Signal Information Research Articles

An accurate semi-empirical model for PMT pulse signal analysis.

The energy information of pulse signals is significantly important for applications such as computed tomography (CT), positron emission tomography (PET), and research on defects in condensed matter. Time-over-threshold (TOT) and multi-voltage threshold (MVT) are commonly used digitization methods in sampling pulse signal. However, both approaches rely on a mathematical model of the pulse signal to derive energy information. This study proposes a semi-empirical mathematical model for pulse signals formation process in scintillation crystal-coupled photomultiplier tube(PMT) probes, by utilizing the CR-RC shaping method. This mathematical model accurately describes output of the PMT pulse signals. This study analyzes a substantial dataset of pulse signals, comparing the performance of the newly designed mathematical model with that of the double exponential function in terms of their ability to fit pulse signals. The results indicate that the mathematical model developed herein achieves an average R2 of 0.9255, significantly surpassing the 0.9155 of the double exponential function, thereby demonstrating its superior fitting efficacy.

MSMGE-CNN: a multi-scale multi-graph embedding convolutional neural network for motor related EEG decoding

Abstract Deep learning (DL) technique has been widely used for decoding motor related electroencephalography (EEG) signals, which has considerably driven the development of motor related brain-computer interfaces (BCIs). However, traditional convolutional neural networks (CNNs) cannot fully represent spatial topology information and dynamic temporal characteristics of multi-channel EEG signals, resulting in limited decoding accuracy. To address such challenges, a novel multi-scale multi-graph embedding convolutional neural network (MSMGE-CNN) is proposed in this study. The proposed MSMGE-CNN contains two crucial components: multi-scale time convolution and multi-graph embedding. Specifically, we design a multi-branch CNN architecture with mixed-scale time convolutions based on EEGNet to sufficiently extract robust time domain features. Afterward, we embed multi-graph information obtained based on physical distance proximity and functional connectivity of multi-channel EEG signals into the time-domain features to capture rich spatial topological dependencies via multi-graph convolution operation. We extensively evaluated the proposed method on three benchmark EEG datasets commonly used for motor imagery/execution (MI/ME) classification and obtained accuracies of 79.59 % (BCICIV-2a Dataset), 69.77 % (OpenBMI Dataset) and 96.34 % (High Gamma Dataset), respectively. These results powerfully demonstrate that MSMGE-CNN outperforms several state-of-the-art algorithms. In addition, we further conducted a series of ablation experiments to validate the rationality of our network architecture. Overall, the proposed MSMGE-CNN method dramatically improves the accuracy and robustness of MI/ME-EEG decoding, which can effectively enhance the performance of motor related BCI system.

Automatic Modulation Recognition Based on Multimodal Information Processing: A New Approach and Application

Automatic modulation recognition (AMR) has wide applications in the fields of wireless communications, radar systems, and intelligent sensor networks. The existing deep learning-based modulation recognition models often focus on temporal features while overlooking the interrelations and spatio-temporal relationships among different types of signals. To overcome these limitations, a hybrid neural network based on a multimodal parallel structure, called the multimodal parallel hybrid neural network (MPHNN), is proposed to improve the recognition accuracy. The algorithm first preprocesses the data by parallelly processing the multimodal forms of the modulated signals before inputting them into the network. Subsequently, by combining Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) models, the CNN is used to extract spatial features of the received signals, while the Bi-GRU transmits previous state information of the time series to the current state to capture temporal features. Finally, the Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA) are introduced as two attention mechanisms to handle the temporal and spatial correlations of the signals through an attention fusion mechanism, achieving the calibration of the signal feature maps. The effectiveness of this method is validated using various datasets, with the experimental results demonstrating that the proposed approach can fully utilize the information of multimodal signals. The experimental results show that the recognition accuracy of MPHNN on multiple datasets reaches 93.1%, and it has lower computational complexity and fewer parameters than other models.

