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The physiological and social behaviors differ widely between honey bee workers and drones. All the organ rudiments of adult bees are formed during the embryonic stage. The initial molecular bases at the proteomic level for both embryonic development have been identified, but a comprehensive understanding of the significant events involved in embryonic establishment remains elusive. To elucidate the molecular regulatory mechanisms underlying tissue differentiation during the embryogenesis of drones and workers, we implemented a state-of-the-art approach that combines in-hive inspection and targeted sampling (at nine embryogenesis stages) with high-throughput proteomics technology to investigate the developmental differences. In-hive inspection of hatching timing revealed an average developmental gap of approximately 3.6 hours between the two embryos. Furthermore, proteomic analyses indicate that drone and worker embryos adopt distinct developmental strategies. Notably, proteins involved in fatty acid metabolism and key biological pathways related to organ formation-such as the Hedgehog and Wnt signaling pathways-are activated earlier in drones, suggesting that tissue development begins sooner in drone embryos than in workers. Additionally, the up-regulation of cytoskeletal proteins and antioxidants in drone embryos likely supports their larger cell size and higher metabolic stress, reflecting distinct molecular characteristics of male development. Ribosomal proteins essential for biosynthetic support remain consistently expressed throughout the late stages in male embryos, indicating that drone embryogenesis lasts longer than that of workers. This work provides novel insights into the molecular foundations of honey bee embryogenesis and lays both theoretical and practical groundwork for future research into the mechanisms driving embryonic development.

The Internet of Drones (IoD) has emerged as a critical extension of the Internet of Things, enabling unmanned aerial vehicles to support diverse applications, including precision agriculture, logistics, disaster monitoring, and security surveillance. Despite its rapid growth, securing IoD communications remains a significant challenge due to the open wireless environment, high drone mobility, and strict computational and energy constraints. Existing authentication mechanisms either rely on computationally expensive cryptographic operations or remain validated only at the protocol or simulation level, leaving a critical gap in practical, hardware-validated solutions suitable for resource-constrained drone platforms. This gap motivates the need for a lightweight, privacy-preserving authentication scheme that is both theoretically sound and experimentally deployable on real hardware. To address this, we propose a Physically Unclonable Functions (PUF)-assisted lightweight authentication scheme for IoD environments that binds cryptographic keys to each drone's intrinsic hardware characteristics via PUFs. The scheme employs dynamically generated pseudo-identities to conceal permanent drone identities and prevent tracking, while authentication and key agreement are achieved using efficient symmetric cryptographic primitives, including SHA-256 for key derivation and updates, AES-256 for secure communication, and lightweight XOR operations to minimize overhead. Forward secrecy is ensured through rolling key updates, and periodic renewal of PUF challenges enhances resistance to replay and modeling attacks. To validate practicality, both software-based and hardware-based implementations were developed and evaluated. The software evaluation demonstrates a low communication overhead of 708.5 bytes and an average computation time of 18.87 ms. The hardware implementation on a Nexys A7-100T FPGA operates at 100 MHz with only 12.49% LUT utilization and low dynamic power consumption of approximately 182.5 mW. These results confirm that the proposed framework achieves an effective balance between security, privacy, and efficiency. The significance of this work lies in providing a fully hardware-validated, PUF-based authentication framework specifically tailored to the real-world constraints of IoD environments, offering a practical foundation for securing next-generation drone networks.

This study explores the question: "Can changing the appearance of drones through AR influence user operational behavior and impressions of drones?" To the best of our knowledge, this is the first study to empirically examine the influence of AR-based appearance modification of drones on user perception and operational behavior. To investigate this, a method is proposed to modify the appearance of drones by projecting objects onto them using Augmented Reality (AR), and its effects are evaluated through subjective experiments involving a relatively large number of participants. Data for drone operation and survey responses are collected from participants and analyzed using factor analysis. The factor scores are then compared between the conditions with and without AR-based appearance modification. The results do not indicate significant differences in factors related to the number of operations, suggesting consistency in this aspect. However, significant differences are observed in factors related to appearance and social evaluation (age, animal likeness, gender), as well as in physical characteristics (e.g., size, weight) of drones. In addition, open-ended survey responses reveal notable differences in appearance assessment. These findings demonstrate the potential of this method to improve the social acceptance of drone technology, particularly in the context of this research.

