Search Results (2,883)

Background: Myocardial ischemia (MI) is a major cause of morbidity and mortality worldwide and requires timely and reliable detection. Although Spin-Exchange Relaxation-Free (SERF) magnetocardiography (MCG) provides femtotesla-level sensitivity for identifying non-linear cardiac repolarization anomalies, its clinical deployment is currently impeded by the computational bottlenecks inherent to portable edge platforms. Methods: We propose a “Sensor-to-Image” Edge-AI framework that links quantum sensing with computer vision. Single-channel SERF-MCG signals from a large cohort of 2118 subjects (1135 Healthy, 983 Ischemia) were transformed into phase-space images using three distinct encoding modalities: Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Markov Transition Fields (MTF). These visual representations were subsequently analyzed by a streamlined MobileNetV3-Small architecture, optimized for low-latency inference. To maximize diagnostic precision, an adaptive weighted fusion mechanism was engineered to combine the chaotic specificity captured by RP with the morphological sensitivity of GASF through a validation-optimized fixed global weighting strategy. Results: In our experiments, the fusion model achieved an Area Under the Curve (AUC) of 0.865, which was higher than the 1D-CNN baseline (AUC 0.857) and the single-modality models. Notably, the fusion strategy significantly elevated sensitivity to 88.3% while maintaining a specificity of 66.5%. Although specificity is moderate, this trade-off prioritizes high sensitivity to minimize false negatives in pre-hospital screening scenarios. The average inference time was 4.7 ms per sample on a standard CPU, suggesting suitability for real-time Point-of-Care (PoC) scenarios under further on-device validation. Conclusions: The results suggest that multi-view phase-space fusion can capture subtle spatio-temporal changes associated with ischemia. The proposed lightweight framework may support the development of portable SERF-MCG systems with embedded AI screening. Full article

Many existing reinforced concrete (RC) structures have undergone increases in service loads due to changes in use, functional upgrades, and evolving design codes. This highlights the need for reliable requalification methods that account for long-term degradation mechanisms, particularly those related to sustained loading and creep. This study investigates the residual flexural behavior of RC beams after long-term loading and evaluates its effects on stiffness and ultimate strength. Three RC beams were loaded to 43% of their short-term yielding moment and kept under sustained load for 210 days, while three identical specimens were maintained as unloaded references. Afterward, all beams were subjected to repeated four-point loading–unloading cycles to detect changes in stiffness, strength, and cyclic response. The results indicate that long-term loading did not significantly affect the beams’ ultimate load-carrying capacity compared with the reference specimens. However, the long-term-loaded beams exhibited a clear reduction in initial stiffness. This difference was most evident during the first loading cycle and gradually decreased in subsequent cycles. To interpret these findings, a layered fiber model was developed to simulate cyclic behavior while incorporating time-dependent concrete effects. The model successfully reproduced the main experimental trends, reinforcing the reliability of both the testing program and the analytical approach. The study enhances understanding of stiffness degradation in RC elements subjected to increased service loads. Full article

