Spatial Vector Research Articles

Dimension reduction method of high-dimensional fault datasets based on C_M_t-SNE under unsupervised background

The unlabeled fault datasets often contain much non-sensitive redundant, and uncertain information. This study designs a novel interpretable and unsupervised dimension reduction method for unlabeled data containing redundancy and uncertainty. Firstly, a fuzzy-based way for pseudo-label generation is given, and feature cloud models under pseudo labels are established; Secondly, this study takes the expectation, entropy, and hyper entropy of the cloud models representing uncertainty in features as spatial vectors. The difference degree between vectors is treated as the evaluation standard to filter out non-sensitive features based on the maximum initial difference; Moreover, redundant elements are fused by t-SNE, and lower dimensional feature components conducive for fault classification are obtained; Finally, the effectiveness of the method is demonstrated by comparative experiments. The results show that this method has a higher factor, which means that the method can better mine the difference among different faults and improve the performance of fault identification.

Spatial Modulation With Joint Permutation, Group and Antenna Indexes

In this letter, a new schematic of spatial modulation with jointing permutation, group and antenna indexes (JPGA-ISM) is proposed to achieve more extra information bits by exploiting the permutation index (PI) domain. In the JPGA-ISM system, in order to increase the squared minimum Euclidean distance (MED) between the transmit vectors, two algorithm devices are designed. Based on the design of two algorithms, by exploiting the index domain, one PI matrix with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D\times 2^{D}$ </tex-math></inline-formula> dimensions, in which each column of elements is treated as a spatial vector (SV), is developed to convey the PI bits. Then, each of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> elements of the specific SV with the PI bits is used to control two Switches, which feed the symbol index bits into the specific modulator and the corresponding element of the SV into the specific algorithm device, respectively. Furthermore, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> outputs from the specific algorithm devices are multiplied by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> components of the selected group index symbol and then modulated on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> active transmit antennas to form a transmit vector, respectively. In addition, the modified pulse amplitude modulation (PAM) and secondary PAM constellations are designed. The spectral efficiency, the MED comparisons and the average bit error probability (BEP) are analyzed. Simulation results show that the JPGA-ISM outperforms other systems in terms of the BEP performance.

Predictive Algorithm for Team Mental Model Convergence

Is everyone on your team on the same page about the task? This is a question team leaders want to know. Herein, we take an approach that can help managers move the team in the right direction of team mental models (TMMs), individually held cognitive representations of task components that when similar and/or accurate can promote task success. We begin by defining the new concept of mental model shifts (MMSs) as the directional shift in each team member’s mental model, leading to an increase or decrease in convergence toward a shared or quality referent mental model. Next, we propose an algorithm that applies the concepts of Markov chains and vector geometry on communication patterns to predict future patterns and TMM convergence (i.e., sharedness and quality) levels. We base the model on a dataset of teams conducting the National Aeronautics and Space Administration (NASA) human exploration research analog (HERA) missions from which we draw communication attributes of MMS, process topic, and message purpose. We show that tasks can be modeled as vectors using the frequency of attribute patterns. Our initial experiments show that an accuracy of up to 86.21% can be achieved in predicting future communication patterns with data from real-world tasks. Furthermore, we validate the estimation of TMM sharedness, showing that the model results are comparable to sharedness ratings provided by subject matter experts with an average accuracy of 71.29%. Research and practical implications are discussed.

Ocean drifter velocity data assimilation, Part 1: Formulation and diagnostic results

Numerical ocean circulation forecasts diverge from reality due to non-deterministic processes. Through data assimilation, ocean forecasting systems routinely make corrections to scalar ocean variables of temperature and salinity derived from ocean observations. Velocity observations, however, are vector quantities that require different processing. To assimilate existing ocean velocity observations, which generally are depth limited, vertical error covariances are needed to relate the velocity errors throughout the water column. In this paper we show a velocity assimilation approach that relies on historical observations and geostrophy to relate velocity, temperature, and salinity. Geopotential calculations transform temperature and salinity covariances into corresponding geopotential covariances. This paper describes the methods and examines details of the solution process for ocean velocity data assimilation. The approach employs two separate three-dimensional variational (3DVAR) analyses combined to create one set of initial conditions during each daily forecast cycle. The first 3DVAR analysis utilizes temperature, salinity, and sea surface height anomaly data and reduces the background error variance based on these observations. The second 3DVAR analysis uses the reduced error variance and performs velocity data assimilation using surface drifter data, and these observations further reduce the error variance of temperature and salinity for the next cycle. The second 3DVAR analysis for velocity data assimilation utilizes the new full depth error covariances. We show diagnostic results from a Gulf of Mexico simulation during extensive drifter deployments in the summer of 2020. Results reveal that the two-step data assimilation cycle applies vector velocity innovations to create increments that correct the background field towards a geostrophic balance in temperature and salinity.

