Spatial Vector Research Articles

An experiential program on the foundations of quantum mechanics for final-year high-school students

Teaching and learning quantum mechanics is one of the most demanding educational and conceptual challenges, in particular in secondary schools where students do not possess an adequate mathematical background to effectively support the description of quantum behaviour. Educational research shows that traditional approaches, generally based on historical and narrative perspectives, are only partially effective. The reason is that they do not address in depth those basic quantum concepts that radically question the fundamentals of classical physics. A research-based educational program has been proposed to two final-year classes of an Italian scientific high school. In order to build the main concepts of quantum mechanics and their formal basic representation via real and simulated experiments, the program uses the light polarization as a context. A quantum game was then integrated in the educational program, to support students’ learning. Their conceptual paths, monitored by means of tutorials and questionnaires, show significant student learning especially on the concept of state and on appropriating the formalism meaning, whereas students more frequently referred to the geometrical vector representation instead of the algebraic-analytic formula. The quantum game has emerged to support intuition and operative experience in distinguishing the foundational concepts of superposition and entanglement.

Bilateral deep brain stimulation of the subthalamic nucleus: Targeting differences between the first and second side

Introduction and objectivesDeep brain stimulation (DBS) of the subthalamic nucleus (STN) is a recognized treatment for drug-refractory Parkinson's disease (PD). However, the therapeutic success depends on the accuracy of targeting. This study aimed to evaluate potential accuracy differences in the placement of the first and second electrodes implanted, by comparing chosen electrode trajectories, STN activity detected during microelectrode recording (MER), and the mismatch between the initially planned and final electrode positions on each side. Materials and methodsIn this retrospective cohort study, we analyzed data from 30 patients who underwent one-stage bilateral DBS. For most patients, three arrays of microelectrodes were used to determine the physiological location of the STN. Final target location depended also on the results of intraoperative stimulation. The choice of central versus non-central channels was compared. The Euclidean vector deviation was calculated using the initially planned coordinates and the final position of the tip of the electrode according to a CT scan taken at least a month after the surgery. ResultsThe central channel was chosen in 70% of cases on the first side and 40% of cases on the second side. The mean length of high-quality STN activity recorded in the central channel was longer on the first side than the second (3.07±1.85mm vs. 2.75±1.94mm), while in the anterior channel there were better MER recordings on the second side (1.59±2.07mm on the first side vs. 2.78±2.14mm on the second). Regarding the mismatch between planned versus final electrode position, electrodes on the first side were placed on average 0.178±0.917mm lateral, 0.126±1.10mm posterior and 1.48±1.64mm inferior to the planned target, while the electrodes placed on the second side were 0.251±1.08mm medial, 0.355±1.29mm anterior and 2.26±1.47mm inferior to the planned target. ConclusionThere was a tendency for the anterior trajectory to be chosen more frequently than the central on the second side. There was also a statistically significant deviation of the second electrodes in the anterior and inferior directions, when compared to the electrodes on the first side, suggesting that another cause other than brain shift may be responsible. We should therefore factor this during planning for the second implanted side. It might be useful to plan the second side more anteriorly, possibly reducing the number of MER trajectories tested and the duration of surgery.

Entropy and its significance in transport of ions through the cell membrane

The aim of this paper is to present the concept of entropy in a simple way and to show its key importance in the transport processes of ions through the cell membrane using the latest discoveries in biophysics. Using a real-life example, we show how processes within a system lead to an increase in entropy. We also show how this entropy increase is directly related to the irreversibility of the process, and how it defines the arrow of time (direction of the flow of time). Using an abstract example, we clarify the meaning of the concept of disorder in a system, which is often used in defining entropy by connecting it with the number of microstates that realise a macroscopic state of a system. The importance of entropy in transport processes of ions through the cell membrane is considered. We show that passive transport processes through the cell membrane are the result of an entropy increase in the cell membrane-transported substance system. A model of active ion transport through the cell membrane following Rubi et al. (2017) is presented. The force that transports ions through the channel in the transport protein arises due to the entropy gradient formed along the transport channel, which is a consequence of the funnel shape of the channel. The entropic force is proportional to the ratio of the ion-available cross-sections of the exit and entrance surface of the channel. That means that only a very funnel-shaped channel can produce a sufficiently large force on the ions to overcome the concentration gradient of the substance. We analyse the final result for the force of entropy in the limits of a very wide and very narrow channel and find that the entropic force is proportional to the ratio of the areas of the exit to entrance surfaces of the channel, i.e., when the channel is very wide, while it becomes high as the width of the channel tends to the ion diameter, i.e., when the channel is very narrow. We explicitly explain how the presented model solves several fundamental questions about the active transport of substances: how is energy, a scalar quantity, converted into the directional motion of the ion (a vector quantity), how does energy drive ions considering that the point of release of energy is far from the point of binding of an ion in a transport protein and finally, how does energy, which is released in a very limited space, transport the ions over a very large spatial scale.

