Source Vector Research Articles

Forward problem of electrocardiography based on cardiac source vector orientations

To localize the unusual cardiac activities non-invasively, one has to build a prior forward model that relates the heart, torso, and detectors. This model has to be constructed to mathematically relate the geometrical and functional activities of the heart. Several methods are available to model the prior sources in the forward problem, which results in the lead field matrix generation. In the conventional technique, the lead field assumed the fixed prior sources, and the source vector orientations were presumed to be parallel to the detector plane with the unit strength in all directions. However, the anomalies cannot always be expected to occur in the same location and orientation, leading to misinterpretation and misdiagnosis. To overcome this, the work proposes a new forward model constructed using the VCG signals of the same subject. Furthermore, three transformation methods were used to extract VCG in constructing the time-varying lead fields to steer to the orientation of the source rather than just reconstructing its activities in the inverse problem. In addition, the unit VCG loop of the acute ischemia patient was extracted to observe the changes compared to the normal subject. The abnormality condition was achieved by delaying the depolarization time by 15ms. The results involving the unit vectors of VCG demonstrated the anisotropic nature of cardiac source orientations, providing information about the heart’s electrical activity.

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets.

In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method.

Environmental drivers impact the accumulation and diversity of antibiotic resistance in green stormwater infrastructure

Antibiotic resistance poses an urgent public health concern, with the environment playing a crucial role in the development and dissemination of resistant bacteria. There is a growing body of research indicating that stormwater is a significant source and transport vector of resistance elements. This research sought to characterize the role of green stormwater infrastructure (GSI), designed for stormwater infiltration, in accumulating and propagating antibiotic resistance in the urban water cycle. Sampling included 24 full-scale GSI systems representing three distinct types of GSI - bioswales, bioretention cells, and constructed wetlands. The results indicated that GSI soils accumulate antibiotic resistance genes (ARGs) at elevated concentrations compared to nonengineered soils. Bioretention cells specifically harbored higher abundances of ARGs, suggesting that the type of GSI influences ARG accumulation. Interestingly, ARG diversity in GSI soils was not impacted by the type of GSI design or the diversity of the microbial community and mobile genetic elements. Instead, environmental factors (catchment imperviousness, metals, nutrients, and salts) were identified as significant drivers of ARG diversity. These findings highlight how environmental selective pressures in GSI promote ARG persistence and proliferation independently of the microbial community. Therefore, GSI systems have the potential to be a substantial contributor of abundant and diverse ARGs to the urban water cycle.

Aeroacoustic two-port characterisation technique for ventilation valves

This paper describes a measurement method to characterise the low-frequency aeroacoustic properties of ventilation extraction and supply valves based on an active two-port formulation for network modelling purposes. The method uses a more compact controlled environment compared to commonly used semi-(anechoic) or reverberations rooms, and relies on a radiation model that assumes the valve radiates similarly to plane waves from an open duct. Using a loudspeaker excitation inside the set-up, a passive two-port model representing the reflection and transmission properties of the valve is obtained. This passive two-port model is then extended to an active two-port model including the source properties by characterising a reflection-free source vector. The resulting properties do not include the influence of the radiation impedance of the environment and can be used to estimate the reflection coefficient and the sound power radiated from the valve when the valve is backed by a different environment. Predictions of the one-port reflection coefficient and the predicted radiated airborne sound power, obtained from the experimentally characterised two-port properties of a ventilation valve, are compared to measurements using established methods in a semi-anechoic environment, showing good agreement in the targeted frequency range where plane wave propagation can be assumed.

Fast 3D simulation of magnetotelluric data in anisotropic media using a rational Krylov method

This study introduces a computational method aimed at accelerating multifrequency 3D magnetotelluric (MT) forward modeling in 3D conductivity structures with general anisotropy. The approach combines an edge-based finite-element (FE) method with a rational Krylov subspace method. In this method, the MT source term is expressed as a planar current source, and the frequency-dependent electric field response is the product of a transfer function and a constant vector of the current source. Consequently, the rapid approximate electric field for multifrequency calculations can be obtained by constructing the orthogonal basis of the rational Krylov subspace. By applying this technique, the large sparse matrix resulting from FE discretization can be projected onto a much lower-order matrix, significantly reducing its size to tens or hundreds. Incorporating the direct solver PARDISO to construct the orthogonal basis enables a considerably faster solution compared with the previous FE method. Moreover, the algorithm is implemented using Julia, a high-level programming language known for its readability, maintainability, and extensibility. The validity of the algorithm is demonstrated through a 1D anisotropic model, and two additional 3D models are designed to further evaluate the applicability and effectiveness of our algorithm. Numerical experiments illustrate a notable increase in modeling speed compared with the traditional edge-based FE method.

