Advanced Vector Research Articles

An advanced quantum support vector machine for power quality disturbance detection and identification

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O(N2log(N))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$O(N^{2} \\log (N))$\\end{document}, superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.

Expression levels and stoichiometry of Hnf1β, Emx2, Pax8 and Hnf4 influence direct reprogramming of induced renal tubular epithelial cells

Generation of induced renal epithelial cells (iRECs) from fibroblasts offers great opportunities for renal disease modeling and kidney regeneration. However, the low reprogramming efficiency of the current approach to generate iRECs has hindered potential therapeutic application and regenerative approach. This could be in part attributed to heterogeneous and unbalanced expression of reprogramming factors (RFs) Hnf1β (H1), Emx2 (E), Pax8 (P), and Hnf4α (H4) in transduced fibroblasts. Here, we establish an advanced retroviral vector system that expresses H1, E, P, and H4 in high levels and distinct ratios from bicistronic transcripts separated by P2A. Mouse embryonic fibroblasts (MEFs) harboring Cdh16-Cre; mT/mG allele are utilized to conduct iREC reprogramming via directly monitoring single cell fate conversion. Three sets of bicistronic RF combinations including H1E/H4P, H1H4/EP, and H1P/H4E have been generated to induce iREC reprogramming. Each of the RF combinations gives rise to distinct H1, E, P, and H4 expression levels and different reprogramming efficiencies. The desired H1E/H4P combination that results in high expression levels of RFs with balanced stoichiometry. substantially enhances the efficiency and quality of iRECs compared with transduction of separate H1, E, P, and H4 lentiviruses. We find that H1E/H4P-induced iRECs exhibit the superior features of renal tubular epithelial cells, as evidenced by expressing renal tubular-specific genes, possessing endocytotic arrogation activity and assembling into tubules along decellularized kidney scaffolds. This study establishes H1E/H4P cassette as a valuable platform for future iREC studies and regenerative medicine.

Motor fault diagnosis based on multisensor-driven visual information fusion

To need of accurate motor fault diagnosis in industrial system, we propose a fault diagnosis framework that utilizes motor current and electromagnetic signals, combining them with a self-attention-enhanced capsule network for enhanced signal analysis and accuracy. Firstly, the original signal extracted by multiple sensors is constructed into a symmetric point mode (SDP) image, and the visual fault information of different sensors and fusion signals of different motion health states are obtained by the proposed multi-channel image fusion method. Then, the capsule network, combined with self-attention, extracts spatial features from the high-dimensional tensor of the multi-channel fused image for adaptive recognition and extraction. Subsequently, advanced feature vector information is obtained through softmax for diagnosis. Diagnosis results of several datasets indicate that the developed diagnosis framework with compressed image information can availably identify 8 kinds of motor fault states under various loads, and the fault diagnosis rate is as high as 99.95 %, it is helpful for low cost and high-speed diagnosis of motors. In addition, by learning multiple sensor signals in the same state, it obtains stronger robustness and effectiveness than a single signal model.

Dengue Hemorrhagic Fever in Quang Nam Province (Vietnam) from 2020 to 2022-A Study on Serotypes Distribution and Immunology Factors.