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

Comparative analysis of four types of mesoscale eddies in the North Pacific Subtropical Countercurrent region - part II seasonal variation

The North Pacific Subtropical Countercurrent area (STCC) is high in mesoscale eddy activities. According to the rotation direction of the eddy flow field and the sign of temperature anomaly within the eddy, they can be divided into four categories: cyclonic cold-core eddy (CCE), anticyclonic warm-core eddy (AWE), cyclonic warm-core eddy (CWE) and anticyclonic cold-core eddy (ACE). CCE and AWE are called normal eddies, and CWE and ACE are named abnormal eddies. Based on the OFES data and vector geometry automatic detection method, we find that at the sea surface, the maximum monthly number of the CCE, AWE, CWE, and ACE occurs in December (765.70 ± 52.05), January (688.20 ± 82.53), August (373.40 ± 43.09) and August (533.00 ± 56.92), respectively. The number of normal eddies is more in winter and spring, and less in summer and autumn, while abnormal eddies have the opposite distribution. The maximum rotation velocity of the four types of eddies appears in June (11.71 ± 0.75 cm/s), June (12.24 ± 0.86 cm/s), May (10.63 ± 0.99 cm/s) and June (9.97 ± 0.91 cm/s), which is fast in winter and spring. The moving speed of the four types of eddies is almost similar (about 10 ~ 11 cm/s). The amplitude of normal and abnormal eddies is both high in summer and autumn, and low in winter and spring, with larger amplitudes in normal than abnormal eddies. The eccentricity (defined as the eccentricity of the ellipse obtained by fitting the eddy boundary) of the four types of eddies is also close to each other, and their variation ranges from 0.7 to 0.8, with no apparent seasonal variation. The vertical penetration depth, which has no significant seasonal difference, is 675.13 ± 67.50 m in cyclonic eddies (CCE and CWE), which is deeper than that 622.32 ± 81.85 m in anticyclonic eddies (ACE and AWE). In addition, increasing the defined temperature threshold for abnormal eddies can significantly reduce their numbers but does not change their seasonal variation trend.

A technique for volumetric incoherent scatter radar analysis

Abstract. Volumetric measurements of the ionosphere are important for investigating spatial variations of ionospheric features, like auroral arcs and energy deposition in the ionosphere. In addition, such measurements make it possible to distinguish between variations in space and time. While spatial variations in scalar quantities such as electron density or temperature have been investigated with incoherent scatter radar (ISR) before, spatial variation in the ion velocity, which is a vector quantity, has been hard to measure. The upcoming EISCAT3D radar will be able to do volumetric measurements of ion velocity regularly for the first time. In this paper, we present a technique for relating volumetric measurements of ion velocity to neutral wind and electric field. To regularize the estimates, we use Maxwell's equations and fluid-dynamic constraints. The study shows that accurate volumetric estimates of electric field can be achieved. Electric fields can be resolved at altitudes above 120 km, which is the altitude range where auroral current closure occurs. Neutral wind can be resolved at altitudes below 120 km.

Solar Power Prediction Using Dual Stream CNN-LSTM Architecture.

The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power is challenging due to the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual-stream convolutional neural network (CNN) and long short-term nemory (LSTM) network followed by a self-attention mechanism network (DSCLANet). Here, CNN is used to learn spatial patterns and LSTM is incorporated for temporal feature extraction. The output spatial and temporal feature vectors are then fused, followed by a self-attention mechanism to select optimal features for further processing. Finally, fully connected layers are incorporated for short-term solar power prediction. The performance of DSCLANet is evaluated on DKASC Alice Spring solar datasets, and it reduces the error rate up to 0.0136 MSE, 0.0304 MAE, and 0.0458 RMSE compared to recent state-of-the-art methods.

G-RANK: an equivariant graph neural network for the scoring of protein-protein docking models.