Pre-analytical conditions are critical to ensure the reliability of laboratory results, as emphasized by ISO 15189 standards. Drone transport has emerged as a promising alternative to conventional logistics, but its impact on sample integrity remains insufficiently characterized. This pilot study aimed to assess the pre-analytical stability of blood samples transported by drone versus ground transport. In this prospective study, 30 healthy volunteers were included. Six blood tubes per participant were collected simultaneously and assigned to ground or drone transport (20 min). A panel of 23 biochemical, hematological, and hemostatic parameters was analyzed. Agreement between transport modalities was assessed using paired comparisons, coefficients of variation, intraclass correlation coefficients (ICC), Pearson correlation, and Bland-Altman analysis. No clinically meaningful differences were observed between transport modalities. Mean values and variability were comparable across parameters. Most analytes showed excellent agreement, with ICC and Pearson correlation coefficients >0.90. Although ALT and LDH showed statistically significant differences (p = 0.039), these were small and clinically negligible. Bland-Altman analysis confirmed minimal bias for ALT (-0.63 U/L), whereas LDH exhibited wider limits of agreement, suggesting increased sensitivity to transport-related factors (-9.7 U/L). No hemolysis, temperature deviation, or safety incidents were observed. Drone transport ensures robust pre-analytical stability of blood samples across a wide range of laboratory parameters. However, analyte-specific variability, particularly for LDH, highlights the need for targeted validation. These findings support the integration of drone-based logistics into laboratory workflows, while emphasizing the importance of analyte-dependent evaluation.

Drone (or unmanned aerial vehicle) has been extensively applied in many modern artificial intelligence systems in the past decade. In this work, we propose a novel deep hashing framework that can detect objects from drone-captured pictures extremely fast. Our method can intrinsically and flexibly encode various topological structures from each target object, based on which multiscale objects can be discovered in a view- and altitude-invariant way. Moreover, by leveraging $l_{F}$ and $l_{1}$ norms collaboratively, the calculated hash codes are robust to low-quality drone pictures and possibly contaminated semantic labels. More specifically, for each drone picture, we extract visually/semantically salient object parts inside it. To characterize their topological structure, we construct a graphlet by linking the spatially adjacent object patches into a small graph. Subsequently, a binary matrix factorization (MF) is designed to hierarchically exploit the semantics of these graphlets, wherein three attributes: 1) deep binary hash codes learning; 2) contaminated pictures/labels denoising; and 3) adaptive data graph updating are seamlessly incorporated. Accordingly, a manifold-regularized feature selector is adopted to further obtain more discriminative deep hash codes. Finally, the selected hash codes corresponding to graphlets within each drone photograph are utilized for ranking-based object discovery. Comprehensive experiments on the DAC-SDC, MOHR, and our self-compiled dataset have demonstrated the competitive speed and accuracy of our method.

Digital technologies in school physical education (PE) may broaden participation and social interaction, particularly for students marginalized in traditional sport-based PE. However, qualitative evidence remains limited regarding how technology-based team sports such as drone soccer shape students' motivational and relational experiences and how teachers' pedagogical practices evolve during implementation. This study examined how a school-based drone soccer program shaped students' motivational and relational experiences in PE, with particular attention to sport-marginalized students. It also explored changes in teachers' pedagogical practices and beliefs. A qualitative multiple-case study design was employed to examine commonalities and contrasts across two school contexts. The drone soccer program was implemented as part of a district-led digital PE initiative in one girls' and one boys' middle school in Busan, South Korea. The program followed a standardized curriculum and was delivered over 6 weeks, with 1 weekly session consisting of two consecutive class periods (12 class periods total). Data were generated through semi-structured interviews with six purposefully selected students and three teachers, field observations, and document analysis, and were analyzed using inductive category analysis with cross-case comparison. Across both schools, learning progressed from basic drone-control training to role-differentiated gameplay and preparation for an inter-school tournament, creating multiple participation pathways through differentiated roles. In the girls' school, initial anxiety and hesitant participation shifted toward supportive peer interaction and a student-led practice culture. In the boys' school, an initially competition-oriented climate evolved into more deliberate teamwork and strategy sharing. In both contexts, sport-marginalized students assumed core roles and reported increased engagement, enhanced perceived competence and self-efficacy, and stronger peer connections through sustained collaboration and shared goals. Teachers described an expanded instructional orientation toward technology-integrated and more inclusive PE, emphasizing role design, scaffolding, and team processes. Drone soccer appears to offer a feasible "high-tech, high-touch" team-based PE approach that may enhance motivational quality, belonging, and socially meaningful participation, particularly for sport-marginalized students, while supporting shifts toward more inclusive and technology-integrated pedagogical practices. Conceptually, the findings suggest that redesigning participation structures through role differentiation may broaden legitimate forms of contribution in school PE.