Background: Dengue hemorrhagic fever (DHF) is the most prevalent and fastest-growing vector-borne disease globally, with symptoms ranging from mild to severe and, in some cases, fatal. Quang Nam province in Vietnam can serve as a model for dengue epidemiological study, as it is an endemic region for DHF with a tropical climate, which significantly constrains the health system. However, there are very few epidemiological and microbiological reports on Dengue virus (DENV) serotypes in this region due to the limited availability of advanced surveillance infrastructure. Aims of the study: This study aims to (1) assess the PCR positivity rates among hospitalized patients with clinical Dengue presentation; (2) identify the circulating DENV serotypes; and (3) assess the impact of secondary DENV infections on outbreak severity by detecting the presence of DENV-specific IgG antibodies in the plasma of DENV-infected patients. Materials and methods: Blood samples from patients clinically diagnosed with DHF and admitted to Quang Nam General Hospital (2020-2022) were analyzed. RNA extraction was performed using the NKDNA/RNAprep MAGBEAD kit, followed by Multiplex Reverse Transcription real-time Polymerase Chain Reaction (MLP RT-rPCR) for DENV detection and serotype identification. Positive samples were further tested for DENV-specific IgG antibodies using an enzyme-linked immunosorbent assay (ELISA). Results: The PCR positivity rate among hospitalized patients was approximately 68% throughout the study period. A significant shift in DENV serotypes was observed, with DENV-2 initially dominant and later giving way to DENV-1. IgG was detected in nearly half of the MPL RT-rPCR-positive samples, indicating secondary DENV infections. Conclusions: Our study highlights persistent dengue prevalence and dynamic shifts in DENV serotypes in Quang Nam province, emphasizing the need for improved diagnostic strategies and timely sample collection. The significant serotype shifts and the presence of IgG in hospitalized patients suggest potential severe outcomes from recurrent DENV infections, possibly linked to antibody-dependent enhancement (ADE) effect, underscoring the importance of advanced surveillance, vector control, vaccination campaigns, and public education to predict and prevent future DHF epidemics.

Quantile connectedness and network among Green bonds, Renewable Energy, and G7 sustainability markets

This paper examines the quantile spillovers and interconnectedness between green bonds, renewable energy, and the G7 sustainability markets under extreme market conditions. It reaches beyond the widely employed studies based in the mean values of variables by resorting to the advanced quantile vector auto-regression methodology. The results show that the overall connectedness is stronger during extreme, either bearish or bullish market situations, than during normal market conditions. Furthermore, we show that all sustainability markets, except Japan, are the net transmitters of shocks, while all the green bonds are the net receivers. Moreover, we document that spillovers vary significantly over time and that extreme market conditions reduce the net spillover influences. The relative tail dependence analysis shows that the connectedness at the lower quantile relatively dominates the connectedness at the upper quantile. Finally, the total spillovers are higher at the extremes than at the median quantile, whereas the periods of major crises are associated with unusually high connectedness, as consistent with the literature on contagion effects.

Research Project Topic Recommender System Using Generative Language Model

Education has become a driver of a person's continuous innovation to improve their quality. Currently, the use of artificial intelligence determines progress in education. In this research, artificial intelligence technology was applied to develop a web-based recommendation system to help students at the Faculty of Computer Science, Klabat University, choose appropriate research topics for their final assignments. To provide personalized and contextually relevant suggestions, the recommendation system leverages deep learning and generative language models, specifically GPT-3. The Rapid Application Development process model is employed to develop the system. Its key components include semantic search, rapid engineering, and an advanced vector database for effective data management and retrieval. The functions provided by the system include user account registration, login, input of major subject grades and research preferences, and personalized recommendation results. Some additional features such as profile management, previous recommendation history, and password reset options are also provided. All these functions have been tested using the black box method.

Experimental and numerical investigations on defect location detection of multi-damage steel beams using advanced damage location vector approach

Experimental and numerical investigations on defect location detection of multi-damage steel beams using advanced damage location vector approach

Bioacoustics in population control of insects of medical importance: A review.

Vector transmitted diseases are accountable for more than 17% of all infectious disease cases worldwide according to World Health Organization. Insect vectors play a key role in transmitting diseases and loss of lives. Modified and advanced vector control strategies with chemical insecticides are needed as vectors are resistant to a particular insecticide. Moreover, chemical control is cost-inductive and may give rise to health issues. In this review, bioacoustics have been narrated as a novel technology for eco-friendly and cost-effective control of insect vectors. Many insects that rely on sounds for communication and copulation can be trapped, killed, and repelled through the mimicked sounds of conspecific males or females, generating disturbing noises. Sound can also be harnessed to prevent the mating success of insects. There is need for future studies on rejection calls and harmonic convergences in insect vectors. In-depth investigations on the higher harmonics of insect calls, along with artificial intelligence, will be beneficial for the development of successful sound- evoked control of insects of medical importance.