Design of a Differential Chaotic Shift Keying Communication System Based on Noise Reduction with Orthogonal Double Bit Rate

In this paper, a differential chaotic shift keying communication system based on noise reduction with orthogonal double bit rate (NR-ODBR-DCSK) is proposed. The system incorporates Walsh orthogonalization at the transmitter side to orthogonalize the information signals so that two mutually orthogonal signals can be superimposed. At the receiving end, because the principle of orthogonal signals is used, it achieves the characteristic of double information transmission rate for information signal transmission while avoiding the problem of chaotic synchronization. In addition, the system employs a noise reduction transmission mechanism, which reduces the noise variance in the received signal, further reducing the BER of the system and thus improving the performance of the communication system. By analyzing the signal format of the system, the transmitter and receiver structures of the communication system are designed. Subsequently, theoretical analyses and simulations in an additive white Gaussian noise (AWGN) channel demonstrate the good performance of the system, including a low bit error rate (BER) and a good data-energy to bit-energy ratio (DBR). Finally, a simulation test of the NR-ODBR-DCSK system for a semi-physical communication system was carried out using two USRP devices to verify the experimental feasibility of the system. The simulation analysis results show that comparative analyses with conventional DCSK and SR-DCSK systems highlight the superior performance of the NR-ODBR-DCSK system.

All time-scale decomposition method and its application in gear fault diagnosis

Adaptive signal decomposition methods, especially without parameters, have become a popular way of diagnosing mechanical faults due to their capability to process mechanical vibration signals adaptively. Empirical mode decomposition (EMD), local mean decomposition (LMD), and local characteristic-scale decomposition (LCD) are typical parameterless adaptive signal decomposition methods currently applied to mechanical fault diagnosis. All of these methods use extreme points to construct baselines, and the mono-component signals are decomposed from an original signal by multiple sift. However, since these methods define time-scale parameters only through extreme points, they are prone to lose the local feature information of an original signal and lead to mode mixing. Aiming at the above problems, the time-scale parameters is defined by using extreme points and zero crossing points simultaneously in this paper. Therefore, we propose a new adaptive signal decomposition method called all time-scale decomposition (ATD). A complex signal can be adaptively decomposed into multiple independent all time-scale components by the ATD method. The baselines of ATD are constructed jointly by extreme points and zero crossing points, so ATD can extract more local feature information of a signal to suppress the mode mixing. First, the principle of ATD is proposed and the method of determining zero crossing points is introduced in this paper. Then, an empirical formula for compensation factor used to determine zero crossing points is deduced. Finally, ATD is verified by the simulation signals and gear signals, respectively. The results indicate that ATD has stronger mode mixing suppression capability and decomposition performance than EMD, LMD, and LCD, and it can be effectively used for gear fault diagnosis.

Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance

Abstract In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a Self-Weighted Graph Attention Networks (SW-GAT) based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using Stacked Auto-Encoders (SAE), and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.

ENHANCING OPERATIONAL EFFECIENCY IN CRUSHERS THROUGH THE USE OF AN INDUSTRY 4.0 BASED CRUSHER CONTROL SYSTEM

In the aggregate and mining industry, an excessive flow rate of raw material from the feeder, caused by irregularities in the raw material being processed by crushers, can lead to blockages or excessive strain on the crusher. Conversely, a low flow rate of raw material can result in high energy consumption by the crusher, despite operating at a low capacity. The issues encountered in the first group result in excessive energy usage in the secondary and tertiary groups. The study focuses on a system that utilizes artificial intelligence and is based on industry 4.0 principles. The system aims to maintain production in the crusher within a specific range by controlling the flow rates of the feeders using an algorithm. This control is done automatically without the need for user intervention. The system optimizes energy consumption while maximizing production capacity and ensures uninterrupted operation. The system was developed during the installation phase at an aggregate pilot plant in the Kahramanmaraş Evri region. It assesses the material capacity data using a belt scale on the crusher, feeder, and output conveyor. This data is then compared to the limit values stored in the database, and the system generates an information signal to initiate the required control actions. Based on this matching result, it sends information to the inverter, coordinates the production cycle, manages and documents the process stages using a structured learning system and artificial intelligence logic.The installation procedure was conducted using two distinct density gradation inputs. As a consequence of the reporting, the records in the report were compared during both the active and inactive states of the system. The project achieved an efficiency of 22% in terms of energy consumption per unit capacity. Based on the whole yearly energy usage, a total of 368609.7 kg of carbon emissions were averted. The facility's aggregate crushing capacity was increased by 40%.

