Positional Relationship Research Articles

Dogs with severe tracheal flattening exhibit lower degrees of left lateralization of the cervical esophagus.

Herein, we investigated the positional relationship between the cervical esophagus and trachea using computed tomography (CT) images in dogs with tracheal flattening. From these CT images, the ratio of the tracheal diameters in the short (vertical) and long (horizontal) axes of the tracheal ring (S/L ratio) and the vertebra - trachea - esophagus (VTE) angle were measured at the point from the 3rd cervical (C3) to the 3rd thoracic vertebra (T3) levels. The VTE angle in dogs with tracheal flattening with an S/L ratio under 0.24 tended to be smaller than the angles at the level from C4 to T2 in dogs with an S/L ratio over 0.25. The positional relationship between the esophagus and trachea seems to be possibly related to tracheal cross-sectional shape.

DPNet: Scene text detection based on dual perspective CNN-transformer.

With the continuous advancement of deep learning, research in scene text detection has evolved significantly. However, complex backgrounds and various text forms complicate the task of detecting text from images. CNN is a deep learning algorithm that automatically extracts features through convolution operation. In the task of scene text detection, it can capture local text features in images, but it lacks global attributes. In recent years, inspired by the application of transformers in the field of computer vision, it can capture the global information of images and describe them intuitively. Therefore, this paper proposes scene text detection based on dual perspective CNN-transformer. The channel enhanced self-attention module (CESAM) and spatial enhanced self-attention module (SESAM) proposed in this paper are integrated into the traditional ResNet backbone network. This integration effectively facilitates the learning of global contextual information and positional relationships of text, thereby alleviating the challenge of detecting small target text. Furthermore, this paper introduces a feature decoder designed to refine the effective text information within the feature map and enhance the perception of detailed information. Experiments show that the method proposed in this paper significantly improves the robustness of the model for different types of text detection. Compared to the baseline, it achieves performance improvements of 2.51% (83.81 vs. 81.3) on the Total-Text dataset, 1.87% (86.07 vs. 84.2) on the ICDAR 2015 dataset, and 3.63% (86.72 vs. 83.09) on the MSRA-TD500 dataset, while also demonstrating better visual effects.

Atomic force microscopy wide-field scanning imaging using homography matrix optimization

During the offline combination of multiple atomic force microscopy (AFM) images, changes in probe displacement can be affected by dynamic noise, leading to dislocation and tearing in the stitching. To overcome this, we designed a homography matrix optimization method to describe the relative positional relationship between images by constructing the original image matrix and applying singular value decomposition to denoise the homography matrix. Additionally, we implemented a scale-invariant feature transform (SIFT) to extract feature points. To verify the effectiveness of this method, the SIFT + RANSAC (R), SIFT + affine transformation (AT), and oriented FAST and rotated BRIEF (ORB) algorithms were compared with the proposed algorithm. The experimental results demonstrate that the proposed method precisely computes the transformation matrix, thereby guaranteeing the geometric consistency of mosaic imaging. The proposed method preserves the intricate details of the original image and enhances the stitching quality of wide-field images.

Signal-receiving UAV Positioning Model Based on Geometric Analysis

A drone is an aerial vehicle without a pilot or controller that is remotely controlled using aerodynamics to fulfil a designated task under certain circumstances. With the encouragement and support of national policies, the development of China's UAV industry has a good prospect. In real life, location information is the key information for UAVs to accurately complete operations and realise mission requirements, and the importance of its accurate positioning is self-evident. For the UAV localisation model, the bias-free localisation model is established first, and then the localisation model in the case of bias is solved on this basis. When there is a deviation in the received signal UAV, the UAV may be located outside the circle, on the circle, and inside the circle. Specifically divided into four scenarios, see Figure 1-4. (Analysed: outside the circle: θ1’<θ1; inside the circle: θ1’>θ1; circle on the left: θ2>θ2’, right: θ2<θ2’; no deviation: θ1’=θ1, θ2’<θ2). When the position of the passive receiving signal UAV does not exist deviation, according to the position relationship obtained four cases, respectively, the establishment of the quadrilateral, the use of sinusoidal theorem to obtain the receiving signal UAV positioning model, the same can be obtained under the existence of deviation, the specific positioning model of the receiving signal UAV.

