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An Intelligent Multi-Task Supply Chain Model Based on Bio-Inspired Networks
Full text linkData Availability Statement:
The datasets used in this study are publicly available at the following address links: https://www.kaggle.com/datasets/azminetoushikwasi/supplygraph-supply-chain-planning-using-gnns (accessed on 1 January 2024); https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis (accessed on 1 January 2019).Acknowledging recent breakthroughs in the context of deep bio-inspired neural networks, several architectural deep network options have been deployed to create intelligent systems. The foundations of convolutional neural networks are influenced by hierarchical processing in the visual cortex. The graph neural networks mimic the communication of biological neurons. Considering these two computation methods, a novel deep ensemble network is used to propose a bio-inspired deep graph network for creating an intelligent supply chain model. An automated smart supply chain helps to create a more agile, resilient and sustainable system. Improving the sustainability of the network plays a key role in the efficiency of the supply chain’s performance. The proposed bio-inspired Chebyshev ensemble graph network (Ch-EGN) is hybrid learning for creating an intelligent supply chain. The functionality of the proposed deep network is assessed on two different databases including SupplyGraph and DataCo for risk administration, enhancing supply chain sustainability, identifying hidden risks and increasing the supply chain’s transparency. An average accuracy of 98.95% is obtained using the proposed network for automatic delivery status prediction. The performance metrics regarding multi-class categorization scenarios of the intelligent supply chain confirm the efficiency of the proposed bio-inspired approach for sustainability and risk management.This research received no external funding
Nucleation control strategy for sustainable aluminium: Improving Fe removal efficiency or increasing Fe-tolerance
Full text linkAchieving ultra-low Fe level or refining Fe-containing intermetallic compounds (Fe-IMCs), both governed by heterogeneous nucleation, remains a major challenge for development of high-performance recycled Al alloys. This study demonstrates that the sensitivity of Fe-IMC formation to casting conditions is dictated by nucleation difficulty, which is controlled by both kinetic factors (diffusion time) and thermodynamic driving forces (nucleation and continuous undercooling). We provide the first direct evidence for dual-size primary Fe-IMCs and their distinct nucleation pathways: large P1-α-Al₁₅(Fe,Mn)₃Si₂ particles originating from non-equilibrium θ-Al₁₃Fe₄ nucleated at higher temperatures, and nanoscale P2-α-Fe particles nucleating heterogeneously on MgAl₂O₄ oxides at lower temperatures with larger nucleation undercooling. Building on this new mechanistic understanding, two casting strategies were developed: (1) promoting Fe-IMC nucleation to enhance Fe removal down to 0.3 wt%, and (2) suppressing Fe-IMC formation to increase Fe tolerance and refine second-phase particles, enabled by tuning pouring temperature, cooling rate, and casting routes. A comprehensive process map linking Fe-IMC formation to cooling rate and pouring temperature is established, providing a predictive framework for process optimization. These insights position nucleation-control-based design as a powerful approach for sustainable aluminium production.This work was financial supported by the EPSRC (UK) for under grant number EP/N007638/1 (Future Liquid Metal Engineering Hub) and Brunel University of London BRIEF award (11937131)
Agreement testing of AMSTAR-PF, a tool for quality appraisal of systematic reviews of prognostic factor studies
No full textStrengths And Limitations Of This Study:
⇒ The testing protocol was preregistered and standardised across all appraisers.
⇒ The 14 appraisers, who had varying levels of experience, tested A MeaSurement Tool to Assess systematic Reviews of Prognostic Factor studies on eight articles covering a range of topics.
⇒ Gwet’s agreement coefficient and kappa values were calculated across interrater, inter- pair and intrapair agreement, and time of use and time to consensus were recorded.
⇒ Appraisers had limited experience in prognostic factor research and reviews were often outside their expertise.Data availability statement:
Data are available upon reasonable request.A preprint version of the article is available on MedRxiv at https://doi.org/10.1101/2025.04.10.25325555 and has not been certified by peer review. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.Objectives: To test the agreement and usability of a novel quality appraisal tool: A MeaSurement Tool to Assess systematic Reviews of Prognostic Factor studies (AMSTAR-PF).
Design: Observational study.
Participants: 14 appraisers of varied experience levels and backgrounds, including undergraduate, master’s and PhD students, postgraduate researchers, research fellows and clinicians.
Study procedure: Eight systematic reviews were rated by all reviewers using AMSTAR-PF.
Outcome measures: Planned measures included intrapair and inter-pair agreement using Cohen’s and Fleiss’ kappa, time of use and time to reach consensus. Interrater agreement was an added measure, and Gwet’s agreement coefficient was calculated and presented due to its greater stability across agreement levels. The percentage of intrapair agreements identical or one category apart was also presented.
