Search Results (97)

Accurate extraction of the arch profile, the key spatial geometric parameter of the core load-bearing component in arch bridges, is crucial for construction process control and for achieving the designed final bridge configuration. To overcome the limitations of existing methods—geometric information loss, sensitivity to noise, and inefficiency—when extracting continuous, precise profiles from point clouds of complex spatially curved arch ribs, this paper proposes a multi-step point cloud processing workflow. The approach integrates geometric feature constraints specific to arch bridges to enable automated, high-precision extraction of the arch profile during construction. The approach comprises three steps. First, arch point cloud subset partitioning: the primitive arch point cloud is efficiently divided using parameters from down-sampling arch point cloud data. Second, component segmentation: a Random Sample Consensus (RANSAC) algorithm, optimized with cylindrical geometric constraints, is then employed to precisely segment the point cloud of individual arch tube components from each subset point cloud. Third, arch profile extraction: the geometric invariance of the bottom edge of each arch tube is leveraged to identify feature points via local coordinate system transformation and longitudinal constraints. These feature points are then spliced together to reconstruct the complete arch profile. The proposed method is employed in multiple construction stages of a concrete-filled steel tubular (CFST) arch bridge and quantifies the vertical deformation between adjacent stages. Compared with Total Station (TS) measurements, the average error ranged from 0.24 mm to 4.13 mm, with an overall average error of 2.105 mm, demonstrating accuracy and reliability. Full article

Determining the genetic variations of candidate genes in affected subjects will help identify early pathological biomarkers of Alzheimer’s disease (AD) and develop effective treatments. It has recently been found that some genes that are linked share an increase in intron retention (IR). In this review, we discuss a few instances of mRNA-IR in various genes linked to AD, including APOE, MAPT-Tau, Psen2, Farp1, Gpx4, Clu, HDAC4, Slc16a3, and App genes. These genes are vulnerable to IR, encompassing additional crucial proteins for brain functionality, but they are frequently involved in pathways linked to the control of mRNA and protein homeostasis. Despite the advancements in human in vivo RNA therapy, as far as we know, there are no reports of data generated regarding artificial in vivo splicing in either animal models or humans. To prevent genetic variations and improve or repair errors in expression of desired genes, humans have adopted new gene editing techniques like CRISPR-Cas9 and RNAi modalities. Ultimately, IR could be utilized as a therapeutic potential biomarker for disorders related to intronic expansion. Full article

Accurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these limitations, we propose a B-spline Interpolation and Clustered Means (BICM) method, which fuses multi-view point cloud data captured by RGB-D cameras to enable robust 3D surface reconstruction and precise volume estimation. By incorporating point cloud splicing, down-sampling, clustering, and 3D B-spline interpolation, the proposed method effectively mitigates issues such as surface notches and misalignment, significantly enhancing the accuracy of grain pile volume calculations across different viewpoints and sampling resolutions. The results of this study show that a volumetric measurement error of less than 5% can be achieved using an RGB-D camera located at two orthogonal viewpoints in combination with the BICM method, and the error can be further reduced to 1.25% when using four viewpoints. In addition to providing rapid inventory assessment of grain stocks, this approach also generates accurate local maps for the autonomous navigation of grain silo robots, thereby advancing the level of intelligent management within grain storage facilities. Full article