Multi-line laser 3D reconstruction method based on spatial quadric surface and geometric estimation

The traditional multi-line laser 3D reconstruction is based on binocular epipolar constraints and the equation of the laser optical plane. Firstly, matching points are identified based on the epipolar constraints. Then, the correct matching points are selected based on the optical plane equation of the multi-line laser. Finally, 3D reconstruction is performed using the selected matching points. In actual production processes involving multi-line lasers, noticeable distortion occurs due to the projection of Diffractive Optical Elements (DOE) at a large angle. This results in curved laser light planes and curved reflections on the measured objects. Under such circumstances, efficiently screening matching points using the traditional method based on the laser plane equation becomes challenging. Additionally, inherent noise in multi-line laser systems introduces errors in extracted laser center coordinates, making it impossible to directly obtain high-precision 3D data. To address these issues, this paper proposes a method that utilizes spatial quadric surfaces and geometric estimation for completing multi-line laser 3D reconstructions. By analyzing the distortion principle of DOE and the positional relationship after optical diffraction, the multi-line laser manifests as a quadric surface on the optical output plane. Consequently, by calibrating the equations of the quadric surface and applying binocular polar constraints, suitable matching points for the multi-line laser can be chosen. Once an accurate matching point is identified, a minimum geometric distance estimation can be established based on the distance constraint between the point and its corresponding epipolar line. This distance represents the separation between the left and right camera’s laser center points and their respective epipolar lines. Utilizing this estimate of the minimum geometric distance, a refined calculation can be performed to better satisfy epipolar constraints and obtain new matching points. Ultimately, utilizing these new matching points enables the completion of 3D reconstruction for multi-line lasers. In comparison with methods relying on spatial plane and epipolar constraints, our algorithm exhibits a superior degree of matchability and accuracy.According to multiple groups of test data, the average error before optimization is 0.3126 mm, and can be increased to 0.0336 mm after optimization.

The influence of equilibrium points and initial values on multistability of a multi-scroll chaotic system and predefined-time synchronization

Abstract In order to explore the effect of the initial value on the symmetry of the coexisting attractor, a novel multi-scroll chaotic system is designed in this paper. The system is proved to be chaotic by analysing the phase diagram, Lyapunov exponential spectrum and dissipativity of the system. Then the equilibrium point of the system is investigated and it is found that the system has four symmetric saddle focus of index 2. By analysing the dynamical behaviour of the system, it is found that the system has a special kind of multistability. Combining the properties of the orbits near the saddle focus of indicator 2, the reason for this special multistability is explained, and the effect of the positional relationship between the initial value and the saddle focus on the symmetry of the coexisting attractors is illustrated, which provides a new way of thinking to analyse the symmetric coexistence of chaotic systems. In order to verify the feasibility and application value of the system, simulation circuits are designed and predefined-time synchronization between systems of different dimensions is achieved.

Improved YOLOv8n for Lightweight Ship Detection

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature.

A Three-Dimensional Modeling Approach for Carbon Nanotubes Filled Polymers Utilizing the Modified Nearest Neighbor Algorithm.

Carbon nanotubes (CNTs) are extensively utilized in the fabrication of high-performance composites due to their exceptional mechanical, electrical, and thermal characteristics. To investigate the mechanical properties of CNTs filled polymers accurately and effectively, a 3D modeling approach that incorporates the microstructural attributes of CNTs was introduced. Initially, a representative volume element model was constructed utilizing the modified nearest neighbor algorithm. During the modeling phase, a corresponding interference judgment method was suggested, taking into account the potential positional relationships among the CNTs. Subsequently, stress-strain curves of the model under various loading conditions were derived through finite element analysis employing the volume averaging technique. To validate the efficacy of the modeling approach, the stress within a CNT/epoxy resin composite with varying volume fractions under different axial strains was computed. The resulting stress-strain curves were in good agreement with experimental data from the existing literature. Hence, the modeling method proposed in this study provides a more precise representation of the random distribution of CNTs in the matrix. Furthermore, it is applicable to a broader range of aspect ratios, thereby enabling the CNT simulation model to more closely align with real-world models.

