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A novel Si/Cu-modified Al-Mg alloy processed by laser powder bed fusion: Crack inhibition, microstructures and mechanical properties
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Data will be made available on request.The Al-Mg alloys fabricated via additive manufacturing normally suffer from poor printing ability and mechanical properties. Here, the cost-effective Si and Cu were used to modify an Al-5.5Mg alloy for laser-powder bed fusion (L-PBF), which achieved a synergetic combination of crack-free feature and high strength. It is found that the hot cracking was effectively eliminated in the L-PBFed samples due to the reduction of brittleness temperature range and the formation of fine Al-Mg2Si eutectics. A hierarchical structure featured by the refined ɑ-Al grains, sub-micron cellular structures decorated with Mg2Si, S-Al2CuMg nanoparticles and substantial dislocation networks was responsible for the obvious strength improvement in the L-PBFed Al-5.5Mg-2Si-Cu alloy, in which the ultimate tensile strength of 560 MPa, yield strength of 453 MPa and elongation of 9.1 % have been obtained. The synergistic effects of crack elimination and precipitation strengthening induced via the incorporation of Si/Cu provide the innovative guideline to fabricate crack-free Al-Mg alloys with high strength.The authors would like to acknowledge the National Natural Science Foundation of China (Grant No. 52071343)
Editorial
Get PDFIn this editorial for the first issue of 2025, we describe our year in post, the challenges and opportunities we face, including the threats of AI to the editorial process and the initiative we have launched since become the editorial team of BERJ
Pons metabolite alterations in narcolepsy type 1
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All data relevant to this study have been disclosed in the manuscript. Further information may be shared upon request.Introduction:
Narcolepsy type 1 (NT1) is a rare central sleep disorder characterized by a selective loss of hypocretin/orexin (hcrt)-producing neurons in the postero-lateral hypothalamus that project to widespread areas of the brain and brainstem. The aim of this study was to explore in a group of NT1 patients the metabolic alterations in the pons and their associations with disease features.
Methods:
Twenty-one NT1 patients (16 M) and twenty age-matched healthy controls (10 M) underwent a brain 1H MRS on a 1.5 T GE Medical Systems scanner. Metabolite content of N-acetyl-aspartate (NAA), choline (Cho), and myo-inositol (mI) were estimated relative to creatine (Cr), using LCModel 6.3. Clinical data were also collected with validated questionnaires, polysomnography, the Multiple Sleep Latency Test (MSLT), Cerebrospinal fluid hypocretin-1 (CSF hcrt-1) concentration and genetic markers.
Results:
NT1 patients compared with healthy controls showed lower NAA/Cr ratio (p = 0.007) and NAA/mI ratio (p = 0.011) in the pons. The Epworth Sleepiness Scale score showed a significant negative correlation with NAA/Cr content (p = 0.023), MSLT sleep latency a negative correlation with the mI/Cr ratio (p = 0.008), and sleep onset REM periods a positive correlation with the mI/Cr ratio (p = 0.027). CSF hcrt-1 levels were positively correlated with the NAA/Cr ratio (p = 0.039) and negatively with the mI/Cr ratio (p = 0.045) and the Cho/Cr ratio (p = 0.026).
