Late Holocene vegetation dynamics, hydrological change, and fire history on the Seward Peninsula, Arctic Alaska

Recent climate change has significantly impacted Arctic ecosystems, with peatlands being particularly sensitive to shifts in hydrology. A widespread deepening of the water table due to permafrost thaw has driven substantial changes in vegetation composition, accelerated organic matter decomposition, increased carbon emissions, and increased fire activity. However, the complexity of local drivers means that detailed studies on potential peatland trajectories remain limited. To address this gap, we conducted high-resolution, multi-proxy palaeoecological analyses—including plant macrofossils, pollen, testate amoebae, macro- and microcharcoal, and peat stoichiometry—on two radiocarbon-dated peat sequences from Seward Peninsula, Arctic Alaska. Our findings indicate that a deeper water table in recent decades has altered dominant peat-forming species, promoting shrub expansion and a shift from sedge dominance to Sphagnum , brown mosses, and lichens. This water table drop, likely driven by permafrost thaw, has restricted further peat accumulation and led to organic layer degradation. We also document a strong link between increased fire activity following the Little Ice Age—particularly in the second half of 20th century—and periods of deep water tables. Overall, our multi-proxy approach demonstrates that peatlands in this Alaskan region have diverged from the predominantly wet conditions that prevailed before 1850 CE. These hydrological shifts have not only altered plant composition and peat formation but also diminished the ecosystem's carbon storage capacity.</p

Pancreatic cancer education: a scoping review of evidence across patients, professionals and the public

Background: Pancreatic cancer is the least survivable malignancy, with five-year survival below 10%. Its vague, non-specific symptoms contribute to late diagnosis and poor outcomes. Targeted education for healthcare professionals, students, patients, carers, and the public may improve awareness, confidence, and early help-seeking. This scoping review aimed to map and synthesize peer-reviewed evidence on pancreatic cancer education, identifying intervention types, outcomes, and gaps in knowledge. Methods: A scoping review was undertaken using the Joanna Briggs Institute (JBI) framework and the Arksey and O’Malley framework and reported in accordance with PRISMA-ScR guidelines. The protocol was registered on the Open Science Framework. Four databases (MEDLINE, Embase, CINAHL, PsycINFO) were searched for English-language, peer-reviewed studies evaluating educational interventions on pancreatic cancer for healthcare students, professionals, patients, carers, or the public. Grey literature was excluded to maintain a consistent methodological standard. Data were charted and synthesised narratively. Results: Nine studies (2018–2024) met inclusion criteria, predominantly from high-income countries. Interventions targeted students and professionals (n = 3), patients (n = 2), the public (n = 2), or mixed groups (n = 2), using modalities such as team-based learning, workshops, virtual reality, serious games, and digital animations. Four interrelated themes were identified, encompassing (1) Self-efficacy; (2) Knowledge; (3) Behavior; and (4) Acceptability. Digital and interactive approaches demonstrated particularly strong engagement and learning gains. Conclusions: Pancreatic cancer education shows clear potential to enhance knowledge, confidence, and engagement across diverse audiences. Digital platforms offer scalable opportunities but require quality assurance and long-term evaluation to sustain impact. The evidence base remains limited and fragmented, highlighting the need for validated outcome measures, longitudinal research, and greater international representation to support the integration of education into a global pancreatic cancer control strategy. Future studies should also evaluate how educational interventions influence clinical practice and real-world help-seeking behaviour

A lightweight learning-based approach for online edge-to-cloud service placement

The integration of edge and cloud computing is critical for resource-intensive applications which require low-latency communication, high reliability, and efficient resource utilisation. The service placement problem in these environments poses significant challenges owing to dynamic network conditions, heterogeneous resource availability, and the necessity for real-time decision-making. Because determining an optimal service placement in such networks is an NP-complete problem, the existing solutions rely on fast but suboptimal heuristics or computationally intensive metaheuristics. Neither approach meets the real-time demands of online scenarios, owing to its inefficiency or high computational overhead. In this study, we propose a lightweight learning-based approach for the online placement of services with multi-version components in edge-to-cloud computing. The proposed approach utilises a Shallow Neural Network (SNN) with both weight and power coefficients optimised using a Genetic Algorithm (GA). The use of an SNN ensures low computational overhead during the training phase and almost instant inference when deployed, making it well suited for real-time and online service placement in edge-to-cloud environments where rapid decision-making is crucial. The proposed method (SNN-GA) is specifically evaluated in AR/VR-based remote repair and maintenance scenarios, developed in collaboration with our industrial partner, and demonstrated robust performance and scalability across a wide range of problem sizes. The experimental results show that SNN-GA reduces the service response time by up to 27% compared to metaheuristics and 55% compared to heuristics at larger scales. It also achieves over 95% platform reliability, outperforming heuristics (which remain below 85%) and metaheuristics (which decrease to 90% at larger scales)

Techniques and metrics for evasion attack mitigation

Evasion attacks pose a substantial risk to the application of Machine Learning (ML) in Cybersecurity, potentially leading to safety hazards or security breaches in large-scale deployments. Adversaries can employ evasion attacks as an initial tactic to deceive malware or network scanners using ML, thereby orchestrating traditional cyber attacks to disrupt systems availability or compromise integrity. Adversarial data designed to fool AI systems for cybersecurity can be engineered by strategically selecting, modifying, or creating test instances. This paper presents novel defender-centric techniques and metrics for mitigating evasion attacks by leveraging adversarial knowledge, exploring potential exploitation methods, and enhancing alarm detection capabilities. We first introduce two new evasion resistance metrics: adversarial failure rate (afr) and adversarial failure curves (afc). These metrics generalize previous approaches, as they can be applied to threshold classifiers, facilitating analyses for adversarial attacks comparable to those performed with Receiver Operating Characteristics (ROC) curve. Subsequently, we propose two novel evasion resistance techniques (trainset size pinning and model matrix), extending research in keyed intrusion detection and randomization. We explore the application of proposed techniques and metrics to an intrusion detection system as a pilot study using two public datasets, ‘BETH 2021’ and ‘Kyoto 2015’, which are well-established cybersecurity datasets for uncertainty and robustness benchmarking. The experimental results demonstrate that the combination of the proposed randomization techniques consistently produces remarkable improvement over other known randomization techniques.<br/

Life cycle assessment of a wave cycloidal rotor: environmental performance and improvement pathways

Wave energy technology needs to be reliable, efficient, and environmentally sustainable. Therefore, life cycle assessment (LCA) is a critical tool in the design of marine renewable energy devices. However, LCA studies of floating type wave cycloidal rotors remain limited. This study builds on previous work by assessing the cradle-to-grave environmental impacts of a cycloidal rotor wave farm, incorporating updated material inventories, site-dependent energy production, and lifetime extension scenarios. The farm with the steel cyclorotor configuration exhibits a carbon intensity of 21.4 g CO2 eq/kWh and an energy intensity of 344 kJ/kWh, which makes it a competitive technology compared to other wave energy converters. Alternative materials, such as aluminium and carbon fibre, yield mass reductions but incur higher embodied emissions. Site deployment strongly influences performance, with global warming potential reduced by up to 50% in high-power-density sites, while extending the operational lifetime from 25 to 30 years further reduces the impact by 17%. Overall, the results highlight the competitive environmental performance of floating wave cycloidal rotors and emphasize the importance of material selection, site selection, and lifetime extension strategies in reducing life cycle impacts