Organizational ethics and position relationship moderators among knowledge workers: a regional study of Poland

ABSTRACT This research explores organizational ethics among knowledge workers using ICT4Ds in regional enterprises in Poland before, during, and after the COVID-19 pandemic. The study addresses the importance of understanding how ethics in organizations, particularly ethical optimism, corporate social responsibility, and top management action, have evolved across these periods. This topic is crucial as organizational ethics influence employee behavior and decision-making, particularly during crises. The research question focuses on how organizational ethics, influenced by gender and the pandemic, differ by position. Online surveys and random sampling were employed to collect data among knowledge workers (139 before, 134 during, and 98 after the pandemic). The study applied multivariate statistical methods, including variance analysis and causal moderation. Findings were that superiors rated organizational ethics higher than subordinates, and ethical optimism fluctuated, rising during the pandemic and dropping afterward. The study contributes to understanding the role of gender and crises as moderators in organizational ethics, benefiting managers and policymakers.

Optimized position analysis of multi-directional active mounting system based on quantification formula

This paper mainly studies the influence of active mounting system for vehicle engine vibration from both theoretical and experimental aspects. First, an active mounting system that considers both horizontal and vertical directions is proposed, modeled, analyzed and verified. The structure contains a bent beam representing the engine, two sets of actuator and elastomer forming active paths, and a straight beam representing the chassis. Second, the force and phase of the active mounts at each location combination are calculated based on static and dynamic methods. The dynamic analysis method is used to examine how the control signal changes with the input signal to learn the correlation between them. The static analysis is employed to obtain the change in force applied to the paths when the position of the input signal changes and to find the optimized placement criteria. Through the position relationship between force and mounting, a specific position where the applied force is relatively small was considered the optimized position of the active mounting system. Finally, these criteria were verified with experiments and the results are matched well with each other.

LGTDA: Bandwidth exhaustion attack on Ethereum via dust transactions

Dust attacks typically involve sending a large number of low-value transactions to numerous addresses, aiming to facilitate transaction tracking and undermine privacy, while simultaneously disrupting the market and increasing transaction delays. These transactions not only impact the network but also incur significant costs. This paper introduces a low-cost attack method called LGTDA, which achieves network partitioning through dust attacks. This method hinders block synchronization by consuming node bandwidth, leading to denial of service(DoS) for nodes and eventually causing large-scale network partitioning. In LGTDA, the attacker does not need to have real control over the nodes in the network, nor is there a requirement for the number of peer connections to the nodes; the attack can even be initiated by simply invoking RPC services to send transactions. Under the condition of ensuring the validity of the attack transactions, the LGTDA attack sends a large volume of low-value, high-frequency dust transactions to the network, relying on nodes for global broadcasting. This sustained attack can significantly impede the growth of block heights among nodes, resulting in network partitioning. We discuss the implications of the LGTDA attack, including its destructive capability, low cost, and ease of execution. Additionally, we analyze the limitations of this attack. Compared to grid lighting attacks, the LGTDA attack has a broader impact range and is not limited by the positional relationship with the victim node. Through experimental validation in a controlled environment, we confirm the effectiveness of this attack.