Protein complex structure prediction is important for many applications in bioengineering. A widely used method for predicting the structure of protein complexes is computational docking. Although many tools for scoring protein-protein docking models have been developed, it is still a challenge to accurately identify near-native models for unknown protein complexes. A recently proposed model called the geometric vector perceptron-graph neural network (GVP-GNN), a subtype of equivariant graph neural networks, has demonstrated success in various 3D molecular structure modeling tasks. Herein, we present G-RANK, a GVP-GNN-based method for the scoring of protein-protein docking models. When evaluated on two different test datasets, G-RANK achieved a performance competitive with or better than the state-of-the-art scoring functions. We expect G-RANK to be a useful tool for various applications in biological engineering. Source code is available at https://github.com/ha01994/grank. kds@kaist.ac.kr. Supplementary data are available at Bioinformatics Advances online.

Ultralow Power On-the-Eye Vergence and Distance Sensing Through Differential Magnetometry

We demonstrate the realization of a very low energy, on-the-eye vergence-type distance ranger based on sensing of a locally-uniform vector field, specifically the earth’s magnetic field. This ranging method is passive, only requiring measurement of the magnetic field vector at both eyeballs utilizing magnetometer chips placed on the eye scleral regions. The eye vergence angle and range distance are calculated from these two vector quantities. The method can obtain a range reading with as little as 118 nJ of energy consumed per eye for 3.3V and 50 nJ when operated at 1.9V. This method is thus suitable for applications where energy storage is very limited such as in smart contacts vision correcting microsystems.

Spinors and Spatial Vectors Are Part of the Number System; They Are Extensions of Complex Numbers Into Three-dimensional Space

Spinors and Spatial Vectors Are Part of the Number System; They Are Extensions of Complex Numbers Into Three-dimensional Space

Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

Now a days, Remote Sensing (RS) techniques are used for earth observation and for detection of soil types with high accuracy and better reliability. This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics. There are a few challenges that is present in soil classification using image enhancement such as, locating and plotting soil boundaries, slopes, hazardous areas, drainage condition, land use, vegetation etc. There are some traditional approaches which involves few drawbacks such as, manual involvement which results in inaccuracy due to human interference, time consuming, inconsistent prediction etc. To overcome these draw backs and to improve the predictive analysis of soil characteristics, we propose a Hybrid Deep Learning improved BAT optimization algorithm (HDIB) for soil classification using remote sensing hyperspectral features. In HDIB, we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral (HS) image. Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology. Then, a recurring Deep Learning (DL) Neural Network (NN) is used for classifying the HS images, considering the datasets of Pavia University, Salinas and Tamil Nadu Hill Scene, which in turn improves the reliability of classification. Finally, the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron (SLP), Convolutional Neural Networks (CNN) and Deep Metric Learning (DML) and it shows an improved classification accuracy of 99.87%, 98.34% and 99.9% for Tamil Nadu Hills dataset, Pavia University and Salinas scene datasets respectively.

Multimodal spatial-temporal feature representation and its application in action recognition