강화학습 상태 텐서 차원에 따른 학습 효과도

In reinforcement learning, an agent takes actions in an environment, which is interpreted into a reward and a “representation of the state”. It is well known that the performance of the reinforcement learning is dependent on the “data model representing the state” of a given environment. This paper proposes a data model representing the state which is suitable for a FPS (First Person Shooting) game, a military tactics simulator that changes state extremely and needs decision making quickly. The proposed data model consists of matrix (multi-dimensional tensors) for spatial features and vectors for non-spatial features. To prove the usefulness of the proposed data model, this paper shows experimental results for a FPS game.

Модальный синтез регуляторов полиномиальным методом для систем автоматического управления с избыточной размерностью вектора управления

When using polynomial methods of synthesis of multichannel regulators, there is a need for polynomial matrix calculus. However, when using this method, objects with the number of output channels equal to the number of input channels are mainly considered. This is necessary for the convenience of solving a system of linear algebraic equations in matrix polynomial computation. At the same time, a fairly large number of real technical systems have an unequal number of input and output channels. At the same time, the issue of the synthesis of generators by the polynomial method for multichannel objects with an unequal number of input and output impacts has not been worked out sufficiently deeply. One of the special cases of such systems can be considered systems with excessive dimension of the control vector. Within the framework of this work, examples of such systems and the purposes of their use are given. An illustrative example of a linear model of an unstable object with three channels for input action and two channels for output action is given. It is necessary to achieve certain quality indicators of the output vector quantity, while the control is carried out in the feedback of the system and is summed up with the input effect. The simplicity of the system under consideration is connected with the convenience of demonstrating on this example a modal synthesis method using a polynomial matrix decomposition of the transfer functions of an object and a controller for such a class of objects. In accordance with the recommendations presented in the algorithm for synthesizing regulators for objects with a non-square matrix transfer function, to solve the problem of synthesizing a regulator for systems with an excessive dimension of the control vector, the transfer function of the control object is represented as a right polynomial matrix decomposition, and the regulator is represented as a left one. During the demonstration of the example of the algorithm, some clarifications and edits are proposed in it.

Interference-induced anisotropy in a two-dimensional dark-state optical lattice

We describe a two-dimensional optical lattice for ultracold atoms with spatial features below the diffraction limit created by a bichromatic optical standing wave. At every point in space these fields couple the internal atomic states in a three-level Lambda coupling configuration. Adiabatically following the local wavefunction of the resulting dark state yields a spatially uniform Born-Oppenheimer potential augmented by geometric scalar and vector potentials appearing due to spatially rapid changes of the wavefunction. Depending on system parameters, we find that the geometric scalar potential can interpolate from a 2D analogue of the Kronig-Penney lattice, to an array of tubes with a zig-zag shaped barrier. The geometric vector potential induces a spatially periodic effective magnetic field (the Berry's curvature) that can be tuned to cause destructive interference between neighboring tubes, thereby decoupling them at a critical point in parameter space. We numerically investigate the energy spectrum including decay from the excited state, and find that the adiabatic approximation is sound for strong coupling strengths, leading to negligible loss in the dark state manifold. Furthermore, the spectrum is well-described by a non-Hermitian tight binding model with on-site losses, and hopping characterized by both loss and, surprisingly, gain.

