the AI-Guard mobile app. Multiple CNN architectures—including VGG19, InceptionV3, Xception, EfficientNetB0, ResNet50, and MobileNetV3Large—were fine-tuned using this dataset. The best-performing model (VGG19) achieved a validation accuracy of 98.92% and performed reliably on unseen real-world data. Lightweight models like MobileNetV3Large and EfficientNetB0 also showed promising results for mobile deployment
Overall water effectiveness: a new lean indicator for digital evaluation of water efficiency in industrial processes
No full textWater management is becoming an increasingly critical challenge for manufacturing industries due to growing environmental concerns, stricter regulatory requirements, and rising pressure from clients demanding more sustainable practices. Efficient and transparent use of water resources is no longer optional but a strategic necessity across industrial sectors. In this paper a new Lean performance indicator for evaluating water usage in industrial processes is presented. The proposed indicator, named Overall Water Effectiveness, aims to systematically assess industrial water performance by quantifying the gap between actual and ideal performance. It builds on the logic of Overall Equipment Effectiveness to identify water-related losses and support informed decision-making for continuous improvement while introducing a comprehensive industrial loss structure specifically designed for water use and consumption. Jointly, two key additional indicators are introduced: one measures how effectively the production process consumes input water, while the other evaluates the dependency on external water sources, taking into account the contributions of recycled and returned water. By translating high-level sustainability goals into actionable operational metrics, this new set of indicators enables the integration of water management into daily industrial operations through a practical, easy-to-use tool. The approach is applied in a major textile manufacturing company, demonstrating its practical utility in evaluating water use and consumption, identifying loss patterns, and leading the identification of improvement actions.Journal of Industrial Information Integratio
Life-cycle assessment of titanium swarf cleaning methods
Get PDFAlternative title: Environmental impact assessment of titanium swarf cleaning methodsThis paper evaluates the environmental impacts of chemical based and CryoClean swarf cleaning techniques through a comprehensive Life Cycle Assessment (LCA) aimed at identifying sustainable practices for recycling titanium swarf in additive manufacturing processes. Employing ISO 14040 and 14044 standards, the study focuses on a functional unit of cleaning 100 gr of titanium swarf, enabling direct comparison of environmental footprints across these methods. Data for the inventory analysis includes specific inputs such as energy consumption, water use, and chemicals, alongside outputs like emissions and waste generation, with supplemental secondary data sourced from Ecoinvent. The impact assessment utilizes the ReCiPe methodology, concentrating on key environmental indicators such as Global Warming Potential (GWP) and Acidification Potential (AP). The findings reveal distinct environmental trade-offs between the chemical based and CryoClean methods. Chemical-based cleaning, while effective at removing contaminants, often involves the use of hazardous substances that can lead to significant ecological impacts. In contrast, CryoClean, which utilizes liquid nitrogen to freeze and remove impurities, shows a lower environmental impact across several categories but may require higher energy inputs. By estimating the specific environmental impacts of the selected swarf cleaning techniques, the study contributes valuable insights towards optimizing material recovery and advancing circular economy principles in industrial manufacturing. The paper aims to guide industry stakeholders toward adopting more environmentally sustainable practices that align with the transition to greener manufacturing processes.The authors gratefully acknowledge the work carried out by Shanker Rajeev for his Master thesis and the funding by the UK EPSRC project "Sustainable Additive Manufacturing EP/W01906X/1"11th International Conference on Sustainable Design and Manufacturin
Understanding the difference between the nano and micro bubble size distributions generated by a regenerative turbine microbubble generator using ozone
Get PDFThere is a genuine paucity of data concerning the relative significance of the nano and the microbubble size distributions that are collectively generated when operating microbubble generation devices. Accordingly, the current work aimed to address this knowledge gap by measuring the two size distributions generated by a regenerative turbine microbubble generator using ozone and assess the relative significance of the nanobubble fraction. The microbubble fraction was measured with a focus-beam reflectance measurement device and the nanobubble fraction with a nano particle tracking instrument. The latter was calibrated using latex spheres to understand method uncertainty and to optimise the measurement approach. Sauter mean diameters of 217 nm and 37 μm were reported for the nano and microbubble fractions, respectively, with half of the microbubbles being <5000 nm in size. A comparison of the size and number concentrations of the different bubble types revealed that the majority of the gas was contained within the microbubble fraction, and hence, this controlled the overall mass transfer performance of the system. Further, the nanobubbles were observed to be stable for 18 h with little change in their size or number, indicating there was no net transfer of their gaseous contents. Overall, the work revealed that when considering enhancing gas-liquid mass transfer processes with micro-nano bubble generators, the microbubble fraction is key.Engineering and Physical Sciences Research CouncilThis research is gratefully supported by the Engineering and Physical Sciences Research Council (EPSRC) through their funding of the STREAM Industrial Doctorate Centre (EP/ G037094/1) and from the project sponsor Anglian Water.Journal of Water Process Engineerin
Quantitative microbial risk assessment of bioaerosol emissions from squat and bidet toilets during flushing