Bees are essential pollinators with species differing morphologically and physiologically. Understanding the variations in reproductive parameters between phenotypes is crucial. This study compares the sperm characteristics of Italian (Apis mellifera ligustica) and Africanized drones (Apis mellifera L.) raised in the Caatinga biome. Nine sexually mature Italian drones and sixteen Africanized drones from different colonies were used. Semen was collected using the endophallus eversion technique and diluted in saline solution (1:20). The parameters analyzed included: motility (optical microscopy), sperm viability (Hoechst 33342; propidium iodide), functional integrity of the plasma membrane (hypo-osmotic test), morphology and morphometry (Rose Bengal), and scanning electron microscopy. The results were expressed as mean &#xb1; standard error. Statistical analyses included the Shapiro-Wilk test to the normality of residuals and the Bartlett test to verify homoscedasticity. Comparisons between groups were performed using the Mann-Whitney and Student's t-tests (P < 0.05). Both phenotypes presented 90% sperm motility with viability of 82.4 &#xb1; 2.5% for Italians and 81.1 &#xb1; 2.4% for Africanized ones; the functional integrity of the plasma membrane was 93.4 &#xb1; 1.8% and 91.6 &#xb1; 1.5%, respectively. Regarding morphology, the percentage of normal sperm was 10.89 &#xb1; 1.66% for Italian and 12.06 &#xb1; 1.01% for Africanized, with the curled tail being the most predominant feature of sperm morphology. No statistically significant differences (P > 0.05) were observed for the above-mentioned parameters. Sperm head morphometry was significantly larger (P < 0.05) in Italian (10.04 &#xb1; 0.03 &#xb5;m) compared to Africanized (9.33 &#xb1; 0.04 &#xb5;m). Scanning electron microscopy analysis revealed no ultrastructural differences between phenotypes. In conclusion, there is a high degree of similarity in sperm parameters of both phenotypes under the same environmental conditions, indicating the feasibility of applying similar reproductive strategies.

Visible and infrared feature fusion plays a critical role in drone-based RGB-IR object detection. Previous studies have shown that insufficient feature fusion limits its performance, particularly in detecting objects with confusing structures or small objects. However, most transformer-based cross-modal fusion methods rely on downsampled, low-resolution intermediate features for interaction, which can lead to the loss of local details and contextual information, ultimately reducing detection accuracy. To address this issue, we propose a novel Cross-Modal Transformer Fusion via Local Sampling (CTFLS) network based on the two-stream strategy, which effectively captures both intra-modal and inter-modal information from high-resolution feature maps. Specifically, we introduce a Local Cascade Transformer (LCT) module, consisting of a cascade of local intra-modal and cross-modal transformer blocks. This module enables the network to extract richer modality-specific and modality-invariant information from high-resolution intermediate features. Within the local cross-modal transformer block, local fusion is achieved by computing cross-attention on sampled points of local features, exploring deeper complementary relationships between different modalities. Additionally, we propose a Detail-Enhanced Mixed-Convolution Attention (DMA) module to enhance the representation capabilities of the fused features, particularly in capturing subtle textures and global contextual dependencies. We perform experiments on the DroneVehicle and FLIR datasets. Experimental results show that our method outperforms other state-of-the-art methods in the drone-based object detection task, especially in effectively detecting small objects and distinguishing objects with similar structures.