目的 在人体行为识别研究中，利用多模态方法将深度数据与骨骼数据相融合，可有效提高动作的识别率。针对深度图像信息数据量大、冗余度高等问题，提出一种通过获取关键时程信息动作帧序列降低冗余的算法，即质心运动路径松弛算法，并根据不同模态数据的特点，提出一种新的时空特征表示方法。方法 质心运动路径松弛算法根据质心在相邻帧之间的运动距离，计算图像差分后获得的活跃部分的相似系数，然后剔除掉相似度高的帧，获得足以表达行为的关键时程信息。根据图像动态部分的变化特性、人体各部分在运动中的协同性和局部显著性特征构建一种新的时空特征表示方法。结果 在MSR-Action3D数据集上对本文方法的效果进行验证。在3个子集中进行交叉验证的平均分类识别率为95.743 2%，分别比Multi-fused，CovP3DJ，D3D-LSTM（densely connected 3DCNN and long short-term memory），Joint Subset Selection方法高2.443 2%，4.763 2%，0.343 2%，0.213 2%。本文方法在使用完整数据集的扩展实验中进行交叉验证的分类识别率为93.040 3%，具有很好的鲁棒性。结论 实验结果表明，本文提出的去冗余算法在降低冗余后提升了识别效果，提取的特征之间具有相关性低的特点，在组合识别中具有良好的互补性，有效提高了分类识别的精确度。;Objective Human body motion-related recognition has been developing in the context of computer vision and pattern recognition like auxiliary human-computer interaction，motion analysis，intelligent monitoring，and virtual reality. To obtain two-dimensional information for its behavioral recognition，conventional motion behavior recognition is mainly used the RGB image sequence captured by RGB camera. To improve the ability to detect short-duration fragments，current feature descriptors for RGB image sequences are employed to characterize human behavior，such as histogram of oriented gradient（HOG），histogram of optical flow （HOF），and a three-dimensional feature pyramid. Some researchers are focused on the feature that image depth is insensitive to ambient light since RGB images are oriented to behavior image sequences of objects in terms of two-dimensional information. The depth information of the image is coordinated with the features of RGB image to describe the related behavior. Human behavior recognition-relevant multi-modal method can be used to fuse depth data and skeleton data，which can improve the recognition rate of action effectively. Recent depth map is widely used in relevant to human behavior recognition. But，the collection of depth information data is required to be optimized because of time complexity of feature extraction and space complexity of feature storage. To resolve the problems，we develop an algorithm to optimize frames of the depth map and resource consumption. At the same time，a new representation of motion features is facilitated as well according to the motion information of the centroid. Method First，the temporal feature vector is used in terms of depth map sequence-extracted time sequence information. The centroid motion path relaxation algorithm is used to realize depth image de-duplication and de redundancy，and the skeleton map-extracted spatial structure feature vector from are spliced to form the spatio-temporal feature input. Next，spatial features are extracted in terms of the original skeleton points coordinates-spliced three-channel spatial feature map. Finally，the fusion probability of spatio-temporal features and spatial features is used for classification and recognition. Our centroid motion path relaxation algorithm is focused on the optimization of redundant information，the time complexity of feature extraction，and the space complexity of feature storage. For the skeleton data，the global feature of motion direction is proposed to fully reflect the integrity and coordination of limb movements. The extracted features are concatenated to obtain the spatio-temporal feature vector，and they can be fused and enhanced through the original coordinates of skeleton points-built three-channel spatial feature map. Its effectiveness is verified on the MSR-Action3D dataset. Result The experimental setting 1 demonstrate that it is 0. 826 0% higher than the depth motion map（DMM）-local binary pattern（LBP）algorithm，1. 015 2% higher than DMM-CRC（collaborative representation classifier），3. 450 1% higher than gradient local auto correlation（DMM-GLAC） algorithm，0. 605 8% higher than EigenJoint algorithm，and 0. 605 8% higher than space-time auto correlation of gradient （STACOG）algorithm is 10. 624 5% higher. After removing redundancy，the result of experimental setting 1 is 0. 126 1% higher as well. The cross-validation on experimental setting 2 show that the average classification and recognition rate in the three subsets is 95. 743 2%，2. 443 2% higher than multi-fused method，4. 763 2% higher than CovP3DJ method，0. 343 2% higher than D3D-LSTM method，and 0. 213 2% higher than joint subset selection method. For the overall data set，it is 2. 030 3% higher than low latency method，0. 240 3% higher than combination of deep models method，and 2. 340 3% higher than complex network coding method. The experimental setting 2 illustrates that the average classification recognition rate of cross-validation in three subsets is 95. 743 2%，and the classification recognition rate of the complete dataset is 93. 040 3%. Conclusion Our algorithm proposed can improve the recognition effect based on redundancy-optimized，and the featuresextracted have lower correlation mutually，which can improve the accuracy of classification recognition effectively.

Research on tracking and decomposing method of aerobics movement based on machine learning

In order to overcome the low success rate of tracking and decomposing traditional aerobics movement tracking and decomposition method, this paper proposes a method of aerobics action tracking and decomposition based on machine learning. In this method, a multi-layer pyramid structure is built to segment the aerobics video image, and the key frame and the frame with the highest energy value are detected to obtain the binary image. This paper constructs a standardised tracking target model, uses convolution filter to detect multi-objective feature vectors in the image, uses binary classifier to build machine learning framework, and realises aerobics action tracking decomposition under the vector constraints of impact strength, spatial and frequency domain vectors. The experimental results show that this method can effectively deal with the interference from the external environment in partial sequence tracking, and compared with the traditional method, this method has higher success rate of tracking and decomposition and has reliability.

