Plane Features Research Articles

Comparison and risk factors analysis of multiple breast cancer screening methods in the evaluation of breast non-mass-like lesions

ObjectiveTo compare multiple breast cancer screening methods for evaluating breast non-mass-like lesions (NMLs), and investigate new best screening method for breast non-mass-like lesions and the value of the lexicon of ACR BI-RADS in NML evaluation.MethodsThis retrospective study examined 253 patients aged 24–68 years who were diagnosed with breast NMLs and described the lexicon of ACR BI-RADS from April 2017 to December 2019. All lesions were evaluated by HHUS, MG, and ABUS to determine BI-RADS category, and underwent pathological examination within six months or at least 2 years of follow-up. The sensitivity, specificity, accuracy, positive predictive values (PPV), and negative predictive values (NPV) of MG, HHUS and ABUS in the prediction of malignancy were compared. Independent risk factors for malignancy were assessed using non-conditional logistic regression.ResultsHHUS, MG and ABUS findings significantly differed between benign and malignant breast NML, including internal echo, hyperechoic spot, peripheral blood flow, internal blood flow, catheter change, peripheral change, coronal features of ABUS, and structural distortion, asymmetry, and calcification in MG. ABUS is superior to MG and HHUS in sensitivity, specificity, PPV, NPV, as well as in evaluating the necessity of biopsy and accuracy in identifying malignancy. MG was superior to HHUS in specificity, PPV, and accuracy in evaluating the need for biopsy.ConclusionsABUS was superior to HHUS and MG in evaluating the need for biopsy in breast NMLs. Compared to each other, HHUS and MG had their own relative advantages. Internal blood flow, calcification, and coronal plane feature was independent risk factors in NMLs Management, and different screening methods had their own advantages in NML management. The lexicon of ACR BI-RADS could be used not only in the evaluation of mass lesions, but also in the evaluation of NML.

Performance of highly irradiated FBK planar and 3D pixel detectors

The High Luminosity upgrade of the CERN Large Hadron Collider (HL-LHC) requires new radiation tolerant silicon pixel sensors. In the case of the CMS experiment, the first layer of pixel detectors will be installed at about 3 cm from the beam line, where an integrated fluence of about 20×1015neqcm−2 (1 MeV equivalent neutrons) is expected (HL-LHC Runs 4 and 5). The first tracker layer will be substituted before the start of HL-LHC Run 6. The 3D concept for silicon pixel sensors presents several advantages with respect to traditional (planar) sensors. Thanks to short anode-to-cathode distances, 3D sensors are much more resistant to radiation damage, making them suitable for use in the inner layer of the future tracker. This paper describes results from beam tests with highly irradiated planar and 3D sensor and RD53A readout chip combinations. RD53A is the first prototype in 65 nm technology developed by the RD53 collaboration for use in HL-LHC pixel detectors. The sensors were made in FBK foundry in Trento, Italy, and their development was done in collaboration with INFN (Istituto Nazionale di Fisica Nucleare, Italy). Both planar and 3D sensors feature a pixel area of 2500 μm2 and an active thickness of 150 μm. The pixel detectors, irradiated to fluences up to 24×1015neqcm−2, were tested in the DESY test beam facility and the analysis of the data shows excellent performances even for the highest irradiation fluences. All results are obtained in the framework of the CMS R&D activities.

Eğitim Kurumlarında Kaliteleşme Çabalarının Globalleşme Süreci

The facilitation of the transfer of knowledge and technology through globalization has also caused changes in the educational understandings of nations. Especially after the economic crises of the late 1970s and early 1980s, reforms were made that supported continuous development in teaching, and instead of heavy approaches that made the student a porter of information, strategic learning methods were organized. This situation has brought with it the conditions for the emergence of an understanding of quality in educational institutions. The understanding of quality is a management model that is spreading rapidly in educational institutions in our country in parallel with global developments. Although it first appeared in industrial enterprises, today it is applied in the form of a plane feature in all social work and operations. Key Words: Educational Institutions, The Concept of Quality, Globalization

