Frequency Matrix Research Articles

An accurate and efficient method based on the dynamic stiffness matrix for analyzing wave propagation in defective lattice structures

In this study, we present an efficient and accurate method for analyzing wave propagation in lattice structures with periodic defects, which are composed of three-dimensional (3D) unit cells arranged infinitely in two or three directions, with defects existing periodically along the directions of the arrangement. The unit cell is composed of 3D beams, and the dynamic stiffness formulation of the 3D beam is developed by combining the Timoshenko-Ehrenfest, Rayleigh-Love and torsion theories. Based on the dynamic stiffness matrix, any number or order of natural frequencies of defective lattice structures can be calculated accurately and efficiently using the Wittrick-Williams algorithm. By combining it with the Bloch theorem, the proposed method can be used to calculate the dispersion curves of lattice structures with periodic defects. The accuracy and efficiency of the proposed method are demonstrated through numerical examples. Additionally, the effects of periodic defects in the lattice structures on the bandgap are analyzed.

Modeling the delay propagation dynamics in high-speed railway networks with a traffic-driven SIS epidemic model

The propagation of delays is a prevalent issue in high-speed railway (HSR) networks. However, the underlying properties of delay propagation have not been fully explored, hindering the effective management of this problem. In this paper, considering the impact of traffic flow, we model the delay propagation as a traffic-driven susceptible–infected–susceptible (SIS) epidemic spreading process. We introduce a traffic frequency matrix to estimate the traffic rate and further derive the propagation dynamic equations using a continuous-time Markov chain method. We then derive the epidemic threshold of the model, which is proportional to the recovery rate and inversely proportional to the average path length. Finally, through simulation, we study the impacts of various factors including the infection rate, recovery rate, and the degree and number of initial delay stations. Our work provides some insights for the prediction and control of delay propagation in HSR networks.

A reciprocity-aware, low-rank regularization for multidimensional deconvolution

A novel factorization-based, low-rank regularization method is introduced to solve multidimensional deconvolution (MDD) problems in the frequency domain. In this approach, each frequency component of the unknown wavefield is represented as a complex-valued square matrix and approximated as the product of a rectangular matrix and its transpose. The benefit of such a parameterization is twofold. First, each frequency matrix is guaranteed to be symmetric. From a physical standpoint, the recovered wavefield inherently adheres to the principle of reciprocity; in other words, the response to a receiver from a virtual source does not change when the two are swapped. Therefore, we refer to this low-rank parameterization as reciprocity-aware in that it respects the underlying physics of wave propagation associated with the MDD solution. Second, the size of the unknown matrix is greatly reduced compared with that of the original wavefield of interest (and halved compared with the existing factorization-based low-rank approximations). We further show that the associated objective function can be successfully optimized using the accelerated proximal gradient algorithm and present a robust strategy to define the initial guess of the solution. Numerical examples on synthetic and field data demonstrate the effectiveness of the proposed method in compressing the retrieved Green’s function while preserving its accuracy.

Evaluation of Aedes aegypti control intervention with pyriproxyfen by lcWGS in Manacapuru, Amazonas, Brazil.

Ae. aegypti mosquitoes are considered a global threat to public health due to its ability to transmit arboviruses such as yellow fever, dengue, Zika and Chikungunya to humans. The lack of effective arboviral vaccines and etiological treatments make vector control strategies fundamental in interrupting the transmission cycle of these pathogens. This study evaluated Ae. aegypti mosquito populations pre- and post-intervention period with disseminating stations of the larvicide pyriproxyfen to understand its potential influence on the genetic structure and population diversity of these vectors. This study was conducted in Manacapuru city, Amazonas, Brazil, where 1,000 pyriproxyfen dissemination stations were deployed and monitored from FEB/2014 to FEB/2015 (pre-intervention) and AUG/2015 to JAN/2016 (post-intervention). Low-coverage whole genome sequencing of 36 individuals was performed, revealing significant stratification between pre- and post-intervention groups (pairwise FST estimate of 0.1126; p-value < 0.033). Tajima's D estimates were -3.25 and -3.07 (both p-value < 0.01) for pre- and post-intervention groups, respectively. Molecular diversity estimates (Theta(S) and Theta(Pi)) also showed divergences between pre- and post-intervention groups. PCA and K-means analysis showed clustering for SNP frequency matrix and SNP genotype matrix, respectively, being both mainly represented by the first principal component. PCA and K-means clustering also showed significant results that corroborate the impact of pyriproxyfen intervention on genetic structure populations of Ae. aegypti mosquitoes. The results revealed a bottleneck effect and reduced mosquito populations during intervention, followed by reintroduction from adjacent and unaffected populations by this vector. We highlighted that low-coverage whole genome sequencing can contribute to genetic and structure population data, and also generate important information to aid in genomic and epidemiological surveillance.