An advanced machine vision-based method for abnormal detection of transverse vibrations in ship propulsion shafting

The working environment of ship propulsion shafting is harsh and the force condition is complex, which often produces all-directional vibration. Its working condition will directly affect the navigation performance of the ship. To overcome the limitations of complicated installation route, tedious maintenance process and high cost of traditional contact vibration sensors, an approach of transverse vibration identification model based on machine vision was proposed to realize multi-point vibration displacement sensing and anomaly analysis of shafting. The displacement information of the video signal is extracted by the displacement sensing strip labeling method, and the abnormal state of the ship propulsion shafting is analyzed by the dynamic kernel principal component analysis (DKPCA) algorithm. The experimental results show that the approach can accurately detect the continuous vibration displacement of shafting in the range of 180 r/min, and can work normally under two abnormal conditions: sudden external excitation and continuous uneven external excitation. In addition, this approach can quickly and accurately monitor the motion state of shafting, and realize the perception and recognition of abnormal vibration state of shafting. The research and application of this approach in ship shafting vibration monitoring is of great significance to the development of unmanned and intelligent ships.

High-Precision Rotor Position Fitting Method of Permanent Magnet Synchronous Machine Based on Hall-Effect Sensors

The high-performance vector control technology of permanent magnet synchronous machines (PMSMs) relies on high-precision rotor position. The Hall-effect sensor has the advantages of low cost, simple installation, and strong anti-interference ability. However, it can only provide six discrete rotor angles in an electrical cycle, which makes high-precision vector control of PMSMs difficult. Hence, to obtain the necessary rotor position of PMSMs, a rotor position fitting method combining the Hall signal and machine flux information is proposed. Firstly, the rotor position signal output by the Hall-effect sensors is used to calibrate and update the stator flux obtained under pure integration. Then, based on the corrected stator flux and its relationship with current and angle, the rotor position and speed are obtained. Experimental verification shows that the rotor position observer combining Hall signal and flux information can reduce the initial value bias and integral drift caused by traditional average speed method hysteresis and pure integration method calculation of flux and can quickly and accurately track and estimate the rotor position, achieving high-performance vector control of PMSMs.

Stochastic neuropeptide signals compete to calibrate the rate of satiation.

Neuropeptides have important roles in neural plasticity, spiking and behaviour1. Yet, many fundamental questions remain regarding their spatiotemporal transmission, integration and functions in the awake brain. Here we examined how MC4R-expressing neurons in the paraventricular nucleus of the hypothalamus (PVHMC4R) integrate neuropeptide signals to modulate feeding-related fast synaptic transmission and titrate the transition to satiety2-6. We show that hunger-promoting AgRP axons release the neuropeptide NPY to decrease the second messenger cAMP in PVHMC4R neurons, while satiety-promoting POMC axons release the neuropeptide αMSH to increase cAMP. Each release event is all-or-none, stochastic and can impact multiple neurons within an approximately 100-µm-diameter region. After release, NPY and αMSH peptides compete to control cAMP-the amplitude and persistence of NPY signalling is blunted by high αMSH in the fed state, while αMSH signalling is blunted by high NPY in the fasted state. Feeding resolves this competition by simultaneously elevating αMSH release and suppressing NPY release7,8, thereby sustaining elevated cAMP in PVHMC4R neurons throughout a meal. In turn, elevated cAMP facilitates potentiation of feeding-related excitatory inputs with each bite to gradually promote satiation across many minutes. Our findings highlight biochemical modes of peptide signal integration and information accumulation to guide behavioural state transitions.

