Sequence Of Vectors Research Articles

Forensic Analysis of Novel SARS2r-CoV Identified in Game Animal Datasets in China Shows Evolutionary Relationship to Pangolin GX CoV Clade and Apparent Genetic Experimentation

Pangolins are the only animals other than bats proposed to have been infected with SARS-CoV-2 related coronaviruses (SARS2r-CoVs) prior to the COVID-19 pandemic. Here, we examine the novel SARS2r-CoV we previously identified in game animal metatranscriptomic datasets sequenced by the Nanjing Agricultural University in 2022, and find that sections of the partial genome phylogenetically group with Guangxi pangolin CoVs (GX PCoVs), while the full RdRp sequence groups with bat-SL-CoVZC45. While the novel SARS2r-CoV is found in 6 pangolin datasets, it is also found in 10 additional NGS datasets from 5 separate mammalian species and is likely related to contamination by a laboratory researched virus. Absence of bat mitochondrial sequences from the datasets, the fragmentary nature of the virus sequence and the presence of a partial sequence of a cloning vector attached to a SARS2r-CoV read suggests that it has been cloned. We find that NGS datasets containing the novel SARS2r-CoV are contaminated with significant Homo sapiens genetic material, and numerous viruses not associated with the host animals sampled. We further identify the dominant human haplogroup of the contaminating H. sapiens genetic material to be F1c1a1, which is of East Asian provenance. The association of this novel SARS2r-CoV with both bat CoV and the GX PCoV clades is an important step towards identifying the origin of the GX PCoVs.

The switching decomposition pulse width modulation with reduced common mode voltage for reduced switch counts neutral point clamped inverter

Compared with a conventional three-levelconverter, a reduced switch counts three-level neutral point clamped (RSC-3LNPC) inverter uses only 10 switching devices to achieve three-leveloutput at a lower cost. However, in the RSC-3LNPC inverter, the common modevoltage (CMV) reduction and neutral point voltage (NPV) balance are mutuallycoupled, and the buck switching module eliminates medium vectors, making conventional modulation methods no longer applicable. To address this problem, this paperdevelops a generation mechanism of the voltage and switching vectors for RSC-3LNPC inverter and proposes a switching decomposition pulse width modulation (SDPWM) scheme. First, a virtual medium vector with two large vectors having the samedwell time is introduced. Then, the small and zero vectors with low CMV, along with the large and virtual medium vectors, are adopted, and by adjusting their dwell time, the NPV is balanced while reducing CMV. In addition, to reduce computational burden, the switching vector isdecomposed into a buck switching vector and a two-level switching vector. Both switching vector sequences with transformed duty cycles are carefully designed to achieve only three switching transactions per switching period. Finally, validation and correctness of the proposed SDPWM scheme for RSC-3LNPC inverterare verified by simulation and experiments.

Chinese Named Entity Recognition Based on BERT and Lightweight Feature Extraction Model

In the early named entity recognition models, most text processing focused only on the representation of individual words and character vectors, and paid little attention to the semantic relationships between the preceding and following text in an utterance, which led to the inability to handle the problem of multiple meanings of a word during recognition. To address this problem, most models introduce the attention mechanism of Transformer model to solve the problem of multiple meanings of a word in text. However, the traditional Transformer model leads to a high computational overhead due to its fully connected structure. Therefore, this paper proposes a new model, the BERT-Star-Transformer-CNN-BiLSTM-CRF model, to solve the problem of the computational efficiency of the traditional Transformer. First, the input text is dynamically generated into a character vector using the BERT model pre-trained in large-scale preconditioning to solve the problem of multiple meanings of words, and then the lightweight Star-Transformer model is used as the feature extraction module to perform local feature extraction on the word vector sequence, while the CNN-BiLSTM joint model is used to perform global feature extraction on the context in the text. The obtained feature sequences are fused. Finally, the fused feature vector sequences are input to CRF for prediction of the final results. After the experiments, it is shown that the model has a significant improvement in precision, recall and F1 value compared with the traditional model, and the computational efficiency is improved by nearly 40%.