Sequence-Order Frequency Matrix–Sampling and Machine Learning with Smith–Waterman (SOFM–SMSW) for Protein Remote Homology Detection

Sequence-Order Frequency Matrix–Sampling and Machine Learning with Smith–Waterman (SOFM–SMSW) for Protein Remote Homology Detection

Fault location method for new distribution networks based on waveform matching of time–frequency travelling waves

AbstractSome existing fault location methods for distribution networks rely too much on the local wave head information of the time or frequency domain signals, making it difficult to adapt to the increasingly complex structure and operating conditions of the distribution network after the new energy access. For improvement, the travelling wave (TW) time and frequency ranges that can effectively avoid waveform distortion caused by new energy access are analysed. The one‐to‐one matching relationship between the TW waveforms in these ranges and the fault positions is revealed. A fault TW time–frequency matrix with specific time and frequency windows is constructed, and a new distribution network fault location method is proposed based on the matching technique of waveform features, which realises the accurate fault location by exploring the proportionality between the cumulative trend of the matrix energy amplitude deviation and the fault point position. Simulation test results show that the proposed method is not affected by the complex structure of distribution networks such as new energy access and overhead‐cable line mixing on fault location and flexibly transforms the fault location problem into a time–frequency TW waveform matching problem, which improves the accuracy and robustness of the fault location for new distribution networks to a degree.

Quasinormal modes and universality of the Penrose limit of black hole photon rings

We study the physics of photon rings in a wide range of axisymmetric black holes admitting a separable Hamilton-Jacobi equation for the geodesics. Utilizing the Killing-Yano tensor, we derive the Penrose limit of the black holes, which describes the physics near the photon ring. The obtained plane wave geometry is directly linked to the frequency matrix of the massless wave equation, as well as the instabilities and Lyapunov exponents of the null geodesics. Consequently, the Lyapunov exponents and frequencies of the photon geodesics, along with the quasinormal modes, can be all extracted from a Hamiltonian in the Penrose limit plane wave metric. Additionally, we explore potential bounds on the Lyapunov exponent, the orbital and precession frequencies, in connection with the corresponding inverted harmonic oscillators and we discuss the possibility of photon rings serving as effective holographic horizons in a holographic duality framework for astrophysical black holes. Our formalism is applicable to spacetimes encompassing various types of black holes, including stationary ones like Kerr, Kerr-Newman, as well as static black holes such as Schwarzschild, Reissner-Nordström, among others.

Visualization-based comprehensive feature representation with improved EfficientNet for malicious file and variant recognition

Malicious file attacks seriously affect network and data security, and recognizing malicious files and variants is crucial for preventing network attacks. Faced with the challenge of traditional methods in quickly, effectively, and efficiently recognizing malicious files or variants, visualization-based feature representation methods have shown promising results. However, practical applications encounter issues such as loss of crucial information, high spatiotemporal overhead, and the need for model performance improvement. Therefore, this paper introduces a novel recognition framework focusing on feature representation and model performance. The framework uses the proposed visualization-based comprehensive feature representation method (VCFR) to extract file information into the Gray-Level Co-occurrence Matrix (GLCM), 2-gram frequency matrix, and interval 2-gram frequency matrix, followed by feature fusion to generate the three-channel RGB images. Subsequently, the proposed lightweight model is applied for recognizing those files, which utilizes ideas such as group convolution, channel shuffle, and attention mechanisms to improve model performance while significantly reducing model parameters, size, and FLOPs. In summary, through a series of experiments conducted on manually collected malicious file dataset (MFD) and public dataset MMCC, the proposed framework significantly outperformed other state-of-the-art technologies and has F1-Score as high as 94.10% and 98.58%, respectively, further verifying its outstanding effectiveness and efficiency.

