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Males in Hymenopteran societies are understudied in many aspects and it is assumed that they only have a reproductive function. We studied the time budget of male honey bees, drones, using multiple methods. Changes in the activities of animals provide important information on biological clocks and their health. Yet, in nature, these changes are subtle and often unobservable without the development and use of modern technology. During the spring and summer mating season, drones emerge from the hive, perform orientation flights, and search for drone congregation areas for mating. This search may lead drones to return to their colony, drift to other colonies (vectoring diseases and parasites), or simply get lost to predation. In a low percentage of cases, the search is successful, and drones mate and die. Our objective was to describe the activity of Apis mellifera drones during the mating season in Northwestern Argentina using three methods: direct observation, video recording, and radio frequency identification (RFID). The use of RFID tagging allows the tracking of a bee for 24 h but does not reveal the detailed activity of drones. We quantified the average number of drones’ departure and arrival flights and the time outside the hive. All three methods confirmed that drones were mostly active in the afternoon. We found no differences in results between those obtained by direct observation and by video recording. RFID technology enabled us to discover previously unknown drone behavior such as activity at dawn and during the morning. We also discovered that drones may stay inside the hive for many days, even after initiation of search flights (up to four days). Likewise, we observed drones to leave the hive for several days to return later (up to three days). The three methods were complementary and should be considered for the study of bee drone activity, which may be associated with the diverse factors influencing hive health.

Background Medical transportation is an essential step in health care services, and includes ground, air and water transportation. Among the important uses of medical transportation is the delivery of blood products in the event of a clinical emergency. Drone technology is the latest technological advancement that may revolutionize medical transportation globally. Nonetheless, its economic evaluation is scant and insufficient, whilst its cost-effectiveness remains controversial. The aim of this study was to compare the cost-effectiveness of drone transportation versus the ambulance. Methods The setting of the study was within a developing nation. An economic evaluation study of drone versus ambulance for emergency blood products transportation between the Sabah Women and Children Hospital (SWACH) and the Queen Elizabeth II Hospital (QEH2) on Borneo Island was conducted using the Cost-Effectiveness Analysis (CEA) technique. The total cost of each mode of transportation was calculated using the Activity Based Costing (ABC) method. Travel time was used as a denominator to estimate the Incremental Cost Effectiveness Ratio (ICER). Results For one clinical emergency in SWACH, a round trip of blood products transportation from SWACH to QEH2 costs RM1,266.02 (USD307.09) when using the ambulance, while the drone costs RM1,313.28 (USD319.36). The travel time for the drone was much shorter (18 min) compared to the ambulance (34 min). The Cost-Effectiveness Ratio (CER) of ambulance transportation was RM37.23 (USD9.05) per minute whilst the CER of drone transportation was RM72.96 (USD17.74) per minute. The ICER of drone versus ambulance was − 2.95, implying an increase of RM2.95 in cost for every minute saved using a drone instead of an ambulance. Conclusion Although drone transportation of blood products costs more per minute compared to the ambulance, the significantly shorter transport time of the drone offset its cost. Thus, we believe there is good economic potential for drone usage for blood products transportation in developing nations particularly if the drone price decreases and its operational lifespan increases. Our limitation of a non-clinical denominator used in this study leads to the recommendation for use of clinical outcomes in future studies.

Unmanned aerial vehicles (UAVs), known as drones, have played a significant role in recent years in creating resilient smart cities. UAVs can be used for a wide range of applications, including emergency response, civil protection, search and rescue, and surveillance, thanks to their high mobility and reasonable price. Automatic recognition of human activity in aerial videos captured by drones is critical for various tasks for these applications. However, this is difficult due to many factors specific to aerial views, including camera motion, vibration, low resolution, background clutter, lighting conditions, and variations in view. Although deep learning approaches have demonstrated their effectiveness in a variety of challenging vision tasks, they require either a large number of labelled aerial videos for training or a dataset with balanced classes, both of which can be difficult to obtain. To address these challenges, a hybrid data augmentation method is proposed which combines data transformation with the Wasserstein Generative Adversarial Network (GAN)-based feature augmentation method. In particular, we apply the basic transformation methods to increase the amount of video in the database. A Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model is used to learn the spatio-temporal dynamics of actions, then a GAN-based technique is applied to generate synthetic CNN-LSTM features conditioned on action classes which provide a high discriminative spatio-temporal features. We tested our model on the YouTube aerial database, demonstrating encouraging results that surpass those of previous state-of-the-art works, including an accuracy rate of 97.83%.

The use of drones for triage in mass-casualty incidents has recently emerged as a promising technology. However, there is no triage system specifically adapted to a remote usage. Our study aimed to develop a remote triage procedure using drones. The research was performed in three stages: literature review, the development of a remote triage algorithm using drones and evaluation of the algorithm by experts. Qualitative synthesis and the calculation of content validity ratios were done to achieve the Aerial Remote Triage System. This algorithm assesses (in this order): major bleeding, walking, consciousness and signs of life; and then classify the injured people into several priority categories: priority 1 (red), priority 2 (yellow), priority 3 (green) and priority * (violet). It includes the possibility to indicate save-living interventions to injured people and bystanders, like the compression of bleeding injuries or the adoption of the recovery position. The Aerial Remote Triage System may be a useful way to perform triage by drone in complex emergencies when it is difficult to access to the scene due to physical, chemical or biological risks.

Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images. The YOLO (You Only Look Once) algorithm detects and extracts humans from each frame, obtaining their skeletons for further processing. The joint angles, displacement and velocity, histogram of oriented gradients (HOG), 3D points, and geodesic Distance are included. These features are optimized using Quadratic Discriminant Analysis (QDA) and utilized in a Neuro-Fuzzy Classifier (NFC) for activity classification. Real-world evaluations on the Drone-Action, Unmanned Aerial Vehicle (UAV)-Gesture, and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods. In particular, the system obtains recognition rates of 93% for drone action, 97% for UAV gestures, and 81% for Okutama-action, demonstrating the system’s reliability and ability to learn human activity from drone videos.

Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios from environmental monitoring to search and rescue. It faces a number of difficulties mainly due to image acquisition and contents, and processing constraints. Since drones’ flying conditions constrain image acquisition, human subjects may appear in images at variable scales, orientations, and occlusion, which makes action recognition more difficult. We explore low-resource methods for ML (machine learning)-based action recognition using a previously collected real-world dataset (the “Okutama-Action” dataset). This dataset contains representative situations for action recognition, yet is controlled for image acquisition parameters such as camera angle or flight altitude. We investigate a combination of object recognition and classifier techniques to support single-image action identification. Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and evaluate the performance of our method on Okutama-Action dataset. Our approach outperformed previous architectures applied to the Okutama dataset, which differed by their object identification and classification pipeline: we hypothesize that this is a consequence of both YoloV5 performance and the overall adequacy of our pipeline to the specificities of the Okutama dataset in terms of bias–variance tradeoff.

Unmanned Aerial Vehicles (UAVs), or drones, have recently emerged as a relatively affordable and accessible method for studying wildlife. Vertical Take-off and Landing (VTOL) UAVs are appropriate for morphometric, behavioural, abundance and demographic studies of marine mammals, providing a stable, nonintrusive and highly manoeuvrable platform. Previous studies using VTOL UAVs have been conducted on various marine mammal species, but specific studies regarding behavioural responses to these devices are limited and scarce. The aim of this study was to evaluate the immediate behavioural responses of common (Delphinus delphis) and bottlenose (Tursiops truncatus) dolphins to a VTOL UAV flown at different altitudes. A multirotor (quadcopter) UAV with an attached GoPro camera was used. Once a dolphin group was located, the UAV was flown at a starting height of 50 m directly above the group, subsequently descending 5 m every 30 s until reaching 5 m. We assessed three behavioural responses to a VTOL UAV at different heights: (i) direction changes, (ii) swimming speed and (iii) diving. Responses by D. delphis (n = 15) and T. truncatus (n = 10) groups were analysed separately. There were no significant responses of T. truncatus to any of the studied variables. For D. delphis, however, there were statistically significant changes in direction when the UAV was flown at a height of 5 m. Our results indicate that UAVs do not induce immediate behavioural responses in common or bottlenose dolphins when flown at heights > 5 m, demonstrating that the use of VTOL UAVs to study dolphins has minimal impact on the animals. However, we advise the use of the precautionary principle when interpreting these results as characteristics of this study site (e.g., high whale-watching activity) may have habituated dolphins to anthropogenic disturbance.

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has become a useful tool for acquiring high-resolution topographic data, especially in active tectonics studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements, aiding in the understanding of fault zones, among other applications. Despite its effectiveness, challenges persist in regions with rapid deformation, dense vegetation, and human impact. We propose an adapted workflow transitioning from the conventional airborne LiDAR system to the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally, drones offer a more cost-effective solution, both in an initial investment and ongoing operational expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area, north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing elevation datasets for the same area. We discuss the advantages and limitations of each DTM product in relation to our results, particularly when applied to fault mapping. By analyzing derivative DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features allowed us to identify the primary factors influencing the current morphological arrangement of the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is difficult to identify due to vegetation cover and inaccessibility.

The idea of deploying electric vehicles and unmanned aerial vehicles (UAVs), also known as drones, to deliver packages in logistics operations has attracted increasing attention in the past few years. In this paper, we propose an innovative problem where a battery electric vehicle (BEV) paired with drone is utilized to deliver first-aid items in a rural area. This problem is termed battery electric vehicle traveling salesman problem with drone (BEVTSPD). In BEVTSPD, the BEV and the drone perform delivery tasks coordinately while the BEV can serve as a drone hub. The BEV can also refresh its battery energy to full capacity in battery-swap stations available in the network. An arc-based mixed-integer programming model defined in a multigraph is presented for BEVTSPD. An exact branch-and-price (BP) algorithm and a Variable Neighborhood Search (VNS) heuristic are developed to solve instances with up to 25 customers in one minute. Numerical experiments show that the heuristic is much more efficient than solving the arc-based model using the ILOG CPLEX solver and BP algorithm. A real-world case study and the sensitivity analysis of different parameters are also conducted and presented. The results indicate that drone speed has a more significant effect on delivery time than the BEV’s driving range.

Active and healthy aging is in great demand in an aging society. Maintaining a high level of physical function is a key aspect of healthy aging. However, many older adults engage in low levels of exercise and physical activity, often due to limited motivation or access to proper guidance. Here, we demonstrate a novel home-based exercise guidance system named Pei-Wo Drone , which assists users perform exercise or physical movements effectively in limited space. By following the drone’s trajectory and receiving real-time feedback, users can ensure correct movements and make necessary adjustments. The system provides real-time continuous sound feedback to notify the users if they deviate from the drone’s path. The study results indicate that older adult participants (12 females, 3 males; 67.40 ± 5.85 years) successfully followed the drone’s movements. In summary, using a drone with real-time feedback to guide physical exercises has the potential to support healthy aging in older adults.