Search Results (70)

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article

The increasing popularity of drones for Internet of Things (IoT) applications has led to significant research interest in autonomous navigation within unknown and dynamic environments. Researchers are utilizing supervised learning techniques that rely on image datasets to train drones for autonomous navigation, which are typically used for rescue, surveillance, and medical aid delivery. Current datasets lack data that allow drones to navigate in a 3D environment; most of these data are dedicated to self-driving cars or navigation inside buildings. Therefore, this study presents an image dataset for training drones for 3D navigation. We developed an algorithm to capture these data from multiple worlds on the Gazebo simulator using a quadcopter. This dataset includes images of obstacles at various flight altitudes and images of the horizon to assist a drone in flying at an appropriate altitude, which allows it to avoid obstacles and prevents it from flying unnecessarily high. We used deep learning (DL) to develop a model to classify and predict the image types. Eleven experiments performed with the Gazebo simulator using a drone and a convolution neural network (CNN) proved the database’s effectiveness in avoiding different types of obstacles while maintaining an appropriate altitude and the drone’s ability to navigate in a 3D environment. Full article

To address challenges such as large-scale variations, high density of small targets, and the large number of parameters in deep learning-based target detection models, which limit their deployment on UAV platforms with fixed performance and limited computational resources, a lightweight UAV target detection algorithm, YOLO-LSM, is proposed. First, to mitigate the loss of small target information, an Efficient Small Target Detection Layer (ESTDL) is developed, alongside structural improvements to the baseline model to reduce parameters. Second, a Multiscale Lightweight Convolution (MLConv) is designed, and a lightweight feature extraction module, MLCSP, is constructed to enhance the extraction of detailed information. Focaler inner IoU is incorporated to improve bounding box matching and localization, thereby accelerating model convergence. Finally, a novel feature fusion network, DFSPP, is proposed to enhance accuracy by optimizing the selection and adjustment of target scale ranges. Validations on the VisDrone2019 and Tiny Person datasets demonstrate that compared to the benchmark network, the YOLO-LSM achieves a mAP0.5 improvement of 6.9 and 3.5 percentage points, respectively, with a parameter count of 1.9 M, representing a reduction of approximately 72%. Different from previous work on medical detection, this study tailors YOLO-LSM for UAV-based small object detection by introducing targeted improvements in feature extraction, detection heads, and loss functions, achieving better adaptation to aerial scenarios. Full article

Unmanned aerial vehicles (UAVs) are emerging as powerful tools for transporting temperature-sensitive payloads, including medical supplies, biological samples, and research materials, to remote or hard-to-reach locations. Effective thermal management is essential for maintaining payload integrity, especially during extended flights or harsh environmental conditions. This review presents a comprehensive analysis of temperature control mechanisms for UAV payloads, covering both passive and active strategies. Passive systems, such as phase-change materials and high-performance insulation, provide energy-efficient solutions for short-duration flights. In contrast, active systems, including thermoelectric cooling modules and Joule heating elements, offer precise temperature regulation for more demanding applications. We examined case studies that highlight the integration of these technologies in real-world UAV applications, such as vaccine delivery, blood sample transport, and in-flight polymerase chain reaction diagnostics. Additionally, we discussed critical design considerations, including power efficiency, payload capacity, and the impact of thermal management on flight endurance. We then presented an outlook on emerging technologies, such as hybrid power systems and smart feedback control loops, which promise to enhance UAV-based thermal management. This work aimed to guide researchers and practitioners in advancing thermal control technologies, enabling reliable, efficient, and scalable solutions for temperature-sensitive deliveries using UAVs. Full article