Application of text mining in analysing notes to financial statements: A Hungarian case

Company stakeholders must have reliable and accurate information about the companies falling into their sphere of interest. In Hungary, one of the key sources of information for company stakeholders is the financial statements and related explanations, which are included in the notes of the financial statements (notes). This study used text mining to analyse the Hungarian annual financial statements notes for 2017, 2019 and 2021. The selection of the notes was based on the proportions of each sector in the national economy. The research analysed 28,700 company notes annually, totalling 86,100 documents for the three years. The text mining and generation of the Term Frequency Matrix have performed 'quanteda' packages of the R statistical system, which incorporate the results of artificial intelligence research to enhance the efficiency of text mining. Based on the results, the contents of the notes to the financial statements appear to be a rather mixed picture in Hungary. Analysing the term frequency matrix for the 67 most common terms has revealed no significant difference between the years. However, considerable differences have been caused by size categories and sectors. The notes are statistically significant using Jaccard similarity analysis, considering the year, corporate size, and sector.

Estimating Haplotype Structure and Frequencies: A Bayesian Approach to Unknown Design in Pooled Genomic Data.

The estimation of haplotype structure and frequencies provides crucial information about the composition of genomes. Techniques, such as single-individual haplotyping, aim to reconstruct individual haplotypes from diploid genome sequencing data. However, our focus is distinct. We address the challenge of reconstructing haplotype structure and frequencies from pooled sequencing samples where multiple individuals are sequenced simultaneously. A frequentist method to address this issue has recently been proposed. In contrast to this and other methods that compute point estimates, our proposed Bayesian hierarchical model delivers a posterior that permits us to also quantify uncertainty. Since matching permutations in both haplotype structure and corresponding frequency matrix lead to the same reconstruction of their product, we introduce an order-preserving shrinkage prior that ensures identifiability with respect to permutations. For inference, we introduce a blocked Gibbs sampler that enforces the required constraints. In a simulation study, we assessed the performance of our method. Furthermore, by using our approach on two distinct sets of real data, we demonstrate that our Bayesian approach can reconstruct the dominant haplotypes in a challenging, high-dimensional set-up.

An optimal sensor layout method based on noise reduction estimation for active road noise control

Active noise control (ANC) is an effective method to suppress vehicle road noise. The rapid selection of the reference sensor set and rational selection of its layout position are crucial for determining the vibration noise transmission paths with high contributions, which determines the noise reduction performance of the active road noise control (ARNC) system. The reference sensor set selected by the conventional multiple coherence function (MCOH) method does not always ensure the optimal noise reduction performance and the matrix singularity problem based on the Fisher information matrix (FIM) expansion method. To alleviate this problem, this paper proposes a method based on the overall level of noise reduction (OANR) and the FIM to select a set of reference sensors and determine their positions. This method uses an optimal causally constrained Wiener filter to estimate the theoretical OANR for each control area to ensure the reference sensor selected has the optimal noise reduction performance, and it expands efficiently based on the FIM method to select a set of reference sensors. Besides, the matrix singularity problems in the FIM-based expansion method are solved using noise frequency summation and matrix operation, which makes the method more universally applicable. Numerical simulations are performed to analyze and evaluate the noise reduction performance and selection efficiency of the proposed method under different conditions. In addition, a series of real vehicle ANC experiments are conducted. The results show that the reference sensor set selected based on the proposed method has an obvious advantage in noise reduction performance, and only four reference sensors are needed to achieve 4.63 dB(A) noise reduction in ARNC experiments. It fully confirms the effectiveness of the proposed method in real vehicle applications.

Embedded exponential Runge–Kutta–Nyström methods for highly oscillatory Hamiltonian systems

Exponential/extended Runge–Kutta–Nyström (ERKN) methods have been validated by numerous theoretical and numerical results to be more efficient and suitable than classical Runge–Kutta–Nyström (RKN) methods in dealing with highly oscillatory Hamiltonian systems. Once numerical solutions are required to achieve a prescribed local error tolerance for practical problems, the embedded ERKN pairs with adaptive stepsize are needed. Given the higher efficiency of high-order methods than low-order methods, this paper focuses on high-order embedded ERKN pairs. Using the particular mapping from RKN methods into ERKN methods, we establish an approach to constructing high-order embedded ERKN pairs. By diagonalizing the frequency matrix, an implementation algorithm with less calculation amount than the direct calculation procedure is proposed for high-dimensional problems. In addition, the dispersion and dissipation of the ERKN pairs ERKN4(3) and ERKN8(6) are analyzed. Furthermore, the application of ERKN pairs to an important highly oscillatory problem, i.e., the wave equation prescribed with different boundary conditions is discussed. For the periodic boundary condition, a special implementation algorithm incorporated with FFT techniques is presented, which decreases the calculation amount in one time step from O(N2) to O(Nlog⁡N). Finally, numerical results with the FPU problem, the Klein–Gordon equation in the nonrelativistic limit regime, and a two-dimensional wave equation demonstrate the superiority of ERKN pairs over classical RKN pairs.