Consumers believe that expired products are unsafe, and, in most cases, misinterpreting the information on food labels often leads to large amounts of food waste. Yogurt is among the most widely eaten dairy products that can still be consumed after its expiration date, even though most consumers throw it away the very day it expires. The aim of this study was to determine whether commercial yogurts currently available on the market remain safe for consumption after their expiration date, with a view to reducing the amount of food waste generated in households. Therefore, the quality, stability, and edible safety of 10 commercial yogurts (two plain with 2% and 4% fat and the others with fruit, such as apricots, strawberries, bananas, blueberries, berries and strawberries, blackberries and raspberries, and cherries) stored at 4°C before and at the expiration date were investigated. Physicochemical, textural, microbiological, and sensory analyses were performed to evaluate changes in functionality, safety, and acceptability of these yogurts. The results showed that, prior to their expiration date, certain yogurt samples (with apricots, strawberries, and blueberries, as well as plain yogurt with 4% fat) tested positive for total coliform bacteria, with values ranging from 20 to 50 CFU/g, suggesting substandard hygiene practices and insufficient sanitary conditions during and following the production process. No Escherichia coli, Listeria, Salmonella, Enterobacter spp., or Enterococcus spp. were detected in any of the yogurt samples that were within their expiration date. Blueberry, berry, and strawberry yogurts change their physical and chemical properties less than other types of yogurts analyzed after expiration. Yogurts containing berries and strawberries, blackberries, and raspberries remain safe at the expiration date, as they do not show the presence of harmful microorganisms such as coliform bacteria, Escherichia coli, Enterobacter spp., Enterococcus spp., Listeria, or Salmonella. Yogurt with berries and strawberries appears to be the most suitable from a microbiological point of view at expiration, as it has a low total mesophilic bacteria count and lactic acid bacteria exceeding 1 × 10⁶ CFU/g. At the time of expiration, this fruit yogurt type (with berries and strawberries) had a total solids content of 21.29%, 5.22% protein, 2.11% fat, 13.19% carbohydrates, 4.07 pH, 26.79% syneresis, 73.21% water retention capacity, 64.78% total phenolic content, and 10.55% DPPH (inhibition percentage). Nevertheless, at the time of expiration, from a sensory perspective (only appearance and consistency, odor, and color, without taste), the yogurt samples that were most appreciated contained blackberries and raspberries. The obtained results indicate that only certain types of fruit yogurts stored unopened at 4 °C may remain safe and edible after the expiration date, but further studies are needed to help the dairy industry and policymakers promote the reduction in food waste in households. Full article

Background: Post-cardiac arrest syndrome (PCAS) following out-of-hospital cardiac arrest (OHCA) is driven by global ischemia–reperfusion injury, endothelial dysfunction, and a dysregulated inflammatory response. This cascade frequently culminates in profound vasoplegia and multiorgan failure, even when guideline-directed post-resuscitation management is applied. Hemoadsorption using the CytoSorb device may attenuate hyperinflammation and vasoplegia by removing circulating inflammatory and injury-related mediators. Methods: This single-centre, retrospective cohort study compared adults with PCAS following OHCA who received hemoadsorption with propensity score-matched controls (1:1 matching; n = 50 per group). For patients treated with hemoadsorption, data were analyzed within predefined intervals covering the 24 h preceding therapy initiation (T1) and the 24 h following the completion of the hemoadsorption treatment period (T2). Controls were evaluated at time points aligned to those of their matched hemoadsorption counterparts. Hemodynamic, metabolic, respiratory, and organ injury markers were assessed. Results: Formal between-group comparisons of temporal change between T1 and T2 showed no statistically significant differences between hemoadsorption-treated patients and matched controls across key parameters, including VIS (Δ −18.7 vs. −7.7; p = 0.183) and lactate (Δ −1.8 vs. −1.25 mmol/L; p = 0.780), as well as markers of organ injury, pH, and oxygenation. In exploratory ANCOVA models, only base excess was associated with treatment group (p = 0.035). Survival to hospital discharge was comparable (48% vs. 40%; p = 0.423), with similar neurological outcomes. Within the hemoadsorption group, pre–post comparisons around hemoadsorption initiation (T1–T2) demonstrated marked improvements, including reduced vasoactive support (VIS 70.0 to 12.1; p = 0.039), substantial lactate clearance (4.1 to 1.1 mmol/L; p < 0.001), and declines in organ injury markers (AST, ALT, LDH, myoglobin), alongside more pronounced platelet reduction compared with controls (129 to 57 × 10³/µL vs. 189 to 123 × 10³/µL). However, adjusted analyses indicated that these changes were primarily driven by baseline shock severity rather than a treatment-specific effect. Conclusions: In this propensity score-matched cohort of PCAS patients after OHCA, hemoadsorption was associated with within-group physiological changes but showed no detectable advantage over matched controls, with similar survival. These findings are hypothesis-generating and warrant prospective studies with standardized timing and phenotype-guided patient selection. Full article

