38139 research outputs found
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UAV-Based Visual Detection and Tracking of Drowning Victims in Maritime Rescue Operations
No full textMaritime search and rescue (SAR) operations are challenged by vast search areas, poor visibility, and the time-critical nature of victim survival, particularly in dynamic coastal areas. This study presents an intelligent unmanned aerial vehicle (UAV) framework for real-time detection, tracking, and prioritization of people in distress at sea. Unlike existing UAV-based SAR systems that rely on visual sensing or offline human intervention, the proposed framework integrates RGB-thermal multimodal sensing and posture recognition to enhance victim prioritization and survivability estimation. Visual-thermal data support human posture detection, inference of physiological indicators, and autonomous UAV navigation. Metadata are transmitted to a ground control station to enable adaptive altitude control, trajectory rejoining, and multi-target prioritization. Field-inspired experiments in Quang Ninh Province, Vietnam demonstrated robust real-time performance, achieving 23 FPS with detection accuracy up to 84% for swimming subjects and over 50% for drowning postures. These findings demonstrate that Edge-AI-enabled UAVs can serve as a practical and efficient solution for maritime SAR, reducing response times and improving mission outcomes
Rational design of perlite-bauxite-based low-density ceramic proppants for hydraulic fracturing
No full textTo develop lightweight ceramic proppants and to promote the high-value utilization of two different forms of perlite, this study employed raw perlite and expanded perlite as partial substitutes for bauxite and fabricated low-density proppants via disk granulation followed by high-temperature sintering. The effects of perlite type, dosage (5–20 wt%), and sintering temperature (1400–1500 °C) on the microstructure and properties were systematically investigated. The results reveal that a moderate addition of raw perlite and expanded perlite significantly promotes mullite formation and reduces proppant density. However, excessive addition increases porosity, decreases the aspect ratio of rod-like mullite crystals, and induces the formation of ellipsoidal mullite, thereby elevating the breakage ratio. The optimized expanded perlite proppants sintered at 1450 °C exhibited the optimal overall performance, with a bulk density of 1.46 ± 0.01 g/cm3 and an acid solubility of 5.98 ± 0.09 %. The breakage ratio of 8.21 ± 0.56 % under the industry-standard closure stress of 35 MPa, meeting the requirement of SY/T 51088–2014 (breakage ratio < 9 %). At a higher closure stress of 41.4 MPa, the proppants achieve a conductivity of 29.435 µm2·cm. Notably, Fe3+ incorporation into the mullite structure enhanced structural stability, thereby improving mechanical performance. This work demonstrates a sustainable strategy for fabricating low-density ceramic proppants and highlights the potential of perlite-based materials as an environmentally friendly substitute for bauxite in hydraulic fracturing applications
eBPF-Guard: a detection method for container escape via multi-level monitoring and enhanced analysis model
No full textIn recent years, cloud-native technologies have rapidly penetrated containerized environments. Their lightweight, flexible, and portable features have made them highly popular among developers. However, the extensive use of containers has also made them a prime target for network attacks, with container vulnerabilities and dangerous mounts frequently leading to container escapes. To address this, this paper proposes a new method that combines eBPF-based multi-level container behavior monitoring with LLM-based anomaly detection. Probes are deployed in the system kernel to conduct multi-level monitoring of container system activities, call features, and behavior logs. The strong semantic understanding and pattern-recognition capabilities of LLMs are utilized to uncover hidden features and abnormal behaviors in the data, enabling precise detection of container issues. Specifically, lightweight eBPF probes are deployed in Linux kernel Namespaces and Cgroups. By parsing node identifiers (Node IDs) in Cgroup hierarchy management and the process isolation features of PID/UID Namespaces, a monitoring chain for cross-host container interactions is constructed. This enables non-intrusive collection of file operations, system calls, and network traffic. During data processing, a dual-window partitioning mechanism and a feature extraction framework based on event type distribution and time-series dependencies are employed. In the behavior analysis layer, we designed a three-layer Chain-of-Thought (CoT) prompt template ("Question-Reasoning-Answer"). Container behavior logs are converted into natural language reasoning chains and embedded into a Q-A-formatted dataset with logical reasoning chains. The Qwen1.5-1.8B-Chat model is fine-tuned using Low-Rank Adaptation (LoRA) technology. Experimental results show that this method performs excellently in container escape anomaly identification scenarios, achieving a detection accuracy of 99.22% in simulated malicious attack scenarios