Online Battery SOH Prediction under Intra-Cycle Variation of Discharge Current and Non-Standard Charging and Discharging Practices

Online SOH prediction of a battery is essential for battery management in real-world applications. Although many approaches have been proposed to date, most conventional approaches to SOH prediction neither incorporate intra-cycle variation of discharge currents nor consider non-standard charging and discharging practices along cycles. For real-world applicability, the above two factors, varying discharge current and non-standard practices, are crucial to deal with. This paper presents an approach to online SOH prediction based on charging and discharging cycles under intra-cycle variation of discharge current and non-standard charging and discharging practices. Here, for online SOH prediction, we represent a cyclic history of terminal voltages and currents by a vector sequence formed by four indices: total charge, voltage-time entropy, average and variance of current, where average and variance of current are intended to deal with varying discharge current. Furthermore, we identify various minimum ranges of charging and discharging voltages that can provide accurate relation between four indices and SOH under non-standard practices, while transforming this relation into that of standard practices. For experiments, an LSTM stack is implemented for the proposed online SOH prediction. The results indicate that the proposed approach is capable of accurate online SOH prediction even under randomly varying discharging currents and non-standard practices with RMSE errors of about 0.5% as well as of about 95%.

Pseudorandom vector generation using elliptic curves and applications to Wiener processes

In this paper we present, using the arithmetic of elliptic curves over finite fields, an algorithm for the efficient generation of a sequence of uniform pseudorandom vectors in high dimensions, that simulates a sample of a sequence of i.i.d. random variables, with values in the hypercube [0,1]d with uniform distribution. As an application, we obtain, in the discrete time simulation, an efficient algorithm to simulate, uniformly distributed sample path sequence of a sequence of independent standard Wiener processes. This could be employed for use, in the full history recursive multi-level Picard approximation method, for numerically solving the class of semilinear parabolic partial differential equations of the Kolmogorov type.

ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition

Finger vein recognition has been widely studied due to its advantages, such as high security, convenience, and living body recognition. At present, the performance of the most advanced finger vein recognition methods largely depends on the quality of finger vein images. However, when collecting finger vein images, due to the possible deviation of finger position, ambient lighting and other factors, the quality of the captured images is often relatively low, which directly affects the performance of finger vein recognition. In this study, we proposed a new model for finger vein recognition that combined the vision transformer architecture with the capsule network (ViT-Cap). The model can explore finger vein image information based on global and local attention and selectively focus on the important finger vein feature information. First, we split-finger vein images into patches and then linearly embedded each of the patches. Second, the resulting vector sequence was fed into a transformer encoder to extract the finger vein features. Third, the feature vectors generated by the vision transformer module were fed into the capsule module for further training. We tested the proposed method on four publicly available finger vein databases. Experimental results showed that the average recognition accuracy of the algorithm based on the proposed model was above 96%, which was better than the original vision transformer, capsule network, and other advanced finger vein recognition algorithms. Moreover, the equal error rate (EER) of our model achieved state-of-the-art performance, especially reaching less than 0.3% under the test of FV-USM datasets which proved the effectiveness and reliability of the proposed model in finger vein recognition.

FPGA‐based control strategy of five‐phase induction motor drives

Here, a novel control technique for Five-Phase Induction Motor (FPIM) drives using a field-programmable gate array (FPGA) controller is proposed. Open-loop control is analysed in terms of the performance and measurement of various power quality factors, such as voltage harmonics, current harmonics, total harmonic distortion, crest factor, unbalanced, short and long-term flickering, K-factor, real power, reactive power, apparent power, and power factor. This study experimentally demonstrated a closed-loop system with both PID and DTC controls for the FPIM. Diminished torque pulsation is obtained by efficiently utilizing the voltage vectors from the total states of 25 = 32. A novel EG voltage vector sequence was proposed for DTC techniques and compared with small, medium, and large voltage vector sequences. Proposed innovative control programming techniques in spartan-6 XC6SLX25 series FPGA for switching the five-phase two-level inverter to reduce the total harmonics distortion by less than 2%.

