Signal Analysis Research Articles

Exploring signal interactions in PWR nuclear power plants through noise-based signal processing

Nuclear power plants operate as complex, interconnected systems with tightly interlinked variables. Monitoring systems within such plants gather data from various process parameters. This paper systematically analyzes key signals in a typical PWR power plant. We develop a dynamic model of the PWR plant encompassing mathematical models for the reactor core, steam generator, pressurizer, turbine, and condenser. This model is rigorously validated against reference data. We use this model to analyze signal perturbations in hypothetical scenarios, employing both time-domain and frequency-domain analyses. Our results demonstrate that noise in one signal noticeably impacts others. Furthermore, the extent of this impact varies depending on the nature of their connection. Direct connections exhibit a higher susceptibility to noise-induced interference, whereas intermediate connections typically show a diminished impact. Through signal analysis, we identify correlated signal pairs, offering insights into processes during abnormal conditions. These findings have implications for enhancing the safety and reliability of nuclear power plant operations.

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN performance comparison in respectto expert interpretation of CMR with late gadolinium enhancement (LGE) images, used as ground truth (GT), was conducted on 206 patients (158 scar, 48 control) from Centro Cardiologico Monzino (Milan, Italy) at both slice- and patient-levels. Left ventricle dynamic features were extracted in non-enhanced cine images using parametric images based on both Fourier and monogenic signal analyses. The CNN, fed with cine images and Fourier-based parametric images, achieved an area under the ROC curve of 0.86 (accuracy 0.79, F1 0.81, sensitivity 0.9, specificity 0.65, and negative (NPV) and positive (PPV) predictive values 0.83 and 0.77, respectively), for individual slice classification. Remarkably, it exhibited 1.0 prediction accuracy (F1 0.98, sensitivity 1.0, specificity 0.9, NPV 1.0, and PPV 0.97) in patient classification as a control or pathologic. The proposed approach represents a first step towards scar detection in contrast-free CMR images. Patient-level results suggest its preliminary potential as a screening tool to guide decisions regarding LGE-CMR prescription, particularly in cases where indication is uncertain.

Implementation of microcontroller board on a sustainable and degradable PLA/flax composite substrate: a case study

In this paper, we present a novel polylactic-acid/flax-composite substrate and the implementation of a demonstrator: a microcontroller board based on commercial design. The substrate is developed for printed circuit board (PCB) applications. The pre-preg is biodegradable, reinforced, and flame-retarded. The novel material was developed to counter the increasing amount of e-waste and to improve the sustainability of the microelectronics sector. The motivation was to present a working circuit in commercial complexity that can be implemented on a rigid substrate made of natural, bio-based materials with a structure very similar to the widely used Flame Retardant Class 4 (FR4) substrate at an early technological readiness level (2–3). The circuit design is based on the Arduino Nano open-source microcontroller board design so that the demonstration could be programmable and easy to fit into education, IoT applications, and embedded designs. During the work, the design was optimized at the level of layout. The copper-clad pre-preg was then prepared and processed with subtractive printed wiring technology and through hole plating. The traditional surface mounting methodology was applied for assembly. The resulting yield of PCB production was around 50%. Signal analysis was successful with analogue data acquisition (voltage) and low-frequency (4 kHz) tests, indistinguishable from sample FR4 boards. Eventually, the samples were subjected to highly accelerated stress test (HAST). HAST tests revealed limitations compared to traditional FR4 printed circuit materials. After six cycles, the weight loss was around 30% in the case of PLA/Flax, and as three-point bending tests showed, the possible ultimate strength (25 MPa at a flexural state) was reduced by 80%. Finally, the sustainability aspect was assessed, where we found that ∼95 vol% and ∼90 wt% of the traditional substrate can be substituted, significantly easing the load of waste on the environment.

