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Several animal species use tools for foraging; however, very few manufacture and/or modify those tools. Humpback whales, which manufacture bubble-net tools while foraging, are among these rare species. Using animal-borne tag and unoccupied aerial system technologies, we examine bubble-nets manufactured by solitary humpback whales (Megaptera novaeangliae) in Southeast Alaska while feeding on krill. We demonstrate that the nets consist of internally tangential rings and suggest that whales actively control the number of rings in a net, net size and depth and the horizontal spacing between neighbouring bubbles. We argue that whales regulate these net structural elements to increase per-lunge prey intake by, on average, sevenfold. We measured breath rate and swimming and lunge kinematics to show that the resulting increase in prey density does not increase energetic expenditure. Our results provide a novel insight into how bubble-net tools manufactured by solitary foraging humpback whales act to increase foraging efficiency.

This study presents a pioneering solution to the growing challenge of escalating global crime rates through the introduction of an automated drone-based street crime detection system. Leveraging advanced Convolutional Neural Network (CNN) models, the system integrates several key components for analyzing images captured by drones. Initially, the Embedding Bilateral Filter (EBF) technique divides images into base and detail layers to enhance detection accuracy. The fusion model, IR with attention-based Conv-ViT, combines Inception-V3, ResNet-50, and Convolution Vision Transformer (Conv-ViT) to capture both shape and texture details efficiently. Further enhancement is achieved through the Improved Shark Smell Optimization Algorithm (ISSOA), which optimizes feature selection and minimizes redundancy in image extraction. Additionally, a Multi-scale Contextual Semantic Guidance Network (MCS-GNet) ensures robust image classification by integrating features from multiple layers to prevent data loss. Evaluation on the UCF-Crime and UCSD Ped2 datasets demonstrates superior accuracy, with remarkable results of 0.783 and 0.974, respectively. This innovative approach offers a promising solution to the arduous and continuous task of monitoring security camera feeds for suspicious activities, thereby addressing the pressing need for automated crime detection systems on a global scale. [ABSTRACT FROM AUTHOR]

The use of drones in logistics transportation has significantly increased recently. One application involves transporting loads from one location to a different location. For larger loads, multiple drones are employed to carry the load collectively. This study introduces a single approach by relaxing the problem formulation and dual stage heuristic approach for dual-type multi-drone systems. Comparative analysis with a simulated annealing (SA) algorithm demonstrates that our single stage (SS) and dual stage (DS) approaches yield satisfactory results with minimal deviation from SA, highlighting SS's superior computational efficiency. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) technology is rapidly advancing, offering innovative solutions for various industries, including the critical task of oil and gas pipeline surveillance. However, the limited flight time of conventional UAVs presents a significant challenge to comprehensive and continuous monitoring, which is crucial for maintaining the integrity of pipeline infrastructure. This review paper evaluates methods for extending UAV flight endurance, focusing on their potential application in pipeline inspection. Through an extensive literature review, this study identifies the latest advancements in UAV technology, evaluates their effectiveness, and highlights the existing gaps in achieving prolonged flight operations. Advanced techniques, including artificial intelligence (AI), machine learning (ML), and deep learning (DL), are reviewed for their roles in pipeline monitoring. Notably, DL algorithms like You Only Look Once (YOLO) are explored for autonomous flight in UAV-based inspections, real-time defect detection, such as cracks, corrosion, and leaks, enhancing reliability and accuracy. A vital aspect of this research is the proposed deployment of a hybrid drone design combining lighter-than-air (LTA) and heavier-than-air (HTA) principles, achieving a balance of endurance and maneuverability. LTA vehicles utilize buoyancy to reduce energy consumption, thereby extending flight durations. The paper details the methodology for designing LTA vehicles, presenting an analysis of design parameters that align with the requirements for effective pipeline surveillance. The ongoing work is currently at Technology Readiness Level (TRL) 4, where key components have been validated in laboratory conditions, with fabrication and flight testing planned for the next phase. Initial design analysis indicates that LTA configurations could offer significant advantages in flight endurance compared to traditional UAV designs. These findings lay the groundwork for future fabrication and testing phases, which will be critical in validating and assessing the proposed approach's real-world applicability. By outlining the technical complexities and proposing specialized techniques tailored for pipeline monitoring, this paper provides a foundational framework for advancing UAV capabilities in the oil and gas sector. Researchers and industry practitioners can use this roadmap to further develop UAV-enabled surveillance solutions, aiming to improve the reliability, efficiency, and safety of pipeline monitoring. [ABSTRACT FROM AUTHOR]

Context: Forest traits and characteristics are challenging to measure across ecosystems with traditional field methods. There is a ripe opportunity for unoccupied aerial systems (UAS) to contribute to landscape ecology through mapping forest traits and characteristics and linking scales between ground surveys and airborne/spaceborne remote sensing. Objectives: We consider the unique perspective of UAS in forests and the considerations that come with working with an emerging technology. Methods: We performed a literature review of which forest traits and characteristics have been derived from UAS and dive into a case example of how researchers derive a particular trait, aboveground carbon stock, from UAS-based data. Results: UAS are most useful and cost-effective in contexts where high resolution data are required across a limited spatial extent. Due to the high spatial resolution and ability to fly close to top-of-canopy, UAS excel at measuring morphological and physiological characteristics, like canopy structure and foliar chemical traits. Combining spectral and structural information can be done particularly easily with UAS data and enhances aboveground carbon estimation from UAS. UAS-based lidar is best for measuring forest structural attributes, but RGB imagery with post-processing is an acceptable alternative for a tight budget. Conclusions: UAS contribute to landscape ecology through measuring forest traits and characteristics in novel ways. We need better metadata and validation reporting and method standardization to improve reproducibility and comparison across UAS forest studies. This review is written for ecologists interested in measuring forests at a landscape scale, and particularly for researchers interested in adding UAS to their toolkit. [ABSTRACT FROM AUTHOR]