Urban air mobility (UAM) is expected to provide environmental benefits while enhancing transportation for citizens and businesses, particularly in commercial and emergency medical applications. The rapid development of electric vertical take-off and landing (eVTOL) aircraft has demonstrated the potential to introduce new technological capabilities to the market, fostering visions of widespread and diverse UAM applications. This paper reviews state-of-the-art occupant safety for personal drones and examines existing occupant protection methods in the aircraft. The study serves as a guide for stakeholders, including regulators, manufacturers, researchers, policymakers, and industry professionals—by providing insights into the regulatory landscape and safety assurance frameworks for eVTOL aircraft in UAM applications. Furthermore, we present a functional hazard assessment (FHA) conducted on a reference concept, detailing the process, decision-making considerations, and key variations. The analysis illustrates the FHA methodology while discussing the trade-offs involved in safety evaluations. Additionally, we provide a summary and a featured description of current eVTOL aircraft, highlighting their key characteristics and technological advancements. Full article

Transport by unmanned aerial systems (UASs) (e.g., drones) could save time and personnel. Our study aimed to assess the effect of drone transportation on the clinical laboratory results of biological samples by examining its impact on pre-analytical and analytical processes. We performed a cross-sectional study of healthy volunteers from Sha’ar Menashe Mental Health Center between July and November 2022. Blood and urine samples were transferred to the central laboratory at Hillel Yaffe Medical Center. Overall, 40 healthcare workers aged 21–67 years (57.5% females) with a mean age of 45.8 (SD = 11.3) years from Sha’ar Menashe Mental Health Center were recruited in the study. There were no significant differences between transportation modes in the complete blood count levels. We found a significant difference between the transportation modes for GGT (p = 0.01) and PT (p = 0.04), despite the very similar mean results of these tests. In Bland–Altman plots, GGT and PT samples fell within the 95% limits of agreement and were indicated as not clinically relevant; however, glucose and LDH did not meet the 95% acceptance criterion and showed a potential clinical effect. There was full agreement between the two types of transportation for urine glucose, nitrites, and urine cultures. UAS transport is an appropriate method for maintaining the quality of most routine clinical laboratory specimens, similar to the routine procedure of using a vehicle. For the 34 biochemistry, hematology, and coagulation assay parameters, only glucose and LDH did not meet the 95% acceptance criterion and showed a potential clinical effect. Full article

This study builds on initial ADApp research that identified the factors that influence the intention to use a pharmacy drone app for urgent medication delivery. While previous studies and theories have predominantly focused on user acceptance alone, the present qualitative study introduced a holistic model that integrates user acceptance theories as well as user-centered design principles and technology features. It focused on the user journey to derive core statements from the development of a drone-based application using a qualitative theory synthesis approach (study 1), and explored the perceived participatory collaboration between developers (software and drone developers) and co-developers (core group participants) using final tandem discussions and a qualitative content analysis method (study 2). Study 1 resulted in the identification of eight categories that serve as technical working goals for future participatory technology development. Study 2 identified five critical factors that provide insight into the unique challenges and goals of collaborative development. Both studies contribute to a better understanding of the essential factors that lead to successful participatory processes between developers and co-developers aimed at increasing usability and intention to use. Based on these findings, an integrated model is presented to support participatory design strategies in healthcare technology development. Full article

At the turn of the 20th century, Christian and Catholic churches in Western nations established numerous mission hospitals in non-European regions. In China, mission hospitals represent a significant category of modern architectural heritage, symbolising advancements in healthcare and medical education while also serving as historical artifacts of early cultural interactions between China and the West. With ongoing developments in medical technology, these mission hospital structures no longer meet contemporary healthcare demands; many have been repurposed or temporarily abandoned. Preserving and effectively repurposing mission hospital heritage has thus emerged as a critical issue. In the present study, the Shantung Christian University Union Medical College was examined as a case study in addressing this challenge. The site retains the original Outpatient Building, Inpatient Building, Medical Teaching Building, and other architectural heritage and has preserved the original mixed Chinese and Western architectural styles. A combination of historical research, field investigation, and historic layering was adopted in the present study, drawing primarily on data from historical maps, satellite images from different periods, aerial photography from drones, architectural drawings, and other relevant historical data. Through case studies, methods for characterising and identifying the landscape and architectural heritage of mission hospitals were explored. Principles for the preservation and regeneration of the heritage of church hospitals were also proposed, with a view to providing a reference for the study and preservation of this type of heritage. Full article