Large-scale deformation fields are crucial for monitoring seismic activity, landslides, and other geological hazards. Traditionally, the acquisition of large-area, three-dimensional deformation fields has relied on GNSS data; however, the inherent sparsity of these data poses significant limitations. The emergence of Interferometric Synthetic Aperture Radar (InSAR) data offers an alternative, enabling the retrieval of large-area, high-resolution deformation velocity fields. Nonetheless, the processing of InSAR data is often complex, time-consuming, and requires substantial storage capacity. To address these challenges, various research institutions have developed online InSAR processing platforms. For instance, the LiCSAR processing platform provides interferometric images covering approximately 250 km × 250 km, facilitating scientific applications of InSAR data. However, the transition from individual interferograms to large-scale, three-dimensional deformation fields often requires additional processing steps, including ramp correction within the images, mosaicking between adjacent images, and the joint inversion of InSAR observations from different viewing angles. In this paper, we propose a novel method for splicing several individual InSAR velocity fields into continent-scale InSAR velocity maps, which takes along-track and cross-track mosaicking into consideration. This method integrates GNSS data with InSAR data and also considers the additional constraint of data overlap region. The efficacy of this methodology is substantiated through its implementation in InSAR observations of the eastern Tibetan Plateau. In some tracks, there are overlapping areas on the east and west sides, and the line-of-sight (LOS) value can be effectively corrected by using these overlapping areas with similar size for two cross-track mosaics. The root mean square error (RMSE) of these tracks was reduced by about 4% to 8% on average when verified using true values of GNSS data compared to no cross-track mosaic. In addition, a significant improvement of 30% in RMSE reduction was achieved for some tracks. Full article

This study presents the results of an investigation into the potential use of the DiagBelt+ magnetic diagnostic system for assessing the quality of conveyor belt splices. Splices in conveyor belts are susceptible to damage and irregularities resulting from assembly errors, improper vulcanization parameters, or unfavorable operational conditions. Detecting geometric deviations from the reference standard after splice fabrication can serve as a component of QA/QC systems. Later deviations may indicate material or fabrication defects. To date, applications of the DiagBelt+ system have been limited to locating damage within the belt and its splices. Recently, efforts have been made to extend the system’s functionality to include splice diagnostics. This study was conducted under laboratory conditions on an ST2500 belt featuring five splices (three bias and two straight splices). Data acquisition was performed under various configurations of measurement parameters, including sensor-to-belt distance, belt travel speed, and system sensitivity threshold. For each splice, the signal width was measured and analyzed as a potential indicator of splice geometry and quality. The results indicate that the DiagBelt+ system can be effectively used for splice diagnostics. Work has commenced on automating the splice quality assessment process. Full article

The online calibration algorithm for camera and LiDAR helps solve the problem of multi-sensor fusion and is of great significance in autonomous driving perception algorithms. Existing online calibration algorithms fail to account for both real-time performance and accuracy. High-precision calibration algorithms require high hardware requirements, while it is difficult for lightweight calibration algorithms to meet the accuracy requirements. Secondly, sensor noise, vibration, and changes in environmental conditions may reduce calibration accuracy. In addition, due to the large domain differences between different public datasets, the existing online calibration algorithms are unstable for various datasets and have poor algorithm robustness. To solve the above problems, we propose an online calibration algorithm based on multi-scale cost volume fusion. First, a multi-layer convolutional network is used to downsample and concatenate the camera RGB data and LiDAR point cloud data to obtain three-scale feature maps. The latter is then subjected to feature concatenation and group-wise correlation processing to generate three sets of cost volumes of different scales. After that, all the cost volumes are spliced and sent to the pose estimation module. After post-processing, the translation and rotation matrix between the camera and LiDAR coordinate systems can be obtained. We tested and verified this method on the KITTI odometry dataset and measured the average translation error of the calibration results to be 0.278 cm, the average rotation error to be 0.020°, and the single frame took 23 ms, reaching the advanced level. Full article

