Search Results (558)

A two-dimensional segmentation model based on the P-matrix array was developed to simulate surface acoustic wave (SAW) delay-line devices under irregular loading. Building on coupling-of-modes (COM) theory and P-matrix model, a channelization approach was introduced to enhance conventional response simulation, enabling the systematic extraction of frequency and phase characteristics under varying spatial load distributions. Experimental verification was conducted using SAW devices fabricated by depositing aluminum interdigital transducers (IDTs) on Y-cut 35° quartz crystals through semiconductor lithography. The results demonstrate that the two-dimensional segmentation method effectively and accurately simulates the response of SAW delay line devices under various non-uniform and irregular mass loading distributions, both the phase shift and frequency shift exhibit linear proportionality to the loaded area (R² > 0.99), while the amplitude-frequency characteristics remain stable with increasing load coverage, showing no observable distortion or aberration. Quantitative mass detection experiments employing polystyrene microspheres further demonstrate that the device response increases linearly with the number of sample injections, and the shift magnitude is directly proportional to the amount injected per loading event. Full article

Background/Objectives: Ultrasound shear-wave elastography (SWE) enables quantitative assessment of tissue stiffness and may provide a non-invasive biomarker of carotid plaque vulnerability. This study aimed to evaluate the ability of SWE to differentiate symptomatic from asymptomatic carotid plaques and to assess its reproducibility. Methods: A systematic search of PubMed, Web of Science, EMBASE, and the Cochrane Library was conducted in the last ten years until January 2026 for publications evaluating carotid plaques using ultrasound SWE. Inclusion criteria required original publications that used quantitative ultrasound SWE parameters for the evaluation of carotid plaque in symptomatic and asymptomatic patients and/or investigated the reproducibility of SWE parameters for carotid plaques; non-carotid studies, non-original articles, and studies not comparing symptomatic versus asymptomatic plaques or not reporting reproducibility were excluded. Fourteen studies comprising 1781 carotid plaques were included. Quantitative SWE measurements were meta-analyzed using random effects. Differences between symptomatic and asymptomatic plaques were assessed using standardized mean differences (SMDs). The reproducibility of SWE measurements was evaluated using pooled correlation coefficients. Publication bias was evaluated using funnel plots and Egger’s regression test. Results: Ten studies including 1246 plaques compared SWE stiffness between symptomatic (n = 472) and asymptomatic plaques (n = 774). The meta-analysis demonstrated significantly lower stiffness values in symptomatic plaques compared with asymptomatic plaques (SMD −1.10, p < 0.001). Reproducibility analysis of correlation coefficients extracted from seven studies demonstrated excellent agreement for SWE measurements (r = 0.92, n = 602). Heterogeneity was observed across the included studies. No statistically significant evidence of publication bias was detected. Conclusions: Ultrasound SWE is a promising approach for assessing carotid plaque vulnerability, with lower SWE stiffness observed in symptomatic plaques compared to asymptomatic plaques. This finding should be interpreted with consideration of methodological heterogeneity and the cross-sectional nature of the available assessed evidence. Further prospective studies with standardized imaging protocols and longitudinal follow-up are needed to determine clinically applicable stiffness thresholds and evaluate the prognostic value of ultrasound SWE in cerebrovascular risk stratification. Full article

In the present study, an amperometric aflatoxin B1 sensor was constructed via modifying a nano-graphite carbon paste microelectrode with a cationic surfactant of cetyltrimethylammonium bromide (CTAB) and a perfluorosulfonic acid resin of Nafion through a simple and controllable electrochemical scanning method. The experiment results show that CTAB–Nafion composite film has a good catalytic effect on the electrochemical response of aflatoxin B1. The electrocatalytic mechanism was investigated with the aid of different analytical techniques, including square wave voltammetry, electrochemical impedance spectroscopy, chronocoulometry, energy-dispersive spectroscopy and scanning electron microscopy. Under the optimal conditions, the linear range of the sensor is from 0.1 nM to 100 nM, and its detection limit and sensitivity are 20 pM (S/N = 3) and (24.9 ± 1.51) μA/nM, respectively. The accurate and rapid detection of aflatoxin B1, which has strong carcinogenicity, is of great significance for food quality monitoring and the protection of human health. Therefore, finally, the sensor was used to detect the concentration of aflatoxin B1 in milk and soy sauce samples, and the favorable recovery results indicated its good application prospects. Full article

