Search Results (866)

This study focuses on the preservation of indoor murals, which can be supported by combining RTK and total station technology to explore the optimization of image geometric accuracy based on a control points layout. The study involves placing varying numbers of control points on the mural surface and processing the collected data using a spatial coordinate transformation model to assess the impact of different layouts on image accuracy. Some control points are used to ensure the spatial positioning accuracy of the images, while others serve as check points to validate the geometric precision of the images. After data processing, high-precision digital orthophotos are generated using Agisoft PhotoScan2.0.1 software, with accuracy verified by the check points. The experimental results show that as the number of control points increases, image accuracy improves gradually. When the number of control points reaches 24, the geometric accuracy of the images stabilizes, and further increases in the number of control points have a limited effect on improving accuracy. Therefore, the study proposes an optimal layout scheme: 24 control points for every 16 square meters. This scheme not only meets millimeter-level precision requirements but also effectively optimizes resource allocation and reduces time costs. The research provides reliable data support for the high-precision preservation and restoration of murals and offers important references for similar cultural heritage preservation projects. Full article

T cells play diverse roles in cancer immunology, acting as tumor suppressors, cytotoxic effectors, enhancers of cytotoxic T lymphocyte responses and immune suppressors; providing memory and surveillance; modulating the tumor microenvironment (TME); or activating innate immune cells. However, cancer cells can disrupt T cell function, leading to T cell exhaustion and a weakened immune response against the tumor. The expression of exhausted T cell (Tex) markers plays a pivotal role in shaping the immune landscape of multiple cancers. Our aim was to systematically investigate the role of known T cell exhaustion (Tex) markers across multiple cancers while exploring their molecular interactions, mutation profiles, and potential implications for immunotherapy. The mRNA expression profile of six Tex markers, LAG-3, PDCD1, TIGIT, HAVCR2, CXCL13, and LAYN was investigated in pan-cancer. Utilizing data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), The Cancer Proteome Atlas (TCPA), and other repositories, we characterized the differential expression of the Tex markers, their association with the patients’ survival outcome, and their mutation profile in multiple cancers. Additionally, we analyzed the effects on cancer-related pathways and immune infiltration within the TME, offering valuable insights into mechanisms of cancer immune evasion and progression. Finally, the correlation between their expression and sensitivity to multiple anti-cancer drugs was investigated extensively. Differential expression of all six markers was significantly associated with KIRC and poor prognosis in several cancers. They also played a potential activating role in apoptosis, EMT, and hormone ER pathways, as well as a potential inhibitory role in the DNA damage response and RTK oncogenic pathways. Infiltration of different immune cells was also found to be associated with the expression of the Tex-related genes in most cancer types. These findings underline that the reviving of exhausted T cells can be used to enhance the efficacy of immunotherapy in cancer patients. Full article

Jakarta faces a critical challenge of extensive land subsidence, ranking prominently globally. This research employs a combined technical and policy evaluation approach to analyze the issue, incorporating sustainability considerations to assess the efficacy of Governor Regulation of Jakarta Number 93 of 2021, focusing on how the groundwater-free zone relates to land subsidence in the city. We processed 81 ALOS-2 PALSAR-2 synthetic aperture radar (SAR) data using persistent scatterer interferometric synthetic aperture radar (PS-InSAR) with HH polarization from 2017 to 2022 and ground truthing with 255 global positioning system (GPS) real-time kinematic (RTK) validation points. Our findings reveal a significant misalignment in the designated groundwater-free zone in the central part of Jakarta. At the same time, severe land subsidence primarily affects northern and northwestern Jakarta, with an average land subsidence rate of 5–6 cm/year. We strongly advocate for a thorough evaluation to rectify and redefine the boundaries of groundwater-free zones, improve regulatory frameworks, and effectively address land subsidence mitigation in the study area. The impact of domestic water needs on land subsidence highlights the urgency of action. Based on a combination of land subsidence velocity rates and domestic water demand, we have classified the cities in Jakarta into three levels of recommendations for groundwater-free zones. The cities are ranked in order of priority from highest to lowest: (1) West Jakarta, (2) North Jakarta, (3) South Jakarta, (4) East Jakarta, and (5) Central Jakarta, which holds the lowest priority. Full article

