Sequence Estimation Research Articles

Detection of Earth’s free oscillation and analysis of the non-synchronous oscillation phenomenon of normal modes

Earth’s free oscillation can provide essential constraints for refining Earth models, inverting seismic source mechanisms, and studying the deep internal structure of the Earth. Large earthquakes can simultaneously excite numerous normal modes. Due to the Earth’s ellipticity, rotation, and internal heterogeneities, these normal modes undergo splitting, with the frequencies of singlets of normal modes becoming very close (only a few µHz apart). This imposes greater demands on the detection of normal modes. This paper introduces a novel method for normal mode detection based on the normal time–frequency transform (NTFT). Compared to classical FT spectrum methods and recent optimal sequence estimation (OSE), the proposed method not only detects more weak normal modes but also reveals the spatial distribution of the phase of each normal mode. Taking the detection of 0S2 as an example, the phase measurements of each singlet are spatially inconsistent. This phenomenon can provide prior information for other methods, such as product spectrum analysis (PSA), spherical harmonic stacking (SHS), multistation experiments (MSE), and OSE. Additionally, understanding the phase distribution patterns contributes to further study of geological structures, offering crucial foundational data and observational support.

Applications of ancestral sequence reconstruction for understanding the evolution of plant specialized metabolism.

Studies of enzymes in modern-day plants have documented the diversity of metabolic activities retained by species today but only provide limited insight into how those properties evolved. Ancestral sequence reconstruction (ASR) is an approach that provides statistical estimates of ancient plant enzyme sequences which can then be resurrected to test hypotheses about the evolution of catalytic activities and pathway assembly. Here, I review the insights that have been obtained using ASR to study plant metabolism and highlight important methodological aspects. Overall, studies of resurrected plant enzymes show that (i) exaptation is widespread such that even low or undetectable levels of ancestral activity with a substrate can later become the apparent primary activity of descendant enzymes, (ii) intramolecular epistasis may or may not limit evolutionary paths towards catalytic or substrate preference switches, and (iii) ancient pathway flux often differs from modern-day metabolic networks. These and other insights gained from ASR would not have been possible using only modern-day sequences. Future ASR studies characterizing entire ancestral metabolic networks as well as those that link ancient structures with enzymatic properties should continue to provide novel insights into how the chemical diversity of plants evolved. This article is part of the theme issue 'The evolution of plant metabolism'.

CALCULATION OF FLAT SHELLS BY THE BOUNDARY ELEMENT METHOD

This article describes the iterative process of solving the problem of the stress-strain state of a long flexible cylindrical panel. Stress-strain state and the study of the stability of flat shells taking into account the physical, mechanical properties of the material, the process of deformation change. It is based on sequential approximation methods and the boundary element method (MGE). The results of algorithmic weighting are presented in the form of a table showing what value each band represents. The equations are determined by solving twelve unknown values of functions at the ends of the segment. Linear problems of elasticity theory and plate theory fundamental solutions have a simple form, so the method is widely used here. For flat shells, the matrix of fundamental solutions is determined by complex volumetric expressions, and for flat shells - by special functions. Therefore, there is little research on solving problems in the theory of flat shells by the boundary element method. In this regard, an urgent topic of research is the development of methods of boundary integral equations for solving linear and nonlinear problems in the theory of flat shells based on the application of fundamental solutions defined by simple analytical expressions. The scientific novelty of the work consists in the development of a methodology for assessing the reliability of thin-walled spatial structures using the boundary element method.

Maximum Likelihood Sequence Estimation Optimum Receiver Design with Channel Identification Based on Zero Distribution

In this paper, a novel maximum likelihood sequence estimation (MLSE) optimum receiver design is proposed to reduce the complexity of the conventional optimum receiver. We first propose a multipath channel classification method according to the zero distribution of the channel impulse response; that is, the multipath channel is divided into a minimum phase channel and a non-minimum phase channel. Then, an MLSE optimum receiver design with channel identification based on zero distribution is also proposed. In this design, the distribution of zero can be obtained directly by calculating the Z transform of the channel. Thus, the complexity is reduced. The number of the multiplication reduced is N(2N+1). In addition, 2N+1 memory units storing the autocorrelation sequence are reduced. The simulation results indicate that this channel classification method effectively represents the characteristics of the channel. Moreover, the MLSE optimum receiver proposed performs similarly and is less complex than the conventional one. The performance is greatly improved compared with the minimum mean square error (MMSE) equalizer.