Ochratoxin A (OTA) is a highly toxic mycotoxin commonly detected in food and agricultural products, requiring sensitive analytical methods for reliable monitoring. Herein, we report an ultrasensitive turn-on electrochemical aptasensor for OTA detection based on a target-induced displacement of an aptamer–complementary DNA (cDNA) duplex assembled on an electrochemically reduced graphene oxide (ERGO)-modified glassy carbon electrode (GCE). In the absence of OTA, a methylene blue (MB)-labeled aptamer hybridized with cDNA is immobilized on the ERGO surface via π–π stacking interactions, forming a rigid duplex that suppresses electron transfer and yields a low electrochemical signal. Upon OTA binding, the aptamer undergoes a conformational transition into a G-quadruplex structure, leading to dissociation of the cDNA strand. This target-induced folding brings the MB redox tag into close proximity to the ERGO surface, markedly accelerating electron transfer and enhancing the cathodic reduction current of MB, thereby producing a pronounced signal-on response in square-wave voltammetry (SWV). The ERGO-modified electrode provides a conductive and stable interface without chemical linkers. Under optimized conditions, the aptasensor shows a linear response to OTA from 10 fM to 100 pM with an ultralow LOD of 0.67 fM, together with high selectivity, good reproducibility, and satisfactory stability. This work demonstrates a simple and effective turn-on aptasensing strategy for sensitive electrochemical detection of OTA. Full article

Early and reliable detection of P-wave arrivals is critical for seismic monitoring and earthquake early warning, particularly under low signal-to-noise ratio (SNR) and non-stationary noise conditions. This study presents an automatic detection method based on singular value decomposition (SVD) entropy computed in sliding time windows with local signal filtering. Within this framework, the P-wave onset is interpreted as a local structural change in the signal rather than a simple energy increase. SVD entropy captures the redistribution of energy among dominant signal components, providing high sensitivity to the initial P-wave arrival even at moderate and low noise levels ($SNR\ge 2$). The method was validated using real seismic data from four regional stations operating under different noise conditions. Analysis of detection parameters revealed strong station dependence. For stations affected by low-frequency drift, polynomial detrending was identified as a necessary preprocessing step to ensure a stable entropy response and reliable detection. The proposed approach achieves detection accuracies of up to 93–98% at $SNR\ge 2$, significantly outperforming the classical STA/LTA algorithm and demonstrating performance comparable to modern deep learning methods. Since the method does not require model training or labeled datasets, it provides an interpretable and computationally efficient solution for automatic seismic monitoring. These properties make the proposed approach particularly suitable for real-time seismic monitoring systems and distributed sensor networks operating under limited computational resources. All computational stages were performed at the Farabi Supercomputer Centre of Al-Farabi Kazakh National University. The method requires no model training or labeled data, making it an interpretable, robust, and computationally efficient solution for automatic seismic monitoring and early warning systems. Full article

The application of ultra-supercritical power plant boilers is becoming increasingly widespread. P92 steel, as a typical material used for boiler main steam pipes, plays a critical role in unit safety, making the detection of its creep damage highly significant. However, existing conventional non-destructive testing methods are difficult to effectively detect creep damage. To address this issue, a magnetoacoustic emission (MAE)–magnetic Barkhausen noise (MBN) composite measurement system is developed, which is adapted to 20 Hz and 0.3 A sine wave excitation to trigger the synchronous pickup of MBN and MAE signals of P92 steel. After collecting signals with different creep life ratios (0%~100%) under working conditions of 650 °C and 100 MPa, time-domain (absolute mean, peak value, etc.) and frequency-domain (bandwidth) features are extracted. In response to the non-monotonicity between the magnetoacoustic features and the creep damage grade, principal component analysis (PCA) is introduced to reduce dimensionality. Different creep levels of samples in the two-dimensional principal component space are presented as clear gradient clustering, achieving the accurate differentiation of creep stages. Research has shown that the MAE-MBN composite system combined with PCA can effectively characterize the creep damage of P92 steel, providing a novel non-destructive detection path for the in-service life assessment of power plant components. Full article