Understanding how volcanic systems evolve over time is a major challenge due to their complex behaviour and constantly changing conditions. This study explores a novel approach to detecting significant changes in multiparametric signals of volcanic unrest by analysing how different types of data, such as ground deformation, gas emissions, temperature, and earthquakes, interact with each other. Focusing on the Solfatara–Pisciarelli volcano system, which is a more active area in the Campi Flegrei Caldera (Southern Italy), we used two advanced methods to identify critical transitions in the system: one to model the nonlinear relationships between variables, and the other to detect key moments when the system’s behaviour shifts. By including time delays between signals (LAG), we found that our model became much more accurate in identifying these changes. In contrast, models that ignored time lags showed higher uncertainty. The results highlight the importance and effectiveness of using integrated multivariate approaches such as Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA) to gain deeper insights into the systemic behaviour of the caldera and its temporal evolution within a complex area like the Campi Flegrei over the selected time period. Full article

Time-reversible synchronization (TRS) of nonlinear oscillators is a recently proposed technique that ensures super-exponential convergence of dynamics between master and slave systems, which is beneficial in many real-time applications. Nevertheless, this approach has not been demonstrated in any real-time embedded system to practically verify it and quantitatively estimate its advantages. Furthermore, previous studies did not consider the application of time-reversible synchronization to a wide, practically relevant class of chaotic systems with piecewise-linear nonlinearity. To fill these gaps, in this work, we developed an FPGA-based time-reversible synchronization controller for the analog Chua circuit and its digital counterpart. To achieve complete synchronization, we first reconstructed dynamical equations of the circuit. Then, we performed a rigorous theoretical analysis of synchronization possibility between analog and digital systems by each single variable. Next, we implemented the digital model of the Chua circuit in the MyRIO-1900 FPGA using the reconstructed dynamical model and showed its capability of digital-to-analog and analog-to-digital conventional Pecora–Carroll (PC) synchronization. Then, an algorithm of time-reversible synchronization on MyRIO-1900 was tested, achieving complete synchronization at the predefined normalized RMSE level of 0.01, requiring an average of 8.0 fewer points and a median of 10.1 fewer points than the PC synchronization. Finally, we implemented a proof-of-concept version of a capacitive sensor based on the analog Chua circuit with an FPGA-based observer using PC synchronization or the TRS algorithm with a heuristic selection of a starting point. Our experiments reveal that when using the TRS algorithm, the time needed to detect a pre-selected 3% level of capacitance change is reduced by a mean factor of 4 and a median factor of 4.9 in comparison with the conventional PC synchronization. This allows for using the developed solution in applications where the synchronization rate is crucial, including chaos-based sensing, communication, and monitoring. Full article

Gas concentration series in coal mining faces are jointly affected by multiple coupled factors, including geological conditions, mining disturbances, ventilation organization, and gas drainage intensity, and therefore exhibit pronounced nonstationarity, strong fluctuations, spatiotemporal correlations across multiple monitoring points, and occasional abrupt spikes. To address these challenges, this study proposes a gas concentration prediction and early-warning method that integrates CEEMDAN–SST with GraphPINN-TimesFM (Graph Physics-Informed Neural Network–Time Series Foundation Model). First, based on multi-source monitoring data such as wind speed, gas concentrations at multiple monitoring points, and equipment operating status, anomaly removal, operating-condition segmentation, and change-point detection are performed to construct stable operating-state labels. Feature selection is then conducted by combining optimal time-lag correlation, Shapley value contribution, and dynamic time warping. Second, WGAN-GP is employed to augment samples from minority operating conditions, while CEEMDAN–SST is used to decompose and reconstruct the target series so as to reduce the interference of nonstationary noise and enhance sequence predictability. On this basis, TimesFM is adopted as the backbone for long-sequence forecasting to capture long-term dependency features in gas concentration evolution. Furthermore, GraphPINN is introduced to embed the topological associations among monitoring points, airflow transmission delays, and convection–diffusion mechanisms into the training process, thereby enabling collaborative modeling that integrates data-driven learning with physical constraints. Finally, the predictive performance, early-warning capability, and interpretability of the proposed model are systematically evaluated through regression forecasting, warning discrimination, and Shapley-based interpretability analysis. The results demonstrate that the proposed method can effectively improve the accuracy, robustness, and physical consistency of gas concentration prediction under complex operating conditions, thereby providing a new technical pathway for gas over-limit early warning and safety regulation in coal mining faces. Full article