Projective Hilbert modules and sequential approximations

Projective Hilbert modules and sequential approximations

Multivariate moment least-squares variance estimators for reversible Markov chains

Markov chain Monte Carlo (MCMC) is a commonly used method for approximating expectations with respect to probability distributions. Uncertainty assessment for MCMC estimators is essential in practical applications. Moreover, for multivariate functions of a Markov chain, it is important to estimate not only the auto-correlation for each component but also to estimate cross-correlations, in order to better assess sample quality, improve estimates of effective sample size, and use more effective stopping rules. Berg and Song (2023) introduced the moment least squares (momentLS) estimator, a shape-constrained estimator for the autocovariance sequence from a reversible Markov chain, for univariate functions of the Markov chain. Based on this sequence estimator, they proposed an estimator of the asymptotic variance of the sample mean from MCMC samples. In this study, we propose novel autocovariance sequence and asymptotic variance estimators for Markov chain functions with multiple components, based on the univariate momentLS estimators from Berg and Song (2023). We demonstrate strong consistency of the proposed auto(cross)-covariance sequence and asymptotic variance matrix estimators. We conduct empirical comparisons of our method with other state-of-the-art approaches on simulated and real-data examples, using popular samplers including the random-walk Metropolis sampler and the No-U-Turn sampler from STAN. Supplemental materials for this article are available online.

An integrated optimization method for the damper placements, shape and size of truss structures

This work extends the application of the approximation concept in dynamic topology optimization for truss structures. The corresponding problem contains both continuous and discrete variables, encompassing damper placements, truss shape and cross-sectional area. First, the utilization of a branched multipoint approximation (BMA) function establishes sequential approximation problems that also involve such mixed variables, marking its innovative application in dynamic optimization. Then, a modified genetic algorithm (GA) is used to optimize discrete variables. To enhance convergence stationarity, the concept of ‘adjacent individuals’ is proposed to control the degree of difference in topology configuration between initial population members and the current optimal. Additionally, adjustments to the sequence-based crossover process ensure compliance with damper quantity constraints. Examples demonstrate that the introduction of adjacent individuals is beneficial to convergence stationarity, and the shape change of the truss increases the mode damping ratio by more than 20%. The created optimization platform can also tackle other mixed variables optimization problems.

Estimates of Sequences with Ultralong and Short CDR3s in the Bovine IgM B Cell Receptor Repertoire Using the Long-read Oxford Nanopore MinION Platform.

Cattle produce Abs with an H chain ultralong CDR3 (40-70 aa). These Abs have been shown to have features such as broad neutralization of viruses and are investigated as human therapeutics. A common issue in sequencing the bovine BCR repertoire is the sequence length required to capture variable (V) and isotype gene information. This study aimed to assess the use of Oxford Nanopore Technologies' MinION platform to perform IgM BCR repertoire sequencing to assess variation in the percentage of ultralong CDR3s among dairy cattle. Blood was collected from nine Holstein heifers. B cells were isolated using magnetic bead-based separation, RNA was extracted, and IgM+ transcripts were amplified using PCR and sequenced using a MinION R10.4 flow cell. The distribution of CDR3 lengths was trimodal, and the percentage of ultralong CDR3s ranged among animals from 2.32 to 20.13% in DNA sequences and 1.56% to 17.02% in productive protein sequences. V segment usage varied significantly among heifers. Segment IGHV1-7, associated with ultralong CDR3s, was used in 5.8-24.2% of sequences; usage was positively correlated with ultralong CDR3 production (r = 0.99, p < 0.01). To our knowledge, this is the first study to sequence the bovine BCR repertoire using Oxford Nanopore Technologies and demonstrates the potential for cost-efficient long-read repertoire sequencing in cattle without assembly. Findings from this study support literature describing the distribution of length and percentage of ultralong CDR3s. Future studies will investigate changes in the bovine BCR repertoire associated with age, antigenic exposure, and genetics.

Resolution of Optimal Mitochondrial and Nuclear DNA Enrichment in Target-Panel Sequencing and Physiological Mitochondrial DNA Copy Number Estimation in Liver Cancer and Non-Liver Cancer Subjects.