Background: The effectiveness of prostate-specific antigen (PSA)-based early detection of prostate cancer remains controversial and implementation-dependent. Screening policy changes have substantially altered PSA testing behavior in the United States, yet longitudinal evidence from non-organized European settings is limited. We assessed 12-year changes in awareness and utilization of PSA-based early detection and identified subgroups requiring targeted counseling. Methods: Two cross-sectional survey waves were conducted in 2009 (Study Phase 1) and 2021 (Study Phase 2) among men recruited via general practitioner practices in urban and rural regions of Germany. The survey was developed and reported according to the Consensus-Based Checklist for Reporting of Survey Studies (CROSS). Identical questionnaires were used across phases. Endpoints were awareness of PSA-based early detection and prior PSA testing. Univariable and multivariable logistic regression evaluated independent associations with sociodemographic and behavioral factors. To assess sensitivity to compositional differences between survey waves, post-stratified weighted analyses re-aligning Study Phase 2 to the Study Phase 1 distribution of age category, educational attainment, and smoking status were conducted. Results: The analytic cohort comprised 890 men (Study Phase 1, n = 755; Study Phase 2, n = 135). Compared with Study Phase 1, Study Phase 2 participants more frequently were non-smokers (63.0% vs. 48.5%, p < 0.001) and had a university degree (38.5% vs. 30.5%, p = 0.002). In primary multivariable analyses, higher educational attainment (OR 1.71, 95% CI 1.24–2.36) and paternity (OR 1.94, 95% CI 1.25–3.01) were independently associated with greater awareness, whereas increasing age (OR 1.39, 95% CI 1.29–1.50) and higher educational attainment (OR 1.63, 95% CI 1.19–2.24) were independently associated with utilization. Study phase was not independently associated with either endpoint in primary models. In post-stratified sensitivity analyses, study phase was positively associated with utilization, indicating sensitivity of temporal contrasts to population composition. Conclusions: In primary multivariable analyses, we did not detect statistically significant temporal differences in awareness or utilization of PSA-based early detection within this German non-organized setting. The emergence of a study phase effect in weighted sensitivity analyses suggests that apparent time trends may be influenced by compositional differences between survey waves. Persistent social gradients, particularly related to educational attainment, underscore the importance of targeted, evidence-based counseling in opportunistic early detection systems. Larger, prospectively designed studies are needed to distinguish true temporal change from sampling-related effects. Full article

Sample-wise detection of P-, R-, and T-peaks in electrocardiograms (ECGs) is challenging because each peak type is sparsely represented (≈1:500 samples in a typical 10-s, 500-Hz ECG at 60 bpm), such that even a small number of false-positives (FPs) can markedly degrade positive predictive value (PPV) and limit the practicality of classifier-only approaches. This study proposes a lightweight ECG peak detection framework that combines binary classifiers with physiological temporal constraints (PTC) to address extreme sample-level class imbalance. Local morphological features are first evaluated using lightweight machine-learning models, among which XGBoost (XGB) exhibited the most stable score-ranking performance. Rather than directly thresholding classifier outputs, prediction scores are interpreted within the framework, which encodes physiological timing relationships. R-peaks are detected using score ranking combined with a refractory-period constraint, and the detected R-peaks serve as temporal landmarks for subsequent P- and T-peak detection within physiologically plausible time windows reflecting the P–QRS–T sequence. Quantitative evaluation was conducted using the Lobachevsky University Electrocardiography Database, hereafter referred to as LUDB. With a temporal tolerance of ±20 ms, the XGB-based system achieved an F1-score of 0.87 for R-peak detection (sensitivity 0.96, PPV 0.79), corresponding to approximately 9–10 true R-peaks with only 2–3 FP samples per 10-s segment. For P- and T-peaks, F1-scores of 0.70 and 0.69 were obtained, respectively. Additional evaluation on arrhythmic LUDB records demonstrated robust R-peak detection across rhythm types. In AF-related rhythms, where organized P waves are physiologically absent, the framework appropriately suppressed P-peak detections, with false-positive rates remaining below 0.31%. Qualitative application to ECG recordings from the PTB-XL database further demonstrated physiologically consistent behavior. These results indicate that reliable and interpretable ECG peak detection under extreme class imbalance can be achieved by integrating lightweight classifiers within the proposed framework, without reliance on complex deep learning architectures. Full article