Conclusion:
The metabolic alterations found in the pons of NT1 patients using the MR Spectroscopy technique were associated with subjective and objective disease severity measures, highlighting the crucial role of this biomarker in the pathophysiology of the disease.AV and MM are supported by funding obtained under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 - Call for tender No. 341 of 15/03/2022 of Italian Ministry of University and Research funded by the European Union– NextGenerationEU, Project code PE0000006, Concession Decree No. 1553 of 11/10/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000930002, “A multiscale integrated approach to the study of the nervous system in health and disease” (MNESYS)
Bodies of whiteness: Space invading Viktor Orbán’s ethnonationalism
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Research data are the publicly available Instagram photos of Viktor Orban.The article maps moments of relations between two bodies and their proximity to the somatic norm of whiteness: that of Viktor Orbán, the Prime Minister of Hungary, and of the author, an émigré mother returning to Hungary to vote at the General Election on 3 April 2022. The context of the encounter is the public space of the official Instagram account of the Prime Minister, and the photos of him posted in the run up to the General Election. Viktor Orbán not only displays white hegemonic masculinities in his social media platform but sets out the role for white ethnic Hungarian mothers in reproducing a nation built on racialized exclusion. The article traces affective disruption and unease engendered by the racialized and gendered norms of national belonging delineated by the current leader of the Hungarian government. Working with Nirmal Puwar’s Space Invaders, it examines the possibilities of creating a sense of belonging from an ambivalent position of being both in and out of place.The author received financial support for the research and authorship of this article from the Leverhulme Trust
The unit-Cauchy quantile regression model with variates observed on (0, 1): percentages, proportions, and fractions
Get PDFData availability. Enquiries about data availability should be directed to the authors.In this study, a new parametric quantile regression model is introduced as an alternative to the beta regression and Kumaraswamy quantile regression model. The proposed quantile regression model is obtained by reparametrization of the unit-Cauchy distribution in terms of its quantiles. The model parameters are estimated using the maximum likelihood method. A Monte-Carlo simulation study is conducted to show the efficiency of the maximum likelihood estimation of the model parameters. The implementation of the proposed quantile regression model is shown by using real datasets. Quantile regression models based on unit-Weibull, unit generalized half normal, and unit Burr XII are also considered in the applications. The application results show that the proposed quantile regression model is preferable over its rivals when several comparison criteria are taken into account. In addition, the fitting plots indicate that the proposed quantile regression model fits extreme observations on the right tail better than its strong rivals, which is important in quantile regression modeling.There is no funding regarding this research work
Neural Combinatorial Optimization for Multiobjective Task Offloading in Mobile Edge Computing
Get PDFTask offloading is crucial in supporting resource-intensive applications in mobile edge computing. This paper explores multiobjective task offloading, aiming to minimize energy consumption and latency simultaneously. Although learning-based algorithms have been used to address this problem, they train a model based on one a priori preference to make the offloading decision. When the preference changes, the trained model may not perform well and needs to be retrained. To address this issue, we propose a neural combinatorial optimization method that combines an encoder-decoder model with reinforcement learning. The encoder captures task relationships, while the decoder, equipped with a preference-based attention mechanism, determines offloading decisions for various preferences. Additionally, reinforcement learning is employed to train the encoder-decoder model. Since the proposed method can infer the offloading decision for each preference, it eliminates the need to retrain the model when the preference changes, thus improving real-time performance. Experimental studies demonstrate the effectiveness of the proposed method by comparison with three algorithms on instances of different scales.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U23A20347);
Royal Society International Exchange (Grant Number: IEC-NSFC-211264)
Vacuum Insulation Enabled Energy Efficient Domestic Cooking Ovens – A Validated Numerical Study
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Threat-agnostic resilience: Framing and applications
