Search Results (1,008)

Reliable communications, accurate localization, and efficient safety monitoring remain critical bottlenecks for sustainable development in remote high-altitude regions. On the Qinghai–Tibet Plateau, harsh topography and sparse infrastructure create a persistent “digital divide” that threatens human safety and limits field governance efficiency. This study aims to design, implement, and evaluate an integrated communication and navigation security assurance platform to bridge this gap. The specific research objectives are (i) to develop a hybrid high-precision positioning model integrating PPP-B2b, RTK, and MEMS inertial constraints; (ii) to implement an adaptive multi-link communication strategy combining BeiDou-3 short message communication (SMC), 4G LTE, and VHF; (iii) to design a lightweight SM1/SM2 security-and-compression framework optimized for bandwidth-constrained satellite messaging; and (iv) to conduct a mixed-methods field evaluation of technical performance and user-level impacts. A six-month field evaluation was conducted in Qinghai Province to validate the platform. Results show that the platform achieves sub-metre positioning accuracy across representative plateau scenarios (horizontal RMSE: 0.06–0.45 m). While terrestrial cellular links in marginal-coverage areas frequently failed (<15%), the BeiDou-3 SMC maintained stable message delivery (87.5–94.7%). Sustainability-oriented indicators suggest marked improvements in disaster resilience: the 95th-percentile emergency notification time was reduced from >180 min to <2 min, and effective route coverage increased from ~15% to ~95%. User surveys (n = 112) indicate high acceptance, with 91.1% of respondents reporting improved perceived safety, though usability gaps persist among non-professional groups. Overall, this indigenous satellite-based platform functions as a practical “social safety net,” narrowing digital exclusion and supporting UN sustainable development goals (SDG 9, 10, and 11). Full article

The emergence of low-cost GNSS hardware and affordable RTK correction services has made high-precision positioning more accessible. While prior studies have investigated RTK capabilities using smartphones, most rely on professional geodetic infrastructures. This study shifts the focus toward evaluating smartphone-based RTK positioning within low-cost GNSS networks and comparing the performance against traditional geodetic network setups. The research investigates two main configurations: (1) a smartphone functioning as an RTK rover within a low-cost GNSS network, using a low-cost base station and publicly available or inexpensive correction services, and (2) the same smartphone setup operating within a traditional geodetic network with high-grade base stations. The study aims to assess the viability of smartphones as RTK rovers in low cost networks, exploring metrics such as horizontal and vertical positioning accuracy, fix reliability, initialization time, and system responsiveness. Preliminary findings suggest that smartphones integrated with low-cost GNSS receivers can deliver sub-meter accuracy under favorable conditions, though some trade-offs are noted when compared with geodetic-grade infrastructure. The study emphasizes the potential of cost-effective RTK configurations for practical applications where high precision is required. By comparing performance across traditional and low-cost network configurations, this research demonstrates the increasing potential of using smartphones and low-cost GNSS systems to make high-precision positioning more accessible. Full article

We assessed the vertical accuracy of six global digital elevation models—FABDEM (SRTM-enhanced), SRTM, ASTER GDEM, ALOS AW3D30, DeltaDTM and GEDTM—against a local photogrammetry-derived DEM as a benchmark in a flat coastal plain of the Colombian Caribbean. Using GNSS-RTK ground points and a high-accuracy reference DEM, we computed BIAS, RMSE, and MAE. Errors were analyzed by land cover class and along transverse profiles relative to the reference DEM. We also evaluated hydrologic suitability by comparing flow accumulation and drainage patterns derived from each model, treating the photogrammetry-derived model as the control and the global DEMs as treatments to gauge their ability to represent hydraulic/hydrologic behavior. DeltaDTM, GEDTM and FABDEM showed the best overall performance, with the lowest vertical error (particularly in non-urban areas with sparse vegetation) and the highest drainage agreement, along with their flood extent sensitivity to a 0.5 m water level rise, all of which were comparable to the benchmark. These results provide practical guidance for selecting and preprocessing topographic models for risk management and territorial planning in flat regions. Full article