Low Earth Orbit (LEO) constellations support the transmission of various communication services and have been widely applied in fields such as global Internet access, the Internet of Things, remote sensing monitoring, and emergency communication. With the surge in traffic volume, the quality of user services has faced unprecedented challenges. Achieving accurate low Earth orbit constellation network traffic prediction can optimize resource allocation, enhance the performance of LEO constellation networks, reduce unnecessary costs in operation management, and enable the system to adapt to the development of future services. Ground networks often adopt methods such as machine learning (support vector machine, SVM) or deep learning (convolutional neural network, CNN; generative adversarial network, GAN) to predict future short- and long-term traffic information, aiming to optimize network performance and ensure service quality. However, these methods lack an understanding of the high-dynamics of LEO satellites and are not applicable to LEO constellations. Therefore, designing an intelligent traffic prediction model that can accurately predict multi-service scenarios in LEO constellations remains an unsolved challenge. In this paper, in light of the characteristics of high-dynamics and the high-frequency data streams of LEO constellation traffic, the authors propose a DST-LEO satellite-traffic prediction model (a dynamic spatio-temporal low Earth orbit satellite traffic prediction model). This model captures the implicit features among satellite nodes through multiple attention mechanism modules and processes the traffic volume and traffic connection/disconnection data of inter-satellite links via a multi-source data separation and fusion strategy, respectively. After splicing and fusing at a specific scale, the model performs prediction through the attention mechanism. The model proposed by the authors achieved a short-term prediction RMSE of 0.0028 and an MAE of 0.0018 on the Abilene dataset. For long-term prediction on the Abilene dataset, the RMSE was 0.0054 and the MAE was 0.0039. The RMSE of the short-term prediction on the dataset simulated by the internal low Earth orbit constellation business simulation system was 0.0034, and the MAE was 0.0026. For the long-term prediction, the RMSE reached 0.0029 and the MAE reached 0.0022. Compared with other time series prediction models, it decreased by 22.3% in terms of the mean squared error and 18.0% in terms of the mean absolute error. The authors validated the functions of each module within the model through ablation experiments and further analyzed the effectiveness of this model in the task of LEO constellation network traffic prediction. Full article

The observation and evaluation of vibration signals is crucial for enhancing engineering quality and ensuring the safe operation of equipment. This paper proposes a fiber-optic vibration sensor based on the Sagnac interference principle. The polarization-maintaining fiber (PMF) is spliced between two single mode fibers (SMFs) to form the SMF-PMF-SMF (SPS) fiber structure. The Sagnac interferometer consists of an SPS fiber structure connected to a 3 dB coupler. Due to the principle of the elastic-optical effect, the interferometric spectrum of the PMF-based Sagnac interferometric structure changes when the PMF is subjected to stress, enabling vibration to be measured. The experimental results show that the relative measurement error of the fiber-optic vibration sensor for healthy and faulty bearings is less than 1.8%, which verifies the effectiveness and accuracy of the sensor. The sensor offers benefits of excellent anti-vibration fatigue characteristics, simple production, small size, light weight, and has a wide range of applications in mechanical engineering, fault detection, safety and security, and other fields. Full article

Perpendicular-orbit circular scanning satellites overcome the conflict between ground resolution and width observed in traditional optical satellites by using a perpendicular-orbit circular scanning imaging method and splicing along the orbit, achieving a balance between an ultra-large width and a high resolution. However, laboratory calibrations of perpendicular-orbit circular scanning satellites exhibit large errors due to the influence of factors such as the thermal and mechanical environment of space during the launch and operation of satellites, and thus, they cannot be applied. In this paper, we start by analysing the in-camera azimuth element errors of perpendicular-orbit circular scanning satellites, then derive a probe element pointing angle calibration model from the physical in-camera calibration model and carry out in-camera parameter calibration based on simulated image data from an ultra-wide perpendicular-orbit circular scanning satellite. Edge and centre strips were selected for the experiment, and a certain number of control points were placed uniformly near the middle column (perpendicular orbit) of the image in each strip and covering all row directions (along orbit). Checkpoints were uniformly selected across a range of widths. The results show that in-orbit geometric calibration can significantly improve the direct-to-ground positioning accuracy of perpendicular-orbit circular scanning satellites, with the positioning accuracy error shown to be better than 30 m within a width of 300 km, 30 m within a width of 1000 km, and 50 m within a width of 2000 km. Full article

The energy supply of ocean monitoring buoys is a major challenge, especially for long-term, low-power applications. Data compression can reduce transmission energy and extend system lifespan. In particular, the algorithm cannot introduce delays to ensure real-time monitoring. In this scenario, we propose an efficient real-time compression scheme for lossless data compression (ERCS_Lossless) based on Golomb-Rice coding to efficiently compress each dimensional data independently. Additionally, we propose an efficient real-time compression scheme for lossy data compression with a flag mechanism (ERCS_Lossy_Flag), which incorporates a flag bit for each dimension, indicating if the prediction error exceeds a threshold, followed by further compression using Golomb-Rice coding. We conducted experiments on 24-dimensional weather and wave element data from a single buoy, and the results show that ERCS_Lossless achieves an average compression rate of 47.40%. In real communication scenarios, splicing and byte alignment operations are performed on multidimensional data, and the results show that the variance of the payload increases but the mean decreases after compression, realizing a 38.60% transmission energy saving, which is better than existing real-time lossless compression methods. In addition, ERCS_Lossy_Flag further reduces the amount of data and improves energy efficiency when lower data accuracy is acceptable. Full article

