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Jaguars and raptorial birds: the ancient wood carving arts of Northeastern South America (Trombetas River region, Brazil)
No full textThis paper presents the results of the first systematic study aimed at defining the chronology, provenience and material components of 14 distinctive wood carvings featuring jaguar and raptorial bird imagery broadly attributed to northeastern South America, centred on Brazil’s Trombetas River region. These carvings, largely confined to drug-related paraphernalia and ceremonial objects (trumpets, rattles, staffs/sceptres and a hafted anchor axe), were part of antiquarian collections deposited in European museums mainly in the 19th century, with very little associated information.14C AMS dating of their wood, cotton and resins shows that they have deeper histories, spanning the 14th to late 17th/early 18th centuries. Wood identification indicates a relatively narrow range of taxa, mainly from the genera Brosimum and Swartzia, likely selected for their hardness and striking “snakeskin” or “leopard skin” patterns. Bindings of human hair are identified on a ceremonial weapon. Strontium isotope (87Sr/86Sr) analysis of the wood is used to explore the possible provenience of the carvings, with the results divided into two groups, one from a more radiogenic region consistent with the Trombetas, and the other from a less radiogenic region, possibly the Amazon floodplain.This work is part of the jagWARS (Jaguars, Raptors and the Patterns of War: 14th-18th century South American Indigenous sculptural arts) project funded by the Gerda Henkel Stiftung (AZ 43/F/18).Journal of Archaeological Science: Report
A unified multi-fidelity aero-structural design framework for novel aircraft configurations
No full textThis study presents a novel framework that integrates multi-disciplinary simulation data, robust multi-objective optimization (MOO), and interactive visual analytics to balance multiple objectives and cope with design uncertainties. Using the CRM wing and ATI narrowbody aircraft geometry as a test case, aerodynamic (e.g., lift-to-drag ratio) and structural (e.g., wing mass) performance measures are combined within a Kriging-based surrogate modelling approach. Monte Carlo Simulation is employed to propagate design uncertainties, while NSGA-III guides the exploration of Pareto-optimal solutions using a robust optimisation framework. An HTML-based dashboard, powered by Python servers, allows users to dynamically inspect trade-offs, filter design alternatives, and compare competing objectives in near real-time. Results indicate that increasing the wing aspect ratio and reducing skin root thickness generally enhances aerodynamic efficiency and flight range, although structural weight and associated uncertainties also rise. By visualizing such trade-offs interactively, designers gain insight into where higher fidelity analyses or additional data collection may be most beneficial, and which configurations deliver robust performance under probable variations. The resulting tool fosters a more efficient and informed engineering design process by consolidating data, computations, and insights within a single, accessible environment.This project has received funding from Innovate UK under Grant Agreement No 10003388AIAA SCITECH 2026 Foru
High-concentration polyethylene and polystyrene microplastics co-exposure shorten insect lifespan and impose ecological risk: multi-omics evidence from Drosophila melanogaster
No full textMicroplastics (MPs) are pervasive environmental pollutants, accumulating in ecosystems and posing a long-term exposure risk to both the entire ecosystem and human health. However, the combined impact of such high doses on insect longevity and the consequent ecological consequences remain understudied. Here we used Drosophila melanogaster as a model to quantify lifespan shortening under environmentally realistic and extreme concentrations of Polyethylene (PE) and Polystyrene (PS) co-exposures and to unravel the molecular bases of the observed toxicity. Furthermore, we delved into the underlying mechanism through metabolomics and transcriptomics analysis. Our results demonstrated PE and PS MPs co-exposure with greatly high concentrations significantly reduced the lifespan of Drosophila and influenced age-related phenotypes such as climbing ability, intestinal barrier and hunger resistance. We found that differential metabolites were engaged in various metabolic pathways, including ABC transporters, alanine, aspartate and glutamate metabolism. Differentially expressed genes (DEGs) were closely related to Toll and Imd signaling pathway and Longevity regulating pathway. Gram-level PE and PS co-exposure triggers immune-metabolic crosstalk failure and represents a realistic terrestrial risk factor for insect longevity. Our data highlight the urgent need to include high-dose microplastic mixtures in terrestrial ecotoxicological risk assessments and biodiversity conservation strategies. Synopsis Co-exposure to PE and PS MPs with high concentrations induces changes in gene expression and metabolites associated with immune system and energy metabolism in Drosophila , thereby affecting their lifespan.The authors gratefully acknowledge funding from Project LH2021E097 supported by the Natural Science Foundation of Heilongjiang Province. ZY thanks UKRI NERC Fellowship (NE/R013349/2) and The Leverhulme Trust Research Leaderships Awards (RL-2022-041).Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacolog
Arctic driftwood proposal for durable carbon removal
No full textVarious geoengineering approaches have been proposed for carbon dioxide (CO2) removal but their viability at scale remains unclear. Here, we consider the natural behaviour of driftwood, the warming-induced acceleration of sea-ice loss and tree growth, as well as the stability of cellulose in subfossil wood under cold-anoxic conditions, to introduce the concept of sinking timber from the boreal forest for durable CO2 sequestration at the deep Arctic Ocean floor.This study was supported by the AdAgriF project: “Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation” (CZ.02.01.01/00/22_008/0004635), the ERC Advanced Grant (882727; Monostar), and the ERC Synergy Grant (101118880; Synergy-Plague).npj Climate Actio
Biofibre explorer: an augmented reality (AR) tool to promote circularity through material knowledge
No full textAugmented Reality (AR), which overlays digital information on the physical world, is frequently used in textile retail to improve shopping experiences by simulating product appearance and enabling virtual customisation. While these applications foster brand engagement and purchasing decisions, they largely promote consumption rather than encouraging circular behaviours. This study introduces the AR Biofibre Explorer, an innovative tool designed to reconnect consumers with materials and processes by demonstrating the wet spinning process for producing cellulose-based textiles. Through a mixed-methods evaluation, we reveal how the tool enhances understanding of material origins and their applications, promoting informed decisions and circular practices. Aligning with The wellbeing framework for consumer experiences in the circular economy of the textile industry [1], the tool incorporates dimensions such as learning, attachment, competence, and playfulness. This research establishes AR as a means to foster sustainability and circularity in fashion by bridging material knowledge gaps, enhancing consumer engagement, and enabling sustainable consumption choices.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through the UKRI Interdisciplinary Circular Economy Centre for Textiles: Circular Bioeconomy for Textile Materials [grant number EP/V011766/1].Sustainable Future
ML-Enhanced Visual Inertial Navigation System for UAVs
No full textRana, Zeeshan - Associate Supervisor.Urban Air Mobility (UAM) applications, including passenger taxis, cargo transport
and aerial surveys, are significantly impacted by GNSS vulnerabilities in urban
environments highlighting the requirement of accurate and robust alternative
navigation solutions like Visual-Inertial Navigation Systems (VINS) to ensure
reliable and safe operation. However, ensuring robustness and high accuracy in
the VINS system requires the development of advanced methodologies to
address the limitations of current state-of-the-art solutions, which struggle to
perform effectively in low-light environments, adverse weather conditions, low -texture areas, and during rapid motion variation. Therefore, the primary aim of
this thesis is to develop a robust and resilient vision-based alternative solution to
GNSS outage by mitigating visual faults, enabling safe and reliable navigation
under challenging urban environments for UAM systems.
This thesis designs an advanced methodology to address visual faults by
exploring hybrid architectures in VINS systems to improve robustness in complex
visually degraded urban environments, ensuring operational safety during GNSS
outages. To enhance robustness and reliability, a hybrid VINS system
incorporating multiple error compensation (Multi-ML Hybrid VIO) is proposed that
simultaneously addresses and mitigates visual failure mode effects arising from
weather conditions, lighting effects, low-texture environments and flight dynamics
to ensure integrity. Furthermore, uncertainty prediction for visual failure modes is
proposed to address aleatoric and epistemic uncertainty, providing compensation
for accumulated errors that improve accuracy and robustness. The evaluation of
different hybrid architectures proposed during this study indicates that Multi-ML
Hybrid Visual Inertial Navigation Systems (VIO) solution outperforms in mitigating
visual failure modes and addressing various sources of visual uncertainty,
ensuring navigation integrity. Training and testing results obtained from simulated
complex urban prototypes demonstrate the remarkable performance of the
proposed solution under diverse visually degraded scenarios and its seamless
integration within the multi-sensor navigation system during GNSS outage.