Self-Similar Solutions of a Bianchi Type-III Model with a Perfect Fluid and Cosmic String Cloud in Riemannian Geometry

A Bianchi type-III cosmological model with self-similarity symmetry is investigated in cases with perfect fluid distribution and a cosmic string cloud. We show that this model admits a non-trivial homothetic vector field which possess non-null homothetic bivectors. This homothetic vector field is not parallel to its source vector (current vector). We discussed cases where the homothetic vector is either orthogonal or parallel to the 4-velocity of a fluid element. We solved Einstein’s field equations without making any assumptions on the geometry of space-time, only assuming that it admits self-similarity symmetry. We obtained new exact self-similar solutions for the Bianchi type-III model. We show that all obtained solutions are shear, and space-time is shear free in only the case where the homothetic vector field is parallel to the 4-velocity vector. The kinematical and physical properties of the obtained solutions are discussed.

Progression of parasitism from phoresy in mites

The mites are the smallest (less than a millimeter in length), the most diverse, and the most common of all arachnids. Mites are ubiquitous and inhabit all known terrestrial, marine, and freshwater habitats, including arctic and alpine extremes, tropical plains and desert barrens, and surface and mineral soils (Dunlop and Alberti, 2008). More than 55,000 species have been described up to date, accounting for 5% of all living species today. Mites are experts at transport with the aid of large animals, mostly insects. It is a temporary relationship called phoresy that allows the mites to exploit scarce resources. Phoresy in the subclass Acari includes insects that feed on carriers. Phoresy evolved from free-living ancestors. The primary waste material used by floating mites appears to be rotting logs. However, rapid changes in the later life stage allowed the development of short-term resources. Although phoresy is a form of social interaction, most interact with mites. These relationships can be very complex and context-specific, but they often use the vector's sources or descendants (Seeman and Walter, 2023). The switch from phoretic to parasitism seems popular, but the scientific evidence for a switch from phoretic to permanent parasitism seems to be lacking.

A Universal Random Coding Ensemble for Sample-Wise Lossy Compression.

We propose a universal ensemble for the random selection of rate-distortion codes which is asymptotically optimal in a sample-wise sense. According to this ensemble, each reproduction vector, x^, is selected independently at random under the probability distribution that is proportional to 2-LZ(x^), where LZ(x^) is the code length of x^ pertaining to the 1978 version of the Lempel-Ziv (LZ) algorithm. We show that, with high probability, the resulting codebook gives rise to an asymptotically optimal variable-rate lossy compression scheme under an arbitrary distortion measure, in the sense that a matching converse theorem also holds. According to the converse theorem, even if the decoder knew the ℓ-th order type of source vector in advance (ℓ being a large but fixed positive integer), the performance of the above-mentioned code could not have been improved essentially for the vast majority of codewords pertaining to source vectors in the same type. Finally, we present a discussion of our results, which includes among other things, a clear indication that our coding scheme outperforms the one that selects the reproduction vector with the shortest LZ code length among all vectors that are within the allowed distortion from the source vector.

D-Semifaithful Codes That are Universal Over Both Memoryless Sources and Distortion Measures

We prove the existence of codebooks for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -semifaithful lossy compression that are simultaneously universal with respect to both the class of finite-alphabet memoryless sources and the class of all bounded additive rational distortion measures. By applying independent random selection of the codewords according to a mixture of all memoryless sources, we achieve redundancy rates that are within <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (log <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n/n</i> ) close to the empirical rate-distortion function of every given source vector with respect to every bounded, rational distortion measure.