Basicsynbio and the BASIC SEVA collection: software and vectors for an established DNA assembly method.

Standardized deoxyribonucleic acid (DNA) assembly methods utilizing modular components provide a powerful framework to explore designs and iterate through Design-Build-Test-Learn cycles. Biopart Assembly Standard for Idempotent Cloning (BASIC) DNA assembly uses modular parts and linkers, is highly accurate, easy to automate, free for academic and commercial use and enables hierarchical assemblies through an idempotent format. These features enable applications including pathway engineering, ribosome binding site (RBS) tuning, fusion protein engineering and multiplexed guide ribonucleic acid (RNA) expression. In this work, we present basicsynbio, open-source software encompassing a Web App (https://basicsynbio.web.app/) and Python Package (https://github.com/LondonBiofoundry/basicsynbio), enabling BASIC construct design via simple drag-and-drop operations or programmatically. With basicsynbio, users can access commonly used BASIC parts and linkers while designing new parts and assemblies with exception handling for common errors. Users can export sequence data and create instructions for manual or acoustic liquid-handling platforms. Instruction generation relies on the BasicBuild Open Standard, which is parsed for bespoke workflows and is serializable in JavaScript Object Notation for transfer and storage. We demonstrate basicsynbio, assembling 30 vectors using sequences including modules from the Standard European Vector Architecture (SEVA). The BASIC SEVA vector collection is compatible with BASIC and Golden Gate using BsaI. Vectors contain one of six antibiotic resistance markers and five origins of replication from different compatibility groups. The collection is available via Addgene under an OpenMTA agreement. Furthermore, vector sequences are available from within the basicsynbio application programming interface with other collections of parts and linkers, providing a powerful environment for designing assemblies for bioengineering applications. Graphical Abstract.

LSTM Network based Sentiment Analysis for Customer Reviews

Continuously increasing data bring new problems and problems usually reveal new research areas. One of the new areas is Sentiment Analysis. This field has some difficulties. The fact that people have complex sentiments is the main cause of the difficulty, but this has not prevented the progress of the studies in this field. Sentiment analysis is generally used to obtain information about persons by collecting their texts or expressions. Sentiment analysis can sometimes bring serious benefits. In this study, with singular tag-plural class approach, a binary classification was performed. An LSTM network and several machine learning models were tested. The dataset collected in Turkish, and Stanford Large Movie Reviews datasets were used in this study. Due to the noise in the dataset, the Zemberek NLP Library for Turkic Languages and Regular Expression techniques were used to normalize and clean texts, later, the data were transformed into vector sequences. The preprocessing process made 2% increase to the model performance on the Turkish Customer Reviews dataset. The model was established using an LSTM network. Our model showed better performance than Machine Learning techniques and achieved an accuracy of 90.59% on the Turkish dataset and an accuracy of 89.02% on the IMDB dataset.

Optimal Sector-Based Sequential Model Predictive Control for Current Source Rectifier

Conventional model predictive control (MPC) methods for current source rectifier (CSR) use either a PI controller or weight factor-based methods, which require time-consuming tuning methods to develop an efficient controller. Moreover, the limitations on the bandwidth requirements of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> filter cause resonances and grid current distortions if a variable switching frequency-based predictive control is used. So, a new fixed switching frequency-based MPC algorithm has been developed to control the load and source current simultaneously, thereby facilitating the weight factor less operation and applying a predefined space vector sequence. Unlike the conventional single cost function methods applied to CSR, the proposed method uses separate cost function for each control variable. Simultaneously, to provide fixed frequency operation, duty ratio-based modulation of grid current control has been applied. A virtual impedance-based active damping has been embedded into the supply current control to suppress the oscillations caused by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> filter and grid current harmonics. Simulation and experimental results have been supported to validate the improvements in terms of supply current total harmonic distortion (THD), steady state, and transient switching performance.