Novel technologies with military applications are often implicitly viewed as significant threats to strategic stability. Yet, some implementations of emerging technologies may bolster defenses, mitigate the security dilemma, or both. In particular, this is more likely to happen if diffusion of the technology is combined with arms control that focuses on qualities rather than quantities of weapons. In this paper, we focus on four emerging militarized technologies (cyber weapons, hypersonic glide vehicles, armed drones, and lethal autonomous weapons systems) and evaluate their effects on the relative advantage of the defense and distinguishability, finding that all four have the potential to contribute to strategic stability. [ABSTRACT FROM AUTHOR]

The expansion of drone-based aerial imagery has facilitated an increase in data obtained from free-ranging marine mammal populations, in particular cetacean species. This non-invasive approach allows for body condition assessments, including nutritional and reproductive health. Yet, existing methods of image analysis are time-consuming and lack the granularity to determine early-stage pregnancies and miscarriage rates. In this study, we leveraged a four-year dataset of drone-based aerial imagery paired with known reproductive statuses (i.e., non-pregnant, early-stage pregnant, late-stage pregnant, and lactating) for killer whales (Orcinus orca) to develop a geometric morphometric-based protocol for detecting reproductive status. We demonstrate the significant separation of resulting shapefiles related to reproductive status between all statuses apart from lactating. This approach reliably detects early-stage pregnancy and highlights the morphological locations of major shape changes during the lactation period. We illustrate the applicability of our geometric morphometric protocol for rapid, robust determination of reproductive status in a free-ranging cetacean species. This work helps to satisfy the need for universal tools for non-invasively gleaning population demographic data from free-ranging cetaceans especially of populations which are experiencing prey-related reproductive failures, to understand miscarriage rates and trigger subsequential conservation actions. [ABSTRACT FROM AUTHOR]

Metropolitan areas have diverse land uses (LUs), which can also cause significant differences in land surface temperature (LST), leading to the formation of micro-urban heat islands (MUHIs). Measuring the MUHIs is significant for heat mitiga-tion and adaptation measures and requires high spatial-temporal resolution, which is not feasible through coarser satellite observations (CSOs). Thermal cameras onboard unmanned aerial vehicles (UAVs) can detect such MUHIs because of their high spatial and desired temporal resolution. This study used the Zenmuse H20T onboard a UAV providing LST at ∼8 cm resolution to evaluate MUHIs in an area with diverse and contiguous LUs including three urban built-up LUs: 1) residential high cost (RHC), 2) residential low cost (RLC), 3) industrial area (IA) and one natural area (i.e. park area (PA)). The LST and MUHI were estimated in two seasons: fall (October 2022) and summer (June-July 2023). In each season, six flights were conducted at similar times of day. The findings were compared with Landsat in each season to examine the loss of information between coarser and finer spatial resolution. Using UAV, a maximum MUHI of 25.54◦C and 15.85◦C was identified in the summer and fall seasons, respectively, between 15:30 and 16:20. The maximum LST was observed in RHC, and PA showed the minimum LST in both seasons. Notably, dark-coloured roofs with asphalt shingle coating reported up to 25.78◦C and 27.37◦C higher LST (UAV-estimated) than light-coloured roofs in the fall and summer, respectively. Landsat significantly underestimated MUHI hotspots in the summer and fall seasons. The on-ground validation of the UAV showed better results in the summer season. The study shows the pragmatic use of UAVs to detect localized MUHIs. The findings are useful to devise strategies to mitigate MUHIs utilizing UAVs in the face of climatic and environmental changes. [ABSTRACT FROM AUTHOR]

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure's health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide, with a low survival rate of around 7% globally. Key factors for improving survival include witnessed arrest, bystander cardiopulmonary resuscitation (CPR), and early defibrillation. Despite guidelines advocating for the "chain of survival", bystander CPR and defibrillation rates remain suboptimal. Innovative approaches, such as dispatcher-assisted CPR (DA-CPR) and smartphone-based alerts, have emerged to address these challenges. DA-CPR effectively transforms emergency callers into lay rescuers, and smartphone apps are increasingly being used to alert volunteer first responders to OHCA incidents, enhancing response times and increasing survival rates. Smartphone-based systems offer advantages over traditional text messaging by providing real-time guidance and automated external defibrillator (AED) locations. Studies show improved outcomes with app-based alerts, including higher rates of early CPR, increased survival rates and improved neurological outcomes. Additionally, the potential of unmanned aerial vehicles (drones) to deliver AEDs rapidly to OHCA sites has been demonstrated, particularly in rural areas with extended emergency medical services response times. Despite technological advancements, challenges such as ensuring responder training, effective dispatching, and maintaining responder well-being, particularly during the coronavirus disease 19 (COVID19) pandemic, remain. During the pandemic, some community first responder programs were suspended or modified due to shortages of personal protective equipment (PPE) and increased risks of infection. However, systems that adapted by using PPE and revising protocols generally maintained responder participation and effectiveness. Moving forward, integrating new technology within robust responder systems and support mechanisms will be essential to improving OHCA outcomes and sustaining effective response networks. [ABSTRACT FROM AUTHOR]