ANALYSIS OF EMERGENCY MODES OF TRACTION INDUCTION ELECTRIC DRIVE

The operation process of the traction asynchronous electric drive of the rolling stock may be accompanied by the occurrence of emergency modes, which are characterized by current overloads and may lead to damaging power electric circuits or mechanical part of the electric drive. Therefore, studying electromagnetic processes that occur in emergency modes of operation is relevant, in particular, for developing systems aimed at diagnosing and protecting traction equipment. Ukrainian and foreign studies of emergency modes in an asynchronous electric drive were analyzed. The most frequent causes of emergency modes are failures in static converters, therefore it is advisable to conduct an analysis of electromagnetic processes specifically in autonomous inverters. Using Matlab/Simulink software environment a simulation model of the vector control system of the traction asynchronous electric drive with spatial vector pulse width modulation was developed, and the short-circuit modes of the power switch of the autonomous voltage inverter and the disappearance of the supply voltage were simulated. The oscillograms of electromagnetic processes in these modes were shown and compared with the nominal mode of operation. The simulation results show that the analyzed emergency modes, even with a short duration of less than 0.5 s, can lead to significant damage to the electric drive. Both cases lead to significant current overloads: in the case of a short circuit of the power switch, a three-fold excess of the phase current is observed, during recovery after a supply voltage failure, the phase currents increase to the starting level, as well as an almost instantaneous increase in the supply voltage on the input filter capacitor occurs. The Fourier analysis of the phase A current in the above emergency modes shows an increase in the coefficient of nonlinear distortions by 65 %, a decrease in the amplitude of the main harmonic to 60 %, and the appearance of a significant number of subharmonics. In the case of a short circuit, the amplitudes of the subharmonics even exceed the fundamental harmonic. In addition, the process of returning from the emergency mode to normal at the first moment of time was characterized by a reduced moment of resistance (20 % of the nominal), and then by an exit to the nominal parameters.

Consistency Discrimination and Experimental Study of Rock Discontinuities Based on Borehole Optical Images

The structural plane is a geological interface of a particular scale, and how to determine the extension of the structural plane in space is an essential issue in the study of rock structure. This paper analyses the structural plane information of multiple boreholes collected by the digital panoramic borehole camera system to determine the structural plane consistency. The discriminative conditions of structural plane consistency in multiple boreholes are deduced through mathematical analysis methods such as spatial geometry and vector. The structural planes that may be connected are quickly screened out. The consistency is further judged by combining the relevant characteristics of the structural planes, thus forming a complete method based on digital borehole images. The method has been validated by a series of indoor tests. Structural planes with specific parameters were presented in the test blocks in these tests, and several boreholes were drilled. The experimental results show that (1) the structural plane consistency discrimination method can be applied to both flat structural planes and structural planes with certain undulation angles and roughness; (2) the tilt of the borehole does not affect the structural plane consistency using the borehole camera, and the structural plane consistency can be judged by this method in any two boreholes in space; and (3) the analysis method proposed in this paper can quickly complete the structural plane consistency discrimination, and is also suitable for the consistency discrimination and screening of a large number of structural planes in deep boreholes. Finally, the method based on the optical borehole images is successfully applied in the slope survey of the Xichang iron ore mine.

Students’ understanding of electrostatic force as a vector quantity at the undergraduate level

Electrostatic force is the preliminary and fundamental topic that forms the bedrock on which the conceptual framework of electromagnetism is built. Being a ‘vector’ quantity, electromagnetic force naturally inherits direction as well as magnitude. The conceptualization of such physical quantities may pose a challenge. The present work explores how students conceptualize the construct of electrostatic force from the viewpoint of its vector nature. Our effort is directed towards engaging students in problems aimed at their understanding of the concept of electrostatic force (a physical quantity) and analyse their responses for identifying the conceptual challenges. Our observations reveal that a significant fraction of conceptual difficulties in the concept of electrostatic force are due to insufficient understanding of operations involving vector quantities. The implications of this work indicate that the pedagogical aspects in physics classroom and curriculum design needs to be researched holistically so as to bridge-in the gaps obstructing the desired cognitive framework of students.

On the Douglas–Rachford Algorithm for Solving Possibly Inconsistent Optimization Problems

More than 40 years ago, Lions and Mercier introduced in a seminal paper the Douglas–Rachford algorithm. Today, this method is well-recognized as a classic and highly successful splitting method to find minimizers of the sum of two (not necessarily smooth) convex functions. Whereas the underlying theory has matured, one case remains a mystery: the behavior of the shadow sequence when the given functions have disjoint domains. Building on previous work, we establish for the first time weak and value convergence of the shadow sequence generated by the Douglas–Rachford algorithm in a setting of unprecedented generality. The weak limit point is shown to solve the associated normal problem, which is a minimal perturbation of the original optimization problem. We also present new results on the geometry of the minimal displacement vector. Funding: The research of H. H. Bauschke and W. M. Moursi was partially supported by Discovery Grants of the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-03703 and RGPIN-2019-04803], respectively.