Reducing the Power Consumption of Edge Devices Supporting Ambient Intelligence Applications

Having as a main objective the exploration of power efficiency of microcontrollers running machine learning models, this manuscript contrasts the performance of two types of state-of-the-art microcontrollers, namely ESP32 with an LX6 core and ESP32-S3 with an LX7 core, focusing on the impact of process acceleration technologies like cache memory and vectoring. The research employs experimental methods, where identical machine learning models are run on both microcontrollers under varying conditions, with particular attention to cache optimization and vector instruction utilization. Results indicate a notable difference in power efficiency between the two microcontrollers, directly linked to their respective process acceleration capabilities. The study concludes that while both microcontrollers show efficacy in running machine learning models, ESP32-S3 with an LX7 core demonstrates superior power efficiency, attributable to its advanced vector instruction set and optimized cache memory usage. These findings provide valuable insights for the design of power-efficient embedded systems supporting machine learning for a variety of applications, including IoT and wearable devices, ambient intelligence, and edge computing and pave the way for future research in optimizing machine learning models for low-power, embedded environments.

IProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking

Background and Objective: Gene promoters play a crucial role in regulating gene transcription by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches, including alignment signal and content-based methods for promoter prediction, accurately identifying promoters remains challenging due to the lack of explicit features in their sequences. Consequently, many machine learning and deep learning models for promoter identification have been presented, but the performance of these tools is not precise. Most recent investigations have concentrated on identifying sigma or plant promoters. While the accurate identification of Saccharomyces cerevisiae promoters remains an underexplored area. In this study, we introduced “iPromyeast”, a method for identifying yeast promoters. Using genome sequences from the eukaryotic yeast Saccharomyces cerevisiae, we investigate vector encoding and promoter classification. Additionally, we developed a more difficult negative set by employing promoter sequences rather than nonpromoter regions of the genome. The newly developed negative reconstruction approach improves classification and minimizes the amount of false positive predictions. Methods: To overcome the problems associated with promoter prediction, we investigate alternate vector encoding and feature extraction methodologies. Following that, these strategies are coupled with several machine learning algorithms and a 1-D convolutional neural network model. Our results show that the pseudo-dinucleotide composition is preferable for feature encoding and that the machine- learning stacking approach is excellent for accurate promoter categorization. Furthermore, we provide a negative reconstruction method that uses promoter sequences rather than non-promoter regions, resulting in higher classification performance and fewer false positive predictions. Results: Based on the results of 5-fold cross-validation, the proposed predictor, iProm-Yeast, has a good potential for detecting Saccharomyces cerevisiae promoters. The accuracy (Acc) was 86.27%, the sensitivity (Sn) was 82.29%, the specificity (Sp) was 89.47%, the Matthews correlation coefficient (MCC) was 0.72, and the area under the receiver operating characteristic curve (AUROC) was 0.98. We also performed a cross-species analysis to determine the generalizability of iProm-Yeast across other species. Conclusion: iProm-Yeast is a robust method for accurately identifying Saccharomyces cerevisiae promoters. With advanced vector encoding techniques and a negative reconstruction approach, it achieves improved classification accuracy and reduces false positive predictions. In addition, it offers researchers a reliable and precise webserver to study gene regulation in diverse organisms.

Research on modulation recognition method of electromagnetic signal based on wavelet transform convolutional neural network

The method of electromagnetic signal modulation recognition based on wavelet transform convolutional neural network is studied to improve the effect of electromagnetic signal modulation recognition. By analyzing the electromagnetic signal modulation model, the original electromagnetic signal is preprocessed by wavelet transform to remove the noise of the original electromagnetic signal. The processed electromagnetic signal is used as the input of convolutional neural network, and the electromagnetic signal feature vector is extracted through the convolution layer of convolutional neural network. By using full connection operation, the advanced feature vector of electromagnetic signal is integrated, and the electromagnetic signal is classified by softmax function, and the electromagnetic signal modulation recognition result is output, thus realizing the electromagnetic signal modulation recognition. The experimental results show that when the number of layers of wavelet decomposition is 7 and the wavelet function is Db9, the wavelet transform has the best denoising effect on electromagnetic signal data. At the same time, the network training efficiency of this method is high, and the accuracy of electromagnetic signal modulation recognition is as high as 97.2 %, which improves the effect of electromagnetic signal modulation recognition and is suitable for various types of electromagnetic signal modulation recognition.