Accurate rice row detection is critical for autonomous agricultural machinery navigation in complex paddy environments. Existing methods struggle with terrain unevenness, water reflections, and weed interference. This study aimed to develop a robust rice row detection method by integrating multi-sensor data and leveraging robot travelling prior information. A 3D point cloud acquisition system combining 2D LiDAR, AHRS, and RTK-GNSS was designed. A variable-threshold segmentation method, dynamically adjusted based on real-time posture perception, was proposed to handle terrain variations. Additionally, a clustering algorithm incorporating rice row spacing and robot path constraints was developed to filter noise and classify seedlings. Experiments in dryland with simulated seedlings and real paddy fields demonstrated high accuracy: maximum absolute errors of 59.41 mm (dryland) and 69.36 mm (paddy), with standard deviations of 14.79 mm and 19.18 mm, respectively. The method achieved a 0.6489° mean angular error, outperforming existing algorithms. The fusion of posture-aware thresholding and path-based clustering effectively addresses the challenges in complex rice fields. This work enhances the automation of field management, offering a reliable solution for precision agriculture in unstructured environments. Its technical framework can be adapted to other row crop systems, promoting sustainable mechanization in global rice production. Full article

This paper presents a novel methodology to generate Digital Elevation Models (DEMs) in flat areas, incorporating river channels from relatively coarse initial data. The technique primarily utilizes filtered dense point clouds derived from SfM-MVS (Structure from Motion-Multi-View Stereo) photogrammetry of available crewed aerial imagery datasets. The methodology operates under the assumption that the aerial survey was carried out during low-flow or drought conditions so that the dry (or almost dry) riverbed is detected, although in an imprecise way. Direct interpolation of the detected elevation points yields unacceptable river channel bottom profiles (often exhibiting unrealistic artifacts) and even distorts the floodplain. In our Fluvial Domain Method, channel bottoms are represented like “highways”, perhaps overlooking their (unknown) detailed morphology but gaining in general topographic consistency. For instance, we observed an 11.7% discrepancy in the river channel long profile (with respect to the measured cross-sections) and a 0.38 m RMSE in the floodplain (with respect to the GNSS-RTK measurements). Unlike conventional methods that utilize active sensors (satellite and airborne LiDAR) or classic topographic surveys—each with precision, cost, or labor limitations—the proposed approach offers a more accessible, cost-effective, and flexible solution that is particularly well suited to cases with scarce base information and financial resources. However, the method’s performance is inherently limited by the quality of input data and the simplification of complex channel morphologies; it is most suitable for cases where high-resolution geomorphological detail is not critical or where direct data acquisition is not feasible. The resulting DEM, incorporating a generalized channel representation, is well suited for flood hazard modeling. A case study of the Ranchería river delta in the Northern Colombian Caribbean demonstrates the methodology. Full article

Accurate measurement of running parameters, including the step length (SL), step frequency (SF), and velocity, is essential for optimizing sprint performance. Traditional methods, such as 2D video analysis and inertial measurement units (IMUs), face limitations in precision and practicality. This study introduces and evaluates two methods for estimating running parameters using real-time kinematic global navigation satellite systems (RTK GNSS) with 100 Hz sampling. Method 1 identifies mid-stance phases via vertical position minima, while Method 2 aligns with the initial contact (IC) events through vertical velocity minima. Two collegiate sprinters completed a 400 m sprint under controlled conditions, with RTK GNSS measurements validated against 3D video analysis and IMU data. Both methods estimated the SF, SL, and velocity, but Method 2 demonstrated superior accuracy, achieving a lower RMSE (SF: 0.205 Hz versus 0.291 Hz; SL: 0.143 m versus 0.190 m) and higher correlation with the reference data. Method 2 also exhibited improved performance in curved sections and detected stride asymmetries with higher consistency than Method 1. These findings highlight RTK GNSS, particularly the velocity minima approach, as a robust, drift-free, single-sensor solution for detailed per-step sprint analysis in outdoor conditions. This approach offers a practical alternative to IMU-based methods and enables training optimization and performance evaluation. Full article

The Global Navigation Satellite System (GNSS), renowned for its high precision and automation, has shone brightly in the deformation monitoring of offshore facilities and sea-crossing bridges. However, antennas placed in these locations are often subject to signal interference from various reflective surfaces, such as rivers and oceans, which significantly compromises observation accuracy and reliability. Synthesizing previous research, we first propose a method for multipath dataset construction, which involves GNSS observation linear combinations, detailed mapping of the near-field reflector, and employed static solution residuals as reference. Subsequently, we construct and train a corresponding para-TCN (parametric Temporal Convolution Network) to enable real-time prediction of multipath prediction. Through time domain and frequency domain analysis, it has been demonstrated that the trained network can capture the main features of multipath models and suppress those components in both the data distribution and frequency band, effectively mitigating the interference of multipath errors in observations. Full article