The rapid transportation of blood samples and blood products using drones has high potential in the medical sector. However, before this can be implemented, sufficient evidence that drone transportation is not affecting clinical outcomes is needed. Currently, limited data on the stability of blood, and specifically on platelet activation, after transport using drones are available. Therefore, the impact of drone transportation on platelet activation parameters was analyzed. Blood was drawn from 20 healthy volunteers and lactate dehydrogenase (LDH), potassium, free hemoglobin, PFA-100 closure time, platelet factor 4 (PF4) plasma levels and platelet activation membrane markers were determined in blood that was transported by drone and compared to non-transported samples. In addition, a control group was included where blood samples were transported by car. Transport by both drone and car increased LDH and potassium levels, but the values mostly remained within the total allowable error. Both drone and car transportation impacted platelet activation, as indicated by a small increase in the baseline P-selectin expression and increased PF4 plasma levels. To our knowledge, this is the first study assessing the impact of drone transport on platelet activation. Transportation of blood tubes from healthy individuals using drones has only minimal impacts on blood stability and platelet activation parameters, and is comparable to blood transportation by car. Therefore, the effects observed as a result of drone transportation will likely not impact clinical decision making. Full article

Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of the remaining useful life is essential to ensure device safety and reliability. Conventional RUL prediction methods typically rely on regression analysis, signal processing, and machine learning techniques to assess battery conditions such as charge/discharge cycles, voltage, temperature, and durability. Although effective, these approaches are constrained by their dependence on large amounts of labeled data and the necessity for complex feature engineering to capture battery physical characteristics. In this study, we propose an approach that employs deep transfer learning to address these limitations. By leveraging pretrained model weights, the proposed method significantly improves the efficiency and accuracy of RUL prediction even under limited training data conditions. Furthermore, we investigate the impact of external environmental factors and physical battery characteristics on RUL prediction precision, thereby contributing to a more robust and reliable prediction framework. Full article

Background: The integration of drone technology into healthcare logistics presents a significant opportunity to enhance the speed, reliability, and efficiency of medical sample transportation. Methods: This paper provides a narrative review of current advancements in drone technology, focusing on its application in the rapid and secure delivery of medical samples, particularly in urban and remote regions where traditional transportation methods often face challenges. Drawing from recent studies and case reports, the review highlights the role of technologies such as artificial intelligence (AI)-driven navigation systems, real-time monitoring, and secure payload management in mitigating logistical barriers like traffic congestion and geographical isolation. Results: Based on findings from various case studies, the review demonstrates how drones can significantly reduce transportation time and costs, while improving accessibility to healthcare services in underserved areas. Conclusions: This paper concludes that, while challenges such as regulatory hurdles and privacy concerns remain, ongoing technological advancements and the development of supportive regulatory frameworks have the potential to revolutionize medical logistics, ultimately improving patient outcomes and healthcare delivery. Full article

The integration of unmanned aerial vehicles or uncrewed aerial vehicles (UAVs)—commonly known as drones—into medical logistics offers transformative potential for the transportation of sensitive medical materials, such as blood samples. Traditional car transportation is often hindered by traffic delays, road conditions, and geographic barriers, which can compromise timely delivery. This study provides a comprehensive analysis comparing high-speed drone transportation with traditional car transportation. Blood samples, including EDTA whole blood, serum, lithium-heparin plasma, and citrate plasma tubes, were transported via both methods across temperatures ranging from 4 to 20 degrees Celsius. The integrity of the samples was assessed using a wide array of analytes and statistical analyses, including Passing–Bablok regression and Bland–Altman plots. The results demonstrated that drone transportation maintains blood sample integrity comparable to traditional car transportation. For serum samples, the correlation coefficients (r) ranged from 0.830 to 1.000, and the slopes varied from 0.913 to 1.111, with minor discrepancies in five analytes (total bilirubin, calcium, ferritin, potassium, and sodium). Similar patterns were observed for EDTA, lithium-heparin, and citrate samples, indicating no significant differences between transportation methods. Conclusions: These findings highlight the potential of drones to enhance the efficiency and reliability of medical sample transport, particularly in scenarios requiring rapid and reliable delivery. Drones could significantly improve logistical operations in healthcare by overcoming traditional transportation challenges. Full article