To investigate SARS-CoV-2 antibody kinetics and household transmission, infected children along with their families were tested for anti-nucleocapsid antibodies at 1, 3, 6, 9 and 12 months post-SARS-CoV-2 infection during the Ancestral, Alpha, Delta, and Omicron waves. We prospectively included SARS-CoV-2 acute infected children (n = 189). After household recruitment (n = 76 households), the total study population was 228 children and 105 adults. The median age (IQR) of children and adults was 96 (115) months and 504 (96) months, respectively. Anti-nucleocapsid (anti-N) COI (cut-off index) titers peaked at three months post-infection and declined thereafter (p-value < 0.001), and 89.2% remained seropositive at 12 months. Children displayed significantly higher anti-N COI titers than adults during the Delta (p-value: 0.018) and Omicron (p-value: 0.047) periods. Household contact anti-N positivity (evidence of infection) was associated with pediatric index cases (aOR: 1.61, 95% CI: 1.11–2.35; p-value: 0.013) and elevated early anti-N COI titers (aOR: 1.24 per log₁₀ unit, 95% CI: 1.05–1.48; p-value: 0.011). Higher secondary attacks were detected in Delta (aOR: 2.12, 95% CI: 1.19–3.77; p-value: 0.011) and Omicron (aOR: 2.75, 95% CI: 1.44–5.25; p-value: 0.002) compared to Ancestral. Waning of SARS-CoV-2 anti-N titers was faster in secondary cases (aHR: 1.62, 95% CI: 1.01–2.59; p-value: 0.047, Cox model) and during Omicron infection (aHR: 1.74 vs. Ancestral, 95% CI: 1.08–2.79; p-value: 0.023). In contrast, waning was slower in SARS-CoV-2 cases with higher baseline anti-N COI titers (aHR: 0.77, 95% CI: 0.64–0.93; p-value: 0.011). These findings demonstrate variant-specific, age-dependent antibody kinetics, emphasizing that pediatric index cases were associated with higher odds of household infection. Full article

This study presents a site-specific calibration of the ratio between S-wave and P-wave peak ground acceleration (PGA) for use in low-cost, on-site earthquake early warning (EEWS) systems in Iași, Romania. A dataset of 25 intermediate-depth Vrancea earthquakes (Mw 4.1–5.7; epicentral distances 150–210 km) was analyzed. PGA values were extracted for the P- and S-wave windows on both horizontal components and combined using geometric means. The resulting S/P amplitude ratios yield a median value of k_S/P = 6.19 and a logarithmic standard deviation of σlog₁₀ = 0.31, corresponding to a multiplicative uncertainty factor of approximately ×2. These results indicate that S-wave amplitudes are typically six times larger than P-wave amplitudes at this site, consistent with soft-soil amplification observed in comparable stations in Japan and Italy. The calibrated ratio can be used as a site-specific input for future MEMS-based on-site EEW implementations to estimate the expected S-wave PGA immediately after P-wave detection, with the observed S–P delays in Iași indicating a typical available warning window of 20–22 s. Full article