Real-Time Visual Recognition of Ramp Hand Signals for UAS Ground Operations

We describe the design and validation of a vision-based system that allows the dynamic identification of ramp signals performed by airport ground staff. This ramp signals’ recognizer increases the autonomy of unmanned vehicles and prevents errors caused by visual misinterpretations or lack of attention from the pilot of manned vehicles. This system is based on supervised machine learning techniques, developed with our own training dataset and two models. The first model is based on a pre-trained Convolutional Pose Machine followed by a classifier, for which we have evaluated two possibilities: A Random Forest and a Multi-Layer Perceptron based classifier. The second model is based on a single Convolutional Neural Network that classifies the gestures directly imported from real images. When experimentally tested, the first model proved to be more accurate and scalable than the second one. Its strength relies on a better capacity to extract information from the images and transform the domain of pixels into spatial vectors, which increases the robustness of the classification layer. The second model instead is more adequate for gestures’ identification in low visibility environments, such as during night operations, conditions in which the first model appeared to be more limited, segmenting the shape of the operator. Our results support the use of supervised learning and computer vision techniques for the correct identification and classification of ramp hand signals performed by airport marshallers.

Prediction of tandem cold-rolled strip flatness based on Attention-LSTM model

Flatness is a key quality indicator of tandem cold-rolled strip. Tandem cold-rolled production is a multi-stand simultaneous rolling process. The prediction of flatness is a typical spatial sequence data prediction problem by considering not only the complex generation mechanism but also the spatial dimension dependence. However, the currently available mechanistic and machine learning models for flatness prediction only focus on its highly nonlinear characteristics and ignore its complex spatial correlation. In addition, the over-parameterized “black box” nature of machine learning models has often been questioned. Based on this, a long-short-term memory model with an attention mechanism (Attention-LSTM) is proposed in this paper. The model is structured as a two-layer LSTM network to fully learn the highly nonlinear and complex spatial data correlation of tandem cold-rolled strip flatness. Furthermore, attention mechanisms are also added to the spatial dimension and flatness feature vector dimension, respectively, to enhance the interpretability of the model. The superiority of the proposed model is verified by comparing the error back propagation neural network model (BPNN), the extreme gradient boosting algorithm model (XGBoost), the deep neural network model (DNN), the LSTM model without the attention mechanism, and the Attention-LSTM model with the same dataset and the same optimum training method. The interpretation of the attention mechanism weights is consistent with the mechanistic model, which enhances the credibility of the model. The results of the ablative experiments also validate the effectiveness of incorporating the attention mechanism in the model.