Accurate estimation of population density and spatial distribution is essential for wildlife conservation and management, yet conventional survey methods face substantial limitations. Estimating the density of medium- to large-sized mammals remains particularly challenging due to difficulties in direct observation and methodological constraints. This study qualitatively evaluated the detection performance and species identification capability of real-time drone surveys and assessed the applicability of distance sampling for density estimation. Surveys were conducted in a reedbed in Yeonsu, Incheon, and a plantation forest in Chungju, South Korea. Real-time drone surveys combined with line-transect sampling were applied in Yeonsu, whereas point-transect sampling was used in Chungju. Across nine line-transect surveys, 270 raccoon dogs (Nyctereutes procyonoides) were recorded, yielding an estimated density of 31.8 ind/km2 with a relatively high detection probability (73.7%). In contrast, four point-transect surveys detected 35 water deer (Hydropotes inermis), resulting in an estimated density of 30.6 ind/km2 but with low detection probability (34.4%) and wide confidence intervals due to limited sample size and complex terrain. These results indicate that real-time drone surveys, when integrated with distance sampling, can effectively correct for detection bias in density estimation of medium- to large-sized mammals. Future studies should apply advanced approaches such as Multiple Covariate Distance Sampling to account for variability in detection probability across survey conditions.

This study addresses several key limitations identified in previous research on additively manufactured PLA composites. Unlike most earlier studies that focused primarily on the characterization of as-printed materials, the present work systematically investigates both mechanical and surface behavior before, during, and after artificial aging. In addition, six different printing configurations and reinforcement types (PVC and fiberglass mesh) were analyzed under controlled conditions, enabling a more reliable assessment of their combined influence on composite performance. Printed specimens were artificially aged for 45 and 90 days. The aging protocol combined cyclic changes in moisture, temperature, UV, and IR agents, trying to mimic real exploitation conditions as realistically as possible. The chemical and surface changes during aging were tracked using FTIR spectroscopy, colorimetry, contact angle, and surface free energy measurements. Mechanical performance at 0, 45, and 90 days was evaluated through tensile, three-point bending, and Charpy impact tests, as well as full-scale cantilever loading tests of real printed drone arms. Results show that artificial aging causes measurable chemical and surface modifications, as indicated by changes in the FTIR degradation index and surface wettability. However, these changes do not result in severe mechanical degradation within the investigated aging period. Reinforcement in the form of incorporated PVC and fiberglass mesh significantly affected failure behavior. Specimens printed with higher infill density and thicker infill lines generally exhibit improved mechanical properties. Specimens stiffness and impact resistance were also altered. Results demonstrate that reinforced PLA structures are suitable for lightweight drone applications.

Saline-alkaline soils severely constrain global rice production by impairing nutrient use efficiency (NUE) and soil health. Conventional fertilizers exacerbate these issues through rapid nutrient loss. This study developed a new core-shell slow-release fertilizer (SC-ZEO/BCF) composed of a NPK-loaded biochar-zeolite core encapsulated within a starch coating, designed for enhanced nutrient retention and slow-release in challenging environments. Comprehensive characterization confirmed successful synthesis with a porous core for adsorption and a continuous starch shell as a physical barrier. Soil column leaching tests demonstrated SC-ZEO/BCF's superior slow-release performance, significantly retarding nitrogen and phosphorus release compared to uncoated formulations. A field trial in a saline-alkali paddy showed that SC-ZEO/BCF application resulted in the highest agronomic traits (grains per panicle, 1000-grain weight), yield (7.35 t/ha, 22.14% higher than the compound fertilizer), agronomic nitrogen use efficiency (14.08 kg/kg), and nitrogen recovery efficiency (25.32%). It also improved soil health by increasing organic matter and available nutrients while reducing salinity. The systematic innovation of combining SC-ZEO/BCF with a drone-based variable-rate system (as a hypothetical scenario) was then assessed: the drone would use YOLOv12 to recognize rice density and growth stage in real time, adjusting fertilization rates accordingly. This integration would synergize the fertilizer's slow-release properties with precision delivery, potentially lowering costs and increasing net profit to $597.02/ha compared to conventional practices. This study validates the core-shell SC-ZEO/BCF combined with precision agriculture as a highly effective and economically viable strategy for sustainable rice production in saline-alkaline soils.