Results: Interrater agreement averaged 0.59 (range 0.21–0.90), inter-pair agreement 0.61 (range 0.24–0.91) and intrapair agreement 0.75 (range 0.45–0.95) across the domains, with agreement for the overall rating 0.46 (95% CI 0.30 to 0.62) for interrater agreement, 0.46 (95% CI 0.17 to 0.74) for inter-pair agreement and 0.68 (range of averages 0.22–1.00) for intrapair agreement. The majority (60.7%) of intrapair ratings were identical, with 94.6% of final ratings either identical or only one category different for the overall appraisal. The time taken to appraise a study with AMSTAR-PF improved with use and averaged around 34 min after the first two appraisals.
Conclusions: Despite some variance in agreement for different domains and between different appraisers, the testing results suggest that AMSTAR-PF has clear utility for appraising the quality of systematic reviews of prognostic factor studies.MLH, RPA, SMC, EM, KP, DAV, EW, MVW were supported by Australian Government Research Training Scholarships. GLM, HBL, BM were supported by a Leadership Investigator Grant from the National Health and Medical Research Council of Australia to GLM (ID 1178444). RDR was supported by funding from the MRC Better Methods Better Research panel (grant reference: MR/V038168/1) and by the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. RDR is an NIHR Senior Investigator. ARG was supported by a postgraduate research scholarship from the Rural Doctors Workforce Agency (RDWA) of South Australia. EM was supported by a National Health and Medical Research Council Project Grant (ID 1161634)
Do deepfakes, digital replicas and human digital twins justify personality rights?
Full text linkData Availability Statement:
The author has nothing to report.Unauthorised deepfakes are deeply problematic, from the spreading of misinformation to non-consensual pornographic content. This paper asks whether deepfakes, digital replicas and human digital twins justify personality rights. To address this question, it examines the harms that deepfakes can cause through disinformation, demeaning content and displacing creative workers. It demonstrates that the current UK legal patchwork of passing off, intellectual property, defamation, and criminal laws do not adequately address these harms. Therefore, it proposes the introduction of personality rights into UK law, in the form of an automatic unwaivable personality right for 70 years after the death of the person, with appropriate exceptions to protect freedom of expression. Deepfakes are the hinges on which to open the door of personality rights in the UK, for protection against the harms of unauthorised digital replicas
A Novel Modified Sine Cosine Algorithm for Reducing Side lobe Level of Linear Antenna Array
No full text......This study received no external funding
Vision-Based Dual-Mode Collision Risk-Warning for Aircraft Apron Monitoring
Full text linkHighlights:
What are the main findings?
• Under identical detector inputs (optimised YOLOv8-Seg) and without tracker specific tuning, DeepSORT delivered the most stable identity tracking on the 997-frame Microsoft Flight Simulator (MSFS) simulation-based incident reenactment benchmark using the airplane-only MOTChallenge ground truth: Multi-Object Tracking Accuracy (MOTA) 92.77%, recall 93.27%, and one ID switch.
• A dual-mode incident-warning framework was developed: (i) a reactive module based on segmentation-mask proximity and (ii) a proactive module based on short horizon trajectory extrapolation and future-Intersection-over-Union (IoU) risk triggering. The modules can be used independently or jointly.
What are the implications of the main findings?
• The MSFS reenactment sequence and its associated labels provide a reproducible testbed that helps mitigate the scarcity of annotated apron-incident data for detection, tracking and risk studies.