STFT-TCAN: A TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems

Networks and industrial systems play a pivotal role in modern society, and their security has garnered increasing attention. Anomalies within industrial equipment may propagate through fault transmission, leading to a cascade of failures. Additionally, cyberattacks on equipment can result in significant losses. Therefore, in the realm of industrial and cyberspace domains, an effective multivariate time series anomaly detection system for monitoring equipment is instrumental in ensuring the healthy operation of the machinery. Nevertheless, detecting anomalies in numerous time series remains challenging, stemming from the absence of anomaly labels and the complexity of the data patterns. Existing algorithms predominantly concentrate on modeling within the time domain, falling short in fully leveraging the informative features present in frequency domain data, resulting in diminished detection performance. This paper introduces STFT-TCAN, a model for anomaly detection in time series that seamlessly integrates information from both time and frequency domains for extracting data features. Sliding windows and the Short Time Fourier Transform (STFT) are utilized to construct a frequency matrix, effectively amalgamating the characteristics of both time and frequency domains within the time series. Furthermore, the model employs Temporal Convolutional Networks (TCN) and Transformer attention mechanisms (which combined to form the TCAN module) to capture the features of multivariate time series, thereby resulting in heightened detection accuracy. The proposed model undergoes validation on six publicly available datasets, showcasing the superior performance of the STFT-TCAN model in comparison to current baseline methods. It adeptly extracts features from both frequency and time domains in sequential data, thereby achieving state-of-the-art performance in tasks related to anomaly detection in multivariate time series.

Advancing Wastewater Treatment: A Comparative Study of Photocatalysis, Sonophotolysis, and Sonophotocatalysis for Organics Removal

Clear and sanitarily adequate water scarcity is one of the greatest problems of modern society. Continuous population growth, rising organics concentrations, and common non-efficient wastewater treatment technologies add to the seriousness of this issue. The employment of various advanced oxidation processes (AOPs) in water treatment is becoming more widespread. In this review, the state-of-the-art application of three AOPs is discussed in detail: photocatalysis, sonophotolysis, and sonophotocatalysis. Photocatalysis utilizes semiconductor photocatalysts to degrade organic pollutants under light irradiation. Sonophotolysis combines ultrasound and photolysis to generate reactive radicals, enhancing the degradation of organic pollutants. Sonophotocatalysis synergistically combines ultrasound with photocatalysis, resulting in improved degradation efficiency compared to individual processes. By studying this paper, readers will get an insight into the latest published data regarding the above-mentioned processes from the last 10 years. Different factors are compared and discussed, such as degradation efficiency, reaction kinetics, catalyst type, ultrasound frequency, or water matrix effects on process performance. In addition, the economic aspects of sonophotolysis, photocatalysis, and sonophotocatalysis will be also analyzed and compared to other processes. Also, the future research directions and potential applications of these AOPs in wastewater treatment will be highlighted. This review offers invaluable insights into the selection and optimization of AOPs.

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

Innovative Cultural Transmission Paths Analysis for Local Opera Based on Data Mining

With the rapid development of society and cultural diversity, local opera, as a representative of traditional art, faces dual challenges of inheritance and innovation. The inheritance of traditional Chinese opera often relies on oral instruction, but in modern society, this method is no longer suitable for the rapidly changing cultural environment. The purpose of this study is to explore in depth the innovative cultural dissemination path of local opera through data mining techniques. This paper uses data mining techniques to explore and analyze the inheritance of innovative culture in local opera, and to study the inheritance paths. This paper proposes a data mining-based method for analyzing and identifying the paths of inheritance of innovative culture in local opera. This paper focuses on two aspects of technology clustering and technology theme association, and the research paths are divided into flexible clustering based on data enhancement and technology evolution path identification based on theme extraction. Firstly, the heritage changes of dating tunes are discussed. Secondly, the unique artistic esthetics of the dating tune is compared with the artistic characteristics of Henan opera and the three-stringed book. Through the research and learning experience of the Dai Yongqu, it is easier to thoroughly understand the artistic essence and cultural connotation of the Dai Yongqu, and that exposure to the Dai Yongqu is a process of touching traditional culture. In the text clustering section, the data co-occurrence frequency matrix is constructed based on the results of multiple clustering, transforming the problem of determining the number of clusters into a co-occurrence frequency problem with semantic information of the text, thus achieving the purpose of flexible clustering on demand. In a comparison with existing methods, the text representation method using data-enhanced coding achieves better semantic coding results. The flexible clustering method can differentiate the data better than the clustering method with a defined number of clusters and obtains clustering results with high intra-class similarity and high inter-class differentiation.