Inverse estimation scheme of radioactive source distributions inside building rooms based on monitoring air dose rates using LASSO: Theory and demonstration

Predicting radioactive source distributions inside reactor building rooms based on monitoring air dose rates is one of the most essential steps towards decommissioning of nuclear power plants. However, the attempt is rather a difficult task, because it can be generally mapped onto mathematically ill-posed problem, i.e., information obtained by air dose rate measurements inside the building rooms is generally poor in predicting the contaminated distributions on surface mesh units of structural materials inside the target building rooms. Then, in order to successfully perform the inverse estimations on radioactive source distributions even in such ill-posed conditions, we suggest that a machine learning method, least absolute shrinkage and selection operator (LASSO) minimizing the loss function, ‖CP−Q‖22+λ‖P‖1 is a promising scheme. For the purpose of its feasibility demonstrations in real building rooms, we employ a Monte Carlo simulation code, i.e., Particle and Heavy Ion Transport Systems (PHITS) to make LASSO input as the above matrix C connecting the radioactive source vector P defined on surface meshes of structural materials with the air dose rate vector Q measured at internal positions inside the rooms. We develop a mathematical criterion on the number of monitoring points to correctly predict source distributions based on the theory of Candes and Tao. Then, we confirm that LASSO actually shows extremely high possibility for source distribution reconstructions as far as the number of detection points satisfies our criterion. Besides, we find that source distributions are partly reconstructed even when the number of detections is less than the bound. In addition, we demonstrate that the proposed LASSO scheme is applicable to remote sensing situations as monitoring is permissible only from distant regions like its neighboring rooms. Moreover, we verify that radioactive hot spots can be truly reconstructed in an experiment setup. At last, we examine an influence factor like detector-source distance to enhance the predicting possibility in the inverse estimation. From the above demonstrations, we propose that LASSO scheme is a quite useful way to explore hot spots as seen in damaged nuclear power plants like Fukushima Daiichi nuclear power plants.

A three-dimensional active cloaking strategy for the Helmholtz equation that exploits the symmetry of the platonic solids

An active cloaking strategy for the scalar Helmholtz equation in three dimensions is developed by placing active sources at the vertices of Platonic solids. In each case, a “silent zone” is created interior to the Platonic solid and only the incident field remains in a defined region exterior to this zone. This distribution of sources ensures that implementation of the cloaking strategy is efficient: once the multipole source amplitudes at a single source location are determined, the other amplitudes are calculated by multiplying the multipole source vector by a rotation matrix. The technique is relevant to any scalar wave field.

A S-wave Spectral Decomposition Hydrocarbon Detection Method Based on P-wave and S-wave Source and Its Application

S-wave source vector exploration is a promising technology in future seismic exploration. Conventional P-wave and S-wave joint inversion methods for hydrocarbon detection are limited in some aspects. First, P-wave and S-wave joint inversion is mainly based on the converted wave theory, but no theoretical support for S-wave generated by S-wave source. Second, the inversion method is complex and difficult to operate. Because P-wave and S-wave from S-wave source should accurately match with each other, without an effective approach, it has to follow the method of P-wave matching with converted wave, so it is not helpful to extensive application. This paper proposes a comprehensive s-wave spectral decomposition hydrocarbon detection method based on p-wave and s-wave source. First, the velocity ratio of P-wave to S-wave (Vp/Vs ratio) is derived to be equal to the frequency notch period ratio of P-wave to S-wave, and approximately equal to the dominant frequency ratio of P-wave to S-wave. Second, spectral decomposition is conducted at the target layer to obtain the tuning frequency spectra of P-wave and S-wave seismic data, respectively, and the dominant frequencies of P-wave and S-wave are automatically identified. Third, the Vp/Vs ratio and Poisson's ratio are calculated according to the dominant frequency ratio of P-wave to S-wave for final hydrocarbon detection. This method is independent on well data and accurate matching of P-wave with S-wave data, it is simple, quick and efficient. It’s widely applicable in any areas more or less explored. Application in an oilfield in western China has obtained effective results.