Mitochondria generate energy to support cells. They are important organelles that engage in key biological pathways. The dysfunction of mitochondria can be linked to hepatocarcinogenesis, which has been actively explored in recent years. To investigate the mitochondrial dysfunction caused by genetic variations, target-panel sequencing is a flexible and promising strategy. However, the copy number of mitochondria generally exceeds nuclear DNA, which raises a concern that uneven target enrichment of mitochondrial DNA (mtDNA) and nuclear DNA (ncDNA) in target-panel sequencing would lead to an undesirably biased representation of them. To resolve this issue, we evaluated the optimal pooling of mtDNA probes and ncDNA probes by a series of dilutions of mtDNA probes in both genomic DNA (gDNA) and cell-free DNA (cfDNA) samples. The evaluation was based on read count, average sequencing depth and coverage of targeted regions. We determined that an mtDNA:ncDNA probe ratio of around 1:10 would offer a good balance of sequencing performance and cost effectiveness. Moreover, we estimated the median physiological mtDNA:ncDNA copy ratio as 38.1 and 2.9 in cfDNA and gDNA samples of non-liver cancer subjects, respectively, whereas they were 20.0 and 2.1 in the liver cancer patients. Taken together, this study revealed the appropriate pooling strategy of mtDNA probes and ncDNA probes in target-panel sequencing and suggested the normal range of physiological variation of the mtDNA:ncDNA copy ratio in non-liver cancer individuals. This can serve as a useful reference for future target-panel sequencing investigations of the mitochondrial genome in liver cancer.

A TDOA sequence estimation method of underwater sound source based on hidden Markov model

To address the time difference of arrival (TDOA) estimation problem in the passive positioning system with wideband underwater motion sound sources and distributed hydrophones, a hidden Markov model-based (HMM) TDOA sequence estimation method is proposed in this paper. The method estimates the TDOA with multi-frame output of cross-correlated signals received on hydrophones. The transfer equation of the TDOA is established as a first-order hidden Markov process by analyzing the motion characteristics of the moving sound source and delays obtained from different hydrophones. Dynamic assignment of the HMM parameters is proposed to address the inconsistent change rate of the TDOA. We then achieve an HMM expression of the TDOA sequence by fitting the transfer equation and dynamic assignment of parameters into the HMM. Then, the Viterbi algorithm (VA) is applied to distinguish the optimal sequence of the TDOA among ambiguous estimations. To deal with the problem of data loss or unreliable issues caused by interferences, a data prediction algorithm which could produce possible time delays is added to VA to avoid the impact of outliers on the estimation results. By utilizing multi-frame processing, the proposed method reduces the signal-to-noise ratio (SNR) requirement of single frames since it does not require accurate estimations of TDOA for each frame. Moreover, the method adapts to a lower SNR, which has significant advantages in terms of whole sequence estimation compared with common methods. The results from the simulations and lake experiments validated the proposed TDOA sequence estimation method.

Energy Efficiency for Faster-than-Nyquist Data Transmission Using Processing Algorithms with Decision Feedback

One of the ways to increase the volume of transmitted information is to increase the bit rate above the Nyquist barrier. However, an increase in bit rate in the case of FTN (Faster-Than-Nyquist) signals leads to an increase in energy costs for receiving information on channels with limited bandwidth, for example, in Digital Video Broadcasting satellite systems like DVB-S2/S2X. It is possible to minimize energy losses by using the processing algorithm “maximum likelihood sequence estimation”. However, the computational complexity of this algorithm is extremely high, which limits its use, especially in terrestrial mobile satellite terminals. We propose a new bit-by-bit decision feedback algorithm with maximum likelihood ratio estimation of subsequent symbols in the observation interval. This algorithm provides minimal energy costs comparable to the method “maximum likelihood sequence estimation” at speeds 2–3 times higher than the Nyquist barrier. At the same time, the complexity is two orders of magnitude less. It is shown by simulation for a channel with additive noise that energy losses in relation to the potential bit error rate (BER) are less than 4.5 dB. In the presence of Rayleigh fading, the application of the proposed algorithm makes it possible to provide the processing of FTN signals for double bit rates in urban areas with energy costs equal to 12 dB when using an equalizer. We give numerical estimations of the increase in computational complexity for the proposed processing algorithm. It is shown that an increase in the bit rate by 1.5 times leads to an increase in the computational complexity by more than an order of magnitude. The same conclusion can be reformulated in another form: for the proposed algorithm, each decibel of energy gain is achieved by increasing the number of computational operations by 1.5×105. It is experimentally shown that additional energy losses due to non-ideal phase and timing synchronization are no more than 1 dB when the proposed algorithm is applied in a fading channel. The energy costs in fading channels relative to a stationary channel for twice the Nyquist rate are equal to 13.8 dB when using an equalizer.