Caspian Elegies

Caspian Elegies Layli Shirani (bio) for Ali First Elegy When we were kids, we found Paradise in the dewy Hyrcanian forests and moody, sentient waters of the Caspian seashore. You handsome, a bit serious, dimple-chinned with a drooping lower lip; me scrawny, messy, eager to keep up. Every family has its own unique geometry. Where I was inconsistent, changeful, rebellious, you were steady, committed, argumentative: a Euclidean vector conferring, upholding, the symmetry and harmony that bound us together. Host to endless hectares of shalizar, our Caspian region has always favored the Mehr varietal: the rice that stretches and gives more of itself after a good salty soaking. Our people prize quality over yield. The cultivation of rice is a delicate process involving rice buds—shaltooks—gestating safe and warm and away from any inclemency for a whole year before being plunged in saltwater and gently stirred, allowing the heavier, heartier seeds to announce themselves by sinking to the bottom. The shalikars rinse the more viable seeds, place them in semi-full gunny sacks and submerge them in fresh water, allowing the seeds to swell in size before laying them out to sprout. You would think the surviving seeds are now ready to begin their separate journeys, but they are not. [End Page 69] Picture our homeland as it is often described: in the shape of a cat. Now see its scruff and there we are, in the middle . . . in Vanoosh village, Nowshahr County, Mazandaran Province, on the wrong side of the highway from the seashore, in a modest villa surrounded, saturated, by damp, green shalizar. A cradle. Think: nobody worried about us crossing the busy Nowshahr-Mahmudabad Road during our daily treks to and from the darya, or how often we young shaltooks ventured into those turbulent waters, unaccompanied and unseen. How free we were. I had my big brother next to me. I had you. Iranian shalikars practice the seedling method of cultivation. Every shalizar has a khazaneh—an enclosed area either adjacent to or on the land itself—where the seeds are released into water at least three centimeters deep. Often the khazaneh is covered with a layer of plastic at night to protect the babes from aberrant drops in temperature or other calamities. Here they will germinate, grow tentative roots, begin their adolescence. Once, on our way back to the villa for lunch, you were running ahead of me through a narrow passageway, and the next thing I knew your face was swarmed by attacking bees. I don’t remember which side of the highway we were on, but we sprinted, eyes half-shut and screaming until we reached Mom, who was nearby at the villa if not on the land itself. In the khazaneh, when the plants become teenagers, the shalikars withhold water for three weeks and then flood them, tricking the young seedlings into believing they are ready to separate from the earth. They emerge, quivering, their fledgling roots intact for nearby transplantation. And they are yet a long way from becoming the grains presently soaking in a bowl on my kitchen counter here in California—continents away from their birthplace—resting beneath a watery western salt cap. Suppose I were to tell you that I have never stopped trying to return to that place where danger and separation were neither thoughts nor words, and safety and comfort were always nearby if not on the land itself ? Where the best part of eating balaal by the roadside in Vanoosh was not so much the grilled corn but the process of receiving it by the cob’s end with a bit of newspaper wrapped around and dunking it into a communal saltwater [End Page 70] bucket that was blackened . . . with bits of char, ash, and rootlike filaments. Suppose I were to tell you that my body rejected its transplant, that the distance was too far, the soil too alien. That my roots are still flooded, still seeking familiar earth.1 Second Elegy In one of my earliest memories, we are in Princeton, our leafy anchor to this country. I am scared of the neighbor’s dog who barks whenever we walk past. You grip my...

A learning trajectory for university students regarding the concept of vector

This paper presents and evaluates a hypothetical learning trajectory by which students bridge the transition from elementary to university-level instruction regarding the concept of vector. The trajectory consists of an instructional sequence of five tasks and begins with a problem in context. Each task is carried out with the support of a Virtual Interactive Didactic Scenario, accompanied by exploration and guided learning sheets, in which the problem is introduced through the simulation of the movement of a robotic arm. This proposal was implemented at the beginning of the SARS-CoV-2 pandemic using various digital media. Two teaching experiments were carried out with engineering students at a Mexican public university. We present the hypothetical learning trajectory that should be followed toward solving the task, and contrast it in each case with the students’ actual learning trajectory. The results show that more than 70 % of the students successfully transitioned from the geometrical vector representation of elementary physics to the algebraic one.

Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks

Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the ‘segmentation-stacking’ method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each image’s 90–99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99–1.00 [0.97–1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91–100%; middle cerebral arteries, 82–98%; anterior cerebral arteries, 88–100%; posterior cerebral arteries, 87–100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90–99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.

Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.

Measuring the Effect of Off-Balancing Vectors on the Delivery of High-Quality CPR during Ambulance Transport: A Proof of Concept Study