Unmanned aerial vehicle (UAV) state estimation is fundamental across applications like robot navigation, autonomous driving, virtual reality (VR), and augmented reality (AR). This research highlights the critical role of robust state estimation in ensuring safe and efficient autonomous UAV navigation, particularly in challenging environments. We propose a deep learning-based adaptive sensor fusion framework for UAV state estimation, integrating multi-sensor data from stereo cameras, an IMU, two 3D LiDAR’s, and GPS. The framework dynamically adjusts fusion weights in real time using a long short-term memory (LSTM) model, enhancing robustness under diverse conditions such as illumination changes, structureless environments, degraded GPS signals, or complete signal loss where traditional single-sensor SLAM methods often fail. Validated on an in-house integrated UAV platform and evaluated against high-precision RTK ground truth, the algorithm incorporates deep learning-predicted fusion weights into an optimization-based odometry pipeline. The system delivers robust, consistent, and accurate state estimation, outperforming state-of-the-art techniques. Experimental results demonstrate its adaptability and effectiveness across challenging scenarios, showcasing significant advancements in UAV autonomy and reliability through the synergistic integration of deep learning and sensor fusion. Full article

Electroporation is an efficient method for nucleotide and protein transfer, and is used for clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein 9 (Cas9)-mediated genome editing. In this study, we investigated the effects of electroporation on platelet-derived growth factor receptor alpha (PDGFRA) and receptor tyrosine kinase (RTK) expression in U-251 and U-87 MG cells. PDGFRA mRNA and protein expression decreased 2 days after electroporation in both cell lines, with recovery observed after 13 days in U-87 MG cells. However, in U-251 MG cells, PDGFRα expression remained suppressed, despite mRNA recovery after 13 days. Similar expression profiles were observed for lipofection in the U-251 MG cells. Comprehensive RNA sequencing confirmed electroporation-induced up- and down-regulation of RTK mRNA in U-251 MG cells 2 days post-electroporation. In contrast, recombinant adeno-associated virus (rAAV) transfected with mNeonGreen fluorescent protein or Cas9 did not affect PDGFRA, RTKs, or inflammatory cytokine expression, suggesting fewer adverse effects of rAAV on U-251 MG cells. These findings emphasize the need for adequate recovery periods following electroporation or the adoption of alternative methods, such as rAAV transfection, to ensure the accurate assessment of CRISPR-mediated gene editing outcomes. Full article

With the rapid advancement of drone technology, modern drones have achieved high levels of functional integration, alongside structural improvements that include lightweight, compact designs with foldable features, greatly enhancing their flexibility and applicability in photogrammetric applications. Nevertheless, limited research currently explores data collected by such compact UAVs, and whether they can balance a small form factor with high data quality remains uncertain. To address this challenge, this study acquired the remote sensing data of a peri-urban area using the DJI Mavic 3 Enterprise and applied Object-Based Image Analysis (OBIA) to extract high-density buildings. It was found that this drone offers high portability, a low operational threshold, and minimal regulatory constraints in practical applications, while its captured imagery provides rich textural details that clearly depict the complex surface features in urban villages. To assess the accuracy of the extraction results, the visual comparison between the segmentation outputs and airborne LiDAR point clouds captured by the DJI M300 RTK was performed, and classification performance was evaluated based on confusion matrix metrics. The results indicate that the boundaries of the segmented objects align well with the building edges in the LiDAR point cloud. The classification accuracy of the three selected algorithms exceeded 80%, with the KNN classifier achieving an accuracy of 91% and a Kappa coefficient of 0.87, which robustly demonstrate the reliability of the UAV data and validate the feasibility of the proposed approach in complex cases. As a practical case reference, this study is expected to promote the wider application of lightweight UAVs across various fields. Full article

Sustainable turfgrass management is essential for maintaining healthy and visually appealing green spaces. Autonomous mowers have emerged as an innovative solution, but the efficiency and quality of mowing operations depend on several factors. This study investigates the impact of mowing patterns and cutting heights on the performance of an autonomous mower through updated custom-built software. Three different mowing patterns (vertical, diagonal, and horizontal) and two cutting heights (3 cm and 6 cm) were analyzed to analyze mowing efficiency, coverage, and cutting uniformity. The vertical pattern emerged as the most effective, maximizing speed (0.52 m/s) and efficiency (0.77), while minimizing overlap (4.27 cm) and uncut areas (0.014 m²). In contrast, the horizontal and diagonal patterns showed lower efficiency (0.71 and 0.76) and less coverage percentage (97.05% and 96.71%) compared to the vertical pattern (98.57%). Cutting height influenced performance, with higher heights sometimes requiring adjustments to prevent inefficiencies. The interaction between the mowing pattern and cutting height was critical for optimizing both operational efficiency and cutting quality. These findings highlight the importance of selecting an appropriate mowing pattern and cutting height tailored to the specific operational goals. Full article