71 Exploring DNA-methylation and DNA-hydroxymethylation in clear cell renal cell carcinoma using Dirichlet multinomial regression

Abstract Background DNA methylation alterations have been found related to clear cell renal cell carcinoma (ccRCC). In addition, DNA methylation has been used to identify cell heterogeneity at the tumor level using cell deconvolution methods. Other cytosine modifications are less studied in the field. In this study, we aim to explore the use of compositional method, Dirichlet multinomial regression1, to simultaneously explore DNA methylation and DNA hydroxymethylation in ccRCC. Methods A total of 243 clear cell renal cell carcinoma (ccRCC) samples were collected from the Dartmouth Renal Tumor Biobank. DNA methylation analysis was conducted using the Infinium MethylationEPIC BeadChip, using tandem bisulfite (BS) and oxidative bisulfite (oxBS) treatments to differentiate between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) states. Beta values were calculated for all the arrayed samples. Quality control was performed using the ENmix pipeline to ensure the inclusion of high-quality samples using a stringent pOOBHA <0.05. The oxBS maximum likelihood estimate (oxBS-MLE) method was applied to assess 5mC and 5hmC levels based on paired BS and oxBS datasets. To determine the inclusion threshold for 5hmC beta values in the Dirichlet analysis, the R package oxBSCut was used to mask values close to zero2. Missing 5hmC values and those values masked below the detection limit were imputed using a compositional approach and a Tobit regression model to retain the model properties. The Dirichlet model was applied to analyze three methylation states: unmethylated, methylated, and hydroxymethylated, providing comprehensive insights into the methylation landscape of ccRCC samples. Results Here, we analyzed the top 1% most variable methylated sites (n=3020). When comparing tumor vs normal adjacent samples we tested the cytosine modifications in the same model adjusting for sex, age at diagnosis, tumor stage, and grade. For this exploratory analysis, we selected those results that passed an FDR<0.01 and an absolute difference >0.2 for further exploration. Four sites were hyperhydroxymethylated related to MYO5A, DLG2, ANKRD33B, and one open sea site. A total of 23 sites were significantly hypermethylated. Using eForge-TF, these sites tracked to transcription factors targeting estrogen receptors, retinoic acid receptors alpha and gamma, and FOXC1 among others3. Conclusions In this preliminary analysis, using a new modeling approach to interrogate cytosine modifications simultaneously, we found some intriguing results consistent with previous literature. We will expand this analysis to evaluate the relation between extreme changes in hydroxymethylation in particular in relation to alternative splicing alterations. Reference 1. Tsagris, M., Stewart, C. A Dirichlet Regression Model for Compositional Data with Zeros. Lobachevskii J Math39, 398–412 (2018). https://doi.org/10.1134/S1995080218030198 2. Zhang Z, Lee MK, Perreard L, Kelsey KT, Christensen BC, Salas LA. Navigating the hydroxymethylome: experimental biases and quality control tools for the tandem bisulfite and oxidative bisulfite Illumina microarrays. Epigenomics. 2022;14(3):139-152. doi:10.2217/epi-2021-0490 3. Charles E Breeze, Alex P Reynolds, Jenny van Dongen, Ian Dunham, John Lazar, Shane Neph, Jeff Vierstra, Guillaume Bourque, Andrew E Teschendorff, John A Stamatoyannopoulos, Stephan Beck, eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data, Bioinformatics, Volume 35, Issue 22, November 2019, Pages 4767–4769, https://doi.org/10.1093/bioinformatics/btz456

Simultaneous time-frequency analysis of gait signals of both legs in classifying neurodegenerative diseases