Low-complexity MLSE for coherent optical fiber transmission systems with symbol correlation

This study demonstrates the effectiveness of a simplified maximum likelihood sequence estimation (MLSE) approach in enhancing the performance of coherent optical fiber transmission systems, particularly under conditions of linear and nonlinear symbol correlation. To counter the high complexity of traditional MLSE in high-speed systems, we introduce what we believe to be a novel simplification method. This approach replaces the conventional channel impulse response (CIR) convolution with a look-up table (LUT) and simplifies the Euclidean distance (ED) calculations. Additionally, we provide a detailed analysis of algorithmic operations, complementary metal oxide semiconductor (CMOS) transistor count, and power consumption across various technology nodes. The proposed simplified MLSE scheme has the potential to reduce implementation complexity to approximately 5% of the conventional approach. The proposed low-complexity approach was verified with both simulations and experiments. The results show that the performance loss is less than 0.3 dB in Q-factors for both linear and nonlinear bandwidth-constrained transmission scenarios.

Toward a Semi-Supervised Learning Approach to Phylogenetic Estimation.

Models have always been central to inferring molecular evolution and to reconstructing phylogenetic trees. Their use typically involves the development of a mechanistic framework reflecting our understanding of the underlying biological processes, such as nucleotide substitutions, and the estimation of model parameters by maximum likelihood or Bayesian inference. However, deriving and optimizing the likelihood of the data is not always possible under complex evolutionary scenarios or even tractable for large datasets, often leading to unrealistic simplifying assumptions in the fitted models. To overcome this issue, we coupled stochastic simulations of genome evolution with a new supervised deep-learning model to infer key parameters of molecular evolution. Our model is designed to directly analyze multiple sequence alignments and estimate per-site evolutionary rates and divergence without requiring a known phylogenetic tree. The accuracy of our predictions matched that of likelihood-based phylogenetic inference when rate heterogeneity followed a simple gamma distribution, but it strongly exceeded it under more complex patterns of rate variation, such as codon models. Our approach is highly scalable and can be efficiently applied to genomic data, as we showed on a dataset of 26 million nucleotides from the clownfish clade. Our simulations also showed that the integration of per-site rates obtained by deep learning within a Bayesian framework led to significantly more accurate phylogenetic inference, particularly with respect to the estimated branch lengths. We thus propose that future advancements in phylogenetic analysis will benefit from a semi-supervised learning approach that combines deep-learning estimation of substitution rates, which allows for more flexible models of rate variation, and probabilistic inference of the phylogenetic tree, which guarantees interpretability and a rigorous assessment of statistical support.

Low complexity sub-baud rate sampling reception in a bandwidth-limited IM/DD system.

This Letter presents what is to our knowledge a novel approach to reduce the digital signal processing (DSP) complexity in intensity modulation and direct detection (IM/DD) systems, which is critical for short-reach optical communication systems with severe bandwidth limitations. We propose a sub-baud rate sampling reception method utilizing a polyphase feedforward equalizer-based maximum likelihood sequence estimation (PFFE-MLSE), which could operate effectively under a sampling rate of 0.6 samples per symbol. This new architecture eliminates the need for resampling, allowing the adaptive equalizer to operate with significantly reduced complexity-over 60% compared to traditional FFE-MLSE. An offline experiment, transmitting a 100-Gbaud on-off keying (OOK) signal over a 5-km single-mode fiber (SMF) link, demonstrates the feasibility of our approach with bit error ratio (BER) meeting the KP4-forward error correction (KP4-FEC) threshold in the optical back-to-back (OBTB) scenario and 7% hard-decision FEC (HD-FEC) threshold in the 5-km SMF transmission.

A Complex-Valued Stationary Kalman Filter for Positive and Negative Sequence Estimation in DER Systems

In medium- and low-voltage three-phase distribution networks, the load imbalance among the phases may compromise the network voltage symmetry. Inverter-interfaced distributed energy resources (DERs) can contribute to compensating for such imbalances by sharing the required negative sequence current while providing active power synchronized with the positive sequence voltage. However, positive and negative sequences are conventionally defined in a steady state and are not directly observed from the instantaneous voltage and current measurements at the DER unit’s point of connection. In this article, an estimation algorithm for sequence separation based on the Kalman filter is proposed. Furthermore, the proposed filter uses a complex vector representation of the asymmetric three-phase signals in synchronous coordinates to allow for the implementation of the Kalman filter in its stationary form, resulting in a simple dynamic filter able to estimate positive and negative sequences even during transient operation. The proposed stationary complex Kalman filter performs better than state-of-the-art techniques like DSOGI and very similarly to other Kalman filter implementations found in the literature but at a fraction of its computational cost (23.5%).

Joint equalization and decoding with per-survivor processing based on super-trellis in time-varying underwater acoustic channels.

This Letter proposes a low-complexity joint equalization and decoding reception scheme based on super-trellis per-survivor processing, making it possible to apply maximum likelihood sequence estimation in high-order underwater acoustic communications under fast time-varying channels. The technique combines trellis-coded modulation states and intersymbol interference states and uses per-survivor processing to track channel parameters. Furthermore, a general trellis configuration for arbitrary order quadrature amplitude modulation signal is provided when truncate the channel is used to describe the intersymbol interference state to 1. Sea trials results show that the performance of proposed method can be more than 1.4 dB superiority than conventional schemes.