Chlorophyll content is an essential parameter for evaluating the growth condition of winter wheat, and its accurate monitoring through remote sensing is of great significance for early warnings about winter wheat growth. In order to investigate unmanned aerial vehicle (UAV) multispectral technology’s capability to estimate the chlorophyll content of winter wheat, this study proposes a method for estimating the relative canopy chlorophyll content (RCCC) of winter wheat based on UAV multispectral images. Concretely, an M350RTK UAV with an MS600 Pro multispectral camera was utilized to collect data, immediately followed by ground chlorophyll measurements with a Dualex handheld instrument. Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. The results provided the following indications: (1) Red-edge vegetation indices (RIs) and TIs were key to estimating RCCC. Univariate regression models were tolerable during the flowering and filling stages, while the superior multivariate models, incorporating multiple features, revealed more complex relationships, improving R² by 0.35% to 69.55% over the optimal univariate models. (2) The RF model showed notable performance in both univariate and multivariate regressions, with the RF model incorporating RIS and TIS during the flowering stage achieving the best results (R²_train = 0.93, RMSE_train = 1.36, RPD_train = 3.74, R²_test = 0.79, RMSE_test = 3.01, RPD_test = 2.20). With more variables, BPNN, KELM, and CNN models effectively leveraged neural network advantages, improving training performance. (3) Compared to using single-feature indices for RCCC estimation, the combination of vegetation indices and texture indices increased from 0.16% to 40.70% in the R² values of some models. Integrating UAV multispectral spectral and texture data allows effective RCCC estimation for winter wheat, aiding wheatland management, though further work is needed to extend the applicability of the developed estimation models. Full article

This study systematically analyzed research trends and hot issues in the field of geological hazard prediction using bibliometric analysis methods. A total of 12,123 related articles published from 1976 to 2023 were retrieved from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases. Co-occurrence analysis and burst detection were conducted on the literature using the VOSviewer and CiteSpace tools to identify the research trends in geological hazard monitoring technologies. The results reveal that “data fusion”, “landslide identification”, “deep learning”, and “risk early warning” are currently the main research hot spots. Additionally, the combined application of Global Navigation Satellite System (GNSS) and Real-Time Kinematic (RTK) technologies, as well as GNSS and Long Short-Term Memory (LSTM) models, were identified as important directions for future research. The bibliometric perspective offers a systematic theoretical framework and technical guidance for future research, thereby facilitating the sustainable advancement of safety, security, and disaster management. Full article

With the aging of the population and the increase in the number of people with disabilities, intelligent wheelchairs are essential in improving travel autonomy and quality of life. In this paper, we propose an autonomous outdoor navigation framework for intelligent wheelchairs based on hierarchical cost maps to address the challenges of wheelchair navigation in complex and dynamic outdoor environments. First, the framework integrates multi-sensors such as RTK high-precision GPS, IMU, and 3D LIDAR; fuses RTK, IMU, and odometer data to realize high-precision positioning; and performs path planning and obstacle avoidance through dynamic hierarchical cost maps. Secondly, the drivable area layer is integrated into the traditional hierarchical cost map, in which the drivable area detection algorithm utilizes local plane fitting and elevation difference analysis to achieve efficient ground point cloud segmentation and real-time updating, which ensures the real-time safety of navigation. The experiments are validated in real outdoor scenes and simulation environments, and the results show that the speed of drivable region detection is about 30 ms, the positioning accuracy of wheelchair outdoor navigation is less than 10 cm, and the distance of active obstacle avoidance is 1 m. This study provides an effective solution for the autonomous navigation of the intelligent wheelchair in a complex outdoor environment, and it has a high robustness and application potential. Full article

The hepatocyte growth factor receptor (MET) is a receptor tyrosine kinase (RTK) that mediates the activity of a variety of downstream pathways upon its activation. These pathways regulate various physiological processes within the cell, including growth, survival, proliferation, and motility. Under normal physiological conditions, this allows MET to regulate various development and regenerative processes; however, mutations resulting in aberrant MET activity and the consequent dysregulation of downstream signaling can contribute to cellular pathophysiology. Mutations within MET have been identified in a variety of cancers and have been shown to mediate tumorigenesis by increasing RTK activity and downstream signaling. In lung cancer specifically, a number of patients have been identified as possessing MET alterations, commonly receptor amplification (METamp) or splice site mutations resulting in loss of exon 14 (METex14). Due to MET’s role in mediating oncogenesis, it has become an attractive clinical target and has led to the development of various targeted therapies, including MET tyrosine kinase inhibitors (TKIs). Unfortunately, these TKIs have demonstrated limited clinical efficacy, as patients often present with either primary or acquired resistance to these therapies. Mechanisms of resistance vary but often occur through off-target or bypass mechanisms that render downstream signaling pathways insensitive to MET inhibition. This review provides an overview of the therapeutic landscape for MET-positive cancers and explores the various mechanisms that contribute to therapeutic resistance in these cases. Full article