DR-SLAM: drift rejection SLAM with Manhattan regularity for indoor environments

In this paper, a drift rejection SLAM (Simultaneous Localization and Mapping) method is proposed targeting indoor scenarios, where SLAM generates large drifts due to the lack of reliable features. To provide sufficient features, we leverage multiple feature primitives and geometric restraints (parallel or perpendicular) restraint in man-made environments. Under some satisfy Manhattan World (MW) assumption scene, such as corridors, we can get absolute and drift-free rotation estimation using a Gaussian sphere. By fully utilizing drift-free rotation estimation under MW assumption and the local stability of purely track restricted by point, line, and plane features, our drift rejection SLAM method becomes more accurate and robust. Additionally, by exploiting the constraint of planar motion on ground robot, we propose an ingenious strategy to reduce translation drift by eliminating vertical movement in the Manhattan world. Advantages of our method over other state-of-the-art algorithms are validated on public datasets and real-world experiments. The code is released at https://github.com/WangWen-Believer/DR-SLAM.

A Spatiotemporal Calibration Algorithm for IMU-LiDAR Navigation System Based on Similarity of Motion Trajectories.

The fusion of light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensing information can effectively improve the environment modeling and localization accuracy of navigation systems. To realize the spatiotemporal unification of data collected by the IMU and the LiDAR, a two-step spatiotemporal calibration method combining coarse and fine is proposed. The method mainly includes two aspects: (1) Modeling continuous-time trajectories of IMU attitude motion using B-spline basis functions; the motion of the LiDAR is estimated by using the normal distributions transform (NDT) point cloud registration algorithm, taking the Hausdorff distance between the local trajectories as the cost function and combining it with the hand–eye calibration method to solve the initial value of the spatiotemporal relationship between the two sensors’ coordinate systems, and then using the measurement data of the IMU to correct the LiDAR distortion. (2) According to the IMU preintegration, and the point, line, and plane features of the lidar point cloud, the corresponding nonlinear optimization objective function is constructed. Combined with the corrected LiDAR data and the initial value of the spatiotemporal calibration of the coordinate systems, the target is optimized under the nonlinear graph optimization framework. The rationality, accuracy, and robustness of the proposed algorithm are verified by simulation analysis and actual test experiments. The results show that the accuracy of the proposed algorithm in the spatial coordinate system relationship calibration was better than 0.08° (3δ) and 5 mm (3δ), respectively, and the time deviation calibration accuracy was better than 0.1 ms and had strong environmental adaptability. This can meet the high-precision calibration requirements of multisensor spatiotemporal parameters of field robot navigation systems.

A Mine Intersection Recognition Method Based on Geometric Invariant Point Detection Using 3D Point Cloud

In large-scale underground mines and mine-like deep urban canyons, intersections are crucial geometry structures. Accurate recognition of intersections is of great importance to improve localization precision for Unmanned Aerial Vehicles(UAVs) and Unmanned Ground Vehicles(UGVs). Currently, traditional Lidar-based localization methods are limited in the mine environment. While 3D Lidar data is accurate but sparse and disordered, making it difficult to extract valid semantic information to correct the accumulated error. The similar geometry structure leads to the challenge of forming a uniform description for recognizing the same intersection with different scanning poses. To overcome these issues, this paper presents a novel real-time intersection recognition method in mine scenarios, encoding the 3D point cloud into a compact vector. RANSAC plane fitting algorithm is employed to extract stable ground plane feature. By compressing representative wall points segmented from raw point cloud into a 2D grid image and detecting the geometry invariant point in the ground plane, the proposed method completes a generic intersection description robust to spatial x-y-z translation and roll-pitch rotation variation. In addition, yaw rotation invariance is achieved through maximum cosine similarity searching in matching two point clouds. Experimental results on simulation mine and public deep urban canyon datasets demonstrate its superiority in the recognition of intersections in mine scenarios.