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future. Full article

In urban environments, autonomous vehicles face critical challenges in localization and perception under extreme lighting conditions, including rapid illumination changes, high contrast, and nighttime low-light scenarios. To address the performance degradation of traditional LiDAR-inertial odometry systems under such conditions, this study proposes a high-precision FAST-LIO2-EC algorithm that fuses event cameras into the FAST-LIO2 framework. Event cameras, with their microsecond temporal resolution and 140 dB dynamic range, provide asynchronous edge information that complements LiDAR point clouds and IMU measurements. We validate the proposed system through real-world road tests conducted on public roads and closed test tracks, covering three typical extreme lighting scenarios: tunnel entrance/exit transitions, high-contrast shadow boundaries, and nighttime sparse-lighting conditions. The experimental platform is equipped with a 32-beam LiDAR, a 6-axis IMU, a DVS event camera, and an RTK-GNSS system for ground truth trajectory acquisition. Real-world results demonstrate that the FAST-LIO2-EC system achieves significant improvements in localization accuracy and robustness. In illumination change scenarios, the Absolute Trajectory Error (ATE) is reduced by 32.5% compared to the baseline FAST-LIO2 system, with zero tracking loss events. The point cloud quality is substantially enhanced, with more uniform distribution and clearer obstacle boundaries. In high-contrast scenarios, both systems maintain comparable performance with ATE below 0.15 m. However, in nighttime scenarios, the fusion system shows moderate improvement (15.3% ATE reduction) but reveals sensitivity to event camera noise, indicating the need for adaptive thresholding strategies. Supplementary simulation experiments validate the system’s robustness under varying speeds and sensor noise levels. This work provides a practical solution for autonomous vehicle deployment in complex urban lighting environments, with a comprehensive analysis of real-world performance boundaries and deployment considerations. Full article

The growing number of older bridges has resulted in an increase in structural flaws, demanding frequent inspections and maintenance. Structural degradation accelerates post-damage recovery, emphasizing the necessity of preventive interventions. The use of Uncrewed Aerial Vehicle Systems (UASs) for bridge inspections represents a significant development in structural health monitoring (SHM). Traditional inspection methods are labor-intensive, time-consuming, expensive, and require access to high or difficult-to-reach areas, posing safety risks to inspectors. This study focuses on identifying drones that can efficiently support bridge inspection activities. Key factors influencing UAS selection include flight performance, flying modes, cost, sensor capabilities, payload capacity, and controller communication. The primary objective of this paper is to provide guidance to inspectors and transportation agencies regarding the capabilities and limitations of commercially available drones. It also outlines potential cost considerations associated with drone selection, including pilot skill level, platform cost, and sensor integration. These factors may vary depending on the type and complexity of the bridge being inspected. By addressing these aspects, this paper aims to assist decision-makers in making informed choices regarding the use of UASs for bridge inspection applications. Full article

In September 2021, significant changes in the geophysical and geochemical parameters on Vulcano Island were recorded by the surveillance network activities and periodic surveys. Between October 2021 and June 2024, additional surveys were conducted to acquire LIDAR, thermal, and RGB datasets for the generation of Digital Terrain Models (DTMs), orthophotos, and fumarole field maps. These data were collected using DJI Matrice 300 UAS platforms. Precision positioning was ensured through a POS/NAV RTK georeferencing approach. The instrumentation included Genius R-Fans-16 and DJI Zenmuse L1 laser scanners for structural mapping, alongside Zenmuse H20T infrared cameras for the thermal detection of potential instabilities on the volcano flanks, focused on the northern area and summit of Gran Cratere La Fossa, and these were subsequently repeated in May 2022, October 2022, October 2023, and June 2024. Additionally, 3D reconstruction targeted morphological variations in unstable areas like the cone top, Forgia Vecchia, and the 1988 landslide site. In May 2022, anomalous degassing in the Eastern Bay led to increased gas and hydrothermal fluid emissions, causing water whitening in front of Baia di Levante. Optical-thermal monitoring, both on land and at sea, detected multiple hydrothermal gas streams, aiding in assessing the magnitude and areal extension of fumarolic fields. These findings contribute to establishing a comprehensive monitoring approach for understanding the volcanic unrest evolution cost-effectively and safely. Full article

Factor graph optimization (FGO) has emerged as a powerful alternative to Kalman filtering for high-precision GNSS positioning, particularly under challenging conditions. Its modular structure allows for the seamless integration of motion constraints, ambiguity modeling, and multi-sensor data across diverse platforms and environments. This study reviews recent FGO architectures for high-precision GNSS methodologies (PPP, RTK), comparing ambiguity management strategies, measurement factor designs, and robust optimization techniques. We compare strategies for modeling ambiguities within the graph and evaluate how they interact with measurement factor design, cycle slip detection, and integer ambiguity resolution (IAR). Trade-offs in ambiguity management and optimization techniques are discussed to guide future design choices. Full article