Overall, the findings in this thesis demonstrate a robust and reliable vision-based
alternative navigation solution tailored for UAM applications.PhD in Aerospac
Drones identification and classification using fingerprints in spectrograms
No full textThe rapid proliferation of drones and Wi-Fienabled devices has revolutionized various sectors, including agriculture, entertainment, security, and surveillance. However, this also has magnified the threat space in terms of security, privacy, and efficient spectrum management. Detecting and classifying these devices accurately is crucial to address potential threats to public safety. To alleviate this issue, this paper proposes an advanced signal classification framework to identify drones base on their unique fingerprint. This is done by using spectrogram images of different drones and Wi-Fi devices operating within the 2.4 GHz spectrum which give unique patterns to identify drones fingerprint. The approach combines the features generated by Principal Component Analysis (PCA) with a modulation index to enhance classification accuracy and robustness of different machine learning classifiers. Two tasks are considered in this paper: i) multi-class classification of different drone models and ii) binary classification of drones and Wi-Fi signals. The proposed framework is rigorously tested and challenged using different hyperparameters configurations and ablation studies. The results demonstrate the robustness of the proposed approach in identifying drones accurately.This work was supported by the Engineering and Physical Sciences Research Council under Research Grants EP/X040518/1 and EP/Y037421/12025 11th International Conference on Control, Decision and Information Technologies (CoDIT
Enhancing vortex-flow-meter precision using physics-informed contrastive learning
No full textHigh precision vortex-based flow measurement devices are subject to systematic measurement errors caused by installation effects and complex flow conditions that cannot always be directly measured or compensated. Correcting such systematic measurement errors is crucial for achieving a yet higher measurement accuracy and reliability. In this work we introduce a hybrid framework for error correction of vortex flow meters. The method uses physically-engineered features derived from computational fluid dynamics (CFD) simulations as inputs to a deep contrastive regression neural network. The network learns latent representations that are useful for predicting measurement errors under various pipe geometries and flow regimes. We demonstrate the effectiveness of this representation learning by testing the prediction performance of the framework under new pipe geometries not seen during training. The method demonstrates the potential of advanced deep learning models to extract physically meaningful features for error prediction tasks in complex, highly non-linear flow setups, in which CFD simulations reach their computational limits.The authors would like to express their gratitude to Endress+Hauser Flow and Endress+Hauser InfoServe for supporting the study.Flow Measurement and Instrumentatio
Wind-powered locomotion mechanism for a wandering robot exploring Titan
No full text© The AuthorsThis paper proposes a wind-powered locomotion mechanism design for wandering robots on Titan, WANDER-Bot. Existing planetary robots use a substantial amount of their power budget for locomotion. Typical power sources include RTGs and solar cells, which experience performance degradation in their power output over time, and are not suitable for replacement or repair. Composed of a Savonius wind turbine mechanism, reduction gearbox mechanism, and Jansen linkage walking mechanism, the proposed wandering robot locomotion design addresses these limitations by using wind energy, and simple mechanical links designed with consideration of ISRU manufacturing. The WANDER-Bot lab prototype takes a low-cost, low-storage, low-power approach to the design and locomotion. Performance analysis is conducted on the robot components in replicated Titan conditions.This work is supported by the Engineering and Physical Sciences Research Council UK-RAS Network and Research Activities Funding EP/Y010523/1 and MSc in Astronautics and Space Engineering, Cranfield University.18th Symposium on Advanced Space Technologies in Robotics and Automation (ASTRA
Supply chain resilience and sustainable food systems - comparative Insights from Kazakhstan and Uzbekistan
No full textThis thesis explores how supply chain resilience affects the possibility of sustainable food systems in Kazakhstan and Uzbekistan. Drawing from policy analysis and expert interviews, the study examines risks, barriers, and enabling factors for food security in two landlocked economies affected by climate change. Particular attention is given to institutional responses toward irrigation management, border and trade corridor reliability and post-harvest logistics, which remain key chokepoints for regional agri-food systems. The study also highlights how the quality of governance, subsidy policy and design, and digitalisation initiatives affect predictability in supply chains, often more than resource endowment itself. While both countries have committed to ambitious agricultural reform programs, significant gaps remain between strategic vision and implementation. The study argues that improving cross-border coordination, infrastructure services, and smallholder inclusion is necessary for more sustainable and resilient food systems. By placing these dynamics into the context of food security and agricultural policy reforms, which are already of concern to Central Asian governments, the study will also generate evidence relevant to national reform agendas and regional cooperation.MSc in Food Systems and Managemen