While recent deep learning-based object detection has achieved great success in various fields, it remains challenging to find tiny objects in aerial imagery on-the-fly using mobile devices. Since mobile platforms such as drones operate with limited onboard computing power, handling high-resolution images to find tiny objects with compute-intensive deep learning-based applications often fails to meet their real-time constraints. To mitigate this problem, we propose HashEye, a novel framework that enables fast on-drone tiny object detection by efficiently suppressing spatial redundancy in aerial imagery. HashEye utilizes a lightweight hashing algorithm to rapidly scan image patches; patches exhibiting high hash collision frequencies are identified as background and suppressed. Subsequently, the remaining salient patches are dynamically rearranged into a hardware-friendly dense image for efficient inference. Experimental results on two real-world datasets demonstrate that HashEye achieves up to a 5.25&#xd7; speedup compared to the baseline, maintaining detection capability.

Abandoned oil and gas wells pose significant risks to human health and the environment by emitting air pollutants, contaminating groundwater, and leaving behind hazardous debris. In the United States, approximately 3.9 million documented wells vary widely in the accuracy of their recorded locations and plugging status, creating major challenges for detection, mapping, and remediation. Existing well detection methods show some promise but often lose effectiveness under complex conditions, such as vegetation occlusion or construction without metal components. In this study, we propose a drone-based approach equipped with a highly sensitive methane sensor to identify statistical anomalies in methane concentrations around abandoned oil and gas well sites. To address the noisy and variable nature of environmental sensor data, statistical methods were developed that enable reliable anomaly detection under field conditions. Controlled release experiments with known emission points validated the method's ability to statistically detect methane anomalies that may indicate nearby emission sources. We further tested the approach at a field site containing three abandoned wells with known locations and sparse emission profiles. The results demonstrate that the proposed drone-based sensing method can serve as a rapid survey approach to identify areas with elevated methane signals around well sites, helping to reduce the scope of the ground survey area, and supporting prioritization of follow-up ground investigations. This approach provides a practical means to support targeted monitoring and prioritization of remediation efforts, while supporting the future development of source attribution and localization methods.

Drones equipped with cameras are helpful in wildlife tracking. Deep learning has great potential for detecting wildlife, but is constrained by the challenge of detecting tiny objects, especially from higher altitudes. These limitations are addressed by an enhanced You Only Look Once 11 (YOLOv11-Lite) model. YOLOv11-Lite is a lightweight, edge-friendly variant of YOLOv11 that reduces computational complexity while maintaining high detection accuracy. Standard Convolution + Batch Normalization + SiLU (CBS) blocks are replaced with Depthwise-CBS units, which reduce the number of parameters and FLOPs. The enhanced version employs a Spatial Reasoning-Enhanced Coordinate Attention-based Simple Attention Module (CA-SimAM) for improved feature representation, Dynamic Sampling (DySample) for adaptive sampling, and a bounding-box IoU for accurate localization. The C2 block with the Parallel Split Attention (C2PSA) module is also replaced with a Ghost-ELAN block, as it enables ghost feature generation and multi-branch ELAN aggregation, achieving good performance with fewer computations. The multiscale detection head aids in detecting smaller animals. The enhanced model achieves an mAP@0.5 of 98.5% and an mAP@0.5:0.95 of 94.7% on the WAID dataset. The performance of the model is assessed through comparative tests, which demonstrate the superiority of the enhanced YOLOv11-Lite model over existing algorithms. The proposed approach supports UAV-based wildlife monitoring and improves detection performance and generalization under real-world conditions.