Remote sensing has revolutionized monitoring landscapes that are inaccessible or impractical to survey on the ground. Satellite platforms such as Sentinel-2 enable assessment of ecosystem changes over extensive areas with high temporal frequency, while Unmanned Aerial Systems (UAS) offer flexible, ultra-high-resolution observations ideal for site-specific analysis and sensitive environments. This study compares the performance of Sentinel-2 and Phantom 4 multispectral RTK data for monitoring vegetation dynamics in Mediterranean shrubland ecosystems, focusing on the Normalized Difference Vegetation Index (NDVI). Both platforms produced broadly consistent patterns in seasonal and interannual vegetation dynamics. However, UAS outperformed satellite data in capturing fine-scale heterogeneity, regeneration patches, and subtle disturbance responses, particularly in sparsely vegetated or heterogeneous terrain where satellite metrics may be insensitive. The comparison of NDVI across platforms accounted for standardized processing, harmonization, radiometric and atmospheric correction, and spatial resolution differences. Results show platform selection can be optimized according to monitoring objectives: satellite data are well suited for long-term monitoring of landscape-level vegetation dynamics, as both platforms capture consistent patterns when evaluated at comparable, spatially aggregated scales, while UAS data provide critical detail for localized management, early stress detection, and restoration prioritization by resolving fine-scale features. A combined approach enhances ecosystem disturbance assessments and resource management by binding the strengths of both wide-area coverage and precise spatial detail. Full article

In many IIoT-based yard operations, accurately identifying the spatial position of containers is becoming increasingly relevant as operators try to automate stacking and retrieval processes by technologies like Container Spatial Localization (CSL). Despite this automation in IIoT, RTK-GPS–based container stacker positioning frequently lacks precision, which causes disruptions in stacking and reduces efficiency in space utilisation. Though it offers placement precision accurately up to 3 cm, this is still insufficient in high-volume Yard Management Systems (YMS). Consequently, this yields to variable container orientation, waste of usable space, increased man input is required in handling goods, and potential automated system failures. This research proposes a novel methodology that combines conventional RTK-GPS measurements with angular information captured from a BHI-260AP–based spreader sensor, allowing the system to correct container placement errors arising from orientation rather than only from positioning. In addition to the spatial positioning problem, we found that continuous IIoT operation raises concerns regarding energy use, particularly when micro-controllers remain active throughout the task cycle. As a solution, this integrates a dynamic task scheduling approach that puts the device in sleep modes whenever computation is not required. In our experiments, this strategy improved overall power efficiency by 34.44%, which makes long automated operation more practical. Full article

Background: Cutaneous squamous cell carcinoma (cSCC) represents one of the most common keratinocyte-derived malignancies encountered in clinical practice; however, its genomic landscape remains far less comprehensively characterized than that of other cutaneous cancers. This study aims to identify key molecular drivers and potential therapeutic targets by comprehensively characterizing the genomic landscape of cSCC using data from the American Association for Cancer Research (AACR) Project Genomics, Evidence, Neoplasia, Information, Exchange (GENIE) consortium. Methods: A retrospective cohort analysis of cSCC samples was performed utilizing AACR Project GENIE data accessed via the cBioPortal platform (v18.0-public) on 22 November 2025. Analyses included identification of recurrent somatic and copy-number alterations, pairwise gene–gene co-occurrence testing using Fisher’s exact tests with Benjamini–Hochberg False Discovery Rate (FDR) correction, and exploratory subgroup comparisons by sex and race, with statistical significance defined as p < 0.05. Results: Recurrent mutations were identified in TP53 (83.5%), NOTCH1 (56.3%), KMT2D (47.0%), CDKN2A (44.4%), TERT (41.4%), ROS1 (34.3%), FAT1 (33.3%), NOTCH2 (31.2%), ERBB4 (28.4%), and KMT2A (24.3%), reflecting disruption of the p53 pathway, cell-cycle control, Notch signaling, epigenetic regulation, telomere maintenance, RTK/MAPK pathways, and Wnt signaling. Statistically significant co-occurrence patterns were observed, and exploratory subgroup analyses evaluated mutation frequency differences by sex and race. Conclusions: This large, multi-institutional genomic analysis defines recurrent mutational and structural alterations in cSCC and highlights an integrated pattern of pathway disruption involving genomic integrity, differentiation, epigenetic control, and proliferative signaling. These findings enhance current understandings of the molecular architecture underlying this common yet genomically understudied malignancy and provide a foundation for future mechanistic studies and development of targeted diagnostic and therapeutic strategies. Full article

Accurate and robust positioning is a critical enabler for Unmanned Aerial Vehicle (UAV) applications, ranging from mapping and inspection to emerging Urban Air Mobility (UAM). While Global Navigation Satellite Systems (GNSS) remain the backbone of absolute positioning, their performance is severely constrained by UAV platform characteristics and complex low-altitude environments. This paper presents a system-level review of GNSS-based UAV positioning. Instead of treating GNSS in isolation, we first link mission requirements and platform constraints, such as aggressive dynamics and Size, Weight, and Power (SWaP) limitations, to specific positioning challenges. We then critically evaluate the spectrum of GNSS techniques, from standalone and Satellite-Based Augmentation System (SBAS) modes to high-precision carrier-phase methods including Real-Time Kinematic (RTK), Post-Processed Kinematic (PPK), Precise Point Positioning (PPP), and PPP-RTK. Furthermore, we discuss multi-sensor fusion with inertial, visual, and Light Detection and Ranging (LiDAR) sensors to mitigate vulnerabilities in urban canyons and GNSS-denied conditions. Finally, we outline key challenges and future directions, highlighting integrity-aware architectures, Artificial Intelligence (AI)-enhanced signal processing, and multi-layer Positioning, Navigation, and Timing (PNT) concepts. The review provides a structured framework and system-level insights to guide resilient navigation for UAV operations in low-altitude airspace. Full article