Background: The relationship between facial aesthetic procedures and changes in the stomatognathic system has attracted increasing interest, motivating investigations into their functional and structural impacts. This longitudinal observational study analyzed molar bite force and orofacial tissue pressure in adults who underwent hyaluronic acid injections in the mandibular angle. Methods: Ten adults (eight women and two men; mean age 34.3 ± 11.2 years) with normal occlusion and no temporomandibular disorders were included. The MD Codes guided injection points of 2 mL of hyaluronic acid in the mandibular angle. Maximum right and left molar bite force was measured using a digital dynamometer, and tongue, lip, and cheek pressures were measured with a Pro-Fono Biofeedback device. Assessments occurred before and at 15, 30, and 60 days. Repeated measures ANOVA with Bonferroni correction was applied (p < 0.05), and effect sizes and 95% confidence intervals were calculated. Results: No statistically significant differences were observed in maximum molar bite force throughout the follow-up period. Regarding orofacial pressures, a significant main effect of time was observed for tongue pressure (p = 0.03); however, the effect size was moderate-to-large, and values showed considerable variability across participants. Lip and cheek pressures remained stable over time. Conclusions: Hyaluronic acid injection in the mandibular angle did not show clinically detectable changes in maximum molar bite force, suggesting short-term preservation of masticatory function within the 60-day follow-up period. These findings are limited to short-term observations and specific sample characteristics. The observed variation in tongue pressure may reflect adaptive functional adjustments, although variability across participants was considerable. Full article

Chronic kidney disease (CKD) represents a growing medical, diagnostic and social challenge, and it is estimated to effect 8.5–9.8% of the global population and requires expensive modes of treatment, such as hemodialysis or renal transplants. Currently, a diagnosis of CKD is set based on the level of creatinine in the blood, which is the gold standard of renal function diagnostics. Unfortunately, decrease in GFR is secondary to damage of the kidney parenchyma and indicates that the best time to start more aggressive treatment has already passed. Therefore, several non-invasive methods have been proposed for predicting increased risk of CKD progression; however, in most of the cases kidney biopsy is essential. Currently, the greatest hopes for a method that can confirm CKD are associated with the development of MRI, the most tissue-specific imaging method, and it is already proven to be capable to detect inflammatory and edematous changes, fibrosis, as well as perfusion and oxygenation disturbances. Therefore, in our manuscript we decided to present up-to-date knowledge about kidney MRI from a clinical point of view. Full article

History attempts to make sense of disparate information by trying to create discourse that lays a series of events with crisp cause–effect relationships in a sequence. Epochal shifts, such as the change from Antiquity to the Middle Ages, are especially complex since they involve a large number of economic, political and even religious factors which occur over long periods and that might overlap and interact through reciprocal feedback mechanisms, making this cause–effects sequence difficult to establish. In this research we adopt a data-driven and well-established methodology to identify, with quantifiable statistical precision, the moment when this shift happened, and from there arrive at its possible causes. We will use historical coin hoard data to find out whether such a shift is detected in a peripheral part of the Roman Empire, the Iberian Peninsula. To do so, we will apply different changepoint analysis methods to a time series of trade links created from that data, and conduct a retrospective analysis based on that result, analyzing the structure of the trade networks before and after the link. Thus, we progress from identifying when the shift happened to identifying where it took place, which in turn allows us to get to investigate why it happened, namely, historical events that could have caused it. This methodology can be used to analyze epochal changes in several steps using time-stamped network data, possibly finding disregarded causes or cause–effect links that could have been overlooked by qualitative methods; in this case, we have applied it to a dataset of coin hoards either found in the Iberian Peninsula or including coins minted there, finding a changepoint in the early 5th century, which, through network analysis, has been linked to a loss of trade with the area of Britannia. Full article