Get PDFBioaerosol emissions during toilet flushing are an often‐overlooked source of potential health risks in shared public facilities. This study systematically investigated the emission characteristics of Staphylococcus aureus and Escherichia coli bioaerosols in washrooms with squat and bidet toilets under varying flushing conditions and ventilation scenarios. Using Monte Carlo simulation–based quantitative microbial risk assessment and sensitivity analysis, the study estimated the disease burden and identified key factors influencing risk. The results showed that squat toilets generated 1.7–2.6 times higher concentrations of S. aureus bioaerosols and 1.2–1.4 times higher concentrations of E. coli bioaerosols compared to bidet toilets. After the first flush, bioaerosol concentrations were 1.3–1.8 times (S. aureus) and 1.2–1.4 times (E. coli) lower than those observed after the second flush. The second flush released a higher proportion of fine bioaerosol particles (<4.7 µm), increasing inhalation risks. The disease health risk burden was consistently one order of magnitude lower after the first flush than the second one. Ventilation with a turned‐on exhaust fan further reduced the risk by one order of magnitude. Sensitivity analysis identified exposure concentration as the most influential parameter, contributing up to 50% of the overall risk. This study highlights the importance of optimizing toilet design and ventilation systems to mitigate bioaerosol emissions and associated health risks. It provides actionable insights for improving public washroom hygiene and minimizing bioaerosol exposure.F.C., Z.A.N., and C.Y. gratefully acknowledge the support of the Environmental Microbiology and Human Health Programme (Grant Reference NE/M010961/1) and the SPF Clean Air Programme (Grant NE/V002171/1) in facilitating this collaborative study.Risk Analysi
Deep‐learning‐based vehicle trajectory prediction: a review
Get PDFVehicle trajectory prediction enables autonomous vehicles to better reason about fast‐changing driving scenarios and thus perform well‐informed decision‐making tasks. Among different prediction approaches, deep learning‐based (DL‐based) methodologies stand out because of their capabilities to efficiently summarise historical data, infer nonlinear behavioural patterns from human driving data, and perform long‐horizon prediction. This work reviews the DL‐based methods that have shown promising results, organising them in terms of usage of the input data, separating the encodings of the target vehicle's historical data, surrounding vehicle's historical data, and road layout data. In particular, this paper explores the relationships between the scope of the prediction components and the input data formats, as well as the connections with other elements in the same prediction framework, including vehicle interaction and road scene mining. This information is crucial to understand complex architectural decisions and to provide guidance for the design of improved solutions. This work also compares the performance of the most successful prediction models, establishing that appropriate encodings of vehicle interactions and road scenes improve trajectory prediction accuracy, with the best performance achieved by attention mechanism and Transformer‐based models. Finally, this work discusses future research directions, including considerations for real‐time applications.China Scholarship CouncilThis work was supported in part by the China Scholarship Council under Grant No. 202108690001 and the Graduate Research and Innovation Projects of Jiangsu Province under Grant KYCX21_3334.IET Intelligent Transport System
Enzymes targeting distinct hydrolysis blind-spots of thermal and biological pre-treatments significantly uplift biogas production
Get PDFThermal hydrolysis process (THP) and biological hydrolysis (BH) are key pre-treatment technologies for anaerobic digestion (AD), termed advanced anaerobic digesters (AADs). They target the rate-limiting hydrolysis step in AD. This study evaluates full-scale pre-treatments for macromolecule bias and the implementation of hydrolysis enzymes to enhance biogas yield. Findings show THP significantly improves protein and carbohydrate solubilisation by 30% and 25%, respectively, but fully hydrolyses only carbohydrates. In contrast, BH targets fibres and proteins, achieving 35% and 23% solubilisation, and only partially hydrolyses carbohydrates. Biomethane potential (BMP) tests indicate that protease enzymes raise biomethane yield by 20-30% for AAD with THP pre-treatment. In comparison, α-amylase increases it by over 30% for AAD with BH pre-treatment. This study tailors enzyme selection and dosage to specifically address the unique "hydrolysis blind spot" of each pre-treatment, providing a strategic framework to enhance AD technologies by an improved understanding of macromolecule selectivity and their transformation pathways.Bioresource Technolog
New opportunities for biologically and chemically mediated adsorption and precipitation of phosphorus from wastewater
Get PDFBiologically mediated adsorption and precipitation of phosphorus (P) from waste streams can restrict environmental P discharges. Here, we appraise progress in this field over the past decade. The research discipline has grown considerably in recent years. Industry ‘wastes’, including steel slags, continue to show promise as adsorbents with exceptionally high P retention capacities (>500 mg P g−1). Hydrotalcite, a nanomineral, offers prospects as a P removal technology with imbedded climate change mitigation capacity. Biomineral struvite formation, driven by microbial processes, offers an exciting P removal and recovery approach that can be applied to diverse wastewater types due to its feedstock-independent mechanisms, emerging immobilisation techniques and adaptability to mixed cultures. All of these factors facilitate efficient nutrient recycling and scalable application to the wastewater industry. Adsorbed and precipitated P can be applied to cropland to offset dependence on conventional fertiliser inputs. Therefore, in addition to water treatment, these biologically mediated processes also offer opportunities to support food production. Moreover, as many of the input materials covered in this review are industry byproducts and common organic materials, the removal of P from waste streams by adsorption and precipitation offers strong circularity potential that aligns with the UN's Sustainable Development Goals. We call for future work to focus on long-term full-scale trials involving community, government and industry partners.Current Opinion in Biotechnolog