Aim This study aims to demonstrate the feasibility of quantifying the off-balancing vectors experienced during ambulance transport and comparing them to high-quality cardiopulmonary resuscitation (HQ-CPR) metrics. Methods Ten participants completed a total of 20 evolutions of compression-only HQ-CPR in an ambulance driven in a manner that minimized or increased linear and angular off-balancing vectors. Linear and angular velocity, linear and angular acceleration, and linear jerk were recorded. HQ-CPR variables measured were compression fraction and proportion of compressions with depth >5 cm (depth%), rate 100–120 (rate%), full chest recoil (recoil%), and hand position (hand%). A composite score was calculated: [(depth% + rate% + recoil% + hand%)/4) * compression fraction]. Difficulty of HQ-CPR performance was measured with the Borg rating of perceived exertion (RPE) Scale. A series of mixed effects models were fitted regressing each HQ-CPR metric on each off-balancing vector. Results HQ-CPR data and vector quantity data were successfully recorded in all evolutions. Rate% was negatively associated with increasing linear velocity (slope = −3.82, standard error [SE] 1.12, p = 0.005), linear acceleration (slope = −5.52, SE 1.93, p = 0.013), linear jerk (slope = −17.60, SE 5.78, p = 0.007), angular velocity (slope = −75.74, SE 22.72, p = 0.004), and angular acceleration (slope = −152.53, SE 59.60, p = 0.022). Compression fraction was negatively associated with increasing linear velocity (slope = −1.35, SE 0.37, p = 0.004), linear acceleration (slope = −1.67, SE 0.48, p = 0.003), linear jerk (slope = −4.90, SE 1.86, p = 0.018), angular velocity (slope = −25.66, SE 6.49, p = 0.001), and angular acceleration (slope = −45.35, SE 18.91, p = 0.031). Recoil% was negatively associated with increasing linear velocity (slope = −5.80, SE 2.21, p = 0.023) and angular velocity (slope = −116.96, SE 44.24, p = 0.019)). Composite score was negatively associated with increasing linear velocity (slope = −4.49, SE 1.45, p = 0.009) and angular velocity (slope = −86.13, SE 31.24, p = 0.014) and approached a negative association with increasing magnitudes of linear acceleration (slope −5.54, SE 2.93, p = 0.075), linear jerk (slope = −17.43, SE 8.80, p = 0.064), and angular acceleration (slope = −170.43, SE 80.73, p = 0.051). Borg RPE scale was positively associated with all off-balancing vectors. Depth%, hand%, mean compression depth, and mean compression rate were not correlated with any off-balancing vector. Conclusion Off-balancing vector data can be successfully quantified during ambulance transport and compared with HQ-CPR performance parameters. Increasing off-balancing vectors experienced during ambulance transport are associated with worse HQ-CPR metrics and increased perceived physical exertion. These data may help guide future drive styles, ambulance design, or use of mechanical CPR devices to improve HQ-CPR delivery during selected patient transport scenarios.

Anomalous scaling in kinematic magnetohydrodynamic turbulence: Two-loop anomalous dimensions of leading composite operators.

Using the field theoretic formulation of the kinematic magnetohydrodynamic turbulence, the explicit expressions for the anomalous dimensions of leading composite operators, which govern the inertial-range scaling properties of correlation functions of the weak magnetic field passively advected by the electrically conductive turbulent environment driven by the Navier-Stokes velocity field, are derived and analyzed in the second order of the corresponding perturbation expansion (in the two-loop approximation). Their properties are compared to the properties of the same anomalous dimensions obtained in the framework of the Kazantsev-Kraichnan model of the kinematic magnetohydrodynamics with the Gaussian statistics of the turbulent velocity field as well as to the analogous anomalous dimensions of the leading composite operators in the problem of the passive scalar advection by the Gaussian (the Kraichnan model) and non-Gaussian (driven by the Navier-Stokes equation) turbulent velocity field. It is shown that, regardless of the Gaussian or non-Gaussian statistics of the turbulent velocity field, the two-loop corrections to the leading anomalous dimensions are much more important in the case of the problem of the passive advection of the vector (magnetic) field than in the case of the problem of the passive advection of scalar fields. At the same time, it is also shown that, in phenomenologically the most interesting case with three spatial dimensions, higher velocity correlations of the turbulent environment given by the Navier-Stokes velocity field play a rather limited role in the anomalous scaling of passive scalar as well as passive vector quantities, i.e., that the two-loop corrections to the corresponding leading anomalous dimensions are rather close to those obtained in the framework of the Gaussian models, especially as for the problem of scalar field advection. On the other hand, the role of the non-Gaussian statistics of the turbulent velocity field becomes dominant for higher spatial dimensions in the case of the kinematic magnetohydrodynamic turbulence but remains negligible in the problem of the passive scalar advection.

Investigation of Microwave Electromagnetic Fields in Open and Shielded Areas and Their Possible Effects on Biological Structure.

The article's subject is the investigation of electromagnetic fields (EMF) of the microwave frequency band in a typical human living environment, especially in shielded areas. The point of view of electromagnetic field presence in the environment with the rapid increase in the level of the electromagnetic background is currently an essential point concerning population protection against the potential adverse effects of such EMFs. The authors focus on actual measurements, especially in shielded spaces frequently used in everyday life, such as elevator cabins and cars. The goal is a quantitative evaluation of the distribution of specific vector quantities of the EM field and a comparison with the currently valid hygiene standards. Measured values in shielded spaces show elevated levels in contrast to the open space. However, the values do not exceed limits set by considering the thermal effect on living tissues.