We present DamCrack, a novel dataset comprising high-resolution images of a concrete dam in Spokane, Washington, USA, captured using a drone-including autonomous flights with overlapping images-and mobile devices to document complex surface deterioration. The dataset includes two damage types: cracks and spalling, with pixel-wise annotations provided in different formats. The inclusion of overlapping aerial imagery enables future photogrammetric 3D reconstruction, while the current 2D image data supports immediate computer vision tasks such as damage detection and segmentation. Some damage exhibits visual characteristics resembling alkali-silica reaction (ASR), including map-cracking patterns and surface discoloration. Captured under diverse environmental conditions-such as varying lighting, camera distances, and camera properties-the dataset specifically addresses challenging real-world scenarios where multiple damage types co-occur. DamCrack provides: (1) standardized benchmarks for 2D damage detection and segmentation algorithms, (2) high-quality imagery to support future 3D reconstruction models, and (3) annotated examples of complex, co-occurring damage patterns that contribute to advancing structural health monitoring research for critical infrastructure.

Drone brood homogenate (DBH), a&#xa0;nutrient-rich bee product, has received limited scientific attention despite its potential immunomodulatory and gut-protective properties. This study evaluated the effects of&#xa0;a&#xa0;dietary DBH supplementation on&#xa0;the intestinal barrier-related gene expression, phagocytic activity, and lymphocyte subpopulations in&#xa0;pigs. Eighteen weaned pigs were assigned to&#xa0;three groups (control, DBH100, DBH200) and fed DBH at&#xa0;0, 100, or&#xa0;200&#xa0;mg/kg feed for 18&#xa0;days. The gene expression of&#xa0;tight junction markers (occludin, claudin-1)&#xa0;and mucosal integrity-associated proteins (lumican, OLFM4) was assessed in&#xa0;the ileum by&#xa0;qRT-PCR. Phagocyte function and peripheral blood lymphocyte subpopulations were analysed by&#xa0;flow cytometry. DBH200&#xa0;significantly upregulated the occludin, claudin-1, lumican, and OLFM4 expression, indicating enhanced intestinal barrier support. The phagocytes from both DBH-treated groups exhibited an&#xa0;increased engulfing capacity and an&#xa0;elevated oxidative burst index, though the percentage of&#xa0;active phagocytes was only weakly affected. The DBH supplementation did not alter the total T (CD3+) or&#xa0;B (CD21+) cells; however, both DBH groups showed a&#xa0;significantly increased CD4+ : CD8+ lymphocyte ratio, which is&#xa0;consistent with immune stimulation. These findings suggest that DBH may beneficially modulate the gut barrier integrity and selected components of&#xa0;innate and adaptive cellular immunity in&#xa0;pigs.

Accurate, spatially explicit quantification of the fraction of absorbed photosynthetically active radiation (fPAR) in tall conifer plantations is essential for productivity modelling and breeding, yet standard nadir-view optical UAV imagery yields only two-dimensional surface estimates. We developed an unmanned aerial workflow that fuses centimeter resolution LiDAR point clouds with five band multispectral imagery to produce a three-dimensional voxelized canopy structure in which top-of-canopy multispectral reflectance values are propagated downward within each vertical column. Ground measurements of fPAR and chlorophyll fluorescence were collected contemporaneously and used to calibrate Random Forest, XGBoost, Support Vector Machine (SVM), and Partial Least Squares Regression models built from 14 spectral indices. Random Forest explained 84 % of fPAR variance (RMSE = 0.12), outperforming alternative algorithms. Application of the trained Random Forest model to the voxelized canopy (0.01 m &#xd7; 0.01 m &#xd7; 2 m) across 28 ha generated three-dimensional fPAR maps that revealed a 26 &#xb1; 4 % increase from lower to upper crowns and a seasonal shift of up to 9 %. Compared with conventional plot-level inversion, the workflow significantly reduced field labour and improved prediction accuracy. The fusion pipeline provides a species-specific tool for high-throughput phenotyping, precision silviculture, and genomic selection in slash pine plantations under clear-sky conditions (solar zenith angle 20-30&#xb0;); transferability to other sites, species, or illumination conditions requires further validation.

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