Background/Objective: Immunoglobulin G (IgG) N-glycosylation is an important regulator of immune function and systemic inflammation and has been associated with cardiometabolic diseases. However, little is known about how IgG glycosylation changes during the course of acute coronary syndrome (ACS) and whether these alterations relate to lipid-lowering response after the initiation of statin therapy. The primary aim of this study was to investigate IgG N-glycosylation following ACS and evaluate its association with response to atorvastatin therapy defined as baseline LDL cholesterol reduction of ≥50%. Methods: In this prospective cohort study, 79 statin-naïve patients hospitalized for the first episode of ACS and treated with atorvastatin 80 mg daily after percutaneous coronary intervention were followed longitudinally. Plasma samples were collected at admission (acute phase), discharge (subacute phase), and follow-up (chronic phase). A control group of 21 individuals received atorvastatin for primary prevention. IgG was isolated from plasma, and N-glycans were released, fluorescently labeled with 2-aminobenzamide, and analyzed using hydrophilic interaction-based ultra-high-performance liquid chromatography with fluorescence detection. Derived glycan traits were calculated, including agalactosylated (G0), monogalactosylated (G1), digalactosylated (G2), core fucosylated (F), bisected (B), and sialylated (S) glycans. Results: No significant differences in derived IgG glycan traits were observed between ACS patients and controls at baseline or follow-up. Within the ACS group, a longitudinal analysis revealed significant increases in G0 and F and a decrease in G2 between the acute and chronic phases. A total of 65% of patients achieved ≥50% reduction in LDL cholesterol (LDL-C), whereas only 22% reached the guideline-recommended LDL-C target of <1.4 mmol/L. Patients achieving ≥50% LDL-C reduction exhibited consistently higher G0 and lower G2 and S across disease phases. In a subgroup of patients with baseline LDL-C >3.9 mmol/L, those who failed to achieve ≥50% LDL-C reduction had significantly lower G0 and higher S across all time points. Conclusions: Specific glycan traits are associated with the degree of LDL-C reduction achieved during statin therapy, particularly in patients with high baseline LDL-C. These findings suggest that IgG glycosylation patterns may reflect biological phenotypes associated with differential lipid-lowering responsiveness after ACS. Full article

This study evaluates GNSS water-vapor tomography across the Lisbon metropolitan area and explores how increasing network density with low-cost receivers improves three-dimensional humidity fields for meteorological applications. Three configurations were tested for December 2022, a month characterized by several rainfall events, including a severe urban-impacting one: (i) a hybrid setup combining permanent and low-cost stations (TOMO_PL), (ii) a dense network of only low-cost stations (TOMO_L), (iii) a sparse arrangement using only permanent stations (TOMO_P). Tomographic water vapor density fields were compared with independent references from the Weather Research and Forecasting (WRF) model, ERA 5 reanalysis, and radiosonde data. All products show the expected exponential decline in water vapor with increasing altitude. Tomography consistently underestimates moisture in the lowest 2.0 to 2.5 km and tends to overestimate it at higher levels, with a weaker correlation above mid-tropospheric heights. Vertical RMSE remains below 2 g m⁻³ for all solutions, but TOMO_P performs the worst due to weak and uneven spatial geometry. Time–height analysis reveals that densified setups capture the changing moisture in the lower atmosphere, including increased near-surface humidity during December 11–13, when rainfall exceeded 120 mm in 24 h, although mid-level intrusions and dry layers observed by radiosondes are not captured. Mean PWV patterns show realistically low points over the Sintra mountain range and align best with TOMO_PL (spatial RMSE 0.6 g m⁻³, bias 0.4 g m⁻³, correlation 0.9), while TOMO_P creates artifacts that mimic mesoscale gradients. Categorized skill analysis shows the highest accuracy under high-moisture conditions and limited ability to detect dry conditions, with TOMO_PL showing the best overall performance against both ERA5 and WRF. Overall, low-cost densification significantly enhances boundary-layer humidity and PWV retrievals, supporting their use for urban heavy-rain monitoring and, with error-aware integration, for short-term forecasting. Full article