The notion of RNA-based therapeutics has gained wide attractions in both academic and commercial institutions. RNA is a polymer of nucleic acids that has been proven to be impressively versatile, dating to its hypothesized RNA World origins, evidenced by its enzymatic roles in facilitating DNA replication, mRNA decay, and protein synthesis. This is underscored through the activities of riboswitches, spliceosomes, ribosomes, and telomerases. Given its broad range of interactions within the cell, RNA can be targeted by a therapeutic or modified as a pharmacologic scaffold for diseases such as nucleotide repeat disorders, infectious diseases, and cancer. RNA therapeutic techniques that have been researched include, but are not limited to, CRISPR/Cas gene editing, anti-sense oligonucleotides (ASOs), siRNA, small molecule treatments, and RNA aptamers. The knowledge gleaned from studying RNA-centric mechanisms will inevitably improve the design of RNA-based therapeutics. Building on this understanding, we explore the physiological diversity of RNA functions, examine specific dysfunctions, such as splicing errors and viral interactions, and discuss their therapeutic implications. Full article

Background: The advent of next-generation sequencing (NGS) has revolutionized the analysis of genetic data, enabling rapid identification of pathogenic variants in patients with inborn errors of immunity (IEI). Sometimes, the use of NGS-based technologies is associated with challenges in the evaluation of the clinical significance of novel genetic variants. Methods: In silico prediction tools, such as SpliceAI neural network, are often used as a first-tier approach for the primary examination of genetic variants of uncertain clinical significance. Such tools allow us to parse through genetic data and emphasize potential splice-altering variants. Further variant assessment requires precise RNA assessment by agarose gel electrophoresis and/or cDNA Sanger sequencing. Results: We found two novel heterozygous variants in the coding region of the LYST gene (c.10104G>T, c.10894A>G) in an individual with a typical clinical presentation of Chediak–Higashi syndrome (CHS). The SpliceAI neural network predicted both variants as probably splice-altering. cDNA assessment by agarose gel electrophoresis revealed the presence of abnormally shortened splicing products in each variant’s case, and cDNA Sanger sequencing demonstrated that c.10104G>T and c.10894A>G substitutions resulted in a shortening of the 44 and 49 exons by 41 and 47 bp, respectively. Both mutations probably lead to a frameshift and the formation of a premature termination codon. This, in turn, may disrupt the structure and/or function of the LYST protein. Conclusions: We identified two novel variants in the LYST gene, predicted to be deleterious by the SpliceAI neural network. Agarose gel cDNA electrophoresis and cDNA Sanger sequencing allowed us to verify inappropriate splicing patterns and establish these variants as disease-causing. Full article

This paper proposes a new type of solar trough collector with a spliced cylindrical mirror and develops a new ray-tracing method to predict and optimize its performance. The mirrors of this system are composed of multiple cylindrical mirrors whose centers are on a parabola, and the normal vector of the centers of each cylindrical mirror is consistent with the normal vector of the parabola point where it is located. The new ray-tracing method is based on the transverse distribution of solar radiation, and it has been validated with Soltrace, with the maximum intercept factor error in the calculations being less than 0.31%. This paper compares the spliced cylindrical mirror trough solar system with the conventional parabolic trough system and finds that the influence of cylindrical, spherical, and coma aberration can be reduced to negligible levels by adjusting the system design. At the same time, the slope error and cost of the cylindrical mirror are much less than the parabolic mirror so it has better performance from numerical simulation. The spliced cylindrical mirror system can be further optimized to achieve an annual net efficiency of 65.52% in the north–south horizontal axis tracking mode. Full article