• A scaled Unmanned Aerial Vehicle (UAV)/laboratory validation protocol is defined to assess end-to-end feasibility on UAV-captured imagery (reported qualitatively via representative frames and warning overlays).Data Availability Statement:
The dataset and annotations generated in this study are available on study are available on Roboflowat: https://app.roboflow.com/ecb-wba09/mot-challange-dataset/browse?query-Text=&pageSize=50&startingIndex=0&browseQuery=true (accessed on 28 January 2026).Ground incidents on airport aprons can cause substantial operational disruption and economic loss, while conventional surveillance (e.g., Surface Movement Radar (SMR), Closed-Circuit Television (CCTV)) often lacks the resolution and proactive decision support required for close-proximity operations. This study proposes a UAV-deployable, camera-agnostic Computer Vision (CV) framework for collision-risk warning from elevated viewpoints. An optimised YOLOv8-Seg backbone performs multi-class aircraft segmentation (airplane, wing, nose, tail, and fuselage) and is integrated with four MOT algorithms under identical evaluation settings. For quantitative tracker benchmarking, DeepSORT provides the strongest overall performance on the airplane-only MOTChallenge-format ground truth (MOTA 92.77%, recall 93.27%). To mitigate the scarcity of annotated apron-incident data, a labelled 997-frame MOT dataset is created via an MSFS simulation-based reenactment inspired by the 2018 Asiana–Turkish Airlines wing-to-tail event at Istanbul Ataturk Airport. The framework further introduces a dual-module warning mechanism that can operate independently: (i) a reactive module using image-plane proximity derived from segmentation masks, and (ii) a proactive module that predicts short-horizon conflicts via trajectory extrapolation and IoU-based future overlap analysis. The approach is evaluated on multiple simulated incident scenarios and assessed on a real apron video from Hong Kong International Airport; additionally, laboratory-scale UAV experiments using diecast aircraft models provide end-to-end feasibility evidence on unmanned-platform imagery. Overall, the results indicate timely warnings and practical feasibility for low-overhead UAV-enabled apron monitoring.This research received no specific external project funding. The first author is supported by a PhD scholarship from the Ministry of National Education of Türkiye (no grant number). This scholarship did not directly fund the work reported in this paper
The Roles of Neuroticism and Schizotypy in Emotional Abuse and Mental Health Association: A Replication and Extension of Alnassar et al. (2024)
Full text linkHighlights:
• All forms of childhood trauma have a significant association with adverse mental health outcomes and poor sleep quality.
• Of all trauma types, emotional abuse is most strongly associated with mental health outcomes and sleep quality.
• Neuroticism and schizotypy are significantly correlated and independently mediated the relationship between emotional abuse and poor mental health.
• Only neuroticism mediated the relationship between emotional abuse and poor sleep quality.Data availability:
The data are publicly available at doi:10.17633/rd.brunel.25451407 .Supplementary materials are available online at: https://www.sciencedirect.com/science/article/pii/S2666915326000302#sec0017 .Background:
Childhood emotional abuse (EA) has been consistently linked to adverse mental health outcomes. Recent data suggest that neuroticism may partially mediate this association but the role of schizotypy which overlaps with neuroticism and also predicts negative emotional experiences remains unknown. This study examined the roles of both neuroticism and schizotypy in the association of EA with poor mental health and sleep quality.
Methods:
Data were collected from 478 healthy adults (179 males, 299 females) who completed self-report measures of childhood trauma (emotional abuse and neglect, physical abuse and neglect, sexual abuse), mental health (depression, anxiety, stress), sleep quality, and personality traits (neuroticism, schizotypy) in a single session. Structural equation modelling was used to test neuroticism and schizotypy as potential mediators of EA association with poor mental health and sleep quality.
Results:
All forms of childhood trauma were associated with poor mental health and sleep quality, with EA showing these associations most strongly (r: .30-.42). Neuroticism and schizotypy were significantly correlated (β=.52) and independently mediated the relationship between EA and poor mental health (neuroticism: β=.12; schizotypy: β=.11), while only neuroticism mediated the relationship between EA and poor sleep quality (β=.12).
Conclusions:
Both neuroticism and schizotypy mediate EA-mental health association possibly due to poor cognitive control and heightened stress sensitivity, exaggerating maladaptive emotion regulation. Further research should aim to examine underlying cognitive mechanisms (e.g., selective negative recall bias) through which neuroticism and/or schizotypy exert their influence in these associations and develop suitable psychological interventions to target them.This research did not receive any funding
A social sustainability assessment of a newly developed solar thermal energy system for industrial integration
Full text linkHighlights:
• The social sustainability of an industrial solar thermal system is conducted.
• The system consists of a SunDial, PCM storage tank, and control unit.
• Supply-chain social risks are quantified using a risk-hour SHDB framework.
• PCM storage components showed the highest social risk.
• Manufacturing location strongly influences health, safety and labour risks.Data availability:
Data will be made available on request.The deployment of solar thermal energy (STE) systems plays a critical role in decarbonising industrial heat demand; however, their sustainability performance includes not only technical efficiency and environmental impacts but also social considerations across the supply chain. This study presents a comprehensive social sustainability assessment of a newly developed STE system focusing on the manufacturing stages of its main components: SunDial concentrator, a phase change material (PCM) thermal storage tank, and a control unit. Social risks were quantified using a database driven risk hour (RH) approach, across different impact categories. including health and safety (H&S), fair payment, excessive working time, gender equality and policy compliance. The data were collected using Social Hotspot Database (SHDB) software, which simulated social risk levels based on material quantities and countries of origin of system components.