Gait-ViT: Gait Recognition with Vision Transformer.

Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its advantages, such as being perceivable at a long distance and difficult to replicate. The existing works mostly leverage Convolutional Neural Networks for gait recognition. The Convolutional Neural Networks perform well in image recognition tasks; however, they lack the attention mechanism to emphasize more on the significant regions of the image. The attention mechanism encodes information in the image patches, which facilitates the model to learn the substantial features in the specific regions. In light of this, this work employs the Vision Transformer (ViT) with an attention mechanism for gait recognition, referred to as Gait-ViT. In the proposed Gait-ViT, the gait energy image is first obtained by averaging the series of images over the gait cycle. The images are then split into patches and transformed into sequences by flattening and patch embedding. Position embedding, along with patch embedding, are applied on the sequence of patches to restore the positional information of the patches. Subsequently, the sequence of vectors is fed to the Transformer encoder to produce the final gait representation. As for the classification, the first element of the sequence is sent to the multi-layer perceptron to predict the class label. The proposed method obtained 99.93% on CASIA-B, 100% on OU-ISIR D and 99.51% on OU-LP, which exhibit the ability of the Vision Transformer model to outperform the state-of-the-art methods.

Hierarchy, Not Lexical Regularity, Modulates Low-Frequency Neural Synchrony During Language Comprehension.

Neural responses appear to synchronize with sentence structure. However, researchers have debated whether this response in the delta band (0.5-3 Hz) really reflects hierarchical information or simply lexical regularities. Computational simulations in which sentences are represented simply as sequences of high-dimensional numeric vectors that encode lexical information seem to give rise to power spectra similar to those observed for sentence synchronization, suggesting that sentence-level cortical tracking findings may reflect sequential lexical or part-of-speech information, and not necessarily hierarchical syntactic information. Using electroencephalography (EEG) data and the frequency-tagging paradigm, we develop a novel experimental condition to tease apart the predictions of the lexical and the hierarchical accounts of the attested low-frequency synchronization. Under a lexical model, synchronization should be observed even when words are reversed within their phrases (e.g., "sheep white grass eat" instead of "white sheep eat grass"), because the same lexical items are preserved at the same regular intervals. Critically, such stimuli are not syntactically well-formed; thus a hierarchical model does not predict synchronization of phrase- and sentence-level structure in the reversed phrase condition. Computational simulations confirm these diverging predictions. EEG data from N = 31 native speakers of Mandarin show robust delta synchronization to syntactically well-formed isochronous speech. Importantly, no such pattern is observed for reversed phrases, consistent with the hierarchical, but not the lexical, accounts.

ε-arithmetics for real vectors and linear processing of real vector-valued signals

In this paper, we introduce a new concept, namely ε-arithmetics, for real vectors of any fixed dimension. The basic idea is to use vectors of rational values (called rational vectors) to approximate vectors of real values of the same dimension within ε range. For rational vectors of a fixed dimension m, they can form a field that is an mth order extension Q(α) of the rational field Q where α has its minimal polynomial of degree m over Q. Then, the arithmetics, such as addition, subtraction, multiplication, and division, of real vectors can be defined by using that of their approximated rational vectors within ε range. We also define complex conjugate of a real vector and then inner product and convolutions of two real vectors and two real vector sequences (signals) of finite length. With these newly defined concepts for real vectors, linear processing, such as linear filtering, ARMA modeling, and least squares fitting, can be implemented to real vector-valued signals with real vector-valued coefficients, which will broaden the existing linear processing to scalar-valued signals.