BackgroundNeurodegenerative diseases (NDDs) pose significant challenges due to their debilitating nature and limited therapeutic options. Accurate and timely diagnosis is crucial for optimizing patient care and treatment strategies. Gait analysis, utilizing wearable sensors, has shown promise in assessing motor abnormalities associated with NDDs. Research questionResearch Question 1 To what extent can analyzing the interaction of both limbs in the time-frequency domain serve as a suitable methodology for accurately classifying NDDs?Research Question 2 How effective is the utilization of color-coded images, in conjunction with deep transfer learning models, for the classification of NDDs? MethodsGaitNDD database was used, comprising recordings from patients with Huntington’s disease, amyotrophic lateral sclerosis, Parkinson’s disease, and healthy controls. The gait signals underwent signal preparation, wavelet coherence analysis, and principal component analysis for feature enhancement. Deep transfer learning models (AlexNet, GoogLeNet, SqueezeNet) were employed for classification. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using 5-fold cross-validation. ResultsThe classification performance of the models varied depending on the time window used. For 5-second gait signal segments, AlexNet achieved an accuracy of 95.91 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.49 % and 92.73 %, respectively. For 10-second segments, AlexNet outperformed other models with an accuracy of 99.20 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.75 % and 95.00 %, respectively. Statistical tests confirmed the significance of the extracted features, indicating their discriminative power for classification. SignificanceThe proposed method demonstrated superior performance compared to previous studies, offering a non-invasive and cost-effective approach for the automated diagnosis of NDDs. By analyzing the interaction between both legs during walking using wavelet coherence, and utilizing deep transfer learning models, accurate classification of NDDs was achieved.

Medical intelligence for anxiety research: Insights from genetics, hormones, implant science, and smart devices with future strategies

AbstractThis comprehensive review article embarks on an extensive exploration of anxiety research, navigating a multifaceted landscape that incorporates various disciplines, such as molecular genetics, hormonal influences, implant science, regenerative engineering, and real‐time cardiac signal analysis, all while harnessing the transformative potential of medical intelligence [medical + artificial intelligence (AI)]. By addressing fundamental research questions, this study investigated the molecular and hormonal foundations underlying anxiety disorders, shedding light on the intricate interplay of genetic and hormonal factors contributing to the etiology and progression of anxiety. Furthermore, this review delves into the emerging implications of biomaterials, defibrillators, and state‐of‐the‐art devices for anxiety research, elucidating their potential roles in diagnosis, treatment, and patient management. A pivotal contribution of this review is the development and exploration of an AI‐driven model for real‐time cardiac signal analysis. This innovative approach offers a promising avenue for enhancing the precision and timeliness of anxiety diagnosis and monitoring. Leveraging machine learning and AI techniques enables the accurate classification of persons with anxiety based on real‐time cardiac data, thereby ushering in a new era of personalized and data‐driven mental health care. Identifying emerging themes and knowledge gaps lays the foundation for future research directions and offers a roadmap for scholars and practitioners to navigate this intricate field. In conclusion, this comprehensive review serves as a vital resource, consolidating diverse perspectives and fostering a deeper understanding of anxiety disorders from biological, engineering, and technological standpoints, ultimately contributing to advancing mental health research and clinical practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Classification

Electromagnetic Signal Analysis for Electrical Fault Diagnosis in Synchronous Generators

This study investigates the enhancement of fault diagnostics in synchronous generators by incorporating electromagnetic signal analysis with conventional diagnostic methodologies. The critical role of synchronous generators in maintaining power system stability and efficiency in industrial and power plant environments is underscored. Finite element modeling (FEM) is employed to simulate various fault conditions, such as stator and rotor winding faults. This study proposes a method that integrates stator current and stray magnetic flux analysis to identify five distinct types of short-circuit faults in stator and rotor windings, thereby enhancing the diagnostic capabilities for electrical faults in synchronous generators. This approach successfully identifies these electrical faults using non-invasive methods, offering a cost-effective solution that enhances fault detection. These findings are based on simulation results and serve as a preliminary stage for further validation through experimental studies. This integration is crucial for the development of efficient diagnostic systems that are capable of adapting to complex fault patterns, reducing human intervention, and streamlining maintenance operations, thus improving the reliability of synchronous generators globally.