The results revealed that social risks are strongly dependent on country of origin and economic sector, rather than material quantity alone. The SunDial component manufactured in Spain demonstrated moderate H&S and gender inequality risks within the steel sector, while comparable components produced in Germany showed consistently low social risk levels. Similarly, manufacturing of PCM tank subcomponents in the Polish non-ferrous metals sector showed increased H&S risks, largely driven by high policy non-compliance and exposure to metal dust, while chemical production in the Netherlands showed substantially lower social risks due to stricter regulatory implementation.
The findings highlight the importance of geographically and sector specific social assessments when sourcing components for renewable energy systems. Incorporating social sustainability metrics at early stages of design can guide responsible supply chain decisions, improving the overall sustainability performance and social acceptability of industrial STE technologies.This work was supported by the EU Horizon 2020 research and innovation programme, Application of Solar Thermal Energy in Industrial Processes (ASTEP) [grant agreement No 884411]
Epigenomic subtypes of late-onset Alzheimer’s disease reveal distinct microglial signatures
Full text linkData availability:
The PITT-ADRC datasets used in this study are available on Synapse (https://www.synapse.org/) under Synapse ID: syn23538600. Access requires creating a Synapse user account and submitting a data access request. The UKBBN dataset is accessible via GEO under accession number GSE284764. ROSMAP datasets are also deposited on Synapse (Synapse IDs: syn7357283, syn23650893, syn3157325, syn25006903). Microglial snRNA sequencing data and markers of microglial states were obtained from https://compbio.mit.edu/microglia_states/. The ROSMAP mQTL dataset is accessible at https://mostafavilab.stat.ubc.ca/xqtl/. All codes used for DNA methylation and bulk transcriptomic analyses, clustering, replication, and cross-cohort validations are available at https://github.com/Dementia-Systems-Biology/LOAD_subtyping.Supplementary Information is available online at: https://link.springer.com/article/10.1007/s00401-026-02990-y#Sec30 .Growing evidence suggests that clinical, pathological, and genetic heterogeneity in late-onset Alzheimer’s disease (LOAD) contributes to variable therapeutic outcomes, potentially explaining many trial failures. Advances in molecular subtyping through proteomic and transcriptomic profiling reveal distinct patient subgroups, highlighting disease complexity beyond amyloid-beta plaques and tau tangles. This underscores the need to expand subtyping across new molecular layers, to identify novel drug targets for different patient subgroups. In this study, we analyzed genome-wide DNA methylation (DNAm) data from three independent postmortem brain cohorts ( = 826) to identify epigenetic subtypes of LOAD. We used unsupervised clustering to define subtype-specific DNAm patterns and validated them across cohorts. We then mapped subtype signatures to brain cell types using purified-cell DNAm profiles and integrated bulk and single-nucleus RNA-seq to assess each subtype’s impact on gene expression. Finally, we examined clinical and neuropathological correlates to evaluate biological and clinical significance. We identified two distinct epigenomic subtypes of LOAD, consistently observed across three cohorts. Both subtypes exhibit significant yet distinct microglial methylation enrichment. Bulk transcriptomic analyses highlighted distinct biological mechanisms underlying these subtypes: subtype 1 was enriched for immune-related processes, while subtype 2 was characterized by neuronal and synaptic pathways. Single-nucleus transcriptional profiling of microglia indicated that both subtypes share AD-associated innate-immune remodeling, with subtype differences emerging primarily as state-dependent transcriptional shifts rather than large changes in state abundance. Overall, subtype 1 showed a relative weighting toward more inflammatory microglial programs, whereas subtype 2 showed stronger transcriptional remodeling in specific microglial states alongside relatively greater engagement of regulatory and clearance-associated features. These findings reveal distinct epigenetic and functional microglial states underlying LOAD subtypes, advancing our understanding of disease heterogeneity. This work lays the groundwork for targeted therapeutic strategies tailored to specific molecular and cellular disease profiles.E.P. was supported by a ZonMw Memorabel/Alzheimer Nederland Grant (733050516). V.L. received support through a PhD scholarship funded by the Mental Health and Neuroscience Research Institute (MHeNs), Maastricht University. The generation of DNAm data, bulk RNA sequencing of the PITT-ADRC samples, and the analysis of FANS-purified nuclei from the BDR samples were supported by a grant from the National Institute on Aging (NIA) of the National Institutes of Health (NIH) (R01AG067015) awarded to K.L., B.C., and E.P. The analysis of DNA methylation data and bulk RNA sequencing of the UKBBN samples was funded by a Medical Research Council (MRC) grant (MR/S011625/1) and an Alzheimer’s Society grant (AS-PG-16b-012) awarded to K.L
Data sources:
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