This study examined the within-session (same-day) test–retest reliability of heart rate (HR) and parasympathetic modulation, assessed using the root mean square of successive differences (RMSSD), across exercise and recovery phases in trained soccer players. Twenty-seven male soccer players (age: 24.9 ± 3.7 years) completed a standardized soccer training session. HR and RMSSD were recorded using an ECG-based chest-strap monitor at rest, pre-exercise, and at ~10–20 min, 1 h, and 3 h post-exercise. At each time point, two consecutive 5 min seated recordings were obtained under identical conditions. Test–retest reliability was evaluated using intraclass correlation coefficients (ICC_(3,1)), standard error of measurement (SEM), coefficient of variation (CV%), minimal detectable change (MDC₉₅), paired-samples t-tests, and Hedges’ g effect sizes. HR demonstrated excellent reliability across all time points (ICC = 0.980–0.994; SEM = 0.87–1.25 bpm; CV% = 1.33–3.70%). RMSSD showed excellent reliability at rest (ICC = 0.944) and pre-exercise (ICC = 0.918), moderate reliability during early recovery (~10–20 min; ICC = 0.551), and good reliability at 1 h (ICC = 0.826) and 3 h post-exercise (ICC = 0.873). No significant systematic differences were observed between test and retest measurements (all p > 0.05), and effect sizes were trivial. These findings indicate that within-session reliability of HR remains consistently high across exercise and recovery phases, whereas RMSSD reliability varies according to measurement timing, particularly during early recovery. Full article

Harmonic reducers exhibit non-stationary and phase-dependent degradation behavior during long-term service, challenging the ability of classical stochastic degradation models to accurately assess reliability. To address phase-dependent differences in degradation behavior, this paper proposes a reliability assessment model based on a two-phase hybrid stochastic degradation process. In the proposed framework, the Wiener process is employed to characterize early-phase gradual degradation dominated by stochastic fluctuations, while the Inverse Gaussian process is used to describe later-phase monotonically accelerated degradation driven by cumulative damage. The framework allows for sample-level variability in transition times to more realistically capture individual degradation behavior. The Schwarz Information Criterion is also adopted to detect change points. Maximum likelihood estimation is performed for model parameter inference, and analytical expressions for the reliability function, cumulative distribution function, and probability density function are derived. Numerical results indicate that a change point exists for each tested product and that the proposed model achieves the best goodness of fit among the considered candidates, demonstrating its superiority in capturing phase-dependent characteristics of harmonic reducer degradation. In terms of reliability assessment bias, the proposed model (0.06%) significantly outperforms the Wiener degradation model (32%) and the IG degradation model (9.9%). These results further confirm that, under an identical failure threshold, the proposed approach yields more accurate and realistic reliability assessment outcomes. Full article

Traditional pedestrian inertial navigation (PDR) algorithms usually assume that the carrying mode of a smartphone is fixed and remains horizontal, while ignoring the significant impact of dynamic changes in the carrying mode on heading estimation, which is the core element of PDR algorithms. In practical application scenarios, pedestrians often change their way of carrying smart terminals (e.g., calling) according to their needs, corresponding to the difference in the heading estimation method; especially when the mode is switched, it will cause a sudden change in heading, which will lead to a significant increase in the localization error if it cannot be corrected in time. Existing smart terminal carrying mode recognition methods that rely on traditional machine learning or set thresholds have poor robustness; lack of universality, especially weak diagnostic ability for mutation; and can not effectively reduce the heading error. Based on these practical problems, this paper innovatively proposes a PDR framework that tries to overcome these limitations. Based on this research purpose, firstly, this paper classifies four types of common carrying modes based on practical applications and designs a CNN-LSTM hybrid model, which can classify the four common carrying modes in near real-time, with a recognition accuracy as high as 99.68%. Secondly, based on the mode recognition results, a multi-layer heading correction strategy is introduced: (1) introducing a quaternion-based universal filter (VQF) algorithm to realize the accurate estimation of initial heading; (2) designing an algorithm to accurately detect the mode switching point and developing an adaptive offset correction algorithm to realize the dynamic compensation of heading in the process of mode switching to reduce the impact of sudden changes; and (3) considering the motion characteristics of pedestrians walking in a straight line segment where lateral displacement tends to be close to zero. This study designs a heading optimization method with lateral displacement constraints to further inhibit the drifting of the heading caused by the slight swaying of the smart terminal. In this study, two validation experiments are carried out in two different environment—an indoor corridor and a tree shelter—and the results show that based on the proposed multi-layer heading optimization strategy, the average heading error of the system is lower than 1.5°, the cumulative positioning error is lower than 1% of the walking distance, and the root mean square error of the checkpoints is lower than 2 m, which significantly reduces the positioning error and shows the effectiveness of the framework in complex environments. Full article