Analysis and Application of Pose Mapping from Spatial Line Vector to Projection Plane

Analysis and Application of Pose Mapping from Spatial Line Vector to Projection Plane

Approximate and discrete Euclidean vector bundles

Abstract We introduce $\varepsilon $ -approximate versions of the notion of a Euclidean vector bundle for $\varepsilon \geq 0$ , which recover the classical notion of a Euclidean vector bundle when $\varepsilon = 0$ . In particular, we study Čech cochains with coefficients in the orthogonal group that satisfy an approximate cocycle condition. We show that $\varepsilon $ -approximate vector bundles can be used to represent classical vector bundles when $\varepsilon> 0$ is sufficiently small. We also introduce distances between approximate vector bundles and use them to prove that sufficiently similar approximate vector bundles represent the same classical vector bundle. This gives a way of specifying vector bundles over finite simplicial complexes using a finite amount of data and also allows for some tolerance to noise when working with vector bundles in an applied setting. As an example, we prove a reconstruction theorem for vector bundles from finite samples. We give algorithms for the effective computation of low-dimensional characteristic classes of vector bundles directly from discrete and approximate representations and illustrate the usage of these algorithms with computational examples.

Data Fusion Approach for Extracting Spindle Key Performance Features Under Different Workloads

The spindle is the core component of CNC machine tools, and its performance features are the main factors affecting the overall accuracy and efficiency of machine tools. This study proposes a method for extracting key performance features of machine tool spindles under different workloads. First, it develops a spindle performance test device, which can obtain the performance features of the spindle at different speeds and radial and axial forces. Then, it proposes a spatial vector model for predicting radial runout at any axial position. Test results show that the maximum error between the test and the prediction model is 2.85% at the first prediction position. At the second prediction location, the maximum error is 2.25%. Lastly, this study proposes a data fusion method to calculate the bearing stiffness by combining the measured data, mechanical theory, and finite element analysis. Test results show that the bearing stiffness feature can be simplified to a constant value in the milling process. In the typical working load range, the bearing stiffness fluctuates within 20% of the axial force. The method is applicable and instructive for engineering applications.

Collaborative Layout Optimization for Ship Pipes Based on Spatial Vector Coding Technique

Collaborative Layout Optimization for Ship Pipes Based on Spatial Vector Coding Technique

Symmetries, Noether’s Theorem, Conservation Laws and Numerical Simulation for Space-Space-Fractional Generalized Poisson Equation

In the present paper Lie theory of differential equations is expanded for finding symmetry geometric vector fields of Poisson equation. Similarity variables extracted from symmetries are applied in order to find reduced forms of the considered equation by using Erdélyi-Kober operator. Conservation laws of the space-space-fractional generalized Poisson equation with the Riemann-Liouville derivative are investigated via Noether’s method. By means of the concept of non-linear self-adjointness, Noether’s operators, formal Lagrangians and conserved vectors are computed. A collocation technique is also applied to give a numerical simulation of the problem.

A Vector Tensor Calculus Description of a Euclidean Space

We present a tensor description of Euclidean spaces that emphasizes the use of geometric vectors which leads to greater geometric insight and a higher degree of organization in analytical expressions. We demonstrate the effectiveness of the approach by proving a number of integral identities with vector integrands. The presented approach may be aptly described as absolute vector calculus or as vector tensor calculus.

Image Perceptual Hashing for Content Authentication Based on Geometric Invariant Vector Distance

Image perceptual hashing is broadly applied in image content authentication, recognition, retrieval, and social media hotspot event detection. An image authentication algorithm is put forward based on the Itti visual saliency model and geometric invariant vector distance. To begin with, the image is preprocessed and weighted by the Itti model and contourlet transform. After that, the weighted image is randomly divided into blocks, and the image feature vector is constructed by calculating the geometric invariant vector distance on both Hu invariant moment vector and maximum singular value vector of the random blocks. In the end, the feature vector is quantized and encrypted to generate the ultimate hash. Experimental results illustrate that when the threshold T = 70, the true positive rate P T P R for duplicate images stands at 0.96574, while the false rate P F P R of different images is merely 0.0224, with the total error rate reaching the minimum value (0.0566). Furthermore, the AUC value of the proposed algorithm is 0.9951, which is higher than that of the comparison algorithms, indicating that the algorithm has better performance than other state-of-the-art algorithms in terms of various visual content-preserving attacks.