Accurate picking of microseismic P-wave arrival times is essential for the localization and monitoring of mining-induced seismic events. Conventional Short-Term Average/Long-Term Average (STA/LTA) detectors, while computationally efficient, are highly susceptible to noise interference. Conversely, deep learning approaches exhibit superior noise robustness but often involve substantial computational redundancy and compromised real-time performance. To address these limitations, we propose a novel two-stage picking framework that integrates STA/LTA with a lightweight U-Net, enabling rapid preliminary detection followed by fine-grained refinement. In the first stage, STA/LTA rapidly scans continuous waveforms to identify candidate windows potentially containing P-wave arrivals. In the second stage, a lightweight U-Net performs sample-level regression within each candidate window to refine arrival-time estimates with high precision. This coarse-to-fine paradigm effectively balances computational efficiency and picking accuracy. Experimental validation on 500 Hz microseismic data acquired from a coal mine in Gansu Province demonstrates that the proposed method achieves a hit rate of 63.21% within a tolerance window of ±0.01 s. This represents performance improvements of 25.42% and 40.47% over convolutional neural network (CNN) and STA/LTA methods, respectively, while reducing the mean absolute error to 0.0130 s. Furthermore, the model exhibits consistent performance on independent test sets, confirming its generalization capability and noise robustness. By combining the computational efficiency of STA/LTA with the representational power of deep learning, the proposed approach demonstrates significant potential for real-time industrial deployment. Full article

Electrocardiogram waveform delineation is a fundamental task for quantitative cardiac analysis, yet accurate and consistent estimation of waveform boundaries remains challenging due to heart rate variability, inter-subject morphological differences, and nonlinear temporal distortions across cardiac cycles. Conventional rule-based methods and pointwise Dynamic Time Warping approaches are sensitive to amplitude variations and baseline fluctuations, while deep learning–based models require large annotated datasets and often suffer from limited interpretability and generalization. In this study, we propose a morphology-oriented ECG waveform alignment framework based on Pearson correlation–based Dynamic Time Warping (PCDTW). By integrating window-level matching with a correlation-driven cost function, the proposed method explicitly emphasizes local morphological similarity rather than absolute amplitude differences. Each ECG record is aligned using a subject-specific reference cycle constructed from normalized RR intervals, enabling stable correspondence of waveform boundaries without any training process. The proposed method was evaluated on two publicly available databases, the QT Database (QTDB) and the Lobachevsky University Electrocardiography Database (LUDB). Experimental results show that PCDTW significantly reduces QT and QTcB estimation errors compared with conventional DTW variants, demonstrating improved temporal consistency and lower bias across cardiac cycles. In particular, the mean QTcB error was reduced to 28.14 ms, compared with 124.54 ms obtained using conventional DTW. In addition, on LUDB, the overall mean delineation error for the P wave, QRS complex, and T wave boundaries was 10.68 ms, showing comparable or superior performance to state-of-the-art deep learning–based methods despite requiring no external training data. These findings indicate that morphology-aware, correlation-based temporal alignment provides a robust and interpretable alternative for ECG waveform boundary detection under realistic physiological variability. Full article