Detecting of Robotic Imitation of Human on-the-Website Activity with Advanced Vector Analysis and Fractional Derivatives

Detecting of Robotic Imitation of Human on-the-Website Activity with Advanced Vector Analysis and Fractional Derivatives

An efficient surrogate model for damage forecasting of composite laminates based on deep learning

In this paper, full-field damage forecasting of a laminated composite structure under different low velocity impact (LVI) conditions is realized through the proposed surrogate model, named VQ-SM. First, an efficient surrogate modelling method is proposed based on the advanced Vector Quantised-Variational AutoEncoder (VQ-VAE) proposed by DeepMind. Second, numerical simulation based on the progressive damage model of composite laminates is performed to obtain the training dataset. After training, the performance of VQ-SM is evaluated compared to the surrogate model without a representation learning process. The results show that VQ-SM has better performance with high-precise and good robustness, trained on the small dataset. Finally, the impact damage field of composite laminates is analyzed based on the surrogate model. The proposed surrogate modelling method provides not only the full-field damage forecast model for composite structures, but also an efficient method of improving the performance of the “generative” surrogate model.

Single-Instruction-Multiple-Data Instruction-Set-Based Heat Ranking Optimization for Massive Network Flow

In order to cope with the massive scale of traffic and reduce the memory overhead of traffic statistics, the traffic statistics method based on the Sketch algorithm has become a research hotspot for traffic statistics. This paper studies the problem of the top-k flow statistics based on the Sketch algorithm and proposes a method to estimate the flow heat from massive network traffic using the Sketch algorithm and identify the kth flow with the highest heat by using a bitonic sort algorithm. In view of the performance difficulties of applying multiple hash functions in the implementation of the Sketch algorithm, the Single-Instruction-Multiple-Data (SIMD) instruction set is adopted to improve the performance of the Sketch algorithm so that SIMD instructions can process multiple fragments of data in a single step, implement multiple hash operations at the same time, compare and sort multiple flow tables at the same time. Thus, the throughput of the execution task is improved. Firstly, the elements of data flow are described and stored in the form of vectors, while the construction, analysis, and operation of data vectors are realized by SIMD instructions. Secondly, the multi-hash operation is simplified into a single vector operation, which reduces the CPU computing resource consumption of the Sketch algorithm. At the same time, the SIMD instruction set is used to optimize the parallel comparison operation of the flow table in a bitonic sort algorithm. Finally, the SIMD instruction set is used to optimize the functions in the Sketch algorithm and top-k sorting algorithm program, and the optimized code is tested and analyzed. The experimental results show that the time consumed by the advanced vector extensions (AVX)-instructions-optimized version has a significant reduction compared to the original version. When the length of KEY is 96 bytes, the instructions consumed by multiple hash functions account for less in the entire Sketch algorithm, and the time consumed by the optimized version of AVX is about 67.2% of that in the original version. As the length of KEY gradually increases to 256 bytes, the time consumed by the optimized version of AVX decreases to 53.8% of the original version. The simulation results show that the AVX optimization algorithm is effective in improving the measurement efficiency of network flow.

Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection.

Optimal real-time resonant scanner linearization using filtered Hermite interpolation.

High-speed laser scanning microscopy frequently relies on resonant scanners due to their order of magnitude increase in imaging rate compared to conventional galvanometer scanners. However, the use of a nonlinear scan trajectory introduces distortion that must be corrected. This manuscript derives a new algorithm based on filtered Hermite polynomial interpolation that provides the optimal shot-noise-limited SNR for a fixed number of photons and provides higher spatial accuracy than previous methods. An open-source library is presented using the Intel advanced vector instruction set (AVX) to process up to 32 samples in parallel. Using this approach, I simultaneously demonstrate lower shot noise variance, moderately higher spatial accuracy and greater than 1 gigapixel per second interpolation rate on a desktop CPU.