A refined formula derived from Jiawei-Xiaoyao pill exerts rapid antidepressant-like effects in LPS-induced depression by reducing neuroinflammation and restoring neuroplasticity signaling

Ethnopharmacological relevanceJiawei-Xiaoyao Pill (JWX), a classic formula in traditional Chinese medicine, is derived from Xiaoyao Pill by adding significant amounts of Gardeniae Fructus (GF) and Moutan Cortex (MC). It is frequently used for the treatment of depression. JWX has been demonstrated to uniquely elicit rapid antidepressant-like effects within the prescribed dosage range. To date, GF has been shown to have rapid antidepressant-like effects, but a much higher dose is required than its proportion in JWX. It is assumed that the synergism of GF with a minimum number of other herbs in JWX serves as a refined formula that exerts these rapid antidepressant-like effects. Identification of a refined formula is important for prioritizing the herbs and ingredients to optimize the quality control of JWX. However, such a refined formula for JWX has not been identified yet. Aim of the studyHere we aimed to identify a refined formula derived from JWX for optimized rapid antidepressant-like effects. Since the neuroinflammation mechanism involving in depression treatment has not been previously investigated for JWX, we tested the mechanism for both JWX and the refined formula. Materials and methodsIndividual herbs (MC; ASR, Angelica Sinensis Radix; Bupleuri Radix; Paeonia Radix Alba) that show antidepressant-like responses were mixed with GF at the proportional dosage in JWX to identify the refined formula. Rapid antidepressant-like effects were assessed by using NSF (Novelty Suppressed Feeding Test) and other behavioral tests following a single administration. The identified formula was further tested in a lipopolysaccharide (LPS)-induced depressive model, and the molecular signaling mechanisms were investigated using Western blot analysis, immunofluorescence, and pharmacological inhibition of mTOR signaling. Scopolamine (Scop) was used as a positive control for induction of rapid antidepressant effects. ResultsA combination of GF, MC and ASR (GMA) at their dosages proportional to JWX induced behavioral signs of rapid antidepressant-like responses in both normal and LPS-treated mice, with the antidepressant-like effects sustained for 5 d. Similar to JWX or Scop, GMA rapidly reduced the neuroinflammation signaling of Iba-1-NF-кB, enhanced neuroplasticity signaling of CaMKII-mTOR-BDNF, and attenuated the upregulated expressions of the NMDAR sub-units GluN1 and GluN2B in the hippocampus of LPS-treated mice. GMA, JWX and Scop rapidly restored the number of BDNF-positive cells reduced by LPS treatment in the CA3 region of the hippocampus. Furthermore, rapamycin, a selective inhibitor of mTOR, blunted the rapid antidepressant-like effects and hippocampal BDNF signaling upregulation by GMA. ConclusionGMA may serve as a refined formula from JWX, capable of inducing rapid antidepressant-like effects. In the LPS-induced depression model, the effects of GMA were mediated via rapidly alleviating neuroinflammation and enhancing neuroplasticity.

Post-marketing safety concern of PI3K inhibitors in the cancer therapies: an 8-year disproportionality analysis from the FDA adverse event reporting system

ABSTRACT Background ThePhosphoinositide 3-kinases (PI3Ks) family plays a crucial role intumorigenesis. Alpelisib (inhibiting PI3Kα), copanlisib (inhibiting PI3Kα andPI3Kδ), duvelisib (inhibiting PI3Kδ and PI3Kγ), and idelalisib (inhibitingPI3Kδ) were developed to target the PI3K pathway. However, the toxicity limitstheir application to some extent. It’s necessary to investigate the adverseeffects (AEs) of these inhibitors. Research designand methods We conducted acomparative analysis of the safety signals of AEs in PI3K inhibitors usingdisproportionality analysis in the FDA Adverse Event Reporting System database(FAERS). Results Our studyidentified significant safety signals for metabolic disorders with all PI3Kinhibitors. Notable safety signals for gastrointestinal disorders were observedwith most PI3K inhibitors, with the exception of copanlisib. Common AEs shared amongall PI3K inhibitors included colitis and dehydration. Alpelisib displayedunique AEs associated with metabolic disorders, whereas copanlisib exhibitedidiosyncratic AEs linked to cardiac and vascular disorders. Stevens-Johnsonsyndrome emerged as a common severe adverse event (SAE) among alpelisib,copanlisib, and idelalisib, while febrile neutropenia was prevalent amongcopanlisib, duvelisib, and idelalisib. Intestinal perforation was solelyassociated with alpelisib. Conclusions The safety profiles of the five PI3K inhibitorsvary concerning adverse events. These findings could guide drug selection andinform future prospective research.