Spoken Language Identification System Using Convolutional Recurrent Neural Network

Following recent advancements in deep learning and artificial intelligence, spoken language identification applications are playing an increasingly significant role in our day-to-day lives, especially in the domain of multi-lingual speech recognition. In this article, we propose a spoken language identification system that depends on the sequence of feature vectors. The proposed system uses a hybrid Convolutional Recurrent Neural Network (CRNN), which combines a Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN) network, for spoken language identification on seven languages, including Arabic, chosen from subsets of the Mozilla Common Voice (MCV) corpus. The proposed system exploits the advantages of both CNN and RNN architectures to construct the CRNN architecture. At the feature extraction stage, it compares the Gammatone Cepstral Coefficient (GTCC) feature and Mel Frequency Cepstral Coefficient (MFCC) feature, as well as a combination of both. Finally, the speech signals were represented as frames and used as the input for the CRNN architecture. After conducting experiments, the results of the proposed system indicate higher performance with combined GTCC and MFCC features compared to GTCC or MFCC features used individually. The average accuracy of the proposed system was 92.81% in the best experiment for spoken language identification. Furthermore, the system can learn language-specific patterns in various filter size representations of speech files.

Local-feature and global-dependency based tool wear prediction using deep learning

Evaluation of tool wear is vital in manufacturing system, since early detections on worn-out condition can ensure workpiece quality, improve machining efficiency. With the development of intelligent manufacturing, tool wear prediction technology plays an increasingly important role. However, traditional tool wear prediction methods rely on experience and knowledge of experts and are labor-extensive. Deep learning provides an effective way to extract features of raw data and establish the mapping relationship between features and targets automatically. In this paper, a new local-feature and global-dependency based tool wear prediction method is proposed. It is a hybrid approach combining manual features with automatic features. Firstly, an enhanced CNN network is designed and applied on the transformed wavelet scalogram to learn the local single-scale specific features and multi-scale correlation features automatically. Secondly, sequence of local feature vectors combining manual features with automatic features are fed into multi-layer LSTM step by step for the global dependency. A fully connected layer is then trained to predict tool wear. Finally, two statistics are proposed to illustrate the overall prediction performance and generalization ability of the model. An experiment illustrates the effectiveness of our proposed method under multiple working conditions.

PARA: A New Platform for the Rapid Assembly of gRNA Arrays for Multiplexed CRISPR Technologies.

Multiplexed CRISPR technologies have great potential for pathway engineering and genome editing. However, their applications are constrained by complex, laborious and time-consuming cloning steps. In this research, we developed a novel method, PARA, which allows for the one-step assembly of multiple guide RNAs (gRNAs) into a CRISPR vector with up to 18 gRNAs. Here, we demonstrate that PARA is capable of the efficient assembly of transfer RNA/Csy4/ribozyme-based gRNA arrays. To aid in this process and to streamline vector construction, we developed a user-friendly PARAweb tool for designing PCR primers and component DNA parts and simulating assembled gRNA arrays and vector sequences.

Three‐vector model predictive power control for three‐level T‐type rectifier based on dead‐beat control

This paper presents a three-vector model predictive power control with neutral-point voltage balance (TVMPPC-NPVB) and a dead-beat control for three-phase three-level T-type rectifier. Three vectors are adopted to reduce the current ripple, balance the neutral-point voltage and achieve unity power factor on the AC side simultaneously, while the conventional model predictive power control suffers from large current ripple, difficulty in designing the weighting factor and computational burden. In the proposed strategy, two cost functions are derived to select the optimal space vector sequence and reduce the computational burden. The action time of each vector is introduced in detail. The traditional PWM rectifier utilizes PI controller to regulate the output voltage. But PI controller tends to have a long transition time and big variation on output voltage when the load changes suddenly. The proposed dead-beat control is capable of solving these problems caused by conventional PI controller. Both simulation and experimental results are shown to confirm the effectiveness of the proposed method.

Explicit Duration Recurrent Networks.