Promoting productive metathesis pathway and tuning activity of multidentate molybdenum catalysts in alkyne metathesis: A theoretical perspective

Molybdenum alkylidyne complexes, including those containing monodentate or multidentate ligands, can catalyze CC bond formation via alkyne metathesis, which is of great importance to organic synthesis and materials chemistry. Herein, to understand the catalyst working mechanism and explore the key parameters determining the catalyst performance, alkyne metathesis catalyzed by molybdenum-based multidentate complexes was systematically investigated using density functional theory calculations. By comparing the desired cross metathesis and undesired polymerization pathways in multidentate and monodentate catalytic systems, the former shows significantly higher activation barrier for alkyne polymerization, resulting in the shutdown of ring-expansion by-product formation. Furthermore, the calculations show that the multidentate ligands taking trigonal-pyramidal geometry play two roles: (1) occupying one of substrate coordination sites to inhibit the polymerization pathway, thus promoting the desired productive pathway; (2) changing the electronic and steric environment to tune catalytic activity. It is worth noting that the metallacyclobutadiene complex with hither plane feature may play a key role in determining the activity of the catalysts. This work thus provides a guiding principle toward high-performance multidentate catalysts for alkyne metathesis.

Research on Mechanical Properties and Macroscopic Fracture Evolution of Rock with Different Joint Plane Features

To analyze the safety of geological engineering, experimental research on the mechanical properties of the rock joint plane is critical. In this paper, triaxial compression tests are carried out on rock specimens with the different prefabricated joint planes, including different dip angles, roughness, confining pressure and surrounding rock strength, and the strength characteristics, failure modes, stress evolution laws, and crack propagation laws are revealed. The test results show that when the specimen is damaged, the strain, peak stress, and residual strength all increase with the increase of roughness, confining pressure, and strength of surrounding rock but decrease significantly with the increase of joint plane angle. With the increase of stress, the stress-strain curve shows multiple fluctuations, indicating that the specimen occurs multiple slip-stable phenomena during the loading process. With the increase of the angle, the multiple cracks of the specimen gradually merge, and the cracks are always vertical to the joint plane, showing splitting failure. As the roughness of the joint plane increases, the possibility of the joint plane sliding becomes smaller, and the splitting mode of the specimen develops from multiple fractures to a single fracture. As the strength of the surrounding rock increases, the slope of the stress-strain curve gradually increases, and the splitting mode develops from a small number of single splits to multiple splitting penetration failures. The increase of the confining pressure significantly improves the failure strength of the specimen, and the crack number of the damaged specimen also increases significantly. The research results provide theoretical support for scientific analysis of the stability of underground engineering under the disturbance of fissures or faults.

Spectrum-matched ground motion selection method based on Siamese Convolutional Neural Networks

Selecting spectrum-matched ground motions is one of the critical problems in the seismic analysis and design of structures. The sum of the square errors (SSE) has been commonly used to quantitatively measure the similarity between the response spectrum of a ground motion and a target spectrum. However, it cannot represent the two-dimensional plane feature of the response spectrum because the response spectrum is treated as a one-dimensional vector in the SSE-based approaches. Considering the high-performance feature extraction of images and small-sample learning for the Siamese Convolutional Neural Networks (SCNNs), a new spectrum-matched ground motion selection method is proposed based on the SCNNs. The types of ground A, B, C, and D acceleration design spectra for the Eurocode 8 are selected as the target spectra without loss of generality. The procedures for the sample image generation, network training, testing, and spectrum-matched ground motion selection are elaborated. Analysis results indicate that using 40 samples for each class can achieve satisfactory training results. The mean response spectrum of the ground motions selected by the proposed method is matched with the target spectrum in all periods. Compared to the SSE-based approaches, the results obtained by the proposed method have a minor standard deviation. The proposed method could be used as an alternative in the ground motion selection for the dynamic analysis of structures.