Double effects of mitigating cyanobacterial blooms using modified clay technology: regulation and optimization of the microbial community structure.

Harmful algal blooms (HABs) are global hazards under global climate change and eutrophication conditions. Modified clay (MC) method is widely used to control HABs in Asian and American coastal waters. However, little research has been conducted on the underlying mechanisms by which MC controls blooms in freshwater environments. Herein, experiments and bioinformatics analyses were conducted for MC-based control of freshwater blooms in a closed water body with an area of approximately 240 m2 in the Fuchun River, China. Results revealed that the dominant bloom species were Microcystis, and an 87.68-97.01% removal efficiency of whole algal biomass was achieved after 3 h of MC treatment. The weaker zeta potentials of Microcystis species and hydrophilic groups such as O-H and P-O-P in the extracellular polymeric substances (EPS) surrounding Microcystis cells made them easier to be flocculated and removed by MC particles, and the relative abundance of Microcystis decreased to 29.12% and that of Cyanobium increased to 40.97%. Therefore, MC changes the cyanobacterial community structure, which is accompanied by the elimination of Microcystis sp. apical dominance and enhanced competition between Cyanobium and Microcystis in the phytoplankton community, increasing cyanobacterial community diversity. Under MC treatment, residual microorganisms, including cyanobacteria, had a high potential for DNA damage repair and were more likely to survive after being subjected to oxidative stress. In the meanwhile, the abundance of genes involved in genetic information processing, signal transduction, and photosynthesis was decreased indicating that the residual microbiome was week in proliferation and light energy harvesting. Therefore, accompanied with the destruction of Microcystis colonies, MC changes the function of cyanobacteria and phycosphere microbiome, further hindering bloom development. These findings illustrate that MC can regulate and optimize the microbial community structure through which MC controls cyanobacterial blooms in ecosystems.

Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition

Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only the vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.

A temporal–spectral fusion transformer with subject-specific adapter for enhancing RSVP-BCI decoding

The Rapid Serial Visual Presentation (RSVP)-based Brain–Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies on a substantial amount of training data from new test subjects, which increases preparation time for BCI systems. Several studies introduce data from existing subjects to reduce the dependence of performance improvement on data from new subjects, but their optimization strategy based on adversarial learning with extensive data increases training time during the preparation procedure. Moreover, most previous methods only focus on the single-view information of EEG signals, but ignore the information from other views which may further improve performance. To enhance decoding performance while reducing preparation time, we propose a Temporal-Spectral fusion transformer with Subject-specific Adapter (TSformer-SA). Specifically, a cross-view interaction module is proposed to facilitate information transfer and extract common representations across two-view features extracted from EEG temporal signals and spectrogram images. Then, an attention-based fusion module fuses the features of two views to obtain comprehensive discriminative features for classification. Furthermore, a multi-view consistency loss is proposed to maximize the feature similarity between two views of the same EEG signal. Finally, we propose a subject-specific adapter to rapidly transfer the knowledge of the model trained on data from existing subjects to decode data from new subjects. Experimental results show that TSformer-SA significantly outperforms comparison methods and achieves outstanding performance with limited training data from new subjects. This facilitates efficient decoding and rapid deployment of BCI systems in practical use.

Copy Number Variation and Selection Signal: Exploring the Domestication History and Phenotype Differences Between Duroc and the Chinese Native Ningxiang Pigs.