Naïve Bayes algorithm for timely fault diagnosis in helical gear transmissions using vibration signal analysis

Naïve Bayes algorithm for timely fault diagnosis in helical gear transmissions using vibration signal analysis

Clinical drug interactions between linezolid and other antibiotics: For adverse drug event monitoring.

Detailed data on safety associated with drug-drug interactions (DDIs) between Linezolid (LZD) and other antibiotics are limited. The aim of this study was to investigate the safety signals related to these DDIs and to provide a reference for clinically related adverse drug event monitoring. Adverse event (AE) information from 1 January 2004 to 16 June 2022 of the target antibiotics including LZD using alone or in combination with LZD was extracted from the OpenVigil FDA data platform for safety signal analysis. The combined risk ratio model, reporting ratio method, Ω shrinkage measure model, and chi-square statistics model were used to analyze the safety signals related to DDIs. Meanwhile, we evaluated the correlation and the influence of sex and age between the drug(s) and the target AE detected. There were 18991 AEs related to LZD. There were 2293, 1726, 4449, 821, 2431, 1053, and 463 AE reports when LZD was combined with amikacin, voriconazole, meropenem, clarithromycin, levofloxacin, piperacillin-tazobactam, and azithromycin, respectively. Except for azithromycin, there were positive safety signals related to DDIs between LZD and these antibiotics. These DDIs might influence the incidence of 13, 16, 7, 7, 6, and 15 types of AEs, respectively, and is associated with higher reporting rates of AEs compared with use alone. Moreover, sex and age might influence the occurrence of AEs. We found that the combinations of LZD and other antibiotics are related to multiple AEs, such as hepatotoxicity, drug resistance and electrocardiogram QT prolonged, but further research is still required to investigate their underlying mechanisms. This study can provide a new reference for the safety monitoring of LZD combined with other antibiotics in clinical practice.

Inhibition of RHOA activity preserves the survival and hemostasis function of long-term cold-stored platelets.

Thrombocytopenic patients require platelet transfusion to prevent and stop hemorrhage. Cold storage of platelets results in complex molecular lesions including changes in membrane microdomains that are recognized by host macrophages and hepatocyte counter-receptors, resulting in phagocytosis and clearance upon transfusion. For this reason, platelets are stored at room temperature, a method that confers increased risk of bacterial contamination. By applying signaling analysis as well as genetic and pharmacological approaches, we identified that the cold induced activation of RHOA GTPase is causal for the major hallmarks of platelet cold storage lesions. RHOA deficiency renders murine platelets insensitive to cold storage induced damage, and pharmacological inhibition by a RHOA activation inhibitor, R-G04, can prevent the cold storage induced lesions. RHOA inhibition prevents myosin activation and clathrin-independent formation and internalization of lipid rafts enriched in active glycosyltransferases as well as abnormal distribution of GpIbα. RHOA inhibition further prevents the metabolic reprogramming of cold induced storage lesions and allows the maintenance of glycolytic flux and mitochondrial dependent respiration. Importantly, human platelets transfused in mice after cold storage, in the presence of R-G04 or its more potent enantiomer S-G04, can circulate in vivo at similar levels as room-temperature stored platelets while retaining their hemostatic activity in vivo as assessed by bleeding time correction of aspirin-treated mice. Our studies provide a new mechanism based translational venue to prevent cold storage induced damage useful for human platelet transfusion in thrombocytopenic patients.