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No data was used for the research described in the article.Critical infrastructure is not indestructible. Interdependencies between infrastructure systems and the environment compound consequences at vulnerable locations but can be harnessed to maximize operational efficiency, recovery capability, and long-term sustainability. Threats, both emergent and systemic, have propagated beyond historical norms, exposing the limitations of hazard-specific resilience approaches. These approaches, by their nature, rely on predefined scenarios that fail to capture the full complexity of cascading failures, novel threat combinations, and the dynamic evolution of risks over time, especially in the cases where environment is affected. This leaves critical gaps in planning, response, and recovery, as systems designed around specific hazards are often unable to adapt to disruptions that fall outside their narrowly defined parameters, resulting in unanticipated vulnerabilities and slower recovery trajectories. We propose a paradigm shift toward threat-agnostic resilience, emphasizing adaptability to unforeseen hazards through modularity, distributedness, diversity, and plasticity. These principles foster system-wide robustness by enabling critical functions to persist despite unpredictable challenges. This framework also accounts for the interdependencies between resilience strategies and environmental outcomes, ensuring that adaptability to unforeseen hazards is balanced with sustainability goals. Resilience characteristics, such as modular design and distributed systems, shape patterns of resource use, energy efficiency, and ecological impacts across systems. By identifying methods to assess and optimize these trade-offs, we provide actionable insights for designing infrastructure that simultaneously enhances resilience and minimizes environmental burdens. Challenges exist in developing methodological foundations for these principles within practical applications to prevent sunk cost and over-constraining operational procedures.Any opinions expressed herein are of the authors alone, and should not be considered the opinion or practice of any institution. Prof Stergios-Aristoteles Mitoulis and Prof Sotirios Argyroudis received funding by the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant agreement No: EP/Y003586/1, EP/X037665/1]. This is the funding guarantee for the European Union HORIZON-MSCA-2021-SE-01 [grant agreement No: 101086413] ReCharged - Climate-aware Resilience for Sustainable Critical and interdependent Infrastructure Systems enhanced by emerging Digital Technologies
Extracting Regions of Interest and Selective Feature Application in Leukaemia Image Classification
Get PDFEvaluating the blood smear test images remains the main route of detecting the type of leukaemia, accurate diagnosis is fundamental in providing effective treatment. The changes in the structure of the white blood cells present different morphological characteristics translated into extractable features. This paper explores techniques for manipulating a reduced dataset to increase the classification with CNN (Convolutional neural Network) and feature extraction. Extracting ROI (Regions of Interest) divides the leukaemia images into points of interest respective white blood cells, expanding the dataset an important factor for CNN’s performance. Segmenting the initial dataset into ROI through computation after applying Otsu thresholding results in a new dataset of images. The two datasets are analysed, feature extraction performs better on the initial dataset while CNN’s accuracy is higher for ROI images. Further steps will divide the images into filtered regions of interest where more specific characteristics are extracted to increase the accuracy
Nucleation competition and phase transformation mechanisms in recycled aluminium alloys : Insights into θ-Al13Fe4, Al6(Fe,Mn) and α-Al15(Fe,Mn)3Si2
Get PDFAluminium rich Fe-bearing intermetallic compounds (Fe-IMCs) plays a critical role in determining the mechanical properties of recycled aluminium alloys due to inevitable Fe accumulation during recycling. The Fe-IMCs which have a needle-/plate-like morphology are particularly detrimental, impairing the ductility and overall performance of aluminium alloys. Consequently, optimizing phase selection to favour less harmful Fe-IMCs is a critical strategy for improving alloy design and enhancing material properties. The nucleation of Fe-IMCs, however, is challenging because it requires precise structural and compositional templating, involving multiple alloying elements at specific atomic positions, and thus necessitates substantial undercooling. This study examines a complicated primary phase selection among θ-Al13Fe4 and Al6(Fe,Mn) and α-Al15(Fe,Mn)3Si2 in an Al-5Mg-2Si-0.6Mn-1.3Fe alloy. Experimental results show θ-Al13Fe4 and Al6(Fe,Mn) solidify as non-equilibrium primary phases ahead of the equilibrium α-Al15(Fe,Mn)3Si2, with subsequent transformation to α-Al15(Fe,Mn)3Si2 during later stages. Phase competition and transformation mechanisms were characterized using scanning electron microscope (SEM), electron backscatter diffraction (EBSD) and transmission electron microscope (TEM), with experimental results supported by first-principles modelling. Particular focus was given to the transition from the silicon-unfavourable Al6(Fe,Mn) to the silicon-rich α-Al15(Fe,Mn)3Si2. The findings provide a novel framework for designing recycled aluminium alloys with enhanced mechanical properties by optimizing Fe-IMC phase selection and transformation pathways.This work was financial supported by the EPSRC (UK) for under grant number EP/N007638/1 (Future Liquid Metal Engineering Hub). Brunel University London BRIEF Award (11937131)
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