The Ningxiang pig, one of the well-known Chinese native pig breeds, has the advantages of tender meat, high intramuscular fat (IMF) content, and roughage tolerance, compared to the commercial lean pig breeds. The genetic basis for complex traits in Ningxiang pigs has been previously studied through other genetic markers, such as Single Nucleotide Polymorphism (SNP), while the characteristics of copy number variation (CNV) and the selection signal have not been investigated yet. In this study, GGP 50 k genotyping data of 2242 Ningxiang pigs (NX) and 1137 Duroc pigs (Duroc) were involved in CNV atlas construction and selection signals identification. Annotations of genes and quantitative trait locus (QTLs) were performed on the target candidate regions, as follows: (1) 162 CNVs were detected in Ningxiang pigs, while 326 CNVs were detected in Duroc pigs, and there are 21 copy number variation regions (CNVRs) shared between them; (2) The CNVRs of Duroc are more abundant, with 192 CNVRs, accounting for 1.61% of the entire genome, while those of Ningxiang pigs only have 98 CNVRs, accounting for 0.49%; (3) The QTLs annotated on CNVs and selected regions of Ningxiang pigs were mainly associated with meat quality and fertility. In contrast, the Duroc QTLs' notes relate primarily to the carcass and immunity, and explain why they have a higher slaughter rate and immunity; (4) There is a presence of high-frequency acquired CNVs, specifically in Ningxiang pigs, with 24 genes significantly enriched in the sensory receptor-related pathway in this region; (5) Based on the CNVs atlas, candidate genes such as 3 inositol 1,4,5-triphosphate receptor, type 3 (ITPR3), forkhead box protein K2 (FOXK2), G-protein coupled estrogen receptor 1 (GPER1), Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), triosephosphate isomerase 1 (TPI1), and other candidate genes related to fat deposition and differentiation were screened. In general, this study improved our knowledge about copy number variation and selection signal information of Ningxiang pigs, which can not only further explain the genetic differences between Chinese native and Western commercial pig breeds, but also provide new materials for the analysis of the genetic basis of complex traits.

Personalized blood pressure estimation using multiview fusion information of wearable physiological signals and transfer learning

Continuous blood pressure (BP) monitoring is crucial for individual health management, yet the significant inter-individual variations among patients pose challenges to achieving precision medicine. In response to this issue, we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilities. Our proposed architecture exhibits superior performance in estimating systolic blood pressure and diastolic blood pressure, with average absolute errors of 3.94 mmHg and 2.24 mmHg, respectively. These results surpass existing baseline models and align with the standards set by the British Hypertension Society, the Association for the Advancement of Medical Instrumentation, and the Institute of Electrical and Electronics Engineers for BP measurement. Additionally, this study explores a personalized model fine-tuning strategy by adjusting specific layers and incorporating individual information, presenting an optimal solution. The model's generalization ability is validated through transfer learning across databases (public and self-made). To enhance the proposed architecture's usability in wearable devices, this study employs a knowledge distillation strategy for model lightweighting, with preliminary application in our designed real-time BP estimation system. This study provides an efficient and accurate solution for personalized BP estimation, exhibiting broad potential applications.

HTS and scRNA-seq revealed that the location and RSS quality of the mammalian TRBV and TRBJ genes impact biased rearrangement

The quality of Recombination signal sequences (RSSs), location, and genetics of mammalian V, D, and J genes synergistically affect the recombination frequency of genes; however, the specific regulatory mechanism and efficiency have not been elucidated. By taking advantage of single-cell RNA-sequencing (scRNA-seq) and high-throughput sequencing (HTS) to investigate V(D)J rearrangement characteristics in the CDR3 repertoire, we found that the distal and proximal V genes (or J genes) “to D” gene were involved in rearrangement significantly more frequently than the middle V genes (or J genes) in the TRB locus among various species, including Primates (human and rhesus monkey), Rodentia (BALB/c, C57BL/6, and Kunming mice), Artiodactyla (buffalo), and Chiroptera (Rhinolophus affinis). The RSS quality of the V and J genes affected their frequency in rearrangement to varying degrees, especially when the V-RSSs with recombination signal information content (RIC) score < -45 significantly reduced the recombination frequency of the V gene. The V and J genes that were “away from D” had the dual advantages of recombinant structural accessibility and relatively high-quality RSSs, which promoted their preferential utilization in rearrangement. The quality of J-RSSs formed during mammalian evolution was apparently greater than that of V-RSSs, and the D-J distance was obviously shorter than that of V-D, which may be one of the reasons for guaranteeing that the “D-to-J preceding V-to-DJ rule” occurred when rearranged. This study provides a novel perspective on the mechanism and efficiency of V-D-J rearrangement in the mammalian TRB locus, as well as the biased utilization characteristics and application of V and J genes in the initial CDR3 repertoire.

Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection

Automatic epilepsy seizure detection has high clinical value since it can alleviate the burden of manual monitoring. Nevertheless, it remains a technically challenging task to achieve a reliable system. In this study, we investigated the significance of the phase information in EEG signals in seizure detection using machine learning. We used the Stockwell transform (S-transform) to extract both phase and power spectra of the EEG signal in epilepsy patients. A dual-stream convolution neural network (CNN) model was adopted as the classifier, which takes both spectra as inputs. We demonstrated that the phase input allows the CNN model to capture the heightened phase synchronization among EEG channels in seizure and add network attention to both the low- and high-frequency features of the inputs in the CHB-MIT and Bonn databases. We improved the detection AUC-ROC by 6.68% on the CHB-MIT database when adding phase inputs to the power inputs. By incorporating a channel fusion post-processing to the outputs of this CNN model, it achieves a sensitivity and specificity of 79.59% and 92.23%, respectively, surpassing some of the state-of-the-art methods. Our results show that the phase inputs are useful features in seizure detection. This discovery has significant implications for improving the effectiveness of automatic seizure detection systems.

Preview control for linear parameter‐varying discrete‐time systems using a linear fractional representation

AbstractIn order to create a mixed tracking controller for discrete‐time linear parameter‐varying (LPV) systems, this work introduces a unique control strategy employing a linear fractional representation (LFR). Under the premise that the reference signal can be previewable, a design strategy for a controller with preview compensations is suggested. First, a preview tracking control issue is changed into a regulator problem by adding two additional linked auxiliary parameters into the initial system state/input, which further creates more enhanced error systems incorporating future information of reference signals. The suggested conditions then depend on utilizing slack variables with decision matrices related to the LFR approach in order to give innovative preview controller designs. We also construct reduced conservative synthesis situations by taking into account parameter‐dependent Lyapunov functions and full‐block multipliers. In order to create state‐feedback and output‐feedback preview tracking controllers, design requirements are characterized as linear matrix inequalities. Two simulation studies are completed as the last step to highlight how successful the suggested control strategy is.

The Influence of Social Media Marketing on Brand Awareness, Brand Image, Customer Engagement, and Purchase Decision

This study aims to analyze how Social Media Marketing (SMM) influences Brand Awareness, Brand Image, Customer Engagement, and Purchase Decision. Using Signal Processing and Information Processing theories, this research identifies gaps in the literature regarding the relationship between SMM, Brand Awareness, Brand Image, Customer Engagement, and Purchase Decision. This research employs a quantitative approach with a survey method to collect data from 105 respondents. The sampling technique used is purposive sampling, which means selecting samples based on specific criteria relevant to the research. The criteria used are consumers who have seen or interacted with SilverQueen's social media marketing content. The collected data was analyzed using Smart PLS (Partial Least Squares) to test the structural model and measurement model. This study shows that Social Media Marketing plays a crucial role in enhancing Brand Awareness, Brand Image, Customer Engagement, and Purchase Decision for SilverQueen chocolate. The findings underscore the importance of effective social media marketing strategies for achieving better business outcomes.

A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing.

Chimera artifacts in nanopore direct RNA sequencing (dRNA-seq) can significantly distort transcriptome analyses, yet their detection and removal remain challenging due to limitations in existing basecalling models. We present DeepChopper, a genomic language model that precisely identifies and removes adapter sequences from base-called dRNA-seq long reads at single-base resolution, operating independently of raw signal or alignment information to effectively eliminate chimeric read artifacts. By removing these artifacts, DeepChopper substantially improves the accuracy of critical downstream analyses, such as transcript annotation and gene fusion detection, thereby enhancing the reliability and utility of nanopore dRNA-seq for transcriptomics research.