The rapid development of the Internet of Things (IoT) has fueled the widespread adoption of Unmanned Aerial Vehicles (UAVs) or drones across various fields, including their use in applications such as surveillance and monitoring. UAVs flight capabilities allow it to effortlessly access previously inaccessible locations, providing real-time, high-resolution data-images and videos-of any desired area or target. Furthermore, the growth of Artificial Intelligence (AI), and edge computing technologies has empowered UAVs with high computational capabilities, making them suitable for diverse applications such as agriculture, transportation and border security. These technology advancements also equip UAVs with powerful on-board processing for sophisticated decision-making that enhances UAV activeness and intelligence. This survey explores the promising areas of UAVs for intelligent active surveillance and monitoring across diverse applications. First, the various levels of UAV activeness within applications are discussed; second, prior research is examined to identify the key technologies and architectures that power intelligent UAV systems; and third, several UAV applications in surveillance and monitoring, ranging from basic tasks to highly intelligent operations are explored. Finally, the survey concludes by discussing emerging research challenges and outlines a guiding road map for future research of highly interdisciplinary and emerging areas in UAV-based systems for surveillance and monitoring.
Secondary high explosives (HEs) exhibit rich microstructure that promotes the formation of hot spots responsible for detonation initiation, but the role of microstructural interfaces remains poorly quantified. To this end, we develop extensions for the generalized crystal-cutting method (GCCM) to prepare molecular dynamics (MD) simulation cells containing grain boundaries (GBs) and other crystal-crystal interfaces with prescribed tilt and twist orientations. Using the GCCM, we perform MD simulations of shock interactions with a GB between the (001) and (100) crystal facets in the secondary HE TATB (1,3,5-triamino-2,4,6-trinitrobenzene). Our MD simulations reveal a strong directional dependence to the formation of a hot spot at the GB interface. In particular, transmission of the shock from the (001) grain to the (100) grain yields a hot spot in the (100) grain at the GB interface, whereas no hot spot is produced when an equivalent shock transits the GB in the opposite direction. We trace the origin of this GB anisotropy to three dominant factors: (1) the intrinsic differences in shock-deformation mechanisms and wave structures for the bulk (100) and (001) grains, which leads to distinct geometries and mechanical impedances upon shock arrival to the GB depending on which grains are donor or acceptor for the transmitted shock; (2) the different time intervals separating the initial shock rise and the formation of steady wave structures in the respective donor-acceptor configurations; and (3) the differences in time scales required to re-establish local thermal equilibrium. Interfacial hot spots form when these factors combine to impede development of a steady two-wave structure and instead induce a localized, pseudosingly shocked region that undergoes a higher rate of work production (resulting in a higher temperature) compared to when the steady two-wave structure develops further from the interface. The extensions to the GCCM approach presented here are anticipated to facilitate a wide range of MD studies that focus on understanding the role of crystal-crystal interfaces in molecular materials.
While digital games offer immersive experiences, their role in environmental behavior change remains underexplored. This study investigates how ecological disaster-themed games can stimulate biodiversity conservation intention through emotional mechanisms. Grounded in Cognitive Appraisal Theory, we propose a "game experience-threat appraisal-threat awe-behavioral intention" framework. Data were collected from 406 players and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), Artificial Neural Networks (ANN), and Fuzzy-set Qualitative Comparative Analysis (fsQCA). Results show that immersive, realism and interactive game environments significantly influence threat appraisal, which strongly predicts threat awe-the most potent driver of biodiversity conservation intention. Moreover, fsQCA reveals multiple pathways leading to strong conservation intentions, reflecting the complex interplay of experiential, cognitive, and emotional factors. This study not only extends the application of cognitive appraisal theory to digital gaming but also emphasizes the unique potential of threat awe in digital public engagement with biodiversity issues.
This study analyzes heat waves (HWs), cold spells (CSs), and mean temperature trends in T & uuml;rkiye's three major metropolises (Istanbul, Ankara, and Izmir) using long-term station data. HW and CS events were defined via a percentile-based threshold approach, utilizing daily maximum (Tmax) and minimum (Tmin) temperature data from a total of 15 meteorological stations. Temporal trends in annual and seasonal wave frequencies, alongside mean temperature series, were evaluated using the Mann-Kendall test and Sen's slope estimator. The findings indicate that HW frequencies have significantly increased across the majority of stations, whereas CS frequencies have decreased at most locations. It was determined that while HWs predominantly concentrate in summer and CSs in winter, heat extremes can extend into transitional seasons. Mean temperatures exhibit a statistically significant upward trend across all stations. Furthermore, HWs have become more prominent and CSs have dissipated more rapidly in urban and coastal stations. These results reveal that the risk of heat extremes is escalating while cold extreme events are weakening in T & uuml;rkiye's major cities due to warming climate conditions.
The seed microbiome plays an important role in seed quality, germination, and crop establishment. Soybean (Glycine max [L.] Merr.) is one of the most economically important field crops. However, information about soybean seed microbiomes is lacking, especially regarding how field environmental conditions and the prevalent seed pathogen Diaporthe longicolla across different years shape soybean seed microbiomes. In this study, we used amplicon sequencing to assess the seed endophytic microbiomes of a soybean cultivar, Dudley, collected in four successive years of field trials. The alpha diversity of both fungal and bacterial communities remained relatively stable across years with D. longicolla treatments. The microbial compositions were significantly influenced by years (P = 0.001), with environmental variables (air temperature, precipitation, and relative humidity) contributing to shifts in community structure. The seed core microbiome comprised bacterial members from genera Methylorubrum, Peribacillus, and Priestia and fungal taxa from classes Dothideomycetes, Sordariomycetes, and Tremellomycetes. Pathogen inoculation altered bacterial composition, positively associated with taxa from genera Sphingomonas, Microbacterium, and Nocardioides, but did not notably affect fungal community. Soybean seed microbiome assembly appears driven primarily by interannual environmental variations. Pathogen inoculation exerts a secondary but detectable effect on the bacterial community. To the best of our knowledge, this is the first multiyear field-based study of soybean seed endophytic microbiomes and the impact of seedborne pathogen inoculation. The findings of this research advance our understanding of soybean seed microbiomes and provide insights into the potential use of seed microbes, thereby aiding in the development of novel microbiome-based alternatives to manage soybean seed diseases.Copyright (c) 2026 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