Background/Objectives: Lupus Nephritis (LN) is a major complication of Systemic Lupus Erythematosus (SLE) leading to significant morbidity. While biopsy is the gold standard, non-invasive tools are needed for longitudinal monitoring. This study aims to evaluate the diagnostic utility of Shear Wave Elastography (SWE) in detecting subclinical renal damage (fibrosis) in SLE patients with a history of LN who are currently in clinical remission (inactive disease), and to compare its efficacy with Doppler ultrasonography (DUS). Methods: This cross-sectional study included 80 SLE patients and 41 age- and sex-matched healthy controls. Crucially, all SLE patients were in the clinically inactive disease (SLEDAI-2K < 6) at the time of evaluation. Patients were stratified into two groups: those with a history of LN (LN Group, n = 37) and those without (Non-LN SLE Group, n = 43). Strict exclusion criteria were applied to eliminate non-SLE renal comorbidities. Renal parenchymal stiffness (kPa) was measured using SWE, and the renal resistive index (RI) was assessed using DUS. SWE findings were correlated with renal function tests and disease activity scores. Results: Despite being in clinical remission, the LN group exhibited significantly higher renal stiffness values (Median: 1.60 kPa) compared to the non-LN SLE group (1.40 kPa, p < 0.001) and healthy controls (1.32 kPa, p < 0.001). No significant difference was observed between the non-LN SLE group and controls. Unlike SWE, renal RI values showed no statistically significant difference among the groups (p > 0.05). Correlation analysis revealed that renal stiffness was positively associated with prior serum creatinine and disease activity (SLEDAI-2K), and negatively associated with eGFR. Conclusions: SWE is superior to DUS (RI) in detecting renal parenchymal changes in LN patients. The persistence of elevated stiffness during the inactive disease suggests that SWE captures cumulative chronic damage (remodeling and fibrosis) rather than just acute inflammation. Consequently, SWE holds promise as a non-invasive surrogate for monitoring disease chronicity in SLE patients. Full article

Background and Objectives: Sternocleidomastoid (SCM) dysfunction is commonly implicated in several musculoskeletal conditions. Accordingly, shear-wave elastography has been used to characterize SCM stiffness in asymptomatic and clinical cohorts. However, the only reproducibility study available reported limited reliability, so clinical interpretations should be made with caution. Therefore, this study revisits key methodological aspects of that protocol to assess intra-examiner reliability and includes two examiners with different levels of expertise to evaluate inter-examiner reliability. Materials and Methods: A longitudinal observational study was conducted, recruiting twenty-five asymptomatic participants. Two examiners with different experience levels participated in this study after following structured training. For each side, images were obtained in immediate succession in the sequence experienced–novice–experienced–novice (with side order randomized), using an ROI spanning full muscle thickness, stabilizing approximately 10 s before freezing to record Young’s modulus and shear-wave speed. Results: Inter-examiner agreement was good–excellent: single-measurement ICCs were 0.77–0.86, improving to 0.79–0.87 when averaging two trials, which also reduced the standard error of measurement (SEM) and minimal detectable changes (MDCs). Between-examiner mean differences were small and nonsignificant (p ≥ 0.068). Intra-examiner reliability was excellent (ICC ≈ 0.93–0.94) with small absolute errors. Precision was high (SEM ~5–6 kPa; 0.22 m/s), and MDCs were ~15–16 kPa and ~0.60 m/s, with no trial-to-trial bias (all p ≥ 0.311). Conclusions: The revised protocol showed excellent intra-examiner repeatability and good–excellent inter-examiner reliability with minimal bias. Averaging two acquisitions improved precision, while a single operator optimized longitudinal stability. Full article

This study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations. The number of P waves is often inadequate for phase association and event location, which underscores the importance of identifying S waves. Given that individual RTS cases may consist of only hundreds of events, it is crucial for algorithms to be trained on small datasets or to detect effectively using external, global training data. To evaluate this, we compared the efficiency of a deep learning global detection model, transfer learning to the RTS database, a specialized neural network designed for RTS, and manual detection of seismic signals. Transfer learning efficiency was database dependent. Additional interpretation and parametrization of detection results are assumed. Therefore, the emphasis is on phase detection, rather than phase picking accuracy, and detection sensitivity is more important than its specificity. Phase association plays a vital role in detecting seismic signals, facilitating the elimination of most false picks. As a result, the comparisons of detections were based on parameters related to the location of seismic events. The findings indicate that neither the automatic signal detection methods nor the manual methods alone are sufficient. However, their combination significantly enhances detectability. The final catalogs cover up to 30% more events compared to the previous manual. It fulfills the main aim of applying a neural network detector, which is to increase the number of seismic events in the catalog. It may also be further utilized in the research of the triggering process, such as identifying fluid paths and determining fault geometry. Full article