Hyperspectral Image Reconstruction Based on Spatial-Spectral Domains Low-Rank Sparse Representation

The enormous amount of data that are generated by hyperspectral remote sensing images (HSI) combined with the spatial channel’s limited and fragile bandwidth creates serious transmission, storage, and application challenges. HSI reconstruction based on compressed sensing has become a frontier area, and its effectiveness depends heavily on the exploitation and sparse representation of HSI information correlation. In this paper, we propose a low-rank sparse constrained HSI reconstruction model (LRCoSM) that is based on joint spatial-spectral HSI sparseness. In the spectral dimension, a spectral domain sparsity measure and the representation of the joint spectral dimensional plane are proposed for the first time. A Gaussian mixture model (GMM) that is based on unsupervised adaptive parameter learning of external datasets is used to cluster similar patches of joint spectral plane features, capturing the correlation of HSI spectral dimensional non-local structure image patches while performing low-rank decomposition of clustered similar patches to extract feature information, effectively improving the ability of low-rank approximate sparse representation of spectral dimensional similar patches. In the spatial dimension, local-nonlocal HSI similarity is explored to refine sparse prior constraints. Spectral and spatial dimension sparse constraints improve HSI reconstruction quality. Experimental results that are based on various sampling rates on four publicly available datasets show that the proposed algorithm can obtain high-quality reconstructed PSNR and FSIM values and effectively maintain the spectral curves for few-band datasets compared with six currently popular reconstruction algorithms, and the proposed algorithm has strong robustness and generalization ability at different sampling rates and on other datasets.

LIDAR-Inertial Real-Time State Estimator with Rod-Shaped and Planar Feature

State estimation and mapping based on Light Detection and Ranging (LIDAR) are important for autonomous systems. Point cloud registration is a crucial module affecting the accuracy and real-time performance of LIDAR simultaneous localization and mapping (SLAM). In this paper, a novel point cloud feature selection for LIDAR-inertial tightly coupled systems is proposed. In the front-end, a point cloud registration is carried out after marking rod-shaped and planar feature information which is different from the existing LIDAR and inertial measurement unit (IMU) integration scheme. This preprocessing method subsequently reduces the outliers. IMU pre-integration outputs high-frequency result and is used to provide the initial value for LIDAR solution. In the scan-to-map module, a computationally efficient graph optimization framework is applied. Moreover, the LIDAR odometry further constrains the IMU states. In the back-end, the optimization based on sliding-window incorporates the LIDAR-inertial measurement and loop closure global constraints to reduce the cumulative error. Combining the front-end and back-end, we propose the low drift and high real-time LIDAR-inertial positioning system. Furthermore, we conducted an exhaustive comparison in open data sequences and real-word experiments. The proposed system outperforms much higher positioning accuracy than the state-of-the-art methods in various scenarios. Compared with the LIO-SAM, the absolute trajectory error (ATE) average RMSE (Root Mean Square Error) in this study increases by 64.45% in M2DGR street dataset (street_01, 04, 07, 10) and 24.85% in our actual scene datasets. In the most time-consuming mapping module of each system, our system runtime can also be significantly reduced due to the front-end preprocessing and back-end graph model.

Ultrasonic Nondestructive Characterization of Blockages and Defects in Underground Pipes.

This article explores the use of a 40-kHz air-coupled ultrasonic array in detecting and imaging blockages and defects in buried pipes with 17-26 wavelengths in diameter at short ranges (approximately 20-60 wavelengths). In particular, the imaging performance of arrays with different numbers of transducers is quantified and compared to establish how many are required for adequate performance. Even low numbers of transducers (<25) are capable of producing -6-dB contours of blockages that match reference images to within 95% by restricting the aperture to maintain element density. However, doing so also limits the resolving power, so arrays with more transducers ultimately image better by having great enough density even at larger apertures. Using <25 transducers also gives a poor contrast ratio of features above background noise (as low as 2), resulting in low tolerance for detection and producing unusable images in some cases. More robust performance is achieved with larger numbers of transducers, which achieves sufficient contrast. All images of planar objects feature a low-amplitude band due to interference between direct reflections and reflections via the pipe wall, which was verified by comparison to simulation. When tested in larger pipes in a deployment case, the low-amplitude band was notably larger but was found to decrease in size at longer ranges.