A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling

Accurate wind forecasts for one day ahead or longer periods have significant impacts on the safe and efficient dispatch of power grids, where Numerical Weather Prediction (NWP) serves as the essential tool, such as ensemble NWP integrating multiple single simulations. Typically, ensembles include all single members with fixed weights; however, the relative accuracy of each member may change over time. This study introduces an attractive idea: improving ensemble performance by dynamically recognizing and avoiding low-performing members. It proposes a dynamic ensemble strategy based on NWP, reinforcement learning and error sequence correction. The process begins with Weather Research and Forecasting ensemble simulations. A dynamic framework is then constructed by mapping the multi-step ensemble problem into a Markov decision process, which is further solved using deep deterministic policy gradient. Subsequently, a hybrid deep learning model, comprising temporal convolutional network and bidirectional long short-term memory, is constructed for error sequence estimation of dynamic ensemble, using the high-frequency information of NWP as input. Conducting experiments at two wind farms, and focusing on the 24-h wind speed forecast with a 15-min time resolution, the proposed system demonstrates a reliable and stable ensemble throughout the entire forecasting horizon, significantly reducing the probability of large forecasting errors.

Calculation of the required methanol consumption during the flow of wet hydrocarbon gas in a horizontal pipeline

One of the main problems that have to be solved during the development of hydrocarbon deposits is the formation of gas hydrates in pipelines. In this regard, the article presents a preventive method to struggle against the formation of gas hydrate deposits on the pipes inner walls associated with the supply of a hydrate formation inhibitor to the gas stream. The research was conducted on the basis of a mathematical model of the wet hydrocarbon gas flow in a horizontal pipeline. The research object is to determine the minimum required consumption of methanol, in which there is no formation of gas hydrate deposits on the channel inner walls. The practical significance of this study is that it is aimed at reducing the risks associated with the formation of gas hydrates in pipelines. The numerical implementation of a mathematical model of natural gas flow in a horizontal channel is based on a sequential solution of a system of four differential equations by the Runge-Kutta method of 4 orders of accuracy, followed by a search for sequential approximations of the minimum inhibitor flow rate, in which the "gas + water ↔ gas hydrate phase" transition process does not occur on the inner surface of the channel. The article presents a calculation of the proportion of a hydrate formation inhibitor in the liquid phase by solving a cubic equation using the Cardano method. Based on the computational experiments results, graphs were constructed and interpreted of the dependencies of the minimum inhibitor consumption on the soil temperature, inlet gas pressure, total water concentration in the gas flow, initial gas temperature and total gas flow rate.

All's eco‐friendly that ends eco‐friendly: Short‐term memory effects in carbon footprint estimates of temporal item sequences

AbstractWhen people estimate the summative carbon footprint of a sequence of events, how are the individual events integrated? In three experiments, we found that summative carbon footprint judgments of item sequences are disproportionately influenced by items at the end of the sequence in comparison with those at the beginning—a recency effect. When, for example, sequences ended with a low carbon footprint item, they were assigned a lower carbon footprint than corresponding sequences with an identical content but different item order. The results also revealed that a green peak (presenting many low carbon footprint items at once) had a relatively large effect on estimates when the peak was contextually distinct from other items in terms of its valence. The results are consistent with an account within which distinctiveness of representations within short‐term memory differentially influences decision‐making and suggest that memory processes bias the perceived environmental footprint of temporally separated instances.

Possible effects of selecting different station distributions in the optimal sequence estimation method

Since the inception of the optimal sequence estimation (OSE) method, various research teams have substantiated its efficacy as the optimal stacking technique for handling array data, leading to its successful application in numerous geoscience studies. Nevertheless, concerns persist regarding the potential impact of aliasing resulting from the choice of distinct station distributions on the outcomes derived from OSE. In this investigation, I employ theoretical deduction and experimental analysis to elucidate the reasons behind the immunity of the Yl'm'-related common signal obtained through OSE to variations in station distribution selection. The primary objective of OSE is also underscored, i.e., to restore/strip a Yl'm'-related common periodic signal from various stations. Furthermore, I provide additional clarification that the ‘Yl'm'-related common signal’ and the ‘Yl'm'-related equivalent excitation sequence’ are distinct concepts. These analyses will facilitate the utilization of the OSE technique by other researchers in investigating intriguing geophysical phenomena and attaining sound explanations.