Post-COVID-19 recovery and resilience in passenger and cargo traffic: A Bayesian vector autoregressive analysis of India’s top 10 busiest airports
No full textThis study examines the post-COVID-19 resilience of India’s ten busiest airports using passenger and cargo traffic data from 2016 to 2024. A Bayesian vector autoregression (BVAR) model generates counterfactual forecasts, enabling a comparative assessment to classify airports as outperformers, forecast achievers, or underperformers. Beyond performance categorisation, the study investigates the role of airport infrastructure in shaping resilience outcomes through Spearman correlation and ordered logistic regression (OLOGIT) analysis. Results indicate that infrastructure attributes such as cargo terminal availability, runway capacity, and metro connectivity are significantly associated with higher resilience. Airports with stronger and more adaptive infrastructure recovered more effectively from pandemic disruptions. These findings offer actionable insights for infrastructure planning, crisis preparedness, and long-term policy strategies aligned with national initiatives such as the UDAN regional connectivity scheme
American Institute of Aeronautics and Astronautic Scitech Forum 2026
No full textA team of international scientists from Australia, Germany and the US partnered with NASA SCIFLI to undertake observations of the re-entry of the OSIRIS-REx sample return capsule. The team was split across two aircraft, one run by UniSQ-RTI and one by NASA SCIFLI, and three ground stations. The observation was undertaken on the 24th of September 2023 with nearly all locations measuring valuable data of the re-entry. Utilising the data collected by the team, and further data shared by NASA SCIFLI, the capsule re-entry trajectory was reconstructed and compared with predictions. The measured trajectory showed that the capsule entered at nearly 300 m/s faster than predicted. Subsequent atmospheric measurements showed that the upper atmosphere had a particularly low density at the time of re-entry, and a recomputed trajectory using the lower density matched the measurements very well. A range of spectroscopic measurements were also taken. Successful measurements of the oxygen 777 nm line were compared between instruments on the UniSQ-RTI and NASA522 aircraft. A difference in irradiance was measured between the aircraft and stations and cannot yet be explained. Further investigations into the discrepancy are ongoing
Sallow Hal: Care and Concern in the Henriad
No full textHal’s “imitate the sun” speech in William Shakespeare’s 1 Henry IV assured early audiences that the transformation from wanton youth to worthy king would play out just as it had been described in earlier chronicles. Yet Shakespeare uses Hal’s transformation from Hal to Henry V throughout the second to fourth plays of the second tetralogy to stage the contest between two competing models for the transformation and care of the self in the Reformation
Preserving human relevance, as a new social responsibility of business in the AI age
No full textPurpose
This paper aims to contribute to the scholarly debate, ongoing in this and other journals, on the justification and extent of artificial intelligence (AI)-related responsibilities of a variety of segments of society, such as governments and parliaments, scientists, corporations, media and AI users. Among these, business has received less attention, in both academic and political speech, hence this paper’s attempt to decant the content of a principle of corporate social responsibility related to AI.
Design/methodology/approach
This conceptual paper is built on two pillars. Placing the discussion in a framework of corporate social responsibility, this paper first argues that in the AI age, the list of corporate social responsibility (CSR) principles should be updated to include one relevant to AI development and deployment. Second, this study looks at the possible content of a new CSR principle.
Findings
Born from and still permeated by ethical principles, CSR principles evolve in time, reflecting contemporary societal priorities. If we define CSR as the integration of social concerns in corporate decision-making, then preserving the relevance of the human in the age of AI should qualify as a CSR principle. Like other CSR principles (anticorruption, transparency, community engagement, etc.), this would start as voluntary, but could harden in time, if society deems it necessary. Human relevance is more appropriate than human centrality as a CSR principle, despite the latter being referred to as a desideratum in numerous studies, policies and political statements on AI governance.
Originality/value
To the best of the author’s knowledge, this study is the first to demonstrate that in the age of AI, the list of recognized CSR principle should be updated to include an AI-related one. Introducing human relevance, as opposed to human centrality, as the content of such principle is also highly original, challenging current assumptions