3D point cloud recognition of substation equipment based on plane detection

Substation equipment identification is a key step in the process of intelligent substation designing. The target recognition of 3D point cloud of substation equipment firstly uses a 3D laser scanner to obtain the 3D point cloud data that can express the surface shape and spatial position of a substation device, then identify the device information by matching it with template point cloud data. Because the point cloud data obtained by 3D laser scanner is affected by equipment attitude and substation environment, there is redundant, missing, and shadow phenomena in the data, resulting in a low algorithm recognition rate. This paper proposes a method combining plane detection and point cloud registration algorithm, achieving substation equipment identification better result. The main steps of the method include: point cloud preprocessing, plane detecting, preliminary filtering based on Umeyama registration method, and Iterative Closest Point (ICP) algorithm identifying. Plane detection is used for the pre-processed point cloud to obtain planar features, then samples and the planar feature templates in template library are preliminarily selected by Umeyama registration method, and substation equipment is finally identified by Iterative Closest Point algorithm. Experiments show that the recognition rate of devices point cloud reaches 92.2% and the recognition time is shorter, which proves that this recognition algorithm has a good recognition effect.

Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer.

At present, early lung cancer screening is mainly based on radiologists' experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adenocarcinoma. Moreover, rapid freezing pathology has a low diagnostic accuracy for small-diameter nodules. Because of the above problems, an algorithm for diagnosing invasive adenocarcinoma nodules in ground-glass pulmonary nodules is based on CT images. According to the nodule space information and plane features, sample data of different dimensions are designed, namely, 3D space and 2D plane feature samples. The network structure is designed based on the attention mechanism and residual learning unit; 2D and 3D neural networks are along built. By fusing the feature vectors extracted from networks of different dimensions, the diagnosis results of invasive adenocarcinoma nodules are finally obtained. The algorithm was studied on 1760 ground-glass nodules with 5-20 mm diameter collected from a city chest hospital with surgical and pathological results. There were 340 nodules with invasive adenocarcinoma and 340 with noninvasive adenocarcinoma. A total of 1420 invasive nodule samples were cross-validated on this example dataset. The classification accuracy of the algorithm was 82.7%, the sensitivity was 82.9%, and the specificity was 82.6%.

Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry

This letter proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Experiments on indoor and unstructured outdoor environments with solid-state LiDAR and non-repetitive scanning LiDAR further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns (see our attached video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ). Our codes and dataset are open-sourced on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>

Point Cloud Registration Leveraging Structural Regularity in Manhattan World

Point cloud registration is an essential technique for many tasks including autonomous navigation, augmented/virtual reality and scene reconstruction. In man-made environments, abundant plane features with structural regularities are commonly seen, which is actually beneficial for point cloud registration. This paper presents an accurate and robust registration method that formulates the registration problem as a maximum likelihood estimation (MLE) problem where the useful structure information is also leveraged. To align two point sets efficiently, one point set is first represented as a Gaussian mixture models (GMM) tree, and then the Manhattan frame (MF) with dominant planes is estimated and the degenerated nodes corresponding to the dominant planes in the GMM tree are clustered to embody the structure information. The proposed MLE problem is solved by an Expectation Maximization (EM) framework. In the E step, the probabilistic data association between the GMM and another point set is first estimated and then refined by aligning the MF and planes. In the M step, an optimization problem using Mahalanobis distance is solved. Experimental results on both indoor room and hybrid man-made environments show that the proposed method not only achieves a good balance between accuracy and speed, but also provides robust and accurate performance in sequence registration process. The source code is available on our Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Point cloud registration and localization based on voxel plane features

The 3D point cloud can directly provide accurate distance information, which facilitates many applications such as autonomous driving and environmental modeling. In order to construct more complete environmental information for these applications, it is necessary to register point clouds that are obtained from different poses. Registration after coarse localization is an effective method for understanding the pose of the device among the context of its environment. Here, a method for extracting plane features based on voxels is proposed and used for coarse registration and localization. The point cloud is divided into voxels by an octree, and voxels with the same plane characteristics are merged to obtain plane features. The candidate transformation matrix is calculated by using the corresponding plane set, and the RANSAC process with two-level transformation matrix verification, including quick verification and fine verification, is used to find the optimum transformation matrix from the candidates after clustering. Then, the coarse registration can be achieved. With the extracted plane features, a combined plane feature description of the point cloud frame is constructed to fulfill fast frame-level global localization. With the integration of the registration method presented in the current paper, pose localization can also be achieved. Experimental results show that the proposed method can achieve more than 85% successful registration rate with short time consumptions. This implies that the proposed method is more efficient than the benchmark method. Even when the map is large, frame-level localization is still fast, and has a successful localization rate of over 90%. The corresponding code are available at https://github.com/zhanjiawang/VPFBR-L.