The Research on the Improvement of FP-growth Algorithm

This paper focuses on the issue of low efficiency in the FP-growth algorithm for frequent pattern mining and proposes an improved algorithm, ICFM-growth. Experimental results demonstrate that the improved algorithm outperforms the FP-growth algorithm in terms of both runtime and space utilization. By studying the classical frequent pattern algorithms Apriori and FP-growth, the latter is an improved algo- rithm based on Apriori to address the problems of generating a large number of candidate itemsets and consuming significant memory space. While FP-growth exhibits superior mining efficiency compared to Apriori, it faces challenges when dealing with large and long transactional databases due to the construction of numerous FP-trees, which increases computational tasks and prolongs runtime, leading to lagging mining efficiency. To address this issue, the improved algorithm ICFM-growth is proposed. It constructs a co-occurrence frequency matrix to perform preliminary screening on the transaction set, focusing on high-frequency and co-occurring item pairs, thereby reducing unnecessary computa- tions. In the initial stage, as important item pairs have already been filtered, the algorithm can directly operate on these item pairs and items, rather than the entire dataset, thereby reducing the search space and computational complexity. Additionally, the structure of the FP-tree ena- blesthe algorithm to store and process data more efficiently, avoiding frequent scanning of the entire database, as seen in traditional Apriori algorithms. Finally, through simulation experiments on publicly available datasets such as Movie Data and House Data, validated using cross- validation, the ICFM-growth algorithm proves to be significantly superior to FP-growth in terms of time and space efficiency. It demonstrates faster runtime, lower memory consumption, and superior mining efficiency compared to FP-growth.

Comparative investigation on capture characteristics of chalcopyrite and hematite in PHGMS process

Pulsating high-gradient magnetic separation (PHGMS) is a promising method of separating chalcopyrite from other minerals with similar floatability. However, the capture characteristics of chalcopyrite in PHGMS process remains poorly understood. In this study, the difference in the capture capacity of chalcopyrite and hematite, a typical weak magnetic mineral, was theoretically compared. The effects of the key operating parameters, i.e., magnetic induction, slurry flow rate, and magnetic wire diameter, on the capture difference of chalcopyrite and hematite were investigated through experimental verification. The comparison results showed that chalcopyrite shared similar capture trend with hematite. The capture mass weight of the matrix decreased with an increase in the pulsating frequency, slurry flow rate, and magnetic wire diameter, but it increased with improved magnetic induction. However, chalcopyrite exhibited an obviously smaller capture mass weight due to its lower susceptibility, which required a higher magnetic induction, slower flow rate, lower pulsating frequency and smaller matrix diameter for high efficient recovery of chalcopyrite. This study provides a reference for the PHGMS of separating chalcopyrite from typically applied weakly magnetic minerals by PHGMS process.

Airline environmental sustainability actions and CSR impact on customer behavior

Sustainability and Corporate Social Responsibility (CSR) are growing concerns among passengers and airlines. However, the communication of such actions by airline companies is underexplored. Accordingly, this study aims to analyze airlines' CSR and Environmental Sustainability Actions (ESA) communications' impact on customer loyalty, through a Relationship Marketing (RM) perspective. Following a text-mining approach, 6181 Facebook comments were collected. A word frequency matrix was created from the collected data and used as input for PLS-SEM to test raised hypotheses. Results suggest that communication based on ESA and CSR positively affects behavioral loyalty and commitment. RM moderated ESA's communication impact on customer loyalty but not on the relationship between CSR communication and loyalty. These findings suggest that communicating environmental efforts positively affects customer loyalty, providing airline managers with valuable insights that should be considered within communication strategies to enhance return and customer retention.

Posterior inference of Hi-C contact frequency through sampling.

Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are represented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.