Identification of Damage Modes and Critical States for FRP/Steel-Concrete Composite Beams Based on Acoustic Emission Signal Analysis

Identification of Damage Modes and Critical States for FRP/Steel-Concrete Composite Beams Based on Acoustic Emission Signal Analysis

Analysis and classification of power quality disturbances using variational mode decomposition and hybrid particle swarm optimization

Power quality disturbances (PQD) threaten electrical power systems, especially in distributed generation with renewable energy sources and in smart grids where PQD takes a complex form. Providing accurate information on the status and characteristics of the electrical signal facilitates the identification of practical solutions to this threat. In this paper, a variational mode decomposition (VMD) signal processing tool is proposed to analyze complex PQD. In VMD, the input signal is decomposed into different band-limited intrinsic mode functions (IMF) or non-recursively reconstructed modes. The input signal analysis by VMD, which considers the frequency values and spectral decomposition for each mode, describes the changes in the input waveform, and the IMFs help extract the behavioral patterns of these disturbances. A new hybrid particle swarm optimization-technique for order of preference by similarity to ideal solution (PSO-TOPSIS) algorithm is also proposed to classify the disturbances based on the features extracted from the signals decomposed using VMD. The performance of this method is then extensively validated by using different PQDs (including complex, stationary, and non-stationary (PQDs) and through a comparison with deep learning methods, such as convolutional and recurrent neural networks. Results show that VMD has several advantages over Fourier, wavelet, and Stockwell transforms, such as its lack of any modal aliasing effect, its capability to diagnose disturbances across four noise levels, and its ability to separate harmonics from other events. The proposed VMD in combination with PSO-TOPSIS performs more accurately than the other methods across all noise levels.

Experimental Analysis of Echogenic Signals Appearing in Optic Nerve Ultrasound.

Experimental Analysis of Echogenic Signals Appearing in Optic Nerve Ultrasound.

Energy efficiency and electrochemical characteristic assessment of PEMFC-supercapacitor hybridization via railway profile

Electric trains equipped with efficient electric motors and powered by high-capacity hydrogen fuel cells are a choice of the global transportation industry to accelerate the global transition to a low-carbon transportation system. The operation of distinct vehicle types in varying geographical regions is characterized by unique features. Concurrently, the continually fluctuating demand for electricity necessitates the utilization of fuel cells in conjunction with energy storage devices. Thus, this study centers on examining the role of the PEMFC-supercapacitor direct hybridization system within the authentic driving profile of the subway in the Bangkok metropolitan region. The supercapacitor within the hybridization system serves as an energy management device, enhancing the durability of fuel cells when subjected to diverse load demands associated with train propulsion. In order to validate this hypothesis, an MRT (Mass Rapid Transit) protocol was formulated to emulate and serve as the load demand for the investigation, and then the PEMFC-supercapacitor direct hybridization system was operated using the created profile. Subsequently, the hybridization system undergoes electrochemical characterizations and fuel cell performance tests for diagnostic purposes. Empirical findings validated that the supercapacitor operates as a filter, generating a stable voltage response to varying load requirements, and possesses the capability to assimilate energy originating from regenerative braking. The integration of supercapacitors in stabilizing the operational parameters of proton exchange membrane fuel cells (PEMFCs) has markedly prolonged their operational lifespan. This assertion is substantiated by voltage signal analysis, which demonstrated a negligible increase in surface resistance of merely 0.23%, as depicted by Nyquist plots. Furthermore, cyclic voltammetry revealed that the system attained a peak electrochemical surface area (ECSA) of 0.0089 (106 cm2/mg of Pt). The information presented in this article constitutes data that has been minimally explored in existing research. Specifically, the study concentrates on the practical driving pattern of electric trains. Consequently, experimental results were deemed advantageous for the advancement of heavy-duty electric vehicles.