This study proposes an autonomous recognition and approach method for unmanned aerial vehicles (UAVs) targeting linear splicing sleeves. By integrating deep learning and active stereo vision, this method addresses the navigation challenges faced by UAVs during the identification, localization, and docking of splicing sleeves on overhead power transmission lines. First, a two-stage localization strategy, LC (Local Clustering)-RB (Reparameterization Block)-YOLO (You Only Look Once)v8n (OBB (Oriented Bounding Box)), is developed for linear target splicing sleeves. This strategy ensures rapid, accurate, and reliable recognition and localization while generating precise waypoints for UAV docking with splicing sleeves. Next, virtual reality technology is utilized to expand the splicing sleeve dataset, creating the D_SS dataset tailored to diverse scenarios. This enhancement improves the robustness and generalization capability of the recognition model. Finally, a UAV approach splicing sleeve (UAV-ASS) visual navigation simulation platform is developed using the Robot Operating System (ROS), the PX4 open-source flight control system, and the GAZEBO 3D robotics simulator. This platform simulates the UAV’s final approach to the splicing sleeves. Experimental results demonstrate that, on the D_SS dataset, the RB-YOLOv8n(OBB) model achieves a mean average precision (mAP0.5) of 96.4%, with an image inference speed of 86.41 frames per second. By incorporating the LC-based fine localization method, the five rotational bounding box parameters (x, y, w, h, and angle) of the splicing sleeve achieve a mean relative error (MRE) ranging from 3.39% to 4.21%. Additionally, the correlation coefficients (ρ) with manually annotated positions improve to 0.99, 0.99, 0.98, 0.95, and 0.98, respectively. These improvements significantly enhance the accuracy and stability of splicing sleeve localization. Moreover, the developed UAV-ASS visual navigation simulation platform effectively validates high-risk algorithms for UAV autonomous recognition and docking with splicing sleeves on power transmission lines, reducing testing costs and associated safety risks. Full article

Immune checkpoint blockade (ICB), radiotherapy, chemotherapy and surgery are currently used as therapeutic strategies against melanoma, lung, bladder and renal cancers, but their efficacy is limited. Thus, I need to predict treatment response and resistance to address this challenge. In this study, I analyzed 350 lung cancer, 320 melanoma, 215 bladder cancer, 139 head and neck cancer and 151 renal carcinoma patients treated with ICB to identify tumor mutations associated with response and resistance to treatment. I identified several tumor mutations linked with a difference in survival outcomes following ICB. In lung cancer, missense mutations in ABL1, ASXL1, EPHA3, EPHA5, ERBB4, MET, MRE11A, MSH2, NOTCH1, PAK7, PAX5, PGR, ZFHX3, PIK3C3 and REL genes were indicative of favorable responses to ICB. Conversely, mutations in TGFBR2, ARID5B, CDKN2C, HIST1H3I, RICTOR, SMAD2, SMAD4 and TP53 genes were associated with shorter overall survival post-ICB treatment. In melanoma, mutations in FBXW7, CDK12, CREBBP, CTNNB1, NOTCH1 and RB1 genes predict resistance to ICB, whereas missense mutations in FAM46C and RHOA genes are associated with extended overall survival. In bladder cancer, mutations in HRAS genes predict resistance to ICB, whereas missense mutations in ERBB2, GNAS, ATM, CDKN2A and LATS1 genes, as well as nonsense mutations in NCOR1 and TP53 genes, are associated with extended overall survival. In head and neck cancer, mutations in genes like PIK3CA and KRAS correlated with longer survival, while mutations in genes like TERT and TP53 were linked to shorter survival. In renal carcinoma, mutations such as EPHA5, MGA, PIK3R1, PMS1, TSC1 and VHL were linked to prolonged overall survival, while others, including total splice mutations and mutations in B2M, BCOR, JUN, FH, IGF1R and MYCN genes were associated with shorter overall survival following ICB. Then, I developed predictive survival models by machine learning that correctly forecasted cancer patient survival following ICB within an error between 5 and 8 months based on their distinct tumor mutational attributes. In conclusion, this study advocates for personalized immunotherapy approaches in cancer patients. Full article