Non-iterative constrained compressive beamforming

Compressive beamforming is increasingly researched and employed in underwater acoustic applications. Sparse Bayesian learning (SBL) induces the prior distribution and encourages the sparsity of the source vector, achieving improved accuracy compared with other deterministic compressive beamforming approaches, where the constraint optimization is solved explicitly. Considering the structural complexity of targets and underwater environment, it benefits the reconstruction performance to employ the multiconstraint SBL (MSBL) approach, exploring the sparsity in a joint sparse representation domain. However, the computation load of MSBL is heavy and unpredictable due to the requisite iterative implementation, and regular acceleration approaches are difficult to employ. To overcome such obstacles, an online constrained SBL is proposed, which is non-iterative and computationally efficient. Moreover, the estimation convergence of hyperparameters is proved and guaranteed theoretically. Through empirical and experimental results, the efficacy of the proposed method is validated and compared with other state-of-the-art beamforming approaches.

Semiexplicit time integration of a reduced magnetic vector potential magneto‐quasistatic field formulation

AbstractIn this article, a reduced vector potential formulation for transient magneto‐quasistatic field problems is presented. The finite element method for spatial discretization yields a system of differential algebraic equations (DAE). Applying a generalized Schur complement the DAE system decomposes into a system of ordinary differential equations (ODE) and an algebraic equation. In order to avoid computationally expensive linearization schemes to solve nonlinear eddy current problems the ODE system is integrated in time using a semiexplicit Euler scheme. The vector potential is split into a source vector potential and a reduced vector potential which describes the eddy current and magnetization effects. The reduced formulation is compared with the underlying nonreduced formulation regarding the overall performance. In addition, the proper orthogonal decomposition method is applied to the iterative preconditioned conjugate gradient method to minimize the number of iterations per time step.

Learning asymmetric embedding for attributed networks via convolutional neural network

Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network embedding is to represent network nodes in a low-dimensional vector space while retaining as much information as possible from the original network including structural, relational, and semantic information. However, asymmetric nature of directed networks poses many challenges as how to best preserve edge directions during the embedding process. Here, we propose a novel deep asymmetric attributed network embedding model based on the convolutional graph neural network, called AAGCN. The main idea is to maximally preserve the asymmetric proximity and asymmetric similarity of directed attributed networks. AAGCN introduces two neighbourhood feature aggregation schemes to separately aggregate the features of a node with the features of its in- and out- neighbours. Then, it learns two embedding vectors for each node, one source embedding vector and one target embedding vector. The final representations are the results of concatenating source and target embedding vectors. We test the performance of AAGCN on four real-world networks for network reconstruction, link prediction, node classification and visualization downstream tasks and investigate the impact of hyperparameters of the proposed method on the performance of the tasks. The experimental results show the superiority of AAGCN against state-of-the-art embedding methods.

High-order harmonic generation by sub-cycle laser pulses and associated scaling laws

We studied the high-harmonic generation by the interaction of sub-cycle laser pulses with the He atom. The sub-cycle pulses are modeled using the complex source vector beam model, which is an exact solution to Maxwell's equations and accurately models the sub-cycle field profiles. We observed that the harmonic cutoff could be extended with the variation of the sub-cycle pulse duration, mainly because of the inherent blueshift associated with shorter pulses. The scaling laws for the harmonic yield and harmonic cutoff energy with pulse duration and fundamental driver wavelengths are also deduced. Furthermore, a detailed wavelet analysis of harmonic generation by sub-cycle pulses is carried out. The appropriate filtering and superposition of the harmonics gave rise to a single attosecond pulse of duration ∼100 as.

A Full Loading-Based MVDR Beamforming Method by Backward Correction of the Steering Vector and Reconstruction of the Covariance Matrix

In order to improve the performance of the diagonal loading-based minimum variance distortionless response (MVDR) beamformer, a full loading-based MVDR beamforming method is proposed in this paper. Different from the conventional diagonal loading methods, the proposed method combines the backward correction of the steering vector of the target source and the reconstruction of the covariance matrix. Firstly, based on the linear combination, an appropriate full loading matrix was constructed to correct the steering vector of the target source backward. Secondly, based on the spatial sparsity of the sound sources, an appropriate loading matrix was constructed to further suppress interferences. Thirdly, the spatial response power was utilized to derive a more accurate direction of arrival (DOA) of the target source, which is helpful for obtaining a more accurate steering vector of the target source and a more effective covariance matrix iteratively. The simulation results show that the proposed method can effectively suppress interferences and noise.

Methodological heterogeneity biases physical activity metrics derived from the Actigraph GT3X in multiple sclerosis: A rapid review and comparative study.