Recurrent neural networks (RNNs) can be used to operate over sequences of vectors and have been successfully applied to a variety of problems. However, it is hard to use RNNs to model the variable dwell time of the hidden state underlying an input sequence. In this article, we interpret the typical RNNs, including original RNN, standard long short-term memory (LSTM), peephole LSTM, projected LSTM, and gated recurrent unit (GRU), using a slightly extended hidden Markov model (HMM). Based on this interpretation, we are motivated to propose a novel RNN, called explicit duration recurrent network (EDRN), analog to a hidden semi-Markov model (HSMM). It has a better performance than conventional LSTMs and can explicitly model any duration distribution function of the hidden state. The model parameters become interpretable and can be used to infer many other quantities that the conventional RNNs cannot obtain. Therefore, EDRN is expected to extend and enrich the applications of RNNs. The interpretation also suggests that the conventional RNNs, including LSTM and GRU, can be made small modifications to improve their performance without increasing the parameters of the networks.

Asexual Blood-Stage Malaria Vaccine Candidate PfRipr5: Enhanced Production in Insect Cells.

The malaria asexual blood-stage antigen PfRipr and its most immunogenic fragment PfRipr5 have recently risen as promising vaccine candidates against this infectious disease. Continued development of high-yielding, scalable production platforms is essential to advance the malaria vaccine research. Insect cells have supplied the production of numerous vaccine antigens in a fast and cost-effective manner; improving this platform further could prove key to its wider use. In this study, insect (Sf9 and High Five) and human (HEK293) cell hosts as well as process-optimizing strategies (new baculovirus construct designs and a culture temperature shift to hypothermic conditions) were employed to improve the production of the malaria asexual blood-stage vaccine candidate PfRipr5. Protein expression was maximized using High Five cells at CCI of 2 × 106 cell/mL and MOI of 0.1 pfu/cell (production yield = 0.49 mg/ml), with high-purity PfRipr5 binding to a conformational anti-PfRipr monoclonal antibody known to hold GIA activity and parasite PfRipr staining capacity. Further improvements in the PfRipr5 expression were achieved by designing novel expression vector sequences and performing a culture temperature shift to hypothermic culture conditions. Addition of one alanine (A) amino acid residue adjacent to the signal peptide cleavage site and a glycine-serine linker (GGSGG) between the PfRipr5 sequence and the purification tag (His6) induced a 2.2-fold increase in the expression of secreted PfRipr5 over using the expression vector with none of these additions. Performing a culture temperature shift from the standard 27–22°C at the time of infection improved the PfRipr5 expression by up to 1.7 fold. Notably, a synergistic effect was attained when combining both strategies, enabling to increase production yield post-purification by 5.2 fold, with similar protein quality (i.e., purity and binding to anti-PfRipr monoclonal antibody). This work highlights the potential of insect cells to produce the PfRipr5 malaria vaccine candidate and the importance of optimizing the expression vector and culture conditions to boost the expression of secreted proteins.

Intelligent Deep Learning Empowered Text Detection Model from Natural Scene Images

The scene Text Recognition process has become a hot research topic and a challenging task owing to the complicated background, varying light intensities, colors, font styles, and sizes. Text extraction from natural scene images encompasses two main processes: text detection and text recognition. The latest advancements in Machine Learning (ML) and Deep Learning (DL) concepts can effectually automate the text detection and recognition process by training the model properly. In this view, this paper presents an Automated DL empowered Text Detection model from Natural Scene Images (ADLTD-NSI). The ADLTD-NSI technique includes two important processes: text detection and text recognition. Firstly, a single shot detector (SSD) with Inception-v2 as a baseline model is employed for text detection, an object detector based on the VGG-16 framework for feature map extraction followed by six convolution layers. Secondly, Convolutional Recurrent Neural Network (CRNN) technique is utilized for the text recognition process. Besides, the recurrent layers in the CRNN model utilize long short-term memory (LSTM) for encoding the sequence of feature vectors. Lastly, Connectionist Temporal Classification (CTC) loss is applied to predict text labels equivalent to the sequences from the recurrent layers. A wide range of experiments was carried out on benchmark COCO datasets, and the results are examined in several aspects. The experimental outcomes showcased the better performance of the ADLTD-NSI technique over the other compared methods with a maximum accuracy of 96.78%.