Atomic Defect Mediated Li-Ion Diffusion in a Lithium Lanthanum Titanate Solid-State Electrolyte.

Lithium lanthanum titanium oxide (LLTO) as a fast Li-ion conductor is a promising candidate for future all-solid-state Li batteries. Fundamental understanding of the microstructure of LLTO and its effect on the Li+ diffusion mechanism, especially across different length scales and interfaces, is a prerequisite to improving the material design and processing development of oxide-based solid electrolytes. Herein, through detailed structural analysis of LLTO ceramic pellets by aberration-corrected transmission electron microscopy, we discovered previously unreported intrinsic planar defects in LLTO single-crystal grains. These planar defects feature an antiphase boundary along specific crystal planes with a "rock-salt" structure enriched by Li within a few atomic layers. Corroborated by density-functional-theory-based calculations, we show an increased diffusion barrier across these planar defects inevitably lowers the bulk Li+ diffusivity of the oxide electrolyte.

Feasibility of Constructing an Automatic Meniscus Injury Detection Model Based on Dual-Mode Magnetic Resonance Imaging (MRI) Radiomics of the Knee Joint.

Objective To explore the feasibility of automatically detecting the degree of meniscus injury by radiomics fusion of dual-mode magnetic resonance imaging (MRI) features of sagittal and coronal planes of the knee joint. Methods This retrospective study included 164 arthroscopically confirmed meniscus injuries in 152 patients admitted to the Department of Orthopaedics of our hospital from July 2018 to March 2021. A total of 1316-dimensional radiomics signatures were extracted from single-mode sagittal and coronal plane images of menisci, respectively. Then, the sagittal and coronal plane features were fused to form a dual-mode joint feature group with a total of 2632-dimensional radiomics signatures. The minimum redundancy maximum relevance (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression were used to select features and generate optimal radiomics signatures. The single-mode sagittal plane feature model (Model 1), single-mode coronal plane feature model (Model 2), and the combined sagittal and coronal plane feature model (Model 3) performance were tested by receiver operating characteristic (ROC) curves and Delong test. The calibration curve test was used to verify the reliability of radiomics signatures of the three models. Results The average intra- and interobserver intraclass correlation coefficients (ICCs) of the most significant 8-dimensional radiomics signatures of Model 1 and Model 2 were 0.935 (range 0.832-0.998) and 0.928 (range 0.845-0.998), respectively. All the three models had good detection performance; Model 3 had the most significant performance (the areas under the curve (AUCs) of training, and validation sets were 0.947 and 0.923, respectively), which was superior to Model 1 (AUCs of training set and validation set were 0.889 and 0.876, respectively) and Model 2 (AUCs of training set and validation set were 0.831 and 0.851, respectively). The detection probability of training and validation sets in the three models was highly consistent with the actual clinical probability. Conclusions It is feasible to establish a model for automatic detection of meniscus damage by means of radiomics. The detection performance of the dual-mode knee MRI model is better than that of any single-mode model, showing potent feature analysis ability and outstanding detection performance.

The inscription on the Vimose plane and (other) West Germanic finds from Denmark

Abstract The Vimose plane features an early runic inscription that has long remained opaque, with none of the attempts to explain it having commanded assent in the historiography. Like the inscription on the Vimose buckle, however, the text on the wood plane appears to preserve an early example of West Germanic religious language. The inscription on the sharpener shows some parallels with comparable Roman texts but also distinctively West Germanic phonological development. The text on the plane seems to be one of several early runic texts found in the Southern Scandinavian votive bogs that preserve Ingvaeonic features.