Physical activity (PA) is reduced in persons with multiple sclerosis (MS), though it is known to aid in symptom and fatigue management. Methods for measuring PA are diverse and the impact of this heterogeneity on study outcomes is unclear. We aimed to clarify this impact by comparing common methods for deriving PA metrics in MS populations. First, a rapid review of existing literature identified methods for calculating PA in studies which used the Actigraph GT3X in populations with MS. We then compared methods in a prospective study on 42 persons with MS [EDSS 4.5 (3.5-6)] during a voluntary course of inpatient neurorehabilitation. Mixed-effects linear regression identified methodological factors which influenced PA measurements. Non-parametric hypothesis tests, correlations, and agreement statistics assessed overall and pairwise differences between methods. In the rapid review, searches identified 421 unique records. Sixty-nine records representing 51 eligible studies exhibited substantial heterogeneity in methodology and reporting practices. In a subsequent comparative study, multiple methods for deriving six PA metrics (step count, activity counts, total time in PA, sedentary time, time in light PA, time in moderate to vigorous PA), were identified and directly compared. All metrics were sensitive to methodological factors such as the selected preprocessing filter, data source (vertical vs. vector magnitude counts), and cutpoint. Additionally, sedentary time was sensitive to wear time definitions. Pairwise correlation and agreement between methods varied from weak (minimum correlation: 0.15, minimum agreement: 0.03) to perfect (maximum correlation: 1.00, maximum agreement: 1.00). Methodological factors biased both point estimates of PA and correlations between PA and clinical assessments. Methodological heterogeneity of existing literature is high, and this heterogeneity may confound studies which use the Actigraph GT3X. Step counts were highly sensitive to the filter used to process raw accelerometer data. Sedentary time was particularly sensitive to methodology, and we recommend using total time in PA instead. Several, though not all, methods for deriving light PA and moderate to vigorous PA yielded nearly identical results. PA metrics based on vertical axis counts tended to outperform those based on vector magnitude counts. Additional research is needed to establish the relative validity of existing methods.

Category-learning attention mechanism for short text filtering

In machine translation, the attention mechanism highlights relevant words according to the distances between the source and target vectors dynamically. However, its ability to optimize text classification is limited because this mechanism only calculates weights inside the same text. There is an uneven distribution of words between ham (not spam) and spam categories, and this category-level feature has not previously been utilized in the attention mechanism for filtering short texts. In addition, short text filtering is uniquely challenging due to the length, sparsity and informal writing of texts, as well as a need for rapid processing. We propose a novel category-level attention mechanism called “category-learning attention,” which highlights words intensely distributed in the same category by dynamically calculating a category differentiation matrix for each short text. The category-learning attention mechanism is extended to the category-learning scaled-dot-product attention and the category-learning multi-head attention (CL-MHA) mechanisms. The CL-MHA mechanism is then applied to a bidirectional gate recurrent unit (Bi-GRU) model for performance evaluation by using the SMS spam collection dataset hosted at the University of California, Irvine. Performance metrics including the accuracy, precision, recall, and F1 score demonstrate that the CL-MHA mechanism significantly improves the performance of Bi-GRU for short text filtering with an accuracy of 99.35%, higher than any previously reported machine learning models. In addition, experiments conducted on three datasets - a Chinese SMS spam dataset, a benchmark movie review dataset, and a benchmark customer review dataset - further validate the effectiveness of the proposed model. The proposed CL-MHA Bi-GRU model has an accuracy of 99.46% when evaluated on the Chinese SMS spam dataset.

Simulated Annealing Algorithm-Aided SC Decoder for Polar Codes

A new decoding scheme aided by simulated annealing algorithm is proposed to further improve the decoding performance of successive cancellation (SC) for polar codes at the short block. We use simulated annealing to revise the decoding result of SC which cannot pass the CRC check. To generate the new neighbors, the decoder flips one bit from the set of the least unreliable information bits each time in the estimated source vector of SC decoding. Euclidean distance is used to measure the gap between the new neighbor solution and the received word so that the decoder can obtain a global optimal solution. Simulation shows that the proposed decoder has a performance gain about 0.5 dB in terms of frame error rate (FER) under short blocks in the additive white Gaussian noise (AWGN) channel compared to other basic decoders, while keeping a low time cost through a parameter tuning process.