A star selection strategy based on MDS and fast convex hull algorithm in COSPAS-SARSAT system

Abstract The space segment of the current COSPAS-SARSAT system is mainly supported by the Galileo system, and the positioning technology used is generally three-star time-difference positioning. Most of the antennas equipped in the Mission Control Centre (MCC) are not omnidirectional. In the case of Source of Search and Rescue (SAR) in a heavily shaded environment, it is necessary to use the different orbit time-sharing single satellite geolocations using TDOA, and the time-sharing will generate more satellite position data. In order to achieve the higher positioning accuracy of the solution, it is necessary to select the appropriate time and optimal geometrical distribution from the satellite positions. In this paper, we propose a satellite selection strategy based on Multiple Dimensional scaling (MDS) data degradation and a fast convex hull algorithm. The three-dimensional satellite data are downscaled to two-dimensional while maintaining their relative positional relationships, and the two-dimensional convex hull algorithm is used for Geometric Dilution of Precision (GDOP) minimum search star selection. By simulating the satellite position data in different orbits and time-sharing to complete the localization solution, the results show that compared with the traditional satellite selection method, the satellite selection strategy proposed in this paper is able to achieve better localization accuracy in a shorter period of time.

In-situ study on the differential evolution of He bubbles in the multilayer oxide of Fe9Cr1.5W0.4Si F/M steel corroded in lead-bismuth eutectic

The combined effects of corrosion and irradiation on nuclear components have been an important but not yet fully revealed topic. Here, the irradiation behavior of the oxide scale formed on F/M steel after lead-bismuth corrosion was in-situ investigated during He+ irradiation. The results showed that the oxide scale included a Fe3O4 outer oxide layer, a nanograin Fe(FexCr2-x)O4 spinel inner oxide layer, and an internal oxide layer. He bubbles formed in Fe3O4, Fe-Cr spinel and F/M steel were polygon, irregular elongated pores and small spheres, respectively. These differences were attributed to variations in defect generation, migration, and corrosion-induced crystal defects in different oxides. Numerous corrosion-induced nanograin boundaries and vacancies in Fe-Cr spinel exhibited more effective absorption of irradiation-induced defects. Moreover, rhombic perfect dislocation loops were detected in Fe3O4 at the late stage of irradiation, their relative positional relationship with He bubbles indicated a potential interaction between bubbles and loops.

Speckle autocorrelation separation method based on frequency intensity for tracking moving object beyond optical memory effect

Abstract Imaging objects behind a scattering medium beyond the optical memory effect (OME) range is vital in various imaging scenarios. Despite numerous proposed imaging methods aimed at separating objects, effectively tracking moving objects remains challenging. In this paper, we present a non-invasive scattering imaging method to reconstruct target beyond the OME range and track moving objects using a speckle separation and position estimation technique. A speckle autocorrelation separation method based on frequency intensity is proposed to eliminate the noise from the cross-correlation and to separate the objects from the mixed speckles. A position estimation technique is proposed to determine the positional relationships of the objects using speckle deconvolution using the point spread function of imaging regions. A non-invasive imaging system is designed to track the moving object without prior knowledge of the shape and distribution of objects. Experimental validation shows the efficiency of the method for separation and localization, allowing for the reconstruction of the target behind the scattering medium and tracking of the moving object behind the scattering medium. Moreover, the method is capable of separating and tracking a moving object when multiple objects are in the background.

Deep-learning based single-shot 3D reconstruction with simulated color-crosstalk and randomized extrinsics

In recent years, many single-frame 3D reconstruction schemes based on fringe projection profilometry (FPP) have been proposed. However, most single-frame reconstruction schemes still face the following three issues: (1) obtaining large datasets is very time-consuming, (2) focusing only on achieving single-frame reconstruction for white objects, and (3) requiring fixing the camera-projector positional relationship, increasing operational difficulty. By building a virtual FPP simulation system, our method can quickly render the required datasets, avoiding cumbersome manual operations. When rendering the datasets, we simulate adverse factors such as color channel crosstalk, system extrinsic parameter variations, and object surface colors to guide the training of the neural network. Ultimately, from a single three-frequency color image, the corresponding three-frequency three-step phase-shift images are predicted, achieving single-frame 3D reconstruction of colored objects and allowing some variation in system extrinsic parameters. Real-world experiments demonstrate that the network trained with the diverse data generated by our virtual system has good accuracy, providing valuable guidance for the practical application of deep learning methods.