Glioblastoma is one of the most highly aggressive types of brain tumor in adults. With limited treatment options, current therapies remain insufficient due to its invasiveness and immune evasion, highlighting the urgent need for new treatments. Bifunctional molecules targeting multiple aspects of the disease could be promising to overcome drug resistance and tumor heterogeneity. Metformin has demonstrated protective effects against brain tumors but requires high doses for efficacy, making it of great interest for molecular optimization. In this context, we synthesized a series of nine metformin–phenolic molecules, combining the metformin guanidine framework with phenolic acids, which have well-established properties in inhibiting cancer cell migration and adhesion. Their impact on cytotoxicity, reactive oxygen species inhibition, and signaling pathways was investigated for glioma cell lines and stem cells. Two of these hybrids, 5a and 5h, particularly enhanced cytotoxicity in glioblastoma cells, selectively targeting cancer cells while sparing healthy ones. Their mechanism of action differed significantly from metformin. Unlike metformin, which mainly triggers metabolic stress, the hybrids broadly inhibit RTK–MAPK–PI3K signaling, leading to cell cycle arrest and apoptosis. The results suggest that these compounds could offer a more effective and synergistic approach for glioblastoma treatment. Full article

Prostate cancer (PCa) is the most common male cancer and the second leading cause of cancer death in men. Androgen deprivation therapy (ADT) has been widely used as the first-line treatment for PCa. However, most PCa will progress to castration-resistant PCa (CRPC) that resists ADT 1 to 3 years after the treatment. Steroidogenesis from cholesterol is one of the mechanisms leading to ADT resistance. In PCa cells, low-density lipoprotein (LDL) mediated uptake is the major venue to acquire cholesterol. However, the mechanism of regulating this process is not fully understood. Fibroblast growth factor receptor 1 (FGFR1) is a receptor tyrosine kinase (RTK) that is ectopically expressed in PCa cells and promotes PCa progression by activating downstream signaling pathways. To comprehensively determine the roles of FGFR1 in PCa, we generated FGFR1-null DU145 cells and compared the transcriptomes of FGFR1-null and wild-type cells. We found that ablation of FGFR1 reduced the expression of genes promoting LDL uptake and de novo synthesis of cholesterol, thereby reducing the overall cholesterol pool in PCa cells. Detailed mechanistic studies further revealed that FGFR1 boosted the activation of sterol regulatory element-binding protein 2 (SREBP2) through ERK-dependent phosphorylation and cleavage, which, in turn, increased the expression of low-density lipoprotein receptor (LDLR) and enzymes involved in de novo cholesterol synthesis. Furthermore, in silico analyses demonstrated that high expression of FGFR1 was associated with high LDLR expression and clinicopathological features in PCa. Collectively, our data unveiled a previously unrecognized therapeutic avenue for CRPC by targeting FGFR1-driven cholesterol uptake and de novo synthesis. Full article

This study presents a drone-based method for assessing the condition of road markings from high-resolution imagery acquired by a UAV. A DJI Matrice 300 RTK (Real-Time Kinematic) equipped with a Zenmuse P1 camera (DJI, China) is flown over urban road corridors to capture images with centimeter-level ground sampling distance. In contrast to common approaches that rely on vehicle-mounted or street-view cameras, using a UAV reduces survey time and deployment effort while still providing views that are suitable for marking. The flight altitude, overlap, and corridor pattern are chosen to limit occlusions from traffic and building shadows while preserving the resolution required for condition assessment. From these images, the method locates individual markings, assigns a class to each marking, and estimates its level of deterioration. Candidate markings are first detected with YOLOv9 on the UAV imagery. The detections are cropped and segmented, which refines marking boundaries and thin structures. The condition is then estimated at the pixel level by modeling gray-level statistics with kernel density estimation (KDE) and a two-component Gaussian mixture model (GMM) to separate intact and distressed material. Subsequently, we compute a per-instance damage ratio that summarizes the proportion of degraded pixels within each marking. All results are georeferenced to map coordinates using a 3D reference model, allowing visualization on base maps and integration into road asset inventories. Experiments on unseen urban areas report detection performance (precision, recall, mean average precision) and segmentation performance (intersection over union), and analyze the stability of the damage ratio and processing time. The findings indicate that the drone-based method can identify road markings, estimate their condition, and attach each record to geographic space in a way that is useful for inspection scheduling and maintenance planning. Full article