Test and Identification Analysis of Wear Response Signal of Contact Interface of Rotary Seal

Test and Identification Analysis of Wear Response Signal of Contact Interface of Rotary Seal

Precursor Damage Quantification in Composite Structures Using Coda Wave Interferometry and Nonlinear Ultrasonics

Abstract Methods to quantification of precursor damage in carbon fiber reinforced polymer (CFRP) composite structures are reported herein. These techniques include coda wave interferometry (CWI) and nonlinear ultrasonics (NLU). Since low-frequency Lamb wave propagation is insensitive to the early-stage material degradation, it is shown that decoding the information in coda wave can overcome this well-known limitation. To conclude this possibility, CWI technique is cross verified with a traditional high-frequency ultrasound method. To achieve this goal, a tensile–tensile fatigue experiment was designed for CFRP composite specimens. By inducing controlled fatigue damage in these structures, material states are assessed using low-frequency (<500 kHz) ultrasonic guided wave and high-frequency (>10 MHz) P-wave. Stretching guided coda wave is utilized to quantify the precursor damage as a unique approach in this article. However, such method could be illuded by the changes in the signals due to bonds and contacts. To verify if the CWI is successful, and to evaluate the precursor damage in composite structures, additional nonlinear analysis of ultrasonic signals from both guided waves and P-waves is performed. Higher order nonlinearities in both low-frequency guided wave and high-frequency P-wave propagation demonstrate the growth of precursor damage in CFRP composite structures. So does the CWI of low-frequency guided wave data. Accuracy of these ultrasonic techniques is validated with experimentally obtained remaining strengths of the fatigue specimens. With this verification it is envisioned that both CWI and NLU together could quantify the precursor damage in composite structures.

RawHash2: Mapping Raw Nanopore Signals Using Hash-Based Seeding and Adaptive Quantization.

Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis. Real-time analysis of raw signals is essential to utilize the unique features that nanopore sequencing provides, enabling the early stopping of the sequencing of a read or the entire sequencing run based on the analysis. The state-of-the-art mechanism, RawHash, offers the first hash-based efficient and accurate similarity identification between raw signals and a reference genome by quickly matching their hash values. In this work, we introduce RawHash2, which provides major improvements over RawHash, including more sensitive quantization and chaining algorithms, weighted mapping decisions, frequency filters to reduce ambiguous seed hits, minimizers for hash-based sketching, and support for the R10.4 flow cell version and POD5 and SLOW5 file formats. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0× and up to 9.9×) than RawHash. RawHash2 is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

An Algorithm for Initial Localization of Feature Waveforms Based on Differential Analysis Parameter Setting and Its Application in Clinical Electrocardiograms

In a biological signal analysis system, signals of the same type may exhibit significant variations in their feature waveforms. Biological signals are typically weak, which increases the complexity of their analysis. Furthermore, clinical biomedical signals are susceptible to various interferences from the human body itself, including muscle movements, respiration, and heartbeat. These interference factors further escalate the complexity and difficulty of signal analysis. Therefore, precise and targeted preprocessing is often required before analyzing these clinical biomedical signals to enhance the accuracy and reliability of subsequent feature extraction and classification. Here, we have established an effective and practical algorithm model that integrates preprocessing with the initial localization of target feature waveforms, achieving the following four objectives: 1. Determining the periodic positions of target feature waveforms. 2. Preserving the original amplitude and shape of target feature waveforms while eliminating negative interference. 3. Reducing or eliminating interference from other feature waveforms in the input signal. 4. Decreasing noise in the input signal, such as baseline drift, powerline interference, and muscle artifacts commonly found in biological signals. We have validated the algorithm on clinical electrocardiogram (ECG) data and the authoritative MIT-BIH open-source ECG database demonstrating its effectiveness and reliability.