Classification of Complete-Arch Implant Scanning Techniques Recorded by Using Intraoral Scanners.

To classify the complete-arch implant scanning techniques recorded by using intraoral scanners (IOSs). Different implant scanning techniques have been described for recording complete-arch implant scans by using IOSs. However, dental literature lacks on a classification of these implant scanning techniques. Implant scanning techniques aim is to record the 3-dimensional position of the implants being scanned, while implant scanning workflows require additional scans to record all the information needed for designing an implant prosthesis. This additional information includes soft tissue information, tooth position, antagonist arch, and maxillomandibular relationship. There are five complete-arch implant scanning techniques captured by using IOSs: non-splinting, non-calibrated splinting, calibrated implant scan bodies, calibrated frameworks, and reverse impression methods. The digital workflow varies depending on the implant scanning technique selected. The understanding of the varying implant scanning techniques and the main differences among them may ease the decision criteria for recording digital implant scans by using intraoral scanners.

Sternalis featuring tendomuscular fusion to the contralateral sternocleidomastoid: a cadaveric case report.

To report the morphologic and spatial relationships of a bilateral sternalis muscle variant. Routine cadaveric dissection in an undergraduate anatomy laboratory revealed two sternalis muscles parasternal to the sternal body. Subsequent fine prosection of the anterior thoracic wall and neck was carried out to uncover the soft tissue attachments of both sternalis muscles. Positional relationship to neighboring anterior thoracic and neck structures, and geometric dimensions of the muscle bellies and tendons, were documented. Both sternalis muscles were imbedded in the pectoral fascia, with their muscle fibers running obliquely to the midsternal line. The right sternalis muscle was notably larger in length, width, and thickness compared to the sternalis muscle on the left. The right sternalis muscle featured a distinct superior cord-like intermediate tendon that traversed superolateral and fused directly with the contralateral sternomastoid and sterno-occipital portions of the left sternocleidomastoid muscle. The superior tendon of the left sternalis muscle was aponeurotic and affixed to the cord-tendon of the right sternalis muscle. A distinct tendinous entheses for the sternal head for both sternocleidomastoid muscles were noted at the manubrial body. This case report describes a unique bilateral sternalis muscle variant with conjoined sternocleidomastoid muscle integration. This anatomical description of a sternalis-sternocleidomastoid muscle morphology may supplement radiographic interpretations and support diagnostic accuracy.

Revisiting the anatomy of the pectoral nerves and nerve loops of the brachial plexus in the goat (Capra hircus)

BackgroundThe anatomy of the pectoral nerves and the two nerve loops on the course of the axillary artery was revisited to complement current general descriptions as well as to argue whether the nerves contributing to the formation of the pectoral loop are the cranial pectoral nerves. Besides, the positional relationship between the scalene muscles and the nerve roots of the brachial plexus, which contribute to the nerves aimed in this study, was also examined at the same time as the dissection. MethodsTwenty brachial plexuses of 10 domestic adult goats (8 females and 2 males) were examined using gross dissection in this study. ResultsIn many dissections (95 %), the last bundle of scalenus ventralis muscle was found to pass between the roots of C7 and C8, dividing the brachial plexus into the cranial (ventral) and caudal (dorsal) parts. Two pectoral nerves were noted to contribute to the formation of the first nerve loop around the axillary artery. The first pectoral nerve arose predominantly from the ventral branches of C6 and C7 in company with the n. musculocutaneus, while the second pectoral nerve arose directly from C8 in 70 % of the dissections or as the first branch of the n. thoracicus lateralis (C8, T1) in the remaining 30 %. After the nerve loop, the second pectoral nerve branched off to innervate the superficial surface of the m. pectoralis profundus toward its insertion. The m. subclavius was found to receive its innervation from several sources, including the ventral branches of the brachial plexus. Interestingly, in 4 of the 14 dissections a communication between the n. subclavius and the n. phrenicus heretofore not found in the animal anatomy literature was found. In 16 of the 20 dissections (60 %), the ramus muscularis proximalis of the n. musculocutaneus received the communicating branch(s) from the n. medianus at the site of the second nerve loop, ansa axillaris. ConclusionThe second pectoral nerve contributing to the pectoral loop would be better described as the caudal pectoral nerve rather than the cranial pectoral nerve. Besides the evolutionary perspectives, understanding the findings of this study would be helpful for teaching veterinary anatomy.