In response to escalating climate change concerns, this study evaluates the ecological impact and efficiency of medical sample transportation using drones, combustion cars, and electric cars across various terrains and weather conditions in Liechtenstein and Switzerland. Through a comparative analysis, we found that combustion cars emit the highest average CO₂ at 159.5 g per kilometer (g/km), while electric cars significantly reduce emissions to an average of 3.43 g/km, representing just 2.15% of the emissions from combustion vehicles. Drones emerged as the most environmentally sustainable option, with an average CO₂ emission of 0.09 g/km, which is only 0.07% of combustion car emissions and 2.6% of electric car emissions. Drones also demonstrated superior transport efficiency, covering routes that were, on average, 17% shorter in flat terrain and 24% shorter in mountainous regions compared to cars. Additionally, drones achieved substantial time savings, ranging from 13% to 80% faster delivery times depending on the terrain and traffic conditions. These findings highlight the potential of drone technology to revolutionize healthcare logistics by significantly reducing carbon footprints, optimizing transport routes, and improving delivery efficiency. Integrating drones into healthcare transportation networks offers a promising pathway toward a more sustainable and resilient healthcare system. Full article

The efficient and timely delivery of pharmaceuticals is critical, particularly in regions with dispersed populations and challenging logistics. Inclement weather often disrupts ground transport, complicating the consistent supply of essential medications. Advanced air mobility (AAM), particularly through the use of drones, presents a promising solution to these logistical challenges by enabling smaller, more frequent deliveries to low density populated places and bypassing traditional transport constraints. This study evaluates the potential benefits of AAM for pharmaceutical transport in North Dakota (ND). The authors developed a comprehensive GIS and optimization framework to identify optimal locations for logistical centers and routes for drone and truck transport. The study introduces a person-years-saved (PYS) metric to rank the potential for AAM deployments to foster healthcare improvements in underserved communities. Moreover, the study found that drone trips were significantly more cost-effective and efficient than truck trips, with trucks being 2.3 times more expensive and having a 2.8 times higher underutilization rate. The study concludes with recommendations for regulatory support and future research to validate and expand the application of AAM in pharmaceutical logistics, contributing to improved healthcare delivery and operational efficiency in often overlooked rural populations. These insights provide a foundation for the practical implementation of AAM technologies, emphasizing their potential to revolutionize pharmaceutical logistics in challenging environments. Full article

The application of drones to assist with healthcare delivery has grown rapidly over the last decade. This industry is supported by a growing research field, and we have undertaken a systematic review of the published literature. Web-based searches returned 290 relevant manuscripts published between 2010 and 2024. We applied Topic Modelling to this corpus of literature, which examines word association and connectedness within the research papers. The modelling identified two emerging research themes with little connection between them: those who used drones to deliver time-critical medical items and those who used drones to deliver non-time-critical medical items. The former was in response to medical emergencies, while the latter was for enhancing resilience in the healthcare supply chain. The topics within these research themes exhibited notable differences. The delivery of time-critical medical items theme comprised the topics of ‘Emergency Response’, ‘Defibrillator and Organ Delivery’, and ‘Search and Rescue’, whilst non-time-critical delivery researched the topics of ‘Supply Chain Optimisation’ and ‘Cost-Effectiveness’, ‘Overcoming Remoteness’, and ‘Pandemic Response’. Research on ‘Engineering and Design Considerations’ and ‘Ethical and Social Considerations’ cut across both research themes. We undertook further analysis to assess research topic alignment and identify knowledge gaps. We found that efforts are needed to establish a more standardised terminology for better alignment across the two emerging research themes. Future studies should focus on evaluating the impact of drone delivery on patient health using systematic methods. Additionally, exploring the economic viability of drone-based health services and addressing regulatory barriers are crucial for efficient and effective drone deployment in healthcare delivery systems. Full article