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization—least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.

Faster Implementation of Ideal Lattice-Based Cryptography Using AVX512

With the development of quantum computing, the existing cryptography schemes based on classical cryptographic primitives will no longer be secure. Hence, cryptographers are designing post-quantum cryptographic (PQC) schemes, and ideal lattice-based cryptography has emerged as a prime candidate. Today, as ideal lattice-based cryptography becomes more mature, its performance becomes an important optimization goal. In ideal lattice-based cryptography, polynomial arithmetic and polynomial sampling are the most time-consuming operations and therefore need to be accelerated. In this article, taking advantage of the parallelism of new 512-bit advanced vector instructions (AVX512), we present parallel implementations of polynomial arithmetic and polynomial sampling, thus comprehensively improving their performance. We conduct experiments with the Dilithium scheme(one scheme of NIST PQC Standardization Process Round-4). Our implementation gets a nice performance boost compared to its pure C language and 256-bit advanced vector instructions (AVX2) implementation.

K-XMSS and K-SPHINCS+: Enhancing Security in Next-Generation Mobile Communication and Internet Systems with Hash Based Signatures Using Korean Cryptography Algorithms.

As Mobile Communication and Internet Systems (MCIS) have rapidly developed, security issues related to MCIS have become increasingly important. Therefore, the development and research of security technologies for mobile communication and internet systems are actively being conducted. Hash-Based Signature (HBS) uses a hash function to construct a digital signature scheme, where its security is guaranteed by the collision resistance of the hash function used. To provide sufficient security in the post-quantum environment, the length of hash should be satisfied for the security requirement. Modern HBS can be classified into stateful and stateless schemes. Two representative stateful and stateless HBS are eXtended Merkle Signature Scheme(XMSS) and SPHINCS+, respectively. In this paper, we propose two HBS schemes: K-XMSS and K-SPHINCS+, which replace internal hash functions of XMSS and SPHINCS+ with Korean cryptography algorithms. K-XMSS is a stateful signature, while K-SPHINCS+ is its stateless counterpart. We showcase the reference implementation of K-XMSS and K-SPHINCS+ employing Lightweight Secure Hash (LSH) and two hash functions based on block ciphers (i.e., CHAM and LEA) as the internal hash function. In addition, K-XMSS and K-SPHINCS+ using Advanced Vector Extensions 2 (AVX2) have been provided, demonstrating that they can be optimized for better performance using advanced implementation techniques than previous approaches.

Modelling of thermo-hydraulic behavior of a helical heat exchanger using machine learning model and fire hawk optimizer

The study of the fluid flow in heat exchangers and its related mass and heat transfer processes is a very complicated research subject. Modeling of coupled fluid flow and heat transfer processes using numerical and analytical approaches are cumbersome problems. Hence, the current study investigates the modeling thermo-hydraulic behavior of a helical plate heat exchanger (HPHE) using advanced machine learning models. The outlet temperatures of hot and cold fluids are predicted considering different cross-sectional areas and pitches of the flow channels. This paper fulfills the research gap about fluid flow and heat transfer mechanisms by considering advanced machine learning approaches. The proposed model helps different manufacturers of heat exchangers to estimate the thermal performance and characteristics. The developed model aims to provide an advanced random vector functional link (RVFL) optimized fire hawk optimizer to predict outlet temperatures of working fluids of HPHE. The proposed model was compared with four optimized models using grey wolf, jellyfish, sine-cosine, and hybrid salp swarm-whale optimizers. The results of all models were compared and fire hawk optimizers showed superior accuracy compared with other models. Fire hawk optimizer had the highest R2 (0.999) followed by jellyfish (0.998) for both investigated responses. Hybrid salp swarm-whale and sine-cosine optimizers had the lowest R2 (0.987) in the case of hot fluid.