Optical Coherence Tomography Angiography Analysis of Peripapillary Choroidal Microvascular Density in normal-tension Glaucoma and Primary open-angle Glaucoma.

Glaucoma patients had a reduction in the inner annulus peripapillary choroidal microvascular density (PCMD) that became worse as the glaucoma severity progressed, which might provide new evidence supporting the vascular theory. To compare PCMD among normal-tension glaucoma (NTG), primary open-angle glaucoma (POAG), and healthy controls using optical coherence tomography (OCT) angiography (OCTA). The study included 40 POAG, 25 NTG, and 33 healthy controls. All subjects underwent OCT and OCTA testing. Inner annulus and outer annulus PCMD, as well as peripapillary vessel density (VD), was calculated. One-way analysis of variance was used to compare the vascular parameters of the three groups. Pearson correlation analysis or Spearman correlation test was used to evaluate the correlation between PCMD and glaucomatous severity factors. The spatial positional relationship between PCMD and corresponding peripapillary retinal nerve fiber layer (RNFL) thickness and visual field (VF) mean deviation (MD) was also assessed. The average and four quadrants of the inner annulus PCMD and peripapillary VD in the two glaucomatous groups were significantly lower than in normal eyes (P<0.05). Strong correlations were found between inner annulus PCMD and VF MD, peripapillary VD, and RNFL in POAG patients. Similarly, the inner annulus PCMD in NTG patients was strongly correlated with peripapillary VD and RNFL (all r>0.5). Strong positional correlations were found between inner superior quadrantal PCMD and RNFL thickness in both POAG and NTG patients (r=0.566, r=0.731, respectively). Likewise, inner inferior quadrantal PCMD exhibited a strong correlation with RNFL thickness in POAG patients (r=0.608). Strong positional correlations were also found between inner superior PCMD and VF MD in both POAG and NTG patients (r=0.589, r=0.622, respectively). Inner inferior PCMD exhibited a moderate correlation with VF MD in both POAG and NTG patients (r=0.487, r=0.440, respectively). The study found that the inner annulus PCMD decreased to varying degrees in NTG and POAG patients. The inner annulus PCMD was closely related to the structural and visual function parameters of glaucoma in both NTG and POAG. Furthermore, inner PCMD demonstrated a spatial correlation with corresponding RNFL thickness and VF MD.

SPANet: Spatial perceptual activation network for camouflaged object detection

AbstractCamouflaged object detection (COD) aims to segment objects embedded in the environment from the background. Most existing methods are easily affected by background interference in cluttered environments and cannot accurately locate camouflage areas, resulting in over‐segmentation or incomplete segmentation structures. To effectively improve the performance of COD, we propose a spatial perceptual activation network (SPANet). SPANet extracts the spatial positional relationship between each object in the scene by activating spatial perception and uses it as global information to guide segmentation. It mainly consists of three modules: perceptual activation module (PAM), feature inference module (FIM), and interaction recovery module (IRM). Specifically, the authors design a PAM to model the positional relationship between the camouflaged object and the surrounding environment to obtain semantic correlation information. Then, a FIM that can effectively combine correlation information to suppress background interference and re‐encode to generate multi‐scale features is proposed. In addition, to further fuse multi‐scale features, an IRM to mine the complementary information and differences between features at different scales is designed. Extensive experimental results on four widely used benchmark datasets (i.e. CAMO, CHAMELEON, COD10K, and NC4K) show that the authors’